Future Position Research Articles

A review: Hydrothermal processing of millets and effect on techno‐functional and nutritional properties

AbstractMillets represent a group of at least 13 highly nutritious, climate‐resilient grain species. Currently being considered as potential replacement to conventional staple food crops, there occurs an urgent necessity for their value addition through processing. Parboiling, heat‐moisture treatment, dry heat processes, and extrusion are the prominent hydrothermal processes for grains. In this article, process principles and research reported on hydrothermally processed millets have been exhaustively reviewed to enable the future positioning of millets in the food industry. Each hydrothermal process is unique and exerts a specific effect on millet properties. Efficiency in the reduction of inherent antinutrient compounds, namely tannins, phytic acid, phenols, and trypsin inhibitors is a key consideration in process designing. Intercomponent interactions in processed millets, namely compound entrapment, Maillard browning, starch–lipid complex, disulfide crosslinking in proteins, protein–lipid complex, tannin–protein complex, and others crucially control the product quality. Processed millet grains and products are established as functional foods, based on biochemical and in vivo studies. The review also identified major gaps in research. With distinct morphological, compositional, and functional differences, each millet variety can be individually dealt for setting process parameters. Being rich in lipid and antioxidant compounds, possibilities for rancidity control need to be examined for stored millet flours. Flours have been mostly used for partial replacement of base flour in developed products. The possibility of using hydrothermally modified millet flours in single flour products can be explored.

Exploring Consumer Sentiment on Central Bank Digital Currencies: A Twitter Analysis from 2021 to 2023

Abstract Between the worldwide digital currencies one can also pinpoint those of central banks being a part of the move towards a cashless society. Several worldwide central banks are already planning to issue them, while others are conducting studies on them. Literature of the topic is heavily increasing, including understanding central bank digital currencies (CBDCs), their risks, and central banks’ future position towards them. This paper analyzes Twitter data tagged with the “cbdc” hashtag and posted between January 2021 and January 2023, with the aim of highlighting the change regarding citizens’ perceptions towards central banks’ digital currencies. The authors extracted 124,946 positive, negative and neutral tweets from Twitter which they further analyzed by using a Python script, in the end highlighting different views on the potential benefits and drawbacks of CBDCs. The results show a growing debate and discussion around the use of CBDCs, with citizens expressing concerns about their potential consequences on civil liberties and financial control, while others highlight the benefits of CBDCs such as financial inclusion and tackling money laundering and terrorism. The paper enriches literature related to the study of consumer sentiment towards digital currencies, highlighting the significance of social media platforms for sharing opinions on emerging financial technologies. Central banks can use social media tools to shift citizens’ sentiments and perspectives, including on topics such as CBDCs, by publishing explainers, replying to comments on relevant topics, and increasing posts’ numbers, as they analyze whether and how CBDCs will be implemented. Research on consumer sentiment on this topic is useful as it can help central banks in adapting their strategies accordingly so that they can better achieve their objectives.

From setting up to creating an IPA collective

The Nancy Regional University Hospital is strongly committed to the development of advanced practice nursing. Currently, 23 advanced practice nurses (APNs) are working at the hospital. The care department, which is responsible for their future positioning, has supported their implementation. The desire to work together on common projects quickly became apparent, and a group of APNs was formed over time, culminating in the creation of a collective. This feedback is the first step towards a multi-faceted partnership, and highlights an example of organization around this new profession.

Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data

Abstract. This work introduces a model for all-sky-image-based cloud and direct irradiance nowcasting (MACIN), which predicts direct normal irradiance (DNI) for solar energy applications based on hemispheric sky images from two all-sky imagers (ASIs). With a synthetic setup based on simulated cloud scenes, the model and its components are validated in depth. We train a convolutional neural network on real ASI images to identify clouds. Cloud masks are generated for the synthetic ASI images with this network. Cloud height and motion are derived using sparse matching. In contrast to other studies, all derived cloud information, from both ASIs and multiple time steps, is combined into an optimal model state using techniques from data assimilation. This state is advected to predict future cloud positions and compute DNI for lead times of up to 20 min. For the cloud masks derived from the ASI images, we found a pixel accuracy of 94.66 % compared to the references available in the synthetic setup. The relative error of derived cloud-base heights is 4 % and cloud motion error is in the range of ±0.1ms-1. For the DNI nowcasts, we found an improvement over persistence for lead times larger than 1 min. Using the synthetic setup, we computed a DNI reference for a point and also an area of 500 m×500 m. Errors for area nowcasts as required, e.g., for photovoltaic plants, are smaller compared with errors for point nowcasts. Overall, the novel ASI nowcasting model and its components proved to work within the synthetic setup.

Visual Information Is Predictively Encoded in Occipital Alpha/Low-Beta Oscillations.

Hierarchical predictive coding networks are a general model of sensory processing in the brain. Under neural delays, these networks have been suggested to naturally generate oscillatory activity in approximately the α frequency range (∼8-12 Hz). This suggests that α oscillations, a prominent feature of EEG recordings, may be a spectral "fingerprint" of predictive sensory processing. Here, we probed this possibility by investigating whether oscillations over the visual cortex predictively encode visual information. Specifically, we examined whether their power carries information about the position of a moving stimulus, in a temporally predictive fashion. In two experiments (N = 32, 18 female; N = 34, 17 female), participants viewed an apparent-motion stimulus moving along a circular path while EEG was recorded. To investigate the encoding of stimulus-position information, we developed a method of deriving probabilistic spatial maps from oscillatory power estimates. With this method, we demonstrate that it is possible to reconstruct the trajectory of a moving stimulus from α/low-β oscillations, tracking its position even across unexpected motion reversals. We also show that future position representations are activated in the absence of direct visual input, demonstrating that temporally predictive mechanisms manifest in α/β band oscillations. In a second experiment, we replicate these findings and show that the encoding of information in this range is not driven by visual entrainment. By demonstrating that occipital α/β oscillations carry stimulus-related information, in a temporally predictive fashion, we provide empirical evidence of these rhythms as a spectral "fingerprint" of hierarchical predictive processing in the human visual system.SIGNIFICANCE STATEMENT "Hierarchical predictive coding" is a general model of sensory information processing in the brain. When in silico predictive coding models are constrained by neural transmission delays, their activity naturally oscillates in roughly the α range (∼8-12 Hz). Using time-resolved EEG decoding, we show that neural rhythms in this approximate range (α/low-β) over the human visual cortex predictively encode the position of a moving stimulus. From the amplitude of these oscillations, we are able to reconstruct the stimulus' trajectory, revealing signatures of temporally predictive processing. This provides direct neural evidence linking occipital α/β rhythms to predictive visual processing, supporting the emerging view of such oscillations as a potential spectral "fingerprint" of hierarchical predictive processing in the human visual system.

Metaheuristic for Optimal Dynamic K-Coloring Application on Band Sharing for Automotive Radars

The number of vehicles equipped with radars on the road has been increasing for years and is expected to reach 50% of cars by 2030. This rapid rise in radars will likely increase the risk of harmful interference, especially since radar specifications from standardization bodies (e.g., ETSI) provide requirements in terms of maximum transmit power but do no mandate specific radar waveform parameters nor channel access scheme policies. Techniques for interference mitigation are thus becoming very important to ensure the long-term correct operation of radars and upper-layer ADAS systems that depend on them in this complex environment. In our previous work, we have shown that organizing the radar band into time-frequency resources that do not interfere with each other vastly reduces the amount of interference by facilitating band sharing. In this paper, a metaheuristic is presented to find the optimal resource sharing between radars, knowing their relative positions and thereby the line-of-sight and non-line-of-sight interference risks during a realistic scenario. The metaheuristic aims at optimally minimizing interference while minimizing the number of resource changes that radars have to make. It is a centralized approach where everything about the system is known (e.g., the past and future positions of the vehicles). This and the high computational load induce that this algorithm is not meant to be used in real-time. However, the metaheuristic approach can be extremely useful for finding near optimal solutions in simulations, allowing for the extraction of efficient patterns, or as data generation for machine learning.

