Projection Approach Research Articles

Multimodal knowledge graph construction for risk identification in water diversion projects

The operation risks of water diversion projects involve numerous influencing factors, complex interrelationships, and heterogeneous data from multiple sources. This study presents a multimodal knowledge graph construction approach for water diversion projects, aiming to comprehend and identify the key risks associated with engineering operation and their propagation patterns. Utilizing term-masked pre-trained language models enhances comprehension of specialized terminology and identifies risk entities within the text. Employing high-order residual convolutional neural networks improves the processing capability for complex graph data, extracting risk information from images. Aggregating multimodal knowledge graphs based on the semantic relationships among entities and conditional probability to determine the coupled features of different risks. Employing complex network theory, analyze node degree and betweenness centrality to identify the diffusion effects and propagation levels of risks. The results indicate that the knowledge extraction accuracy of our method is high (with an average F1 score of 95.85%), enabling the qualitative analysis and quantitative calculation of operational risks in engineering. Relevant studies can effectively enhance the reliability of engineering safety management and reduce the impact of engineering risks on water supply security.

Destination Management Organizations’ Roles in Sustainable Tourism in the Face of Climate Change: An Overview of Prince Edward Island

Globally, destination management organizations (DMOs) are becoming increasingly known for their sustainable practices. Despite the importance of destination management organizations’ sustainability initiatives in the face of climate change, minimal research has been undertaken on the topic in Canada. DMOs on PEI can inform tourists better if they know what other adaptive strategies are taken into consideration around Canada. Our work included host-community interviews and perceptions on DMOs’ roles in improving tourism in the face of climate change. The interviews concluded that tourism officials in Prince Edward Island are becoming more conscious of climate change, but more has to be done to slow down the effects of the phenomenon. This paper also identified challenges facing DMOs in the area of sustainable tourism in the context of climate change. One of the recommendations was that DMOs should have access to techniques for mitigation and adaptation in addition to incentives that are sensitive to local situations. They may successfully advocate for climate change in this way and inform visitors if they are staying in risky places because of the consequences of climate change. The information on the standard operating procedures that DMOs use was intended to be useful to travelers, DMOs, and enterprises involved in the tourism industry. Future implications should discover new approaches for sustainability projects and to achieve a better understanding of how to enhance processes within the tourism industry, and more research on DMOs’ sustainability practices in the face of climate change could help improve this field.

Optimising Precision Agriculture Choices for Arable Farmers in Germany and the UK: the LINKDAPA Approach

SummaryFarmer adoption of so‐called Precision Agriculture (PA) or ‘smart’ technologies in the arable sector has grown in the last few decades with a focus on Global Navigation Satellite Systems (GNSS) and variable rate technologies (VRT). This has led to increased generation of large volumes of data about fields and their crop yields which could be used to increase the environmental and financial performance for farmers. However, survey results show that cost and adaptability have been issues for many farmers in the UK and Germany that have held back such adoption. The LINKDAPA (LINKing multi‐source Data for Adoption of Precision Agriculture) project's approach sought to minimise both concerns by creating a customisable web platform that incorporates both GNSS and VRT into one, easy to use, affordable option for farmers. The project developed an online cloud‐based decision support tool which takes into account different fertiliser strategies based on novel algorithms using soil, historic yield and satellite data. Co‐created by researchers, farmers and agricultural technology firms, the LINKDAPA approach offers both economical and easy to implement solutions for farm management to mitigate resource loss‐ratios such as in fertiliser use, provide financial performance analyses, and multi‐year graphical imagery for soil mapping.

Agile meets quantum: a novel genetic algorithm model for predicting the success of quantum software development project

Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve the complex problems more efficiently than classical counterparts. Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored. This study investigates key causes of challenges that could hinders the adoption of traditional agile approaches in quantum software projects and develop an Agile-Quantum Software Project Success Prediction Model (AQSSPM). Firstly, we identified 19 causes of challenging factors discussed in our previous study, which are potentially impacting agile-quantum project success. Secondly, a survey was conducted to collect expert opinions on these causes and applied Genetic Algorithm (GA) with Naive Bayes Classifier (NBC) and Logistic Regression (LR) to develop the AQSSPM. Utilizing GA with NBC, project success probability improved from 53.17 to 99.68%, with cost reductions from 0.463 to 0.403%. Similarly, GA with LR increased success rates from 55.52 to 98.99%, and costs decreased from 0.496 to 0.409% after 100 iterations. Both methods result showed a strong positive correlation (rs = 0.955) in causes ranking, with no significant difference between them (t = 1.195, p = 0.240 > 0.05). The AQSSPM highlights critical focus areas for efficiently and successfully implementing agile-quantum projects considering the cost factor of a particular project.

