J Jumadi, Nick Malleson, Steve Carver and Duncan Quincey: Managing disasters caused by natural events, especially volcanic crises, requires a range of approaches, including risk modelling and analysis. Risk modelling is commonly conducted at the community/regional scale using GIS. However, people and objects move in response to a crisis, so static approaches cannot capture the dynamics of the risk properly, as they do not accommodate objects’ movements within time and space. The emergence of Agent-Based Modelling makes it possible to model the risk at an individual level as it evolves over space and time. We propose a new approach of Spatio-Temporal Dynamics Model of Risk (STDMR) by integrating multi-criteria evaluation (MCE) within a georeferenced agent-based model, using Mt. Merapi, Indonesia, as a case study. The model makes it possible to simulate the spatio-temporal dynamics of those at risk during a volcanic crisis. Importantly, individual vulnerability is heterogeneous and depends on the characteristics of the individuals concerned. The risk for the individuals is dynamic and changes along with the hazard and their location. The model is able to highlight a small number of high-risk spatio-temporal positions where, due to the behaviour of individuals who are evacuating the volcano and the dynamics of the hazard itself, the overall risk in those times and places is extremely high. These outcomes are extremely relevant for the stakeholders, and the work of coupling an ABM, MCE, and dynamic volcanic hazard is both novel and contextually relevant.

Qing Xu, Sylvie Huet, Eric Perret and Guillaume Deffuant: The drivers of conversion to organic farming, which is still a residual choice in agriculture, are poorly understood. Many scholars argue that farm characteristics can determine this choice but do not exclude the role of social dynamics. To study this issue, we developed an agent-based model in which agents' decisions to shift to organic farming are based on a comparison between satisfaction with the current situation and potential satisfaction with an alternative farming strategy. A farmer agent’s satisfaction is modelled using the Theory of Reasoned Action. This makes it necessary to compare an agent's productions over time with those of other agents to whom the former attributes considerable credibility (“important others”). Moreover, farmers make technical changes that affect their productions by imitating other credible farmers. While we first used this model to examine simple and abstract farm populations, here we also adapted it for use with data from an Agricultural Census concerning the farm characteristics of dairy farming in 27 French “cantons”. Based on domain expertise, data and previous research, we propose certain laws for modelling the impact of conversion on the farm production of milk and the environment. The simulations with “real” populations of farms confirm the important impact of farm characteristics. However, our results also suggest a complex impact of social dynamics that can favour or impede the diffusion of organic farming through dynamic implicit networks of similarity and credibility. We confirm the great importance of demographic changes in farm characteristics.

Alireza Mansouri and Fattaneh Taghiyareh: Human interactions and opinion exchanges lead to social opinion dynamics, which is well described by opinion formation models. In these models, a random parameter is usually considered as the system noise, indicating the individual's inexplicable opinion changes. This noise could be an indicator of any other influential factors, such as public media, affects, and emotions. We study phase transitions, changes from one social phase to another, for various noise levels in a discrete opinion formation model based on the social impact theory with a scale-free random network as its interaction network topology. We also generate another similar model using the concept of social power based on the agents' node degrees in the interaction network as an estimation for their persuasiveness and supportiveness strengths and compare both models from phase transition viewpoint. We show by agent-based simulation and analytical considerations how opinion phases, including majority and non-majority, are formed in terms of the initial population of agents in opinion groups and noise levels. Two factors affect the system phase in equilibrium when the noise level increases: breaking up more segregated groups and dominance of stochastic behavior of the agents on their deterministic behavior. In the high enough noise levels, the system reaches a non-majority phase in equilibrium, regardless of the initial combination of opinion groups. In relatively low noise levels, the original model and the model whose agents' strengths are proportional to their centrality have different behaviors. The presence of a few high-connected influential leaders in the latter model consequences a different behavior in reaching equilibrium phase and different thresholds of noise levels for phase transitions.