User trajectory prediction in mobile wireless networks using quantum reservoir computing

AbstractThis paper applies a quantum machine learning technique to predict mobile users' trajectories in mobile wireless networks by using an approach called quantum reservoir computing (QRC). Mobile users' trajectories prediction belongs to the task of temporal information processing, and it is a mobility management problem that is essential for self‐organising and autonomous 6G networks. Our aim is to accurately predict the future positions of mobile users in wireless networks using QRC. To do so, the authors use a real‐world time series dataset to model mobile users' trajectories. The QRC approach has two components: reservoir computing (RC) and quantum computing (QC). In RC, the training is more computational‐efficient than the training of simple recurrent neural networks since, in RC, only the weights of the output layer are trainable. The internal part of RC is what is called the reservoir. For the RC to perform well, the weights of the reservoir should be chosen carefully to create highly complex and non‐linear dynamics. The QC is used to create such dynamical reservoir that maps the input time series into higher dimensional computational space composed of dynamical states. After obtaining the high‐dimensional dynamical states, a simple linear regression is performed to train the output weights and, thus, the prediction of the mobile users' trajectories can be performed efficiently. In this study, we apply a QRC approach based on the Hamiltonian time evolution of a quantum system. The authors simulate the time evolution using IBM gate‐based quantum computers, and they show in the experimental results that the use of QRC to predict the mobile users' trajectories with only a few qubits is efficient and can outperform the classical approaches such as the long short‐term memory approach and the echo‐state networks approach.

Environment-Aware Worker Trajectory Prediction Using Surveillance Camera in Modular Construction Facilities

The safety of workers in modular construction remains a concern due to the dynamic hazardous work environments and unawareness of the potential proximity of equipment. To avoid potential contact collisions and to provide a safe workplace, workers’ trajectory prediction is required. With recent technology advancements, the study in the area of trajectory prediction has benefited from various data-driven approaches. However, existing data-driven approaches are mostly limited to considering only the movement information of workers in the workplace, resulting in poor estimation accuracy. In this study, we propose an environment-aware worker trajectory prediction framework based on long short-term memory (LSTM) network to not only take the individual movement into account but also the surrounding information to fully exploit the context in the modular construction facilities. By incorporating worker-to-worker interactions as well as environment-to-worker interactions into our prediction model, a sequence of the worker’s future positions can be predicted. Extensive numerical tests on synthetic as well as modular construction datasets show the improved prediction performance of the proposed approach in comparison to several state-of-the-art alternatives. This study offers a systematic and flexible framework to incorporate rich contextual information into the prediction model in modular construction. The observation of how to integrate construction data analytics into a single framework could be inspiring for further future research to support robust construction safety practices.

Case study of TCAS implementation in modern FMS

In this paper, uncertainty is included in the extended framework of global air traffic both through the action of the wind and through the incomplete determination of the aerodynamic coefficients and the inevitable imprecision involved in the execution of ATC instructions. In order to study the air conflict detection process in detail, the possibility of potential conflicts must be evaluated, considering the current state of the international airspace, and taking into account the uncertainty in calculating the future position of the aircraft. A mathematical model for predicting potential aerial conflicts is necessary for this objective. In a probabilistic framework, the mathematical model thus created could be either an empirical distribution of future aircraft positions in space or a dynamic model, such as a stochastic differential equation, that describes the movement of the aircraft and implicitly defines a distribution for the position’s future of the aircraft/ future aircraft positions. Based on the prediction model, aircraft flight safety matrices can be evaluated. The mathematical methods applied in the Detection and Resolution of Air Traffic Conflicts - CDR involve a wide range of fields, but also the appropriate modeling of physical systems such as airplanes, the codification of mathematical algorithms for conflict detection, as well as the properly applied procedures for resolving potential air conflicts.