Privacy Preserving Image Retrieval Using Multi-Key Random Projection Encryption and Machine Learning Decryption

Homomorphic Encryption (HE), Multiparty Computation (MPC), Differential Privacy (DP) and Random Projection (RP) have been used in privacy preserving computing. The main benefit of the random projection approach is the lighter time and space complexity compared to the other available techniques. However, RP is typically used in a symmetric encryption mode, with one random projection matrix single key, making it vulnerable to attacks. An enhanced multi-key RP approach is proposed in this paper where a set of N random matrices are used as projection keys. Moreover, a randomly chosen one is used for each new query. Machine learning models are trained to perform specific vector operations on the randomly projected vectors and produce another randomly projected results vector. Another machine learning model is trained to decrypt the final result at the user’s side. The proposed system is shown to offer privacy against known plaintext and cipher-only attacks while preserving Euclidean distance calculations accuracy in the randomly projected domain which are demonstrated on the COREL 1K image retrieval task. Results show that the cyphertext space took sixteen times less than the ciphertext done with homomorphic encryption, and the computation of distance using random projection was 8 times faster than homomorphic encryption distance calculation.

The Australian Traumatic Brain Injury Initiative: Single Data Dictionary to Predict Outcome for People With Moderate-Severe Traumatic Brain Injury.

In this series of eight articles, the Australian Traumatic Brain Injury Initiative (AUS-TBI) consortium describes the Australian approach used to select the common data elements collected acutely that have been shown to predict outcome following moderate-severe traumatic brain injury (TBI) across the lifespan. This article presents the unified single data dictionary, together with additional measures chosen to facilitate comparative effectiveness research and data linkage. Consultations with the AUS-TBI Lived Experience Expert Group provided insights on the merits and considerations regarding data elements for some of the study areas, as well as more general principles to guide the collection of data and the selection of meaningful measures. These are presented as a series of guiding principles and themes. The AUS-TBI Aboriginal and Torres Strait Islander Advisory Group identified a number of key points and considerations for the project approach specific to Aboriginal and Torres Strait Islander peoples, including key issues of data sovereignty and community involvement. These are outlined in the form of principles to guide selection of appropriate methodologies, data management, and governance. Implementation of the AUS-TBI approach aims to maximize ongoing data collection and linkage, to facilitate personalization of care and improved outcomes for people who experience moderate-severe TBI.

RENOVATION OF CRITICAL INFRASTRUCTURE FACILITIES IN UKRAINE AFTER THE WAR: STRATEGY AND PERSPECTIVES

The military actions in Ukraine not only led to the destruction of critical infrastructure (hereinafter referred to as CI), especially the energy and transport sectors, but also actualized the need to ensure its security, immediate restoration to provide basic services to the population in various areas. Local public authorities, operators of CIs, as well as the central government of the country and international partners are involved in the processes of immediate restoration of CI facilities in Ukraine, which, on the basis of cooperation, provide organization, planning, and financing of its reconstruction. Initiatives of the national government to establish cooperation testify to the existing prospects for the long-term development of CI. Therefore, the modernization and construction of new CI facilities takes place on the basis of partnership, which contributes to the attraction of human, financial, material and technical and other types of resources, the establishment of cooperation mechanisms and the interaction of public administration subjects of various institutional levels. Transport and energy infrastructure requires the largest financial investments and efforts of various subjects - participants in the recovery process, because it ensures the provision of basic public services for the population. Taking into account the need for long-term development of CI facilities in various sectors of the economy, the National Recovery Council of Ukraine presented the «Project Recovery Plan». This document provides for the implementation of programs for the restoration of the most priority sectors using a project approach (logistics, infrastructure, including educational, social, healthcare infrastructure and other sectors). For long-term planning of the reconstruction of critical infrastructure, proposals are made for the practical implementation of sectoral and functional approaches in public administration in the relevant field. This position corresponds to the provisions of the Law of Ukraine «On Critical Infrastructure» № 1882-IX of 01.01.2024, providing for the construction and reconstruction of CI facilities according to the importance of their functions for consumers of services in various infrastructure sectors. In general, the study of public administration in the field of reconstruction of various sectors of the economy, as well as the analysis of the growth dynamics of Ukraine’s GDP in 2021-2023 according to the production method, indicates the strength of various types of economic activity.

