Credit Value Research Articles

Process development and optimization of sustainable and integrated large scale ammonia production process using water electrolysis based green hydrogen: Investigating process, energy, economic & environmental perspectives

Ammonia is an important raw material for fertilizer production and significantly contributes globally to agricultural systems. The multi-objective optimization of a large-scale integrated ammonia production process using water electrolysis-based green hydrogen is carried out using the Hybrid Multi-Objective Differential Evolution-Distance Learning Scheme (Hybrid MODE-DLS) algorithm. Three optimization cases are solved with the objectives of ammonia production rate, total production cost, and total energy by considering nine decision variables, and multiple constraints. The maximum ammonia production (66.51 kmol/h) and minimum total production cost (105.07 $/yr × 105) are obtained in Case 1 whereas the minimum energy value (7863.45 kWh) is obtained in Case 2. The minimum heat credit value of 3057300.5 $/annum exists in Case 2 solutions whereas the maximum heat credit value of 7992076 $/annum is seen in Case 3. The ammonia production rate increased by 82.79% in Case 2 results compared to the base case results.

Concordit: A credit-based incentive mechanism for permissioned redactable blockchain

Malicious attacks and the introduction of illegal data put blockchains at risk, and blockchain governance is gaining increasing attention. The redactable blockchain technology has become a mainstream solution for blockchain governance. However, a low completion rate for redaction tasks limits current redactable blockchain technologies, primarily due to the absence of an effective incentive mechanism for participants. This gap underscores the urgent need for designing and implementing robust incentive mechanisms in redactable blockchains. Incentive mechanisms can motivate and guide entities to participate and perform desired behaviors through awards and punishments. This paper proposes Concordit, the first deployable credit-based incentive mechanism for redactable blockchains. Its purpose is to encourage submitters to submit legal redaction requests, modifiers to perform legal redaction operations, and verifiers to maintain the behavior consistent with the consensus algorithm. In the context of permissioned blockchains, Concordit utilizes a credit value system for awards and punishments. Additionally, we use a game theory-based mechanism to analyse and model participants’ behavior utilities in the redactable blockchain. Meanwhile, we evaluate the credibility of nodes by combining their static initial credit values and dynamic behavior-related credit values. This system prioritizes high-credibility nodes as participants, thereby enhancing the completion rate for redaction tasks. Finally, the implementation and performance evaluation of our Concordit incentive mechanism demonstrate its effectiveness and practicality.

Empirical Analysis of the Macroeconomic Drivers of Banking Industry Stability in Nigeria

The main purpose of this study is to determine the macroeconomic drivers of banking industry stability in Nigeria. The study employed Quantitative Research method using ex-post research design to analyse the impact of exchange rate, interest rate, inflation rate, economic growth rate, and unemployment rate on bank credit-asset ratio from 1991 to 2023. Based on preliminary estimations from the Phillips-Perron unit root and F-Bounds tests, the study employed the Autoregressive Distributed Lag (ARDL) technique to establish the following key findings. Macroeconomic variables collectively and significantly influence banking industry stability. Specifically, high economic growth rate, moderate inflation rate, and low unemployment rate are the core drivers of banking industry stability, while high interest rate and volatile exchange rate exert a significant negative influence on banking industry stability. Consequently, bankers and macroeconomic policymakers should collaborate to ensure that macroeconomic policy frameworks provide banks with the opportunities for capital mobilisation necessary to effectively perform their financial intermediation roles for the overall benefit of the economy. The study recommends balancing macroeconomic targets and bank stability through growth-inflation-employment targeting policies to improve households' loan repayment resilience and reduce the credit risk of Nigerian banks. Additionally, exchange rate stabilisation through government intervention is suggested to help banks hedge against asset and credit value loss due to exchange rate volatility.

