Future Information Research Articles

The impact of forward-looking disclosure on the transparency of integrated reports through: the governance system, business model, and rules for preparing and presenting integrated reports.

The research aims to demonstrate the extent to which future disclosure contributes to influencing the transparency of the governance system, the business model, and the rules for preparing and presenting integrated reports. A questionnaire was conducted to clarify the extent to which future information can enhance the transparency of integrated reports. The form was distributed to a group of beneficiaries of future information in the Iraqi stock market, including investors for the most part, and some auditors. The most important conclusions that were reached are that the relationship is positive and has a moral impact between future disclosure, the governance system, and the business model. And the rules for preparing and presenting integrated reports. The most important recommendations are the need for all economic units to hold awareness sessions that expand the awareness of management and its employees about what future disclosure is, the mechanism for preparing such information, how to include it in integrated reports, and its importance

Understanding Antiferromagnetic Coupling in Lead-Free Halide Double Perovskite Semiconductors.

Solution-processable semiconductors with antiferromagnetic (AFM) order are attractive for future spintronics and information storage technology. Halide perovskites containing magnetic ions have emerged as multifunctional materials, demonstrating a cross-link between structural, optical, electrical, and magnetic properties. However, stable optoelectronic halide perovskites that are antiferromagnetic remain sparse, and the critical design rules to optimize magnetic coupling still must be developed. Here, we combine the complementary magnetometry and electron-spin-resonance experiments, together with first-principles calculations to study the antiferromagnetic coupling in stable Cs2(Ag:Na)FeCl6 bulk semiconductor alloys grown by the hydrothermal method. We show the importance of nonmagnetic monovalence ions at the BI site (Na/Ag) in facilitating the superexchange interaction via orbital hybridization, offering the tunability of the Curie-Weiss parameters between -27 and -210 K, with a potential to promote magnetic frustration via alloying the nonmagnetic BI site (Ag:Na ratio). Combining our experimental evidence with first-principles calculations, we draw a cohesive picture of the material design for B-site-ordered antiferromagnetic halide double perovskites.

Complex chemical reaction networks for future information processing.

Tackling the increasing energy demand of our society is one of the key challenges today. With the rise of artificial intelligence, information and communication technologies started to substantially contribute to this alarming trend and therefore necessitate more sustainable approaches for the future. Brain-inspired computing paradigms represent a radically new and potentially more energy-efficient approach for computing that may complement or even replace CMOS in the long term. In this perspective, we elaborate on the concepts and properties of complex chemical reaction networks (CRNs) that may serve as information-processing units based on chemical reactions. The computational capabilities of simpler, oscillatory chemical reactions have already been demonstrated in scenarios ranging from the emulation of Boolean gates to image-processing tasks. CRNs offer higher complexity and larger non-linearity, potentially at lower energy consumption. Key challenges for the successful development of CRN-based computers are associated with their specific physical implementations, operability, and readout modalities. CRNs are sensible to various reaction triggers, and provide multiple and interlinked reaction pathways and a diverse compound space. This bears a high potential to build radically new hardware and software concepts for energy-efficient computing based on neuromorphic architectures-with computing capabilities in real-world applications yet to be demonstrated.

Optimal control for both forward and backward discrete-time systems

Forward discrete-time systems use past information to update the current state, while backward discrete-time systems use future information to update the current state. This study focuses on optimal control problems within the context of forward and backward discrete-time systems. We begin by investigating a general optimal control problem for both forward and backward discrete-time systems. Leveraging the inherent properties of these systems and the Bellman optimality principle, we derive recursive equations as a means to solve such optimal control problems. Using these recursive equations, we obtain analytical expressions for both the optimal controls and optimal values of bang–bang and linear quadratic optimal control problems. Finally, we present a numerical example and an industrial wastewater treatment problem to illustrate and demonstrate our findings.