Bruno Pietzsch, Sebastian Fiedler, Kai G. Mertens, Markus Richter, Cédric Scherer, Kirana Widyastuti, Marie-Christin Wimmler, Liubov Zakharova and Uta Berger: The recent advancement of agent-based modeling is characterized by higher demands on the parameterization, evaluation and documentation of these computationally expensive models. Accordingly, there is also a growing request for "easy to go" applications just mimicking the input-output behavior of such models. Metamodels are being increasingly used for these tasks. In this paper, we provide an overview of common metamodel types and the purposes of their usage in an agent-based modeling context. To guide modelers in the selection and application of metamodels for their own needs, we further assessed their implementation effort and performance. We performed a literature research in January 2019 using four different databases. Five different terms paraphrasing metamodels (approximation, emulator, meta-model, metamodel and surrogate) were used to capture the whole range of relevant literature in all disciplines. All metamodel applications found were then categorized into specific metamodel types and rated by different junior and senior researches from varying disciplines (including forest sciences, landscape ecology, or economics) regarding the implementation effort and performance. Specifically, we captured the metamodel performance according to (i) the consideration of uncertainties, (ii) the suitability assessment provided by the authors for the particular purpose, and (iii) the number of valuation criteria provided for suitability assessment. We selected 40 distinct metamodel applications from studies published in peer-reviewed journals from 2005 to 2019. These were used for the sensitivity analysis, calibration and upscaling of agent-based models, as well to mimic their prediction for different scenarios. This review provides information about the most applicable metamodel types for each purpose and forms a first guidance for the implementation and validation of metamodels for agent-based models.

Katarzyna Growiec, Jakub Growiec and Bogumił Kamiński: We study the impact of endogenous creation and destruction of social ties in an artificial society on aggregate outcomes such as generalized trust, willingness to cooperate, social utility and economic performance. To this end we put forward a computational multi-agent model where agents of overlapping generations interact in a dynamically evolving social network. In the model, four distinct dimensions of individuals’ social capital: degree, centrality, heterophilous and homophilous interactions, determine their generalized trust and willingness to cooperate, altogether helping them achieve certain levels of social utility (i.e., utility from social contacts) and economic performance. We find that the stationary state of the simulated social network exhibits realistic small-world topology. We also observe that societies whose social networks are relatively frequently reconfigured, display relatively higher generalized trust, willingness to cooperate, and economic performance – at the cost of lower social utility. Similar outcomes are found for societies where social tie dissolution is relatively weakly linked to family closeness.

Szymon Talaga and Andrzej Nowak: Real-world social networks often exhibit high levels of clustering, positive degree assortativity, short average path lengths (small-world property) and right-skewed but rarely power law degree distributions. On the other hand homophily, defined as the propensity of similar agents to connect to each other, is one of the most fundamental social processes observed in many human and animal societies. In this paper we examine the extent to which homophily is sufficient to produce the typical structural properties of social networks. To do so, we conduct a simulation study based on the Social Distance Attachment (SDA) model, a particular kind of Random Geometric Graph (RGG), in which nodes are embedded in a social space and connection probabilities depend functionally on distances between nodes. We derive the form of the model from first principles based on existing analytical results and argue that the mathematical construction of RGGs corresponds directly to the homophily principle, so they provide a good model for it. We find that homophily, especially when combined with a random edge rewiring, is sufficient to reproduce many of the characteristic features of social networks. Additionally, we devise a hybrid model combining SDA with the configuration model that allows generating homophilic networks with arbitrary degree sequences and we use it to study interactions of homophily with processes imposing constraints on degree distributions. We show that the effects of homophily on clustering are robust with respect to distribution constraints, while degree assortativity can be highly dependent on the particular kind of enforced degree sequence.

Myong-Hun Chang and Joseph Harrington: The dynamics of social stigma are explored in the context of diffusion models. Our focus is on exploring the dynamic process through which the behavior of individuals and the interpersonal relationships among them influence the macro-social attitude towards the stigma. We find that a norm of tolerance is best promoted when the population comprises both those whose conduct is driven by compassion for the stigmatized and those whose focus is on conforming with others in their social networks. A second finding is that less insular social networks encourage de-stigmatization when most people are compassionate, but it is instead more insularity that promotes tolerance when society is dominated by conformity.