Satellite-based shoreline detection along high-energy macrotidal coasts and influence of beach state

Earth observation coupled with novel image analysis techniques now present a unique and powerful tool for the historical study of shoreline change at local to global scale. However, satellite-derived shoreline (SDS) data is limited in certain areas and is associated with large uncertainties relating to environmental factors, tidal range, and wave action. We use 14 years of monthly topographic surveys at two macrotidal sites in the UK representing end members of beach type (reflective, dissipative) to investigate the influence of tidal elevation and wave action on SDS accuracy. We find that applying appropriate water level corrections can significantly improve SDS accuracy. Results show that a different approach is required for water level definition depending on beach type and reveal that ultimately SDS accuracy is primarily controlled by beach state (beach profile shape). Accounting for tidal elevation led to substantial accuracy improvement at both sites and formed the optimal SDS strategy for the reflective site (Slapton). At the dissipative site (Perranporth) considering wave-induced water level fluctuations (wave setup and/or runup), including wave shoaling, was critical for reducing the tidally corrected SDS RMSE by a third and the mean bias by three quarters. An important realization for areas with high cloud cover such as the UK, and/or low satellite coverage, was that critically low image availability restricts temporally the type of phenomenon that can be detected (e.g., seasonal/interannual variability) and may compromise computed long-term trends. Our results suggest that the optimal approach is site-specific and depends on the shoreline translation method used and is therefore different depending on the application. We propose optimal SDS strategies to increase confidence in SDS extraction in meso-macrotidal environments with potentially low satellite useability (i.e., high cloud cover and/or low satellite coverage) depending on the spatial scale of the intended application. Long-term trends derived using this approach can reproduce trends from ground-based surveys and therefore enable more accurate projections of future shoreline position to be made.

Interferenceless coexistence of 6G networks and scientific instruments in the Ka‐band

Abstract6G networks are envisioned to provide an extremely high quality‐of‐service (QoS). Then, future 6G network must operate in the Ka‐band, where more bandwidth and radio channels are available, and noise and interferences are lower. But even in this context, 6G base stations must adjust the transmission power to ensure the signal‐to‐noise ratio is good enough to enable the expected QoS. However, 6G networks are not the only infrastructure operating in that band. Actually, many scientific instruments are also working on those frequencies. Considering that 6G networks will be transmitting a relevant power level, they can interfere very easily with these scientific instruments. Therefore, in this paper we propose a new solution to enable the interferenceless coexistence between 6G networks and scientific instruments. This solution includes a three‐dimensional model to analyse future positions of user devices. Using this information and an interference model, we design a decision model to adapt the transmitted power, so the QoS achieves the expected level. Besides, when the transmitted power is high enough to interfere with close scientific instruments, a scheduling algorithm based on swarm intelligence is triggered. This algorithm calculates the optimum distribution of time slots and radio channels, so the scientific instruments can operate, and the 6G networks can still provide the required QoS. An experimental validation is provided to analyse the performance of the proposed solution. Results show a complete coexistence may be achieved with an interference level of −26 dBm and a QoS above 95% of the expected level.

Assessing Resident Well-Being: Ethical Considerations for Researchers When Conducting Qualitative Wellness Research With Resident Populations.

Assessing Resident Well-Being: Ethical Considerations for Researchers When Conducting Qualitative Wellness Research With Resident Populations.

Recurrent Encoder–Decoder Networks for Vessel Trajectory Prediction With Uncertainty Estimation

Recent deep learning methods for vessel trajectory prediction are able to learn complex maritime patterns from historical Automatic Identification System (AIS) data and accurately predict sequences of future vessel positions with a prediction horizon of several hours. However, in maritime surveillance applications, reliably quantifying the prediction uncertainty can be as important as obtaining high accuracy. This paper extends deep learning frameworks for trajectory prediction tasks by exploring how recurrent encoder-decoder neural networks can be tasked not only to predict but also to yield a corresponding prediction uncertainty via Bayesian modeling of aleatoric and epistemic uncertainties. We compare the prediction performance of two different models based on labeled or unlabeled input data to highlight how uncertainty quantification and accuracy can be improved by using, if available, additional information on the intention of the ship (e.g., its planned destination).