Technician routing and scheduling with employees’ learning through implicit cross-training strategy

With record high talent shortages and skill mismatches around the world, this paper investigates a variant of multi-period dynamic technician and routing problem that can be modeled as a Markov decision process. To deal with the double tradeoffs between the routing and service time costs, as well as the current and future costs, we propose an approximate dynamic programming (ADP)-based cost function approximation (CFA) algorithm — the implicit cross-training strategy (ICT). A two-phase routing and scheduling heuristic is developed to account for both employees’ learning and future information, and to facilitate an efficient implementation of CFA. Extensive computational results show that ICT can provide a better solution in the current decision with a global view in comparison with the myopic strategy. In depth analysis demonstrates that ICT trains the workforce with more balanced skillsets and workloads, which ensures the flexibility of the workforce and helps buffer against the future uncertainties with substantial routing cost savings. Additionally, ICT has much more advantages in large-scale problems with more diversified service requests and randomly distributed customers.

Improved nonlinear active disturbance rejection control for a continuous-wave pulse generator with cascaded extended state observer

The generator rotor is crucial for the high-rate and reliable continuous-wave mud pulse transmission system. However, its dynamic responses are constrained by resistant load shocks and harsh operating environments. This study proposes an enhanced nonlinear active disturbance rejection control (EADRC) that integrates cascaded extended state observer (CESO) and enhanced nonlinear state error feedback (NLSEF) to address the abovementioned issues. The rotor motion model considering hydraulic torque is analyzed to characterize external load shocks, and an optimized nonlinear function, fal(·), is designed to improve slow convergence speed in high-error states. CESO is constructed to achieve timely and accurate disturbance estimations, while enhanced NLSEF is established to combine current and future system information for efficient feedback. Moreover, estimation capability and stability are theoretically proven based on the Lyapunov function. Simulations and experiments show that the proposed EADRC exhibits rapid response, high accuracy, and strong robustness in rotor speed control.

Predicting future velocity of mineral flotation froth using STMA-LSTM with sequence images

During the mineral flotation process, the surface froth image contains characteristic information that is closely related to the production index of the process. However, a delay often exists between the state of the flotation tank and the taste calculation results, which hinders real-time process regulation. To address this challenge, a video sequence prediction algorithm is proposed to forecast the future sequence of flotation froth, and a Spatiotemporal Motion-Attentive Long Short-Term Memory (STMA-LSTM) network model is introduced to estimate and predict the future image information of the froth by integrating spatiotemporal information. After obtaining the future froth image, an image velocity measurement algorithm is utilized to achieve the future velocity prediction of the flotation froth. To address the ambiguity of future froth images and the real-time demand of future velocity estimation, a Fast Froth Velocity Estimation (FFVE) algorithm is introduced. This algorithm is suitable for fast estimation of flotation froth velocity due to its low sensitivity to image blurring factors and fast feature matching. The proposed approach implements future motion velocity prediction of froth images, thereby addressing the delay in froth taste prediction and enhancing the overall efficiency of the process control in the flotation field.

Winds of Change: Enhancing Wind Power Generation Forecasting with LSTM Models and Advanced Techniques in a Single Turbine Wind Farm

This article presents a comprehensive analysis of wind power generation using a Long Short-Term Memory (LSTM) model in the context of a wind farm with a single turbine. The novelty of the research lies in the integration of a technical model, predictive model, heat map analysis, and LSTM architecture to enhance forecasting accuracy. The performance of the forecasted model is evaluated using standard metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). In the estimation phase, wind power generation is predicted based on current weather conditions and historical trends. Leveraging multivariate time series forecasting with LSTM through the Kera library, different look-back values are explored to optimise prediction accuracy. In particular, the study achieves favourable performance metrics, including MSE of 27.781, RMSE of 5.271, MAE of 3.281, and variance of 0.886 with a 70% training and 30% test split. Furthermore, a 60% training and 40% test split yield improved accuracy with MSE of 28.791, RMSE of 5.366, MAE of 3.376, and variance of 0.888. In the prediction phase, the LSTM model is employed to forecast power generation without relying on future weather information. Through various experiments, the optimal look-back period and number of neurones for the LSTM model are determined. Notably, the achieved mean absolute percent errors (MAPE) of 11.433% and 11.158% for 24 and 48 hours of data respectively showcase the impact of varying neuron counts on prediction accuracy. To further enhance predictive capabilities, optimisation techniques are implemented, involving adjustments to the LSTM model, including increased input batch size, batch normalisation, and dense layers. This optimisation leads to a substantial decrease in MAPE from 92.53% to 87.14% in a monthly prediction experiment. Furthermore, forward predictions are made for multiple days in the future, where LSTM successfully captures patterns and fluctuations, resulting in MAPEs of 70.74% for 24 hours, 39.26% for 2 days, and 51.48% for 1 week. Additionally, a comparison of two LSTM-based models, Autoencoder LSTM and FFT-Encoder-Decoder-LSTM, demonstrates the superior performance of the latter in terms of RMSE. This study emphasizes the potential of LSTM models in wind power generation prediction while introducing novel insights into optimizing model performance for accurate forecasting. The integration of technical and predictive models, along with heat map analysis and LSTM architecture, sets the groundwork for advancing wind power prediction methodologies.

