Inconsistency Handling Research Articles

How to handle glacier area change in geodetic mass balance

Abstract Innovations in geodesy enable widespread analysis of glacier surface elevation change and geodetic mass balance. However, coincident glacier area data are less widely available, causing inconsistent handling of glacier area change. Here we quantify the bias introduced into meters water equivalent (m w.e.) specific geodetic mass balance results when using a fixed, maximum glacier area, and illustrate the bias for five North American glaciers. Sites span latitudes from the northern U.S. Rocky Mountains (48°N) to the Central Alaska Range (63°N) between 1948 and 2021. Results show that fixed (maximum) area treatment subdues the m w.e. mass change signal, underestimating mass balance by up to 19% in our test cases. This bias scales with relative glacier area change and the mass balance magnitude. Thus, the bias for specific geodetic mass balances will be most pronounced across rapidly deglaciating regions. Our analysis underscores the need for temporally resolved glacier area in geodetic mass balance studies.

Modeling and shadowing paraconsistent BDI agents

The Bdi model of rational agency has been studied for over three decades. Many robust multiagent systems have been developed, and a number of Bdi logics have been studied. Following this intensive development phase, the importance of integrating Bdi models with inconsistency handling and revision theory have been emphasized. There is also a demand for a tighter connection between Bdi-based implementations and Bdi logics. In this paper, we address these postulates by introducing a novel, paraconsistent logical Bdi model close to implementation, with building blocks that can be represented as Sql/rule-based databases. Importantly, tractability is achieved by reasoning as querying. This stands in a sharp contrast to the high complexity of known Bdi logics. We also extend belief shadowing, a shallow and lightweight alternative to deep and computationally demanding belief revision, to encompass agents’ motivational attitudes.

Selecting accepted assertions in partially ordered inconsistent DL-Lite knowledge bases

The problem of inconsistency handling in knowledge bases has received considerable research attention in several settings and has met many applications. Inconsistency usually stems from the fact that the information available for reasoning, query answering and decision-making tasks is obtained from different, scattered and potentially unreliable information sources. Performing these tasks is often computationally expensive, especially for knowledge bases in which the data is endowed with preferences and/or uncertainty. In the context of formal ontologies, enriching the assertions (i.e. the data pieces) with conceptual knowledge makes the query answering task harder computationally. Tractable results have been successfully established for the lightweight languages of the DL-Lite family. In this paper, we present two tractable methods for handling inconsistency in partially ordered lightweight ontologies. In essence, both methods are inspired from the well-known Intersection of ABox Repair (IAR) semantics, which proceeds by replacing the initial inconsistent knowledge base with the intersection of all of its maximally consistent sub-bases. The first method assumes a partial preorder over the knowledge base and identifies the accepted assertions, called ‘elected’, in polynomial time in data complexity. The second method captures uncertainty quantitatively in the framework of possibility theory and identifies the so-called ‘π-accepted’ assertions, also in polynomial time in data complexity. However, both characterisations require the conflict set to be readily available. This constitutes a serious limitation in frameworks and applications where the conflict set cannot be exhibited efficiently. In this paper, we propose a new and equivalent characterisation for each method. We show that the elected assertions and also the π-accepted assertions can be identified in polynomial time in the size of the dataset for lightweight ontologies.

An aquatic environmental DNA filtration system to maximize recovery potential and promote filtration approach standardization.

Aquatic environmental DNA (eDNA) has emerged as a promising approach to identify organisms in freshwater and marine environments. While the recovery of eDNA from water most commonly involves capture of biological debris on a filter matrix, practitioners are yet to converge on standardized approaches for filtration, particularly in the field. This lack of standardization has resulted in inconsistent handling of samples following collection, limiting interpretation of results across studies and burdening groups with inconvenient storage and transport logistics that may compromise eDNA integrity. A simple to assemble and low-cost ($350 USD) water filtration system is demonstrated that can be used in field and laboratory settings to reduce time between sample acquisition and eDNA filtration, maximizing eDNA sample recovery. Quantitative PCR is used to show the utility of the platform for laboratory and marine eDNA analysis. The resulting eDNA collection system is easily transported in a rugged water-resistant case, operates for more than eight hours on a 12-volt lead-acid battery, and has an unobstructed filtration rate of 150.05 ± 7.01 mL/min and 151.70 ± 6.72 mL/min with 0.22 µm and 0.45 µm Sterivex filters, respectively. We show that immediate sample filtration increases eDNA recovery in the laboratory, and demonstrate collections in aquaria and marine environments. We anticipate that providing easy to obtain, open hardware designs for eDNA sample collection will increase standardization of aquatic eDNA collection methods and improve cross-study comparisons.