Load-balanced Routing Heuristics for Bandwidth Allocation of AVB Flow in TSN

Time-Sensitive Networking (TSN) is a new technology developed from Ethernet that guarantees deterministic transmission of various types of flows, such as Time-triggered (TT) flows and Audio-video-bridging (AVB) flows, in the same network. Currently, Time-aware Shaping (TAS) and Credit-based Shaping (CBS) are widely used for scheduling hybrid flows, where the credit value in CBS is directly linked to the logical bandwidth value idleSlope , which affects the real-time performance of AVB flows. However, as the network scale increases, existing bandwidth allocation methods result in higher computation times and lower allocation success rates. In this paper, the aforementioned problem is addressed by two-step: first, we design a load-balanced routing heuristic (LBRH) to improve the allocation success rate; second, we accelerate the CBS bandwidth allocation by using unified bandwidth allocation scheme, which integrates LBRH and further improves the allocation success rate. Performance evaluation in multiple test cases shows that LBRH can improve the success rate of bandwidth allocation, and the unified bandwidth allocation scheme integrating LBRH significantly reduces the overall execution time while improving the success rate of bandwidth allocation, which is more suitable for large-scale TSN networks with complex routing.

Adaptive stacked species distribution modelling: Novel approaches to large scale quantification of blue carbon to support marine management

This study introduces a novel concept of ‘Adaptively Stacked’ Species Distribution Models (AS-SDMs) to predict blue carbon habitat distribution, abundance, carbon stocks, and carbon sequestration potential in Orkney. AS-SDMs are built from Weighted Boosted Regression Trees (WBRTs) that adaptively stack blue carbon sediment thickness, sediment carbon content, and sequestration potential to predicted abundance. A novel method to describe substrate types by relative inputs of mud, sand, and gravel is detailed that better characterises the determining factors of seagrass, maerl, and horse mussel abundance. This study also introduces a novel use of indexes to mitigate double counting issues of mixed species distribution models.Seagrass, maerl, horse mussel, and mixed seagrass and maerl (SGM) habitats are estimated to cover a maximum area of 657 km2 in Orkney, have a total sediment carbon stock of 16 Mt. C, and sequester 6000 t C yr−1. Applying a conservative threshold of 50 % abundance to habitat predictions, six key potential areas of blue carbon offset projects are identified. These areas cover just over 9 km2, have a total carbon stock of 330,000 t C, and sequester 330 t C yr−1. When applied to UK carbon credit value, assuming integration with voluntary markets and compliance with accreditation criteria, the habitats in these areas have a potential value of £24.5 million. If applied as annual values, these areas have carbon stocks with a potential value of £0.93 million yr−1 and a carbon sequestration potential value of £24,000 yr−1.

CE-PBFT: An Optimized PBFT Consensus Algorithm for Microgrid Power Trading

Currently, in the blockchain-based distributed microgrid trading system, there are some problems, such as low throughput, high delay, and a high communication overhead. To this end, an improved Practical Byzantine Fault Tolerance (PBFT) algorithm (CE-PBFT) suitable for microgrid power trading is proposed. First, a node credit value calculation model is introduced, and nodes are divided into consensus, supervisory, and propagation nodes according to their credit values, forming a hierarchical network structure to ensure the efficiency and reliability of consensus. Secondly, the consensus process is optimized by adopting a segmented consensus mechanism. This approach calculates the consensus rounds for nodes and selects the methods for node-type switching and consensus based on these calculations, reaching dynamic changes in node states and credit values, effectively reducing the communication overhead of node consensus. Finally, the experiments show that compared with the IMPBFT and PBFT algorithms, the CE-PBFT algorithm has better performance in throughput, delay, and communication overhead, with a 22% higher average throughput and 15% lower average delay than the IMPBFT algorithm and a 118% higher average throughput and 87% lower average delay than the PBFT algorithm.