프로젝트 접근법 기반 유아 생태전환교육 프로그램 개발 및 적용

The purpose of this study is to develop and implement a young children's eco-logical transition education program based on the project approach, with the aim of exam-ining changes in young children's awareness of sustainable development, ecological liter-acy, and problem-solving abilities. The program was applied to 28 five-year-old children in E preschool located in S City focusing on the selected themes of ‘Marine Life’ and ‘Companion Animals and Animal Welfare’ during morning activities over an 8-week period. The collected data, including research journals, play observation logs, interviews, photo-graphs, etc., were qualitatively analyzed during the process of implementing the program To examine changes based on the timing of program implementation, quantitative data collected through observation checklists were subjected to repeated measures analysis of variance. The results of qualitative and quantitative analyses revealed positive changes in young children's awareness of sustainable development, ecological literacy, and prob-lem-solving skills as they engaged in play related to interesting topics they chose during the project-based activities. The significance of this research lies in presenting an eco-logical transition education program applied through children's expressive play with self-selected themes. The findings suggest that this approach could be utilized as a case for implementing play-centered and child-centered educational programs in early childhood education and care settings to promote ecological transition education for a sustainable society.

DEVELOPMENT AND SUBSTANTIATION OF PROMISING MODELS OF MEDICAL CARE FOR DISEASES OF CIRCULATORY SYSTEM IN INDUSTRIAL REGION. 2010–2023 RESULTS

HighlightsWe have identified regional peculiarities of traditional and non-traditional cardiovascular risk factors.We have defined and characterized clinical and organizational characteristics of the specialized care, prevention, and treatment of diseases of the circulatory system in the Kemerovo region - Kuzbass.Moreover, we have described the principles of innovative organizational models for improving care for patients with diseases of the circulatory system, focused on the needs of outpatient organizations. AbstractThe review presents the main research findings obtained at the Department of Optimization of Medical Care for Cardiovascular Diseases of the Research Institute for Complex Issues of Cardiovascular Diseases over the past 13 years of work. The uniqueness of the obtained data lies in the fact that we have identified regional peculiarities of cardiovascular risk factors in residents of an industrial region (using the Kemerovo region data). Data on monitoring of health and risk factors served as the basis for the development of innovative organizational models for improving care for patients with diseases of the circulatory system from the stage of primary care to tertiary care using a systematic and integrated approach, and situational analysis. Management decisions within models are based on international quality standards that reaffirm that risk-based management, the role of personnel and the work environment are the integral to the improvement of performance and the achievement of the main goals of medical organization. Using the project approach, we have implemented management tools for internal quality control and safety of patients and personnel.

Design of prehospital spectral CT with a rotation-free rectangular gantry and deep learning reconstruction

Treatment of stroke depends on a narrow therapeutic time window and requires urgent intervention to be emergently pursued. Despite recent “fast” initiatives that have underscored “time is brain,” many patients still fail to present within the narrow time window to receive maximum treatment benefit from advanced stroke therapies. The convergence of emergency medical services, telemedicine, and mobile technology, including transportable computed tomography scanners, has presented a unique opportunity to advance patient stroke care in the prehospital field by shortening the time to hyperacute stroke treatment with a mobile stroke unit. However, in order to facilitate the integration of computed tomography (CT) scanners into small ambulances, their geometry should be streamlined and made compact. Additionally, it is imperative that radiation exposure from prehospital CT scans comply with established radiation protection guidelines to safeguard both patients and healthcare workers. In this study, we propose a newly designed CT system for a rotation-free rectangular gantry with a photon-counting detector (PCD) and a deep-learning reconstruction approach for undersampled projections. Undersampled projections obtained using the designed CT system were implemented using a deep learning method. The deep learning approach showed superior image quality without distortion compared to state-of-the-art reconstruction methods. Finally, we implemented imaging of brain microhemorrhages using low- and high-energy images obtained from the PCD. Our results indicate that the proposed scanner effectively shows potential for acute stroke detection in prehospital ambulances. Its effectiveness was validated by comparing its image performance with those of other methods such as FBP and compressed sensing.