Improved lithium-ion battery health prediction with data-based approach

The accurate modeling and forecasting of Battery State of Health (SOH) is crucial for ensuring reliable performance and longevity of lithium-ion batteries. This article introduces a data-driven approach for SOH prediction using Gaussian Process Regression (GPR), selected for its ability to model complex data relationships and capture prediction uncertainty without relying on future load information. Recognizing the effect of reversible capacity recoveries on prediction accuracy, this work employs the ISEA-RWTH 48 Li-ion Cells dataset, deliberately devoid of such recoveries prior to training the GPR model. The GPR model was evaluated and compared with Support Vector Regression (SVR) using the publicly available dataset. First and second End of Life (EOL) scenarios were considered, relevant to primary and secondary battery applications. The results demonstrated the GPR model's superior performance. Particularly, mid-life and late-life predictions displayed better accuracy with GPR, showcasing higher R2 values and lower MAPE values (e.g., mid-life prediction: GPR's average R2 = 0.99, SVR's = 0.9789; GPR's average MAPE = 0.1916, SVR's = 1.3028). Moreover, GPR exhibited the ability to quantify uncertainty in capacity degradation and forecast first and second EOL instances effectively (e.g., mid-life predictions had 1.7 cycle error at 1st EOL and 8.9 cycle error at 2nd EOL). The research also offers valuable insights into the application of machine learning methods for predicting the health degradation of lithium-ion batteries.

전업직장인의 상담학 학습경험에 관한 현상학적 연구

Objectives The purpose of this study is, on the one hand, to examine the experiences of individuals who are full-time employees and enrolled in the process of learning in counselor education, and on the other hand, to phenomenologically reveal the meaning behind their experiences.
 Methods Interviews were conducted on seven full-time employees who are in counseling graduate school for more than 3 semesters or within 1 year of graduation, and analyzed by Colaizzi's phenomenological research method. Participants consisted of those in their late 30s to early 50s who had more than 10 years of work experience and experienced vocational activities in different occupations from the counseling field. The data collection period was from September to December 2021, and after an in-depth interview of 90 to 120 minutes, additional interviews were conducted by phone or text message if necessary.
 Results As a result, 5 categories, 19 topic bundles, and 81 topics were derived. The five categories are as follows. ‘studying counseling to break away from current instability’, ‘making good changes during the counseling course of study’, ‘facing psychological difficulties arising from both occupation and academic affairs’, ‘maturing the mind through learning in counselor education’, and ‘running a life as an employee also doing in counseling job’. In conclusion, participants, with over 10 years of full-time work experience and a stable career will go to counseling, a new field, and the world of experience can be summarized as “growth through counseling studies upgrades professionalism in the current workplace and running a life as an employee also doing counseling job.”
 Conclusions The research results confirmed the experiences and meanings experienced in the course of learning in counselor education, and based on this, provided basic data for future related education and information provision.