Volker Grimm, Steven F. Railsback, Christian E. Vincenot, Uta Berger, Cara Gallagher, Donald L. DeAngelis, Bruce Edmonds, Jiaqi Ge, Jarl Giske, Jürgen Groeneveld, Alice S.A. Johnston, Alexander Milles, Jacob Nabe-Nielsen, J. Gareth Polhill, Viktoriia Radchuk, Marie-Sophie Rohwäder, Richard A. Stillman, Jan C. Thiele and Daniel Ayllón: The Overview, Design concepts and Details (ODD) protocol for describing Individual- and Agent-Based Models (ABMs) is now widely accepted and used to document such models in journal articles. As a standardized document for providing a consistent, logical and readable account of the structure and dynamics of ABMs, some research groups also find it useful as a workflow for model design. Even so, there are still limitations to ODD that obstruct its more widespread adoption. Such limitations are discussed and addressed in this paper: the limited availability of guidance on how to use ODD; the length of ODD documents; limitations of ODD for highly complex models; lack of sufficient details of many ODDs to enable reimplementation without access to the model code; and the lack of provision for sections in the document structure covering model design rationale, the model’s underlying narrative, and the means by which the model’s fitness for purpose is evaluated. We document the steps we have taken to provide better guidance on: structuring complex ODDs and an ODD summary for inclusion in a journal article (with full details in supplementary material; Table 1); using ODD to point readers to relevant sections of the model code; update the document structure to include sections on model rationale and evaluation. We also further advocate the need for standard descriptions of simulation experiments and argue that ODD can in principle be used for any type of simulation model. Thereby ODD would provide a lingua franca for simulation modelling.

Flaminio Squazzoni, J. Gareth Polhill, Bruce Edmonds, Petra Ahrweiler, Patrycja Antosz, Geeske Scholz, Émile Chappin, Melania Borit, Harko Verhagen, Francesca Giardini and Nigel Gilbert: The COVID-19 pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers’ demands for advice despite the absence of adequate, trusted data. Understanding the pandemic requires fine-grained data representing specific local conditions and the social reactions of individuals. While experts have built simulation models to estimate disease trajectories that may be enough to guide decision-makers to formulate policy measures to limit the epidemic, they do not cover the full behavioural and social complexity of societies under pandemic crisis. Modelling that has such a large potential impact upon people’s lives is a great responsibility. This paper calls on the scientific community to improve the transparency, access, and rigour of their models. It also calls on stakeholders to improve the rapidity with which data from trusted sources are released to the community (in a fully responsible manner). Responding to the pandemic is a stress test of our collaborative capacity and the social/economic value of research.

Given the Indian economy could well contract in 2020-21,following the disruption to the pandemic, even bigger companies might enourage some employees to work from home. For small businesses there might be no other option.

The concern is genuine as new hotspots are emerging in districts like Idukki and Kottayam that were earlier in the green zone and considered safe.

Using R programming, marketers can create a marketing mix model to determine how sustainable their audience channels are, and make better ad spend decisions. Here's how

Only the Countryman now is available with a diesel engine in India while all other models come with the familiar 2.0-litre petrol motor.

The Mini Cooper SE made its debut last year with a funky wheel design which they then called the ‘Corona Spoke’, which has turned out to be somewhat inappropriate in recent times.

As the nation continues to be under lockdown due to the coronavirus pandemic. Honda cars India has launched an online portal for prospective customers who can book their Honda model of their choosing sitting at home, safe from COVID-19.

Excitement levels are high for the March 2020 release of SAS Customer Intelligence 360, which includes multiple years of research and development culminating in enhancements to the platform's underlying data model. The changes will introduce the unification of a comprehensive data model recording both: Customer behavior -- what users are [...]

SAS Customer Intelligence 360: Unified data model, marketing attribution and AutoML was published on Customer Intelligence Blog.

  Introduction Character modeling is the process of creating a character within the 3D space of computer programs. The techniques for character modeling are essential for third - and first - person e...

The letter has urged, mainly for wage subsidy to large employers, on the lines of the Netherlands and Australia .

The Hybrid Annuity Model (HAM) for highway projects that insulates private developers from virtually any commercial risks, is putting stress on the highly-leveraged balance sheet of the National Highways Authority of India (NHAI), and should therefore be ‘downplayed’.

The ID.3 is the first fully electric model based on the new MEB platform

Jim Harris explains the relevance of DevOps, DataOps and ModelOps for data analytics practitioners.

The post Why Ops-ing is in (DevOps, ModelOps, DataOps) appeared first on The Data Roundtable.

He said within six years of the Modi government, the northeastern region has emerged as the model for development for the entire country.