Can you credit it? Towards a process for ascribing credit to apprenticeships in England

PurposeApprenticeships in England, while defined by level and typical duration, are not quantified regarding the number of learning hours required to achieve the outcomes specified, as with other regulated qualifications and accredited programmes. This paper proposes an approach to ascribe credit to apprenticeships recognising both on-and-off-the-job learning to remove some of the existing barriers to accessing higher education (HE) and the professions.Design/methodology/approachA mixed methodological approach resulting in a total learning hours/credit value was proposed.FindingsThere is significant HE-wide confusion regarding the amount of learning/training that is required to complete apprenticeships in England. Whilst sector guidance made it clear that there was no prescribed method to ascribe credit to qualifications, programmes, modules, units or apprenticeships by drawing out the core principles within current practice, a key outcome of this project was the development of a method to ascribe a credit value to apprenticeships.Research limitations/implicationsThere is potential to support further research into the recognition of prior learning as a specialised pedagogy and for reflecting on apprenticeship practice in other roles and sectors.Practical implicationsWhilst the project underpinning this paper focused on the healthcare sector, the method used to ascribe credit to the level-3 healthcare support worker apprenticeship was not sector specific and can therefore be applied to apprenticeships within other contexts providing more widespread benefits to workforce development.Social implicationsPolicy makers must ensure that employers and providers are clear that the minimum statutory off-the-job hours constitute an apprentice employment entitlement, which must not be conflated with total apprenticeship learning hours requirements. This recommended policy clarification could assist in simplifying the process required for ascribing credit to apprenticeships and at the same time support a move towards better and more consistent recognition of the value of apprenticeship learning.Originality/valueIt is a first attempt to ascribe a credit value to an apprenticeship in England for the specific purpose of facilitating progression to HE.

Domestic Credit Growth Analysis using ARIMA Technique: A Case Study of Kenya

The aim of this paper is to technically analyze domestic credit growth in Kenya using the Box -Jenkin framework. Box -Jenkin technique uses ARIMA model, which is used to understand time series data and forecast future values of the observed data for a given period. The framework involves three iterative step to develop a model The first step involves model parameter identification. Unit root test show that domestic credit is stationary after the first difference. The ACF and PACF indicates cut off at lag zero in both cases. There are seasonal component in the ARIMA model. In the second step of model estimation, ARIMA(2,1,2)(1; 0; 0)12 is obtained as the best fit for the sample period under consideration. Model diagnostics test in the third step, proves that there ARIMA modeling assumptions are met. The estimated model is used to forecast credit growth for the next twelve monthly values in year 2024 upto December. The estimated model projects an increase trend in domestic credit growth for the year 2024, with the percentage growth rate ranging between 1.045 to 1.230 percent. The expected domestic credit value in December 2024 is yellow is highly likely to hit Kenya shilling 8.039 Trillions mark. Kenya government should put in place monetary policy that favors bank credit growth and competition in the banking sector.

A blockchain-based data storage architecture for Internet of Vehicles: Delay-aware consensus and data query algorithms

The recent development of the Internet of Vehicles (IoV) relies heavily on the secure store and analysis of reliable driving data. Blockchain technology has been widely used in the IoV field due to its security advantages, where consensus and query algorithms are two key modules determining its performance. However, existing solutions do not perform well enough in terms of delay performance, thus limiting their ability to support IoV environments with ultra-low-latency requirements. To address this challenge, we design a blockchain-based data storage architecture, focusing on the design of delay-aware consensus and data query algorithms. Specifically, to reduce the consensus delay, we propose a reputation-assisted and grouping-simplified Practical Byzantine Fault Tolerant (RGS-PBFT) algorithm. First, we develop a novel node credit value evaluation model by combining the behavior and configuration reputation to select the primary node with both high security and high computing capacity. Second, after determining the primary node, we reduce the communication complexity of traditional PBFT from O(Z2) to O(Z43) by establishing a grouping-simplified consensus structure. These contributions effectively reduce the consensus delay. To reduce the query delay and index construction time, we propose an index query algorithm based on the data tracking chain (DTC-Index query). By introducing the LevelDB database, it establishes a transaction index chain coupled to vehicle ID in the blockchain. This approach avoids the time-consuming block traversal during data querying. Experimental results demonstrate that the proposed algorithms outperform existing solutions, achieving significant improvements in both consensus delay and query time reduction. Notably, our solutions offer exceptional delay performance, making them well-suited for IoV environments.