Development and Application of Technical Key Performance Indicators (KPIs) for Smart Water Cities (SWCs) Global Standards and Certification Schemes

Smart water cities (SWCs) use advanced technologies for efficient management and preservation of the urban water cycle, strengthening sustainability and improving the quality of life of the residents. This research aims to develop measurement and evaluation tools for SWC key performance indicators (KPIs), focusing on innovative water technologies in establishing unified global standards and certification schemes. The KPIs are categorized based on the stage at which water is being measured, namely the urban water cycle, water disaster management and water supply and treatment. The objective is to assess cities’ use of technologies in providing sufficient water supply, monitoring water quality, strengthening disaster resilience and maintaining and preserving the urban water ecosystem. The assessment is composed of a variety of procedures performed in a quantitative and qualitative manner, the details of which are presented in this study. The developed SWC KPI measurements are used to evaluate the urban water management practices for Busan Eco Delta City, located in Busan, South Korea. Evaluation processes were presented and established, serving as the guideline basis for certification in analyzing future cities, providing integrated and comprehensive information on the status of their urban water system, gathering new techniques, and proposing solutions for smarter measures.

Reproductive biology and feeding habit of Coilia dussumieri Valenciennes, 1848 (Actinopteri: Engraulidae): A review

Coilia dussumieri, regularly referred to as the goldspotted grenadier anchovy, is a ray-finned fish species that pertains to the Engraulidae family. Estuaries and maritime waters bordering Bangladesh, Myanmar, and India are home to it. On top of that, it can be found from Thailand south to Java and the Malay Peninsula. Any species' genetic features must be scrutinized in order to govern its population smoothly and figure out whether it is well-suited for culture. Fish biology encompasses a broad spectrum of disciplines, consisting of an in-depth overview of their stature, progression, gonado-somatic index (GSI), prime mating period, and fetal growth. Biology regarding consumption and procreation has been the focus of numerous research efforts in the recent past. The main goal of this review work is to bring together the disparate information, identify the knowledge gaps for additional research on this fish species, and provide aqua culturists with some information for future, more in-depth research on the culture, management, and breeding of Coilia dussumieri.

Molecular beam epitaxy growth of superconducting tantalum germanide

Developing alternative material platforms for use in superconductor–semiconductor hybrid structures is desirable due to limitations caused by intrinsic microwave losses present in commonly used III/V material systems. With the recent reports on tantalum superconducting qubits that show improvements over the Nb and Al counterparts, exploring Ta the superconductor in hybrid material systems is promising. Here, we study the growth of Ta on semiconducting Ge (001) substrates grown via molecular beam epitaxy. We show that at a growth temperature of 400 °C, the Ta diffuses into the Ge matrix in a self-limiting nature resulting in smooth and abrupt surfaces and interfaces with roughness on the order of 3–7 Å as measured by atomic force microscopy and x-ray reflectivity. The films are found to be a mixture of Ta5Ge3 and TaGe2 binary alloys and form a native oxide that seems to form a sharp interface with the underlying film. These films are superconducting with a TC∼1.8−2 K and HC⊥∼1.88 T, HC∥∼5.1 T. These results show this tantalum germanide film to be promising for future superconducting quantum information platforms.

Mapping factors influencing initiation of antiretroviral treatment among adolescents living with HIV/AIDS in sub-Saharan Africa: A scoping review protocol.

Since the Start Free, Stay Free, and AIDS-Free launch, UNAIDS targets intended to promote interventions to prevent HIV transmission and promote access to ART among adolescents and children, of which none were achieved in 2020. In the sub-Saharan African region, the number of adolescents initiated on ART drugs remained consistently low, with approximately100 000 adolescents succumbing to AIDS-related causes in 2022. Although HIV prevalence among adolescents had been reduced, several HIV- positive adolescents died without being initiated on ART drugs. Therefore, this scoping review protocol aims to map factors influencing the initiation of ART drugs among adolescents living with HIV in sub-Saharan Africa. The methodological framework for scoping reviews will guide this scoping protocol. A search strategy will be used to search literature in electronic databases, including EBSCOhost (PubMed/MEDLINE), Google Scholar, Science Direct, Scopus, BioMed Central, and the World Health Organization library for citations and literature using keywords and the Medical Subjects Heading (MeSH). The electronic databases will be supplemented by hand-searching references on the included studies. The search will be from Jan 01, 2012, to Dec 31, 2022. Articles will be searched and assessed for eligibility by two screeners uploaded on the Endnote software, and duplicates will be identified and removed before the abstract screening. The two screeners will assess the eligibility of the abstracts and the complete articles of the selected studies using the inclusion and exclusion criteria. A third screener will intervene when there is a lack of consensus between the two screeners. The selection process will be documented by following and using the PRISMA flow diagram (Fig 1). A thematic content analysis will present a narrative account of the extracted data. The results of this review will identify and describe factors influencing the initiation of Antiretroviral treatment among adolescents living with HIV in the Sub-Saharan African region. The findings will guide future research and inform tailored interventions and strategies for initiating ART among adolescents. Open Science Framework. https://doi.org/10.17605/OSF.IO/RNF2T.