I have a modeless informational dialog box defined at the beginning of a SKILL script, but its contents don't display until the script finishes.

How do you get a modeless dialog box contents to display while a SKILL script is running?

procedure(myproc()

   prog((myvars)

     hiDisplayAppDBox()    ; opens blank dialog box - no dboxText contents show until script completes!

     ....rest of SKILL code in script...launches child processes

   );prog

);proc

I'm trying to generate IBIS models for the parts that I'm designing.  I'm designing using CADENCE Virtuoso.  

I'm wondering if there is a tutorial for generating IBIS models in CADENCE Virtuoso.   Please pardon me if my question is broad.      

Those who work in the IC Packaging design space have some unique challenges. We bridge between the IC design world (90/45-degree traces with rectangular and octagonal pins) and the PCB domain...

[[ Click on the title to access the full blog on the Cadence Community site. ]]

Hello,

I am using virtuoso version IC 6.1.7-64b.500.1, and I am trying to follow the Spectre RF Workshop-Noise-Aware PLL Design Flow(MMSIM 7.1.1) pdf.

I could find the workshop library "pllMMLib", but I cannot find PLL Macro Model Wizard, and I attached my screen.

Could you please help me install the module "PLL Macro Model Wizard"?

Thanks a lot!

I'm working on a circuit that requires the input voltage to be converted to a frequency, transmitted over an optical cable, and then converted back to a voltage. I am attempting to simulate this circuit using Eagle ngSpice simulations. The voltage to frequency converters that I am using are ADVFC32 and made by Analog Devices. However, I can't seem to find a SPICE model for this component. Analog Devices does not provide it on their website. Can anyone find a SPICE Model for this part? I'm new to working with electronics so any help/advice you can provide would be appreciated.

Hi

     I want develope basic circuit of DC motor which consist of resistor, inductor and back emf in capture and check its simulation in pspice, for reference I have attached image and link.

https://www.precisionmicrodrives.com/content/ab-025-using-spice-to-model-dc-motors/ .

Those who work in the IC Packaging design space have some unique challenges. We bridge between the IC design world (90/45-degree traces with rectangular and octagonal pins) and the PCB domain (any-angle routing, filled planes, and a multitude of pad ...(read more)

I would like to add these components to the component bank in ORCAD simulation. Even an accessible or free course that explained how to create these components.

i had encountered error message like this before. 

but in liberate, i did not find the entry to input section info. 

I have a question regarding simulating IBIS model using Spectre.  IBIS model generation always has the die capacitance included and in the generated IBIS file you will have this value as  “C_comp” value.  Does the Spectre accounts for this capacitance from the IBIS file while computing the time domain voltage waveform during simulation ?  If I add additional capacitance outside in the testbench, to model the die capacitance, then it will be double counting.

Does anyone know if Spectre is already accounting this C_comp during the time domain voltage wave computation from IBIS file, during simulation ?

Hello,

I am trying to import (in spectre) an spice model of a ceramic capacitor manufactured by Samsung EM. The link that includes the model is here :-

http://weblib.samsungsem.com/mlcc/mlcc-ec.do?partNumber=CL05A156MR6NWR

They proved static spice model and interactive spice model.

I had no problem while including the static model.

However, the interactive model which models voltage and temperature coefficients seems to not be an ordinary spice model. They provide HSPICE, LTSPICE, and PSPICE model files and I failed to include any of them.

Any suggestions ?

Hello,

I am using ultrasim Version 18.1.0.314.isr5  64bit 03/26/2019 06:33 (csvcm20c-2).

When I run my netlist, ultrasim is blocked in the first DC stage and takes forever. Then it will fail or never progress. I am using a 22nm BSIMBULK model. I tried to tune different accuracy and convergence aids options but noting works.

 When I run the same netlist with spectre it works fine with no problem.

Also, If I use another model (not BULKSIM), ultrasim will work and converge with no problem.

My first feeling is that ultrasim has a problem with using BSIMBULK model.

Could you please advice,

Thank you,

Kotb

Before I ask this, I am aware that I can output an s-parameter file from an SP analysis.

I'm wondering if there is a simple way of creating an s-parameter model of a component.

As an example, if I have an S-parameter model that has 200 ports and 150 of those ports are to be connected to passive components and the remaining 50 ports are to be connected to active components, I can simplify the model by connecting the 150 passive components, running an SP analysis, and generating a 50 port S-parameter file.