A static replication approach for callable interest rate derivatives: mathematical foundations and efficient estimation of SIMM–MVA

The computation of credit risk measures such as exposure and Credit Value Adjustments (CVA) requires the simulation of future portfolio prices. Recent metrics, such as dynamic Initial Margin (IM) and Margin Value Adjustments (MVA) additionally require the simulation of future conditional sensitivities. For portfolios with non-linear instruments that do not admit closed-form valuation formulas, this poses a significant computational challenge. This problem is addressed by proposing a static replication algorithm for interest rate options with early-exercise features under an affine term-structure model. Under the appropriate conditions, we can find an equivalent portfolio of vanilla options that replicate these products. Specifically, we decompose the product into a portfolio of European swaptions. The weights and strikes of the portfolio are obtained by regressing the target option value with interpretable, feed-forward neural networks. Once an equivalent portfolio of European swaptions is determined, we can leverage on closed-form expressions to obtain the conditional prices and sensitivities, which serve as an input to exposure and SIMM-driven MVA quantification. For a consistent forward sensitivity estimation, this involves the differentiation of the portfolio-weights. The accuracy and convergence of the method is demonstrated through several representative numerical examples, benchmarked against the established least-square Monte Carlo method.

Penerapan Buy Back Guarantee Bagi Pembeli dan Developer Berdasarkan Undang-Undang Perlindungan Konsumen

Housing is one of the needs that must be met by all people in the world, both in developing and developed countries, in addition to food needs, proper housing certainly influences the formation of the nation's personality so that they can socialize well in society. Every year the need for houses as a place to live or occupancy is increasing due to the increasing population both in rural and urban areas. Basically, fulfilling housing needs is the responsibility of each community itself, but the state helps the community to obtain and provides convenience in obtaining housing through housing developers. Housing developers or developers who have built their homes can sell to debtors with a gradual cash payment system and Home Ownership Credit (KPR), a gradual cash payment system. For KPR, a credit agreement can be signed which contains the approved credit value and the credit loan repayment period. The credit agreement made must be known by the housing developer or developer, which can be in the form of a private deed, or a deed drawn up before a notary or land deed official.

Wireless sensor network security defense strategy based on Bayesian reputation evaluation model

AbstractIn order to solve the security problems caused by malicious nodes in wireless sensor networks, a TS‐BRS reputation model based on time series analysis is proposed in this paper. By using the time series analysis method, the matching analysis of two time series is carried out to reduce the interference of channel conflicts on the reputation evaluation model and improve the accuracy of model recognition. In order to improve the adaptability of the evaluation model, the adaptive maintenance function μ is introduced into the update of credit value, which aggravates the influence of node behaviour on credit value at the present stage. The simulation results show that the new reputation evaluation model can effectively improve the detection rate and detection speed of malicious nodes in the network. After the introduction of maintenance function, the reputation value of the captured malicious nodes in the network has a faster convergence speed.

Wrong Way Risk corrections to CVA in CIR reduced-form models

In this paper we provide an efficient methodology to compute the credit value adjustment of a European contingent claim subject to some default event concerning the issuer solvability, when the underlying and the default event are correlated. In particular, in a Black and Scholes market/CIR intensity-default model, we consider a second order expansion around the origin of a vulnerable call option with respect to a correlation parameter rho, which may be used to describe the wrong way risk of the contract, measuring the dependence between the underlying asset price and the option’s issuer default intensity. Numerical implementations of this approach are compared with the benchmark Monte Carlo simulations.

Using industrial wastes for rice-wheat cropping and food-energy-carbon-water-economic nexus to the sustainable food system

This experiment aimed to develop a long-term sustainable food-energy-water-carbon-economic nexus module by converting industrial wastes (IWs) to compost. In the methodology, a split-plot design was employed to apply a different recommended dose of fertilizer (RDF) levels, and carbon decomposing microbes (CDMs) converted IWs compost in the rice-wheat cropping. Four RDF levels were used in the main-plot, and nine combinations of CDMs + IWs were used in the sub-plot and replicated thrice. The result indicated that in the interaction impact of nitrogen, phosphorus, potassium (NPK)100% +zinc (Zn)5kg + iron (Fe)5kg × IW1+CDM1 produced 80.2% more grain yield, 68.1 and 176.2% more net energy output and energy balance. There was higher water productivity and lower WFP than those for NPK0 × IW3+CDM3. Further, the main-plot NPK100% +Zn5kg + Fe5kg was observed sequentially at a maximum of US$ 126 and 2149/ha carbon societal and credit value. Meanwhile, the IW1+CDM1 noted a higher US$ 124 and 2094/ha of carbon societal and credit values, respectively in the sub-plot. The first two components from principal component analysis (PCA) of various energy indices and carbon dynamics explained 81 and 14% of the variability, respectively. The hypothesis of the experiment was that the use of IWs combined with inorganic fertilizers and CDMs for rice-wheat cropping; may help to increase crop productivity, carbon dioxide (CO2) sequestration, soil organic carbon (SOC) pools, and reduce carbon footprints (CFs), water footprint (WFP), energy, and fertilizer load to feed the ever-growing population.

