In recent years, the most widely used method for studying medical accessibility and the balance between the demand and supply of medical and healthcare facilities is floating catchment area (FCA), and associated enhancements such as the two-step floating catchment area (2SFCA) method, enhanced two-step floating catch- ment area (E2SFCA) method and the three-step floating catchment area (2SFCA) method. FCA originated from spatial decomposition , and is a special case of grav- ity model . The application and improvement of this method made calculations simpler and the results more rational [5, 6, 14–20]. Methods used in other studies in- cluded the gravity model [6, 16, 21] and kernel density estimation (KDE) [20, 22, 23]. Neutens (2015)  fur- ther analyzed the advantages and disadvantages of the aforementioned methods when studying medical accessi- bility. However, there remains a lack of research on roadnetworkvulnerability and its impact on residents’ medical convenience levels.
Configuration is major important to the road system. Due to the reason that we will be able to predict what is the effect to the system, when they are most likely to occur and where is the most severe impact on the link. This is the basic information to form the roadnetworkvulnerability analysis. Then after that we need to identify the possible solutions to prevent recover from disturbances. We also have to mind about cost that will happen during the process due to the resource allocation or various actions required as well. Essentially, in planning development and maintenance of roadnetwork, the decision makers make their choice base on some key factors or criteria such as traffic volume, average speed and travel time, in terms of congestion and delay, and economic issues. Typically the road is planned to be constructed, expanded, or maintained when its traffic volume closed to road capacity, or it needed to improve travel time and travel speed of road users. Furthermore, the location of road development is just concentrated to the area that has more population or greater economic growth.
in an individual’s obtained utility compared to the null scenario for a number of reasons. A component that affects people’s accessibility to critical societal ser- vices and is known to be vital for choices related to travel and activity participation is travel time, and network disruptions often lead to increased travel times for trav- ellers that would normally use the disrupted element or nearby roads indirectly affected by congestion. Beside the possible discomfort of spending an unusually long time travelling, an increase in travel time means that the user will reach (or be reached by) societal services later, or must sacrifice time from other activities that may be more desirable than travel. With appropriate information the user may counteract this utility loss to some extent by cancelling the trip, travelling to other destinations or in other ways adjusting her plans. Paper VI proposes an activity- based model to capture some of these utility losses, for work trips in particular, under various levels of available information and schedule flexibility; see further Section 10.
This project will supplement efforts by the Town of Hempstead and Nassau County to clean out stormwater along roads south of Merrick Road within Seaford and Wantagh. In addition to the clean outs, this project will collect and reconfirm the condition and types of stormwater infrastructure that exist in order to understand any other immediate needs as well as feed into the overall capital and asset management of the stormwater infrastructure.
The Main purpose of this study is to evaluate condition of maintenance components affecting road safety, this require development of a methodology for identification of maintenance components that affect road safety. The methodology is aimed at providing guidance in identifying maintenance components for improving road safety. This methodology is developed in such a manner that it includes all maintenance components that affect road safety. An overall methodology is developed for identification and evaluation of road safety hazardous conditions. At the top of this methodology is the main objective of Road safety hazardous conditions which are to be assessed at network level for maintenance. The road safety hazardous conditions are decomposed in four sections (I) poor maintenance carriageway condition (II) poor road side condition (III) poor road geometric conditions and (IV) poor traffic hazardous condition. Overall methodology is presented in Fig 1 attached at the end in Appendix I.