Design optimisation towards lower embodied carbon of prefabricated buildings: Balancing standardisation and customisation

The design principle of standardisation is widely acknowledged for its potential in reducing embodied carbon emissions (CE) in prefabrications through the facilitated reuse of prefabricated elements. Yet, its effectiveness in optimising CE in precast constructions remains underexplored. This article introduces a micro-level sustainable design optimisation approach for precast projects that factors the CE of both building materials and concrete formwork. Using a parametric approach, the method generates design alternatives derived from the original architecture design. Structure analysis is then employed to determine the design of structure elements and formwork pieces. With the objective of minimising CE from building materials and formwork, a genetic algorithm (GA) is used to explore solutions with the lowest CE. Comparative analysis shows this approach's effectiveness over standardisation-only designs, reducing concrete, reinforcement, precast formwork, and overall CE by 14.32%, 16.71%, 10.39%, and 10.51%, respectively. These findings underscore that increased standardisation does not necessarily lead to lower embodied CE. Integrating the benefits of element standardisation and customisation presents a promising carbon reduction strategy. The introduction of micro-level variables enhances the understanding of standardisation and offers novel carbon reduction strategies, such as increasing the adaptability of casting formwork and maximising the use of existing formwork pieces. Designers and manufactures can adopt these strategies for lower embodied CE of projects. This study also addresses the limitations of design tools used in early architecture design stages, providing designers with continuous decision-making assistance, thereby facilitating the implementation of sustainable strategies.

Theoretical and methodological principles of risk management in trade organisations

Traders play a key role in meeting the country's household demand for goods and services. Given that these organizations operate in a competitive environment, the success of their business depends on their ability to respond quickly and effectively to changes in the market situation. The increase in competitiveness is an incentive for business organizations to search for and implement innovative methods of organizational management. The challenges of managing a trading organization can vary significantly depending on the type of business, the specifics of the products, and the company's position in the market. The main characteristics of the training organizations have been identified and have been classified according to the type of activity. The peculiarities of the activities carried out by commercial organizations have been identified and a classification of the typical risks faced by these companies has been presented. In order to increase the efficiency of the business of such organizations, it is considered advisable to apply a project approach. The issues of project management and implementation in trading companies have been analyzed and outlined. The basic principles of managing project risks according to international project management standards, in particular P2M, ICB IPMA, PMBoK PMI, PRINCE2, ISO 21500 and AGILE, have been presented. The main project risks faced by the trade organizations have been identified. It was found that the same risks can accompany both projects and the organization’s operational activities. Thus, the operational risks of the organization overlap with the project risks. To reduce the time and increase the efficiency of resource management, it is deemed reasonable to develop and apply tools for the integrated management of project and operational risks of trading companies.

Space‐Time Projection Enabled Ultrafast All‐Optical Diffractive Neural Network

AbstractAll‐optical neural networks have advantages in higher throughput, higher speed as well as lower energy consumption compared to electrical neural networks. Optical neural networks have already shown great potential in various applications; however, the operation speed of the network is limited by the 2D detector as most optical neural networks rely on space to space projection. Here, a space to time projection approach to build diffractive deep neural network (D2NN) is proposed, which can project spatial intensity distribution into time‐domain intensity variation, thus bypassing the detection speed limit of 2D imaging device. Based on this scheme, high‐speed all‐optical logic gates are theoretically analyzed and experimentally realized. In this case, the network's operation speed is only limited by the photodetector (PD), which can reach GHz levels. Moreover, the method will show great advantage when it comes to wavelengths where 2D detectors are not achievable easily such as infrared, terahertz or microwaves.