Association of congestive heart failure with mortality in individuals with rheumatoid arthritis: a cohort study.

Rheumatoid arthritis (RA) is a risk factor for congestive heart failure (CHF). Evidence is lacking regarding the relationship between CHF and mortality among American adults with RA. We aimed to investigate the relationship between CHF and mortality in patients with RA. We extracted the corresponding data from the National Health and Nutrition Examination Survey (NHANES) database and calculated the hazard ratios (HR) and 95% CIs between CHF and mortality from all causes, cardiovascular disease (CVD), and cancer by using a Cox proportional risk model. A total of 2045 participants with a mean age of 60.32 ± 13.96years were included; 57.60% were female. During a median follow-up period of 109months, there were 602 deaths. After adjusting for potential confounders, compared with participants who are non-CHF, those with CHF had 60% (HR 1.60, 95% CI 1.27-2.01) and 110% (1.45, 1.45 to 3.06) higher all-cause mortality and CVD mortality, respectively. Furthermore, a significant association between CHF and all-cause mortality can also be observed in female individuals and those aged 65 and above. There was no significant association between CHF and cancer mortality. In this cohort study of US adults with RA, CHF was significantly associated with an increased risk of all-cause or CVD-related death. This finding underscores the importance of CHF in the management of patients with RA and may provide future information on maintaining the health status of patients with RA. Key messages • The study findings demonstrate a significant increase in overall mortality and cardiovascular mortality among individuals with RA who develop CHF. • This knowledge can assist healthcare professionals in identifying high-risk patients who could benefit from targeted monitoring and early intervention to prevent or manage CHF.

DNA as a universal chemical substrate for computing and data storage.

DNA computing and DNA data storage are emerging fields that are unlocking new possibilities in information technology and diagnostics. These approaches use DNA molecules as a computing substrate or a storage medium, offering nanoscale compactness and operation in unconventional media (including aqueous solutions, water-in-oil microemulsions and self-assembled membranized compartments) for applications beyond traditional silicon-based computing systems. To build a functional DNA computer that can process and store molecular information necessitates the continued development of strategies for computing and data storage, as well as bridging the gap between these fields. In this Review, we explore how DNA can be leveraged in the context of DNA computing with a focus on neural networks and compartmentalized DNA circuits. We also discuss emerging approaches to the storage of data in DNA and associated topics such as the writing, reading, retrieval and post-synthesis editing of DNA-encoded data. Finally, we provide insights into how DNA computing can be integrated with DNA data storage and explore the use of DNA for near-memory computing for future information technology and health analysis applications.

TSAGNN: Temporal link predict method based on two stream adaptive graph neural network

Temporal link prediction based on graph neural networks has become a hot spot in the field of complex networks. To solve the problems of the existing temporal link prediction methods based on graph neural networks do not consider the future time-domain features and spatial-domain features are limited used, this paper proposes a novel temporal link prediction method based on two streams adaptive graph neural networks. Firstly, the network topology features are extracted from the micro, meso, and middle perspectives. Combined with the adaptive mechanism of convolution and self-attention, the preprocessing of the feature extraction is more effective; Secondly, an extended bi-directional long short-term memory network is proposed, which uses graph convolution to process topological features, and recursively learns the state vectors of the target snapshot by using the future time-domain information and the past historical information; Thirdly, the location coding is replaced by the time-coding for the transformer mechanism, so that past information and future information can be learned from each other, and the time-domain information of the network can be further mined; Finally, a novel two-stream network framework is proposed, which combines the processing results of point features and edge features. The experimental results on 9 data sets show that the proposed method has a better prediction effect and better robustness than the classical graph neural network methods.

Distinct manifold encoding of navigational information in the subiculum and hippocampus.