The assessment of transport network failure consequences has attracted significant attention recently. The main focus of the relatively new notion is to assess not only the actual state of transportation infrastructure but also assess the impact of a network deterioration to the com- munity. The increasing frequency of natural hazards due to global warming (Schneider et al. (6)), recent collapses of transportation links like in New Orleans and Switzerland due to natural hazards and the threat of terrorist attacks enhances the relevance of the topic. Nevertheless, the research community has not reached a common definition of link failure induced transporta- tion related consequences. Several several definitions by different authors provide different perspectives on transportation related consequences including (Berdica (7); Taylor and D’Este (8); Knoop et al. (9); Matisziw et al. (10)). However, what they all have in common, is that they assess the impact of infrastructure failure, though with different measures and methodolo- gies: For instance (Bell (11)) and (Bell and Cassir (12)) have used a game theory approach and forumlated the problem as a 2-player, noncooperative, zero-sum game envisaging between a router, seeking a least-cost path, and a virtual network tester, seeking to maximize transporta- tion consequences by severing one link at a time. Thereby, this approach was applied is to identify the most vulnerable network elements. However, when link costs are assumed to be traffic-dependent, which they are, such a methodology is very computationally intensive and is only applicable within small networks. Besides Monte Carlo Simulation (Chen et al. (13)) and Minimum Cut Set (Iida and Wakabayashi (14)) particularly approaches which incorporate both the demand and supply side of traffic assignment models were used recently for the assessment. They differ mainly in the type of traffic assignment used: Jenelius et al. (15) neglects the traffic dependency of travel times, as the focus of their research was the Swedish roadnetwork with most parts of the country only sparsely populated and link capacity playing only a minor role in the analysis and congestion therefore becomes only a minor problem as result of link fail- ures consequences. This might be a reasonable assumption for spatially disperse countries but Knoop et al. (9) showed for the case of the Rotterdam metro region the need to include capac- ity constraints when analyzing roadnetwork failure consequences in more densely populated areas.
It should be noted again that even in the no-growth scenario, the demand curves for individual links still shift over time. Figure 3 plots the link tolls and capa- cities after the long-run supply–demand equilibria are achieved. The marginal-cost tolls are higher on links in the center of the grid as they attract more traffic and have higher levels of congestion than do links on the edges. The socially optimal capacity distribution is quite (though not perfectly) flat, which is largely explained by the assumed uniform land-use pattern in all zones. Under centralized control, we can observe capacities significantly lower than the optimum levels. Apparently, when the average-cost price implemented as distance-based tolls or fuel taxes is lower than the optimal marginal-cost price (which is the case in congested net- works), it cannot generate sufficient revenues for all desirable network capacity expansions. Quantitative analysis of network evolution under decentralized con- trol has not been available previously. It is therefore interesting to examine the last two graphs in Figure 3. The profit-maximizing capacity is almost the same as the optimal capacity on the edges of the network, and overinvestment can be observed in the center. The higher level of competition among the central links accounts for their higher-than-optimal capacity. On the other hand, tolls are higher in the decentralized private regime, which should reduce traffic volumes and hence the benefit from capacity expansion in terms of congestion relief. The difference between regimes in investment levels is the net effect of these opposing forces. The imperfection of the private road economy is exemplified in the equilibrium toll graphs in Figure 3. The spatial monopoly power of roads is evident and most obvious on the edge and corner links where travelers have fewer or no choices for service. These links are able to maximize profits by charging very high tolls with
Each attribute (i.e. physical connectivity or traffic conditions), can be considered to individually reflect the level of mobility from a certain perspective. Suitable measures can then be introduced to improve the mobility level related to each attribute. However, there is still a need to estimate the overall mobility level by combining the impact of both 𝑃𝐶𝐴 and 𝑇𝐶𝐴. 𝑇𝐶𝐴 is able to clearly reflect the effects of a congested/free flow network, but could underestimate the impact of certain events. For example a link closure could lead to detours with some trips rescheduled or cancelled. As a consequence, network loading will decrease, leading to improved flow in some parts of the network. To reflect these effects on the mobility indicator, 𝑃𝐶𝐴 should also be considered. Consequently, the mobility indicator 𝑀𝐼 should be estimated with consideration to both 𝑃𝐶𝐴 and 𝑇𝐶𝐴. To deal with the complexity and uncertainty of traffic behaviour, the randomised nature of traffic data and to simulate the influences of both 𝑃𝐶𝐴 and 𝑇𝐶𝐴, a fuzzy logic approach was implemented to scale both attributes and combine their impact at the mobility level. The fuzzy logic approach has the ability to interpolate the inherent vagueness of the human mind and to determine a course of action, when the existing circumstances are not clear and the consequence of the course of action have not been identified (Zadeh, 1965). In other words, a fuzzy logic approach deals with the type of uncertainty, which arises when the boundaries of a class of objects are not sharply defined (Nguyen and Walker, 1997).