Using clustering as pre-processing in the framework of signal unmixing for exhaustive exploration of archaeological artefacts in Raman imaging

Analytical chemistry on archaeological material is an essential part of modern archaeological investigations and from year to year, instrumental improvement has made it possible to generate data at a high spatial and temporal frequency. In particular, Raman spectral imaging can be successfully applied in archaeological research by its simplicity of implementation to study past human societies through the analysis of their material remains. This technique makes it possible to simultaneously obtain spatial and spectral information by preserving sample integrity. However, because of the inherent complexity of the samples in Archaeology (e.g. seniority, fragility, lack or full absence of any information about its composition), chemical interpretation can be difficult at first glance. Indeed, specific problems of spectral selectivity related to unexpected chemical compounds could appear due to their state of conservation. Furthermore, detecting minor compounds becomes challenging as major components impose their contributions in the acquired spectra. Therefore, a relevant chemometric approach has been introduced in this context to characterize distinct spectral sources in a Raman imaging dataset of an archaeological specimen – a mosaic fragment. The fragment was unearthed during the Ruscino archaeological dig on the outskirts of Perpignan, France. It dates back to the oppidum period. The aim is to extract selective spectral information from pixel clustering analysis in order to enhance the initial optimisation step within the Multivariate Curve Resolution and Alternating Least-Squares (MCR-ALS) algorithm, a well-known signal unmixing technique. The underlying principle of the MCR-ALS is that the acquired spectra can be expressed as linear combinations of pure spectra of all individual components present in the chemical system under study. Sometimes it can be difficult to obtain the desired results through the algorithm, particularly if initial estimates of spectral or concentration profiles are inaccurate due to complex signals, noise or lack of selectivity, resulting in rank deficiency (i.e. a poor estimation of the total number of pure signals). For this reason, an innovative threshold-based clustering algorithm, combined with multiple Orthogonal Projection Approaches (OPA), has been developed to improve matrix rank investigation and thus the initialisation step of the MCR-ALS approach before optimisation. The effective analysis of Raman imaging data for an archaeological mosaic played a crucial role in uncovering significant chemical information about a particular biogenic material. This insight sheds light on the origins of mortar manufacture during the oppidum period.

Impact of Enterprises Relationship on ML’s Ability to Forecast SME’s Credit Risk in SCF: A Research Based on Complex Network

ABSTRACT In this study, we investigate the impact of introducing enterprise relationship to train machine learning (ML) algorithms on improving its ability of forecasting small and medium-sized enterprises’ (SMEs) credit risk in supply chain finance (SCF). First, we incorporate attributes of SME node in the CEs-SMEs network, a bipartite network constructed with transaction data in the Chinese automobile industry, into financial indicators to train ML algorithms for improving forecasting performance. Second, we employ the weighted one-mode projection approach to extract a projected-SMEs network that reveals the relationship among SMEs from the CEs-SMEs network and examine whether the ML algorithms’ forecasting performances is improved when considering attributes in the projected-SMEs network. Third, we analyze the correlation between SME’s credit risk and individual network attribute to specifically understand the impact of enterprise relationship on credit risk. Overall, the empirical results indicate that the forecasting performance is obviously better when enterprise transaction relationship is considered, and it is further enhanced after we apply weighted one-mode projection approach. Meanwhile, the variable importance analysis and the binomial logistic regression demonstrate how significantly each network attribute is correlated with credit risk, and the discussion on partial dependence plot shows that SMEs with large degree are non-risky generally.

Design and Simulation of Switched Capacitor for Voltage Boost Converter for EV

The concept focuses on using voltage boost converters specifically made for electric vehicle (EV) drives to simulate and design switching capacitors. In the past, standard boost and non-boost inverters have been used to assess the efficiency of electric vehicles. On the other hand, this proposal suggests using voltage boost converters to greatly increase the vehicle's speed and efficiency. The main goal is to maximise the converter topology's design parameters in order to achieve higher voltage-boosting capabilities and improve the vehicle's overall performance. The research intends to address issues with power delivery and power conversion efficiency through careful investigation and simulation. This research aims to push the boundaries of EV technology and aid in the creation of more effective and potent electric vehicles by utilising sophisticated switching capacitor techniques. The project's approach entails a thorough analysis of current converter topologies and design parameters, followed by in-depth simulation studies carried out with the use of suitable software programmes like Matlab. To maximise the efficiency of voltage-boosting, several parameters, including switching frequencies, capacitor values, and circuit designs, will be rigorously examined and optimised. The relevance of this notion is that it has the potential to introduce revolutionary voltage boost converter designs that are specifically tuned to the needs of electric vehicle drives, hence revolutionising the efficiency benchmarks of EVs. By conducting thorough testing and analysis, this research hopes to open the door for the electric car sector to widely embrace cutting-edge power conversion technology, which will ultimately result in more environmentally friendly and sustainable transportation options.