The problem is that this is cumbersome. You've got to wire up 50 PORT components and then after generating the s50p file, create a new cellview with an nport component and connect the 50 ports with 50 new pins.

Wiring up all of those port components takes quite a lot of time to do, especially as the "choosing analyses" form adds arrays in reverse (e.g. if you click on an array of PORT components called X<0:2> it will add X<2>, X<1>, X<0> instead of in ascending order) so you have to add all of them to the analyses form manually.

Is any way of taking a schematic and running some magic "generate S-Parameter cellview from schematic cellview"  function that automates the whole process?

Key Findings:  Nearly 100% of SoCs are mixed-signal to some extent.  Every one of these could benefit from the use of a metrics-driven unified verification methodology for mixed-signal (MD-UVM-MS), but the modeling step is the biggest hurdle to overcome.  Without the magical models, the process breaks down for lack of performance, or holes in the chip verification.

In the last installment of The Low Road, we were at the mixed-signal verification party. While no one talked about it, we all saw it: The party was raging and everyone was having a great time, but they were all dancing around that big elephant right in the middle of the room. For mixed-signal verification, that elephant is named Modeling.

To get to a fully verified SoC, the analog portions of the design have to run orders of magnitude faster than the speediest SPICE engine available. That means an abstraction of the behavior must be created. It puts a lot of people off when you tell them they have to do something extra to get done with something sooner. Guess what, it couldn’t be more true. If you want to keep dancing around like the elephant isn’t there, then enjoy your day. If you want to see about clearing the pachyderm from the dance floor, you’ll want to read on a little more….

Figure 1: The elephant in the room: who’s going to create the model?

 Whose job is it?

Modeling analog/mixed-signal behavior for use in SoC verification seems like the ultimate hot potato.  The analog team that creates the IP blocks says it doesn't have the expertise in digital verification to create a high-performance model. The digital designers say they don’t understand anything but ones and zeroes. The verification team, usually digitally-centric by background, are stuck in the middle (and have historically said “I just use the collateral from the design teams to do my job; I don’t create it”).

If there is an SoC verification team, then ensuring that the entire chip is verified ultimately rests upon their shoulders, whether or not they get all of the models they need from the various design teams for the project. That means that if a chip does not work because of a modeling error, it ought to point back to the verification team. If not, is it just a “systemic error” not accounted for in the methodology? That seems like a bad answer.

That all makes the most valuable guy in the room the engineer, whose knowledge spans the three worlds of analog, digital, and verification. There are a growing number of “mixed-signal verification engineers” found on SoC verification teams. Having a specialist appears to be the best approach to getting the job done, and done right.

So, my vote is for the verification team to step up and incorporate the expertise required to do a complete job of SoC verification, analog included. (I know my popularity probably did not soar with the attendees of DVCON with that statement, but the job has to get done).

It’s a game of trade-offs

The difference in computations required for continuous time versus discrete time behavior is orders of magnitude (as seen in Figure 2 below). The essential detail versus runtime tradeoff is a key enabler of verification techniques like software-driven testbenches. Abstraction is a lossy process, so care must be taken to fully understand the loss and test those elements in the appropriate domain (continuous time, frequency, etc.).

Figure 2: Modeling is required for performance

AFE for instance

The traditional separation of baseband and analog front-end (AFE) chips has shifted for the past several years. Advances in process technology, analog-to-digital converters, and the desire for cost reduction have driven both a re-architecting and re-partitioning of the long-standing baseband/AFE solution. By moving more digital processing to the AFE, lower cost architectures can be created, as well as reducing those 130 or so PCB traces between the chips.

There is lots of good scholarly work from a few years back on this subject, such as Digital Compensation of Dynamic Acquisition Errors at the Front-End of ADCS and Digital Compensation for Analog Front-Ends: A New Approach to Wireless Transceiver Design.

Figure 3: AFE evolution from first reference (Parastoo)

The digital calibration and compensation can be achieved by the introduction of a programmable solution. This is in fact the most popular approach amongst the mobile crowd today. By using a microcontroller, the software algorithms become adaptable to process-related issues and modifications to protocol standards.

However, for the SoC verification team, their job just got a whole lot harder. To determine if the interplay of the digital control and the analog function is working correctly, the software algorithms must be simulated on the combination of the two. That is, here is a classic case of inseparable mixed-signal verification.