On one occasion the threats and vulnerabilities have been evaluated, design the penetration testing to deal with the risks recognized all over the environment. . The penetration testing should be suitable for the complexity and size of an organization. Penetration tests different from assessments and evaluations, penetration tests are adversarial in environment. Will refer to penetration tests as level III assessments. All locations of sensitive data, all key applications that store, process, or transmit such data, all key network connections, and all key access points should be included. The penetration testing should try to exploit security vulnerabilities and weaknesses throughout the environment, attempt to penetrate both at the network stage and key applications. The goal of penetration testing is to resolve if unauthorized access to key systems and accounts can be achieved. If access is achieved, the vulnerability ought to be corrected and the penetration testing re-performed in anticipation of the test is spotless and no longer allows unauthorized access or other malicious actions. These measures classically obtain on an adversarial responsibility and give the impression of being to see what the stranger can access and control inside the organization. Penetration tests are much less concerned with policies and procedures and are more focused on finding "low-hanging fruit" and seeing what a hacker can negotiate on this network. We almost always recommend that organizations complete an assessment and evaluation before beginning a penetration test, because a company with sufficient policies and procedures can't implement real security without documented controls. The general NVA life cycle as shown in the below Fig 4.
The recent flood events in Queensland, Australia had an adverse effect on the country’s social and economic growth. Queensland state controlled roadnetwork included 33,337 km of roads and 6,500 bridges and culverts . 2011-2012 flood in Queensland produced record flood levels in southwest Queensland and above average rainfall over the rest of the state . Frequency of flood events in Queensland, during the past decade appears to have increased. In 2009 March flood in North West Queensland covered 62% of the state with water costing $234 million damage to infrastructure . 2010-2011 floods in Queensland had a huge impact particularly on central and southern Queensland resulting in the state owned properties such as 9,170 roadnetwork, 4,748 rail network, 89 severely damaged bridges and culverts, 411 schools and 138 national parks . Approximately 18,000 residential and commercial properties were significantly affected in Brisbane and Ipswich  during this time. More than $42 million support was provided to individual, families and households while more than $121 million in grants have been provided to small businesses, primary producers and not-for-profit organizations. Furthermore, more than $12 million in concessional loans to small businesses and primary producers have been provided . The Australian and Queensland governments have committed $6.8 billion to rebuilding the state. Pritchard  identifies that urban debris, such as cars, and the insufficient bridge span to through the debris are main cause for damaging bridges in the aftermath of 2011/2012 flood in Queensland. Using 2013 flood event in Lockyer Valley, Lokuge and Setunge  concluded that it is necessary to investigate the failure patterns and the construction practices adopted during the initial construction and rehabilitation stages in the lifetime of bridges. These findings raised a question that what are the failure mechanisms and contributing factors which require consideration in designing of bridges to be resilient to extreme flood events.
The client vulnerabilities generally appear difficult to design shields for. However, none of the client vulnerabilities are likely to result in self-propagating worms, because they cannot be exploited without some kind of user action upon the browser. For example, seven involved application file formats. Of the remainder, two were email client vulnerabilities, one was a media player vulnerability, and the rest were found in the browser, and hence invoked via HTML or client-side scripting. Of the server vulnerabilities, twelve might conceivably be exploitable by worms, under “ideal” conditions—i.e., the server application being very widely deployed in an un- protected, unpatched and unfirewalled configuration. The re- mainder included three denial-of-service attacks, three ”cross- site scripting” attacks, and a potential information disclosure. These are not vulnerable to exploitation by worms.