Investigation of long-term stability of a transmission Raman calibration model using orthogonal projection methods

Transmission Raman Spectroscopy (TRS) was implemented as an ‘Extended’ Content Uniformity (ECU) method for un-coated tablets for a commercial pharmaceutical product. By sampling un-coated tablets throughout the duration of the tablet compression stage, it can be demonstrated that the material from the preceding blend step was of uniform composition, and therefore the blend and compression unit-operations were in a state of control. TRS was selected as a rapid, non-destructive measurement that can be automated through the use of a sample tray that can hold many tablets. In this work, the performance of a multivariate calibration model (PLS) deployed to two Transmission Raman Spectrometers co-located within the same QC laboratory was studied using data obtained over a 3-year period. The aim of the investigation was to assess the impact of various annual instrument maintenance events, and to evaluate several chemometric methods for reducing or eliminating the spectral effects that led to deterioration of a models performance. Linear orthogonal projection approaches such as Transfer by Orthogonal Projection (TOP), Dynamic Orthogonal Projection (DOP) and Unsupervised Dynamic Orthogonal Projection (uDOP) were applied, along with a more recent, non-linear method called Transfer Component Analysis-Orthogonal Projection (TCA-OP). This works shows that each method has merits, depending on the nature of the spectral/model correction required. In most cases, the model performance could be fully restored, or significantly improved. This work also highlights how these various methods can be useful tools to better understand the root-cause for a deterioration in model performance.

Asset Information Model Management-Based GIS/BIM Integration in Facility Management Contract

Achieving efficiency success status inside an organization’s built environment and obtaining a positive return on investments need robust and comprehensive asset management and maintenance processes based on the efficiency of contract information documents within the built asset lifecycle. This paper aims to highlight the appropriate interactive approach for construction projects to build the information flow scope of asset facility management contracts based on GIS (Geographical Information System) and BIM (Building Information Modeling) integration processes and sustainability standards, and project as-built contractual documents to support owners and stakeholders with the intent of improving asset management processes. Expert interviews and contract information flow types in several facility management processes conducted in both local and international facility management organizations were used to assist the information flow scope method. The study classified and built significant integrated information and data flow models for a case study to serve as contract guidelines, including efficiency performance measures and indicators for monitoring procedures, technical evaluation, and financial issues in order to provide high-performance service quality in facility management applications.

The German Center for Mental Health : Innovative translational research to promote prevention, targeted intervention and resilience

Due to the high disease burden, the early onset and often long-term trajectories mental disorders are among the most widespread diseases with growing significance. The German Center for Mental Health (DZPG) was established to enhance research conditions and expedite the translation of clinically relevant findings into practice. The aim of the DZPG is to optimize mental healthcare in Germany, influence modifiable social causes and to develop best practice models of care for vulnerable groups. It seeks to promote mental health and resilience, combat the stigmatization associated with mental disorders, and contribute to the enhancement of treatment across all age groups. The DZPG employs atranslational research program that accelerates the translation of basic research findings into clinical studies and general practice. University hospitals and outpatient departments, other university disciplines, and extramural research institutions are working together to establish acollaboratively coordinated infrastructure for accelerated translation and innovation. The research areas encompass 1)the interaction of somatic and mental risk and resilience factors and disorders across the lifespan, 2)influencing relevant modifiable environmental factors and 3)based on this personalized prevention and intervention. The DZPG aims to develop innovative preventive and therapeutic tools that enable an improvement in care for individuals with mental disorders. It involves acomprehensive integration of experts with experience at all levels of decision-making and employs trilogue and participatory approaches in all research projects.

Discount rates and cash flows: A local projection approach

We develop flexible local projections to quantify the relative contributions of expected discount rates and cash flows to the variation of dividend yields. Local projections enable the incorporation of large information sets, the use of monthly data along with annual data, and the consideration of time variation in the dividend yield decomposition. By expanding the set of state variables and allowing for time-varying parameters, our results show that the variation of expected discount rates remains the primary contributor to market volatility, whereas the contribution of expected cash flows is considerably smaller.