Scoring, ranking, and technoeconomics of carbon capture and storage opportunities in the central valley of California

Previous studies of geological carbon storage are reviewed, redefined, and evaluated to provide proper capturable emission amounts and candidate storage sites in the Southern San Joaquin Basin (California). The LCFS eligible capturable GHG emissions from thermal EOR facilities were 11.4 MtCO2/y. Also, the capturable emission amounts and location of other large emitters in both Kern County and southern California were identified. The estimated CO2 storage resource in the 7 qualified saline formations ranged from 16.6 to 52 GtCO2 whereas that in 35 qualified hydrocarbon fields ranged from 0.45 to 1.15 GtCO2. Among the 35 hydrocarbon fields, 15 CO2-EOR candidate fields with storage resources of 0.36 to 0.88 GtCO2 are located in Kern County. Altogether, 41 storage sites including 7 hydrocarbon fields, 32 saline storage sites, and 2 stacked storage sites were defined as CO2 storage locations. The 41 CO2 storage sites in and around Kern County were linked to selected large CO2 emitters in Imperial, Kern, Los Angeles, Orange, San Bernardino, Santa Barbara, San Luis Obispo, and Ventura Counties. Importantly, the technoeconomics of prospective storage sites and emitters were analyzed using SimCCS (Scalable infrastructure model for Carbon Capture and Storage). Regional GHG emissions from oil and gas facilities such as EOR steam generators and CHPs can be captured and stored economically as a result of LCFS and 45Q credits. The 45Q credit value and duration are critical factors to incentivize CCS deployment. The deployment scenarios evaluated teach that the Southern San Joaquin basin is an excellent potential regional carbon storage hub.

Pricing of New Energy Vehicles Considering the Dual Credit Policy

Considering the “dual credit” policy, this paper builds three different power structures to explore price strategies in the supply chain, which is composed of new energy vehicle manufacturers and traditional automobile manufacturers. It has found that when the positive credit value of new energy vehicles is higher than the negative credit value of fuel consumption, new energy vehicle manufacturers will gain higher profits. When the power structure is imbalanced, wholesale and retail prices are mainly determined by the highest price that consumers can accept for passenger cars. When new energy vehicle manufacturers dominate, the clearing price of new energy vehicle credits is mainly influenced by the production cost of new energy vehicles.

Assessing and improving the outcomes of nonpoint source water quality trading policies in urban areas: A case study in Virginia

Nonpoint source (NPS) water quality trading (WQT) is a market-based approach to improving water quality. Past work has shown that these programs could increase localized pollutant loadings, in part by exporting water quality controls from urban to rural areas. Virginia's NPS WQT program has enabled thousands of transactions and may provide a model for other programs, but its impacts on urban water quality have not been thoroughly assessed. We quantify the impact of NPS WQT purchases in Virginia on water quality and hydrology in an urban catchment. We go on to assess outcomes of a policy alternative where buyers and sellers are collocated in the urban catchment. Simulation results show that NPS WQT increased total phosphorus (TP) loading by an average of 0.8 lbs TP/year for each 1.0 offsite credits purchased in the analyzed catchment. The TP loading increased in years with greater rainfall, such that TP loads were increased by up to 1.2 lbs TP/year for each offsite credit purchased. These loading increases may or may not be acceptable, depending on the cumulative number of purchases within an urban catchment and existing local water quality issues. In our policy alternative with buyers and sellers collocated in the catchment, we found that the TP increase from development was completely offset at the catchment scale, with a decrease of 4.3 lbs TP/year for each 1.0 credits purchased. This suggests that credits awarded for urban mitigation practices are undervalued compared with water quality requirements for credit purchasers. This undervaluation is a result of the Virginia trading program using one approach to compute the credit value for buyers and a different approach to compute the credit value for sellers. We demonstrate how using a single model to determine both buyer and seller credit values in urban areas could provide greater transparency and mitigate the risk of urban pollution hot spots. This work demonstrates the importance of consistency in the scale of pollutant load calculations between buyers and sellers for NPS WQT, and contributes novel insight into the implications of WQT for urban NPS pollution.