Furthermore, we analyzed the typical fault patterns of
5 http://ftp.gnome.org/- pub/GNOME/sources/libgtop/
each type of vulnerability (forth column in Table 1) and de- vised a set of experiments covering the respective faulty be- haviors (last column), such as the execution of a different set of instructions, an increase in some resource utilization, or an incorrect server response. For instance, vulnerabilities of buffer overflow, format string, SQL injection, or exter- nal application execution, force the server into inadvertently change its control flow of execution, making it execute code that was not intended by the developers. Unusual resource consumption is a clear faulty characteristic of resource ex- haustion vulnerabilities and also of denial of service flaws (cpu/time is also a resource). Directory traversal and infor- mation disclosure vulnerabilities can be characterized by the server’s behavior in granting illegal access to some private resources. This behavior is depicted in the server’s output by responding positively when it should be denying access. The same faulty behavior is observed in misconfiguration issues, such as the existence of default accounts.
This section presents the characterisation, using CNA indexes and metrics, of the global Wheat Trade Network (WTN). This will allow us to understand the structure and the main features of the network. Thus, we are interested in the following: mea- suring different topological features such as the density of the network (i.e. what percentage of all pairs of countries trade); the extent that trade links are reciprocal (i.e. bidirectional); the transitivity (often called clustering in the CNA parlance) of the trade relationships in the WTN; the distances among the nodes in the network; what the most central countries in the global wheat trade are from an importer or an exporter point of view; whether the WTN is scale-free (i.e. its degree distribution follows a Power Law so that most countries have a small number of trading partners, however, there are a few “ hub countries” that have a large number of trading links); whether there is homophily (e.g. of geographical type) so that nodes of the same type trade among themselves more than nodes of different types; whether there exist significant motifs (i.e. local connection patterns that occur with a frequency unlikely to be due to randomness); the community structure (i.e. different groups of countries that trade intensively within-group much more than with countries that belong to other groups), etc. All these questions can be effectively answered using CNA tools and techniques. That is why, as the literature review presented in the previous section shows, CNA has generally been used for this characterisation task.
At present a number of methods are being used to update roadnetwork databases including ground survey, driving along roads with GPS and analysing satellite images to register changes. Previous research has aimed at addressing three update functions: road extraction, change detection and change representation (Zhang, 2004). Different types of image processing algorithms have been developed for each purpose. While image-based road updating approaches have had success, their accuracy is directly tied to the quality of the data (Klang, 1998) and object model used for road extractions (Gerke et al., 2004).