Improving Deep Learning Classifiers Performance via Preprocessing and Class Imbalance Approaches in a Plant Disease Detection Pipeline

The foundation of effectively predicting plant disease in the early stage using deep learning algorithms is ideal for addressing food insecurity, inevitably drawing researchers and agricultural specialists to contribute to its effectiveness. The input preprocessor, abnormalities of the data (i.e., incomplete and nonexistent features, class imbalance), classifier, and decision explanation are typical components of a plant disease detection pipeline based on deep learning that accepts an image as input and outputs a diagnosis. Data sets related to plant diseases frequently display a magnitude imbalance due to the scarcity of disease outbreaks in real field conditions. This study examines the effects of several preprocessing methods and class imbalance approaches and deep learning classifiers steps in the pipeline for detecting plant diseases on our data set. We notably want to evaluate if additional preprocessing and effective handling of data inconsistencies in the plant disease pipeline may considerably assist deep learning classifiers. The evaluation’s findings indicate that contrast limited adaptive histogram equalization (CLAHE) combined with image sharpening and generative adversarial networks (GANs)-based approach for resampling performed the best among the preprocessing and resampling techniques, with an average classification accuracy of 97.69% and an average F1-score of 97.62% when fed through a ResNet-50 as the deep learning classifier. Lastly, this study provides a general workflow of a disease detection system that allows each component to be individually focused on depending on necessity.

Actors' perceptions of environmental impact assessment (EIA) benefits to fulfil Sweden's national environmental objectives

Activities that require a permit in Sweden account for a significant part of the environmental impact that jeopardizes the fulfilment of the 16 Swedish National Environmental Quality Objectives (EQOs) set up by the Swedish Parliament. In this study we investigate how the EQOs are perceived as a management tool in the Swedish Environmental Impact Assessment (EIA) process, mainly based on interviews.We have identified several limitations associated with the use of EQOs in EIAs as an effective management tool towards sustainability. These limitations include that the EQOs have a subordinate significance compared with other aspects in the EIA process and that EQOs represent different concepts of sustainability. Furthermore, EQOs have low validity and are set as national objectives, hence separated from the operational level of EIAs.A significant proportion of environmental pressures leading to failure to achieve the EQOs are related to permit requiring activities, hence EIA and the permitting process can be important policy instruments to achieve the Swedish EQOs. Integrating EQOs better into EIAs may facilitate handling of synergies, inconsistencies, and trade-offs between environmental and sectoral objectives. Furthermore, driving forces of environmental problems may be targeted more directly.However, in order for the Swedish EQOs to have a larger impact in the permitting process, the EQO system may need to be adapted, and may also need to be complemented with binding standards or legal norms. Furthermore, clearer guidelines regarding issues to focus on, and necessary templates and documentation may be useful tools to facilitate the process even further.

Implementation of K-Means Algorithm for Diseases Clustering in Elderly Posyandu Participants

The Posyandu of Tirem Village is one of the integrated service posts for the elderly, where they can get proper health services. To get the right health services, Posyandu officers group elderly posyandu participants who suffer from chronic diseases for counseling and treatment. The problems that occur during the data collection and counseling process carried out by Posyandu officers are in the calculations that are still basic and are carried out alternately on the elderly Posyandu participants in Tirem village. So this method has the risk of inaccurate data collection and inconsistent handling for the treatment of elderly residents due to different health histories among the elderly. This study aims to classify the data of elderly posyandu participants in Tirem Village who suffer from chronic diseases with predetermined attributes. This grouping process uses the Clustering method using the K-Means Algorithm. The data used in the form of 40 elderly posyandu participant data in October 2022. The results of data processing using the K-Means algorithm with Microsoft Excel tools and using RapidMiner obtained the same results, namely Cluster 1 and cluster_0 have a total of 32 data from 40 data, Cluster 2 and cluster_1 have a total of 8 data from 40 data.