Research on Green and Low-Carbon Development Path of the Electric Power Industry

In the analogy where achieving the “carbon peaking and carbon neutrality” goals (the “dual carbon” goals) is compared to a battle, energy is the main battlefield, and electric power the main force. The low-carbon transformation path of the electric power industry exerts fundamental impact on progress towards carbon peaking and carbon neutrality of the whole society. Therefore, this paper first analyzes challenges and opportunities faced by the electric power industry to achieve the “dual carbon” goals; secondly, on the basis of deep low-carbon, zero-carbon and negative-carbon scenarios of the electric power industry, this paper quantifies and delves into the consumption structure of primary energy and final energy, as well as the future development positioning and trend of different categories of energy. Based on the research results, this paper proposes low-carbon transformation path of the electric power system at the stages of carbon peaking, deep low-carbon and carbon neutrality, and analyzes the trend of power supply cost changes in different scenarios. On this basis, a series of implementation key points and measures are proposed to adapt to green and low-carbon transformation of the electric power industry, covering areas such as coal-fired electric power development positioning, new energy development and utilization, diversified supply and demand, new energy industry chain, and power supply costs, and so forth.

Advanced Theranostic Strategies for Viral Hepatitis Using Carbon Nanostructures.

There are several treatment protocols for acute viral hepatitis, and it is critical to recognize acute hepatitis in its earliest stages. Public health measures to control these infections also rely on rapid and accurate diagnosis. The diagnosis of viral hepatitis remains expensive, and there is no adequate public health infrastructure, while the virus is not well-controlled. New methods for screening and detecting viral hepatitis through nanotechnology are being developed. Nanotechnology significantly reduces the cost of screening. In this review, the potential of three-dimensional-nanostructured carbon substances as promising materials due to fewer side effects, and the contribution of these particles to effective tissue transfer in the treatment and diagnosis of hepatitis due to the importance of rapid diagnosis for successful treatment, were extensively investigated. In recent years, three-dimensional carbon nanomaterials such as graphene oxide and nanotubes with special chemical, electrical, and optical properties have been used for the diagnosis and treatment of hepatitis due to their high potential. We expect that the future position of nanoparticles in the rapid diagnosis and treatment of viral hepatitis can be better determined.

Fast prediction in marmoset reach-to-grasp movements for dynamic prey

Primates have evolved sophisticated, visually guided reaching behaviors for interacting with dynamic objects, such as insects, during foraging.1,2,3,4,5 Reaching control in dynamic natural conditions requires active prediction of the target's future position to compensate for visuo-motor processing delays and to enhance online movement adjustments.6,7,8,9,10,11,12 Past reaching research in non-human primates mainly focused on seated subjects engaged in repeated ballistic arm movements to either stationary targets or targets that instantaneously change position during the movement.13,14,15,16,17 However, those approaches impose task constraints that limit the natural dynamics of reaching. A recent field study in marmoset monkeys highlights predictive aspects of visually guided reaching during insect prey capture among wild marmoset monkeys.5 To examine the complementary dynamics of similar natural behavior within a laboratory context, we developed an ecologically motivated, unrestrained reach-to-grasp task involving live crickets. We used multiple high-speed video cameras to capture the movements of common marmosets (Callithrix jacchus) and crickets stereoscopically and applied machine vision algorithms for marker-free object and hand tracking. Contrary to estimates under traditional constrained reaching paradigms, we find that reaching for dynamic targets can operate at incredibly short visuo-motor delays around 80ms, rivaling the speeds that are typical of the oculomotor systems during closed-loop visual pursuit.18 Multivariate linear regression modeling of the kinematic relationships betweenthe hand and cricket velocity revealed that predictions of the expected future location can compensate for visuo-motor delays during fast reaching. These results suggest a critical role of visual prediction facilitatingonline movement adjustments for dynamic prey.