The food system is increasingly globalized, which allows for buffering against local shocks, but exposes regions to external shocks. Evaluating exposure to such shocks helps assess vulnerability and risk within the global food system. Here we studied the response of the global seafood trade network to potential environmental and policy perturbations by modeling how negative local impacts propagate through the trade network and how trade ﬂows are redistributed. Vulnerability to shocks in the network was assessed by comparing changes in national ﬁsh supplies to indices of each country ’s nutritional seafood dependency. The regions with higher imports, notably West Africa, Eastern Asia, and Southern and Western Europe tended to be most exposed within the network. As major exporters, Northern Europe, Eastern Asia, and Southeast Asia have the most signi ﬁcant inﬂuence initiating shocks in the network. Comparing exposure to sensitivity, revealed West and Central Africa to be relatively vulnerable to shocks within the network,
how they differ to the more conventional IP-based systems. As most tools for security auditing or ethical/unethical hacking were developed for IP-based networks, it is vital to understand how an ICS or SCADA system differs in terms of the physical devices present on the network, what embedded or bespoke software is installed on those devices, and how that may cause defects or irregularities when faced with the existing scanning tools. Chromik et al.  give an extensive review as to how SCADA systems work on the physical layer of networking. programmable logic controllers (PLCs), Remote Terminal Units (RTUs), and Intelligent Electronic Devices (IED) are all referenced within this paper, with details about how data from field devices is acquired or monitored by one of these physical machines. SCADA servers, historians, and Human Machine Interfaces (HMIs) are also mentioned as part of an informal system description, where the basic hierarchy and control flow of SCADA are outlined at a high level. Although this paper is able to highlight the main devices which are both unique and essential to SCADA and ICS, the amount of detail given as to how each device functions and communicates, in particular focussing on layers 3–7 of the Open Systems Interconnection (OSI) model, is significantly lacking in content and depth. Having knowledge of how each device utilises its data through each one of the OSI layers would be highly advantageous towards developing a clear understanding of how the process and services present on these SCADA nodes could potentially compromise the whole system. This paper fails to provide details in this area. National Communications System (2004) is much more descriptive about not only the physical devices which form a SCADA system but also the protocols they use. Here, SCADA data flow is explained using examples of the devices mentioned in Chromik et al. . However, the description of each devices’ responsibility is more elaborate, referring to how RTUs act as interfaces which convert electronic signals from the field devices into a protocol which can then be utilised by the extended network. This information is then extended to PLCs, demonstrating how the two technologies link together, and provides details about the history and evolution of these devices. The details this paper is providing about Distributed Network Protocol (DNP3) are very thorough and cover all areas of discussion around this protocol, for example, the relationship between DNP3 clients and servers, as well as showing a typical design diagram for a DNP3 network architecture. However, the paper fails to address the wider range of SCADA technologies and protocols which are still utilised in today’s systems, that is, Modbus, Siemens S7, and so forth. The content of this paper seems to skip the details about the higher levels of the SCADA infrastructure, such as the HMIs and SCADA servers, something which was explained within Chromik et al. .
Abstract: Presented in this paper is a major step towards an innovative solution of GIS roadnetwork databases updating which moves away from existing traditional methods where vendors of roadnetwork databases go through the time consuming and logistically challenging process of driving along roads to register changes or GIS roadnetwork update methods that are exclusively tied to remote sensing images. Our proposed road database update solution would allow users of GIS roadnetwork dependent applications (e.g. in-car navigation system) to passively collect characteristics of any “unknown route” (roads not in the database) on behalf of the provider. These data are transferred back to the provider and inputted into an artificial neural net (ANN) which decides, along with similar track data provided by other service users, whether to automatically update (add) the “unknown road” to the road database on probation allowing subsequent users to see the road on their system and use it if need be. At a later stage when there is enough certainty on road geometry and other characteristics the probationary flag could be lifted and permanently added to the roadnetwork database. Towards this novel approach we mimicked two journey scenarios covering two test sites and aimed to group the road segments from the journey into their respective road types using the snap-drift neural network (SDNN). The performance of the SDNN is presented and its potential in the proposed solution is investigated.
In the current research therefore, a new method based on fuzzification and an exhaustive search optimisation technique is employed to combine the various attributes (defined above) into a vulnerability index. Fuzzification is the process of converting a crisp quantity to a fuzzy one (Ross, 2005). It is adopted here to accommodate the complexity and uncertainty in traffic behaviour alongside randomised elements in both traffic data and the simulation process. Each attribute is evaluated according to four assessment levels represented by four fuzzy membership functions. An exhaustive search technique is then employed to identify the optimal weight contribution of each fuzzified attribute. This is determined by the level of weights at which the correlation between the vulnerability index (obtained from the weighted attributes) and the given total travel cost is the strongest. Travel cost could be estimated based on different factors such as travel time, distance or toll. In this research travel time is used as an estimate of travel cost, however, the method is flexible and could accommodate other cost measures. The full details of the technique are presented in the following sub sections.