Credit Default Prediction Based on Blending Learning Model

Currently, the preventions of credit default usually will be evaluated by users credit value before loaning from banks. However, for the loan user, who have no existing record of loaning and the situation of low credit value, it cannot precisely recognize the risk of credit default. After a credit default, the bank not only doesnt get the signed compensation and principal in time, but also the debtor needs to bear the expensive corresponding late fees and credit costs. Therefore, reducing credit defaults can decline more burden of debtors and creditors. In this paper, the authors evaluate multiple machine learning models including algorithms belong to machine learning and deep learning, using blending model to boost the prediction effect and accuracy, while proposing an optimization design to further enhance the stability, accuracy and generalization capacity of proposed algorithm, so as to effectively decrease the credit default rate and the risk of bank loss in practice.

Penerapan Asuransi Dan Akad Murabahah Pada Pembiayaan KPR Di BTN Syariah KC Bandung

Mortgage financing to provide livable and comfortable housing by simply providing a down payment, and the rest is paid in installments every month. Mortgage financing also includes insurance coverage to anticipate if a customer dies and a way for the bank to minimize losses and risks. Qualitative research was conducted to find out the Application of Insurance and Murabahah Contracts in KPR Financing at BTN Syariah KC Bandung which was carried out by studying literature related to Islamic Banking, interviews, and financing procedures. The results of this study are that the application of a murabahah contract at BTN Syariah KC Bandung is used to provide mortgage financing in accordance with the results of the analysis carried out by the bank on prospective customers. First, the bank will inform the assessment of the location and house that the customer wants to own, fit to live in, and is considered comfortable to live in. Second, the customer prepares the required documents for submitting mortgage financing at the BTN Syariah bank. Third, the BTN Syariah bank will consider and assess the completeness of the prospective customer's files, as well as carry out a 5C analysis of the prospective Bank BTN Syariah KPR customers. Fourth, if the results of the analysis are feasible to finance, then proceed to the KPR financing disbursement stage. Fifth, installment payments are made every month according to the agreed period. Furthermore, the application of insurance to KPR financing at BTN Syariah KC Bandung in the form of a customer before signing in front of a notary, the bank asks the customer to prepare a down payment of 10–20% of the property price, booking fee fee, 1% provision fee of the total ceiling, fees credit agreement notary, APHT guarantee costs around 0.25% of 125% credit value, deed of sale and transfer of title, costs of acquisition of building land rights (BPHTB), fire insurance costs, and credit life insurance costs.

Task Load and Work Property-Based Virtual Machine Dispatching Algorithm

This research proposes a virtual machine dispatching algorithm that takes into account the task load and work property of a virtual machine. The algorithm utilizes an interruption monitoring module to assess the interruption frequency of the virtual machine and determine whether it belongs to a CPU-dense or I/O-dense type. Additionally, a CPU monitoring module tracks the current CPU utilization rate to monitor the task load. By considering the historical information within a specified time range, the algorithm calculates a dispatching time segment for the virtual machine and notifies the dispatcher to update the time segment. The dispatcher module adjusts the credit value dispatcher based on inputs from the monitoring and CPU modules. It applies different dispatching time segments to virtual machines with varying work properties, aiming to reduce I/O request delays, provide sufficient time for I/O request handling, and minimize additional overhead. Notably, the modifications occur within the virtual machine monitor, ensuring practicality and adaptability.