Explaining the future position in strategic urban planning

Explaining the future position in strategic urban planning

Improving Maritime Security via Automatic Navigational Monitoring

Uninterrupted monitoring of illegal activities occurring in a large maritime area with numerous vessels is very challenging. This article reports the development of system for automated monitoring of navigational data obtained via the automatic identification system (AIS). The AIS information obtained from a vessel is used to build up a time series dataset where an autoregression integrated moving average (ARIMA) model is used on the dataset to predict the status and future position of the vessel. Since the actual navigational trajectories of vessels are predictable, the projected information obtained from the ARIMA model can be compared against the next actual AIS information. The model could decide to trigger a warning alert using preset criteria after comparing the prediction to the actual data. This article shows two cases where a particular ship displayed suspicious behaviours, prompting the model to trigger a warning. While the preset criteria can initially be decided by the user, such criteria can be shaped or trained ‘on the fly’ to produce more accurate decisions as more similar cases are detected. The author believes that the ARIMA model is simple and robust for monitoring suspicious behaviours. It is versatile and warning criteria can be defined and shaped according to user requirements.

Developing a Wine Tourism Destination Image Measurement Scale

Wine tourism plays an important role in the positioning strategies of wine destinations. As the competitiveness is high among wine destinations, it is important to identify the main factors that affect wine tourists’ decision making. One of the most important factors is the wine tourism destination’s image. To measure the image of a wine region, there is a need for a scale that is adapted to the characteristics of wine tourism destinations. Our purpose is to develop a scale that can measure any wine region’s image. We used a literature review and focus group interviews to collect scale attributes. We gathered responses using an online survey. Our respondents were mostly non-visitors. We used FA to analyze the data. We also tested the reliability of this scale using Cronbach’s Alpha. As a result, we developed a reliable scale with six factors. This can be further tested and used by any wine tourism destination to measure the people’s perceptions about them. The developed scale can have various purposes. It can be used by destination management organizations or marketing agencies. Measured images of wine region can be used in the planning of future positioning strategies and promotions. The scale can be used to compare the images of competitor wine regions. Images can also be studied during a period and any changes can be observed.

Comparative Analysis of the Scientific, Educational and Digital Potential of the BRICS and G7 Countries: Conclusions for Public Administration Systems

The relevance of the study lies in the need to assess the capabilities of the BRICS and G7 international alliances in the context of increasing uncertainty and shifting centers of global leadership. The purpose of the study is to conduct a comprehensive comparative analysis of the BRICS and G7 countries' scientific, educational and digital capabilities to determine the prospects of the impact of the two international alliances on the development of the global economy.Objectives: to study the indicators of the BRICS and G7 countries' scientific and educational potential; to study the components of the BRICS and G7 countries' digital potential; to substantiate the tools for expanding the scientific, educational and digital potential of the countries to strengthen the BRICS position in the global economy.Methodology. The study is based on the use of methods of statistical information collection, comparative and economic analysis, systematization, and the graphical method.Results. The analysis of indicators of the BRICS and G7 countries' scientific and educational potential shows, on the one hand, the advantage of the alliance of developed G7 countries and, on the other hand, the clear leadership potential of China, Russia and Brazil in various groups of indicators, which determines the probability of strengthening the positions of these countries and the entire BRICS alliance in the implementation of an active government policy in the field of education and science. The current gap between the BRICS alliance and the G7 countries in terms of digital capacity has narrowed during the pandemic, which creates the preconditions for developing countries to strengthen and achieve digital leadership in the world.Conclusions. It is concluded that the strengthening of the BRICS countries' position on the world stage in terms of GDP over the past 10 years, as well as the presence of strong elements of scientific, educational and digital potential of individual countries of this alliance, and their strengthening during the pandemic have formed the prerequisites for the alliance to take leadership positions in the near future.