The subiculum (SUB) plays a crucial role in spatial navigation and encodes navigational information differently from the hippocampal CA1 area. However, the representation of subicular population activity remains unknown. Here, we investigated the neuronal population activity recorded extracellularly from the CA1 and SUB of rats performing T-maze and open-field tasks. The trajectory of population activity in both areas was confined to low-dimensional neural manifolds homoeomorphic to external space. The manifolds conveyed position, speed, and future path information with higher decoding accuracy in the SUB than in the CA1. The manifolds exhibited common geometry across rats and regions for the CA1 and SUB and between tasks in the SUB. During post-task ripples in slow-wave sleep, population activity represented reward locations/events more frequently in the SUB than in CA1. Thus, the CA1 and SUB encode information distinctly into the neural manifolds that underlie navigational information processing during wakefulness and sleep.

A hybrid CNN–BiLSTM–AT model optimized with enhanced whale optimization algorithm for remaining useful life forecasting of fuel cell

To further improve the remaining useful life forecasting accuracy of fuel cells using classic deep learning models, a convolutional neural network combining bidirectional long and short-term memory networks (BiLSTM) and attention mechanism (AT) is optimized with the enhanced whale optimization algorithm (EWOA). Singular spectrum analysis preprocesses the attenuation data to eliminate noise and enhance its effective information; the CNN–BiLSTM model extracts spatiotemporal features and learns historical and future information; AT further explores the spatiotemporal correlation; and EWOA optimizes its hyperparameters to reduce human intervention error. Results demonstrate that, compared with long and short-term memory, CNN–LSTM, CNN–BiLSTM, CNN–BiLSTM–AT, and CNN–BiLSTM–AT optimized with other algorithms, the CNN–BiLSTM–AT model optimized with EWOA achieves lower root mean square error, mean absolute error, mean absolute percentage error, and relative errors of 0.1951%–0.2059%, 0.1267%–0.1538%, 0.0319%–0.0366%, and 0.026%–0.036%, respectively, with different training data. Importantly, the proposed model still maintains good prediction robustness with over 40% of the missing data.

Mpox: Awareness, knowledge and information channels used by individuals accessing a sexually transmitted infections Helpline.

Up until recently, monkeypox (mpox) was considered to be a rare zoonotic infection restricted to Central and West Africa. However, in July 2022, the World Health Organization declared mpox to be a public health emergency of international concern, after there were several outbreaks in non-endemic countries. This study assessed the information channels used by individuals accessing the "Telefono Verde AIDS e Infezioni Sessualmente Trasmesse" of Istituto Superiore di Sanità (National Institute of Health in Italy) with regard to mpox. We also evaluated their awareness, and knowledge about mpox focusing on transmission and prevention. This is a cross-sectional study conducted on "Telefono Verde AIDS e Infezioni Sessualmente Trasmesse" users, between January and April 2023. The target population was helpline users who were older than 18 years of age, and were phoning the service for the first time (i.e. who have not phoned in the last 12 months). Participants were interviewed using a questionnaire containing 19 questions filled by the interviewer. Collected data were analysed through descriptive statistics, and significance testing. From January to April 2023, "Telefono Verde AIDS e Infezioni Sessualmente Trasmesse" received 2,389 phone calls, 216 of which participated in this study. Most of these were male (85.2%; n=186), single (79.2%; n=171) and in employment (72.2%; n=156). Awareness about mpox was high (71.3%; n=154), with the central and south/islands regional areas having the highest percentage of participants who heard about the virus (84.3%; n=43 vs 72.4%; n=42; p=0.078). Television was the most frequently mentioned source of information about mpox by participants (63%; n=97; p<0.001). This medium was also the most preferred source of information about mpox (39%; n=60; p=0.109). The majority of participants stated that they did not know what mpox is (67.6%; n=146), and less than half knew that it is preventable (42.4%; n=89). Of those who stated that mpox is a preventable illness, less than half knew about the existence of a vaccine (44.9%; n=40; p<0.001). This study provides insights into the awareness, knowledge, prevention tools and information channels of individuals who contacted the "Telefono Verde AIDS e Infezioni Sessualmente Trasmesse". Useful indications for defining future information campaigns were obtained.