So, what needs to be in the model is the big question. And the answer is, a lot. For this example, the main sources of dynamic error at the front-end of ADCs are critical for the non-linear digital filtering that is highly frequency dependent. The correction scheme must be verified to show that the nonlinearities are cancelled across the entire bandwidth of the ADC. 

This all means lots of simulation. It means that the right level of detail must be retained to ensure the integrity of the verification process. This means that domain experience must be added to the list of expertise of that mixed-signal verification engineer.

Back to the pachyderm

There is a lot more to say on this subject, and lots will be said in future posts. The important starting point is the recognition that the potential flaw in the system needs to be examined. It needs to be examined by a specialist.  Maybe a second opinion from the application domain is needed too.

So, put that cute little elephant on your desk as a reminder that the beast can be tamed.

Steve Carlson

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- It’s Late, But the Party is Just Getting Started

Verification is the top challenge in mixed-signal design. Bringing analog and digital domains together into unified verification planning, simulating, and debugging is a challenging task for rapidly increasing size and complexity of mixed-signal designs. To more completely verify functionality and performance of a mixed-signal SoC and its AMS IP blocks used to build it, verification teams use simulations at transistor, analog behavioral and real-number model (RNM) and RTL levels, and combination of these.

In recent years, RNM and simulation is being adopted for functional verification by many, due to advantages it offers including simpler modeling requirements and much faster simulation speed (compared to a traditional analog behavioral models like Verilog-A or VHDL-AMS). Verilog-AMS with its wreal continue to be popular choice. Standardization of real number extensions in SystemVerilog (SV) made SV-RNM an even more attractive choice for MS SoC verification.

Verilog-AMS/wreal is scalar real type. SV-RNM offers a powerful ability to define complex data types, providing a user-defined structure (record) to describe the net value. In a typical design, most analog nodes can be modeled using a single value for passing a voltage (or current) from one module to another. The ability to pass multiple values over a net can be very powerful when, for example, the impedance load impact on an analog signal needs to be modeled. Here is an example of a user-defined net (UDN) structure that holds voltage, current, and resistance values:

When there are multiple drives on a single net, the simulator will need a resolution function to determine the final net value. When the net is just defined as a single real value, common resolution functions such as min, max, average, and sum are built into the simulator.  But definition of more complex structures for the net also requires the user to provide appropriate resolution functions for them. Here is an example of a net with three drivers modeled using the above defined structural elements (a voltage source with series resistance, a resistive load, and a current source):

To properly solve for the resulting output voltage, the resolution function for this net needs to perform Norton conversion of the elements, sum their currents and conductances, and then calculate the resolved output voltage as the sum of currents divided by sum of conductances.

With some basic understanding of circuit theory, engineers can use SV-RNM UDN capability to model electrical behavior of many different circuits. While it is primarily defined to describe source/load impedance interactions, its use can be extended to include systems including capacitors, switching circuits, RC interconnect, charge pumps, power regulators, and others. Although this approach extends the scope of functional verification, it is not a replacement for transistor-level simulation when accuracy, performance verification, or silicon correlation are required:  It simply provides an efficient solution for discretely modeling small analog networks (one to several nodes).  Mixed-signal simulation with an analog solver is still the best solution when large nonlinear networks must be evaluated.

Cadence provides a tutorial on EEnet usage as well as the package (EEnet.pkg) with UDN definitions and resolution functions and modeling examples. To learn more, please login to your Cadence account to access the tutorial.

Experts from Rocky Mountain Institute, the Advanced Energy Economy Institute and America’s Power Plan have released a new report that shows why new utility business models are key to the energy transition.

New York State is pushing its utility industry to shift away from a century-old business model into a system that can accommodate more power from solar and wind.

Europe’s utilities are re-evaluating their business models due to the energy transition. Members of POWER-GEN Europe’s Advisory Board consider how a reliance on fossil fuels is no longer politically desirable, forcing utilities to transform their portfolios to adapt to radical change.

Check here each week to keep up with the latest from John MacArthur's pulpit at Grace Community Church.

1. FCA Final Report on Motor Finance In March 2019, the FCA published its Final Report on motor finance. Our briefing note on the Final Report can be found Full Article