Substituting serum anti-Müllerian hormone for polycystic ovary morphology increases the number of women diagnosed with polycystic ovary syndrome: a community-based cross-sectional study.

Can serum anti-Müllerian hormone (AMH) replace polycystic ovary morphology (PCOM) determined by ultrasound as a diagnostic component of polycystic ovary syndrome (PCOS)? Despite good correlations between serum AMH and PCOM, the use of a high serum AMH as a proxy for PCOM resulted in the reclassification of PCOS in 5% of study participants, with the main effect being more women identified, although some women previously classified as having PCOS were no longer classified as such. AMH has been proposed as an alternative to PCOM as a diagnostic component of PCOS. Previous studies are limited by poorly defining PCOS, use of infertile women as comparators, measurement of hormones by immunoassay that lack precision in the female range, low-resolution ovarian ultrasound and inconsistent handling and storage of serum samples. This is an Australian cross-sectional study of 163 non-healthcare-seeking women. Serum AMH was measured by both the Ansh picoAMH assay and the Beckman Coulter Access 2 (BA2) assay, in parallel with androgens measured by liquid chromatography-tandem mass spectrometry, in blood samples of women, not pregnant, breast feeding or using systemic steroids, who also underwent high-resolution ovarian ultrasound. PCOS was determined by the Rotterdam criteria with PCOM defined by the Androgen Excess-PCOS Taskforce recommendation of ≥25 follicles in at least one ovary. Cut-off serum concentrations that best identified women as having PCOM were identified by receiver operator characteristic (ROC) curves. A total of 163 women, mean (SD) age 32.5 (5.5) years, who provided a blood sample and had both ovaries visualized on ultrasound were included in the analysis. Women with isolated PCOM had higher median (range) Ansh AMH and BA2 AMH concentrations than those with no PCOS characteristics [56.9 pmol/l (34.6, 104.2) versus 18.7 (3.2, 50.9), P = 0.002 and 38.5 pmol/l (22.2, 100.2) versus 16.7 (3.5, 38.9), P = 0.002, respectively]. An AMH ≥ 44.0 pmol/l, suggested by the ROC curve, identified 80.6% of women with PCOM, falsely identified 15.2% of women without PCOM as having PCOS and had a positive predictive value of 55.6%. The negative predictive value was 94.9%. An AMH BA2 assay cut-off of ≥33.2 pmol/l provided a sensitivity of 80.6%, a specificity of 79.5% and a positive predictive value for PCOM of 48.1%. The negative predictive value was 94.6% for PCOM. When serum AMH was used in the place of PCOM as a diagnostic criterion for PCOS, the Ansh assay resulted in an additional seven women classified as having PCOS and no longer classified one woman as having PCOS. For the BA2 assay, eight additional and two fewer women were classified as having PCOS. Overall, both assays resulted in six more women being classified as having PCOS. Women with functional hypogonadotrophic hypogonadism were not excluded and may have been misclassified as having an oligo-amenorrhoea-PCOM phenotype. As study participants were predominantly Caucasian/White, our findings cannot be generalized to women of other ethnicities. Although serum AMH reflects the number of developing ovarian follicles, the absolute values vary between assays and specific reference ranges for individual assays are required. Irrespective of the assay used, replacing PCOM with serum AMH to diagnose PCOS in a community-based sample altered the number of women classified as having or not having PCOS. Consequently, although overall the risk of women being identified as having PCOS would be increased, some women would no longer be classified as having this condition. The study was supported by the Norman Beischer Research Foundation and the Grollo-Ruzzene Foundation. S.R.D. is an NHMRC Senior Principal Research Fellow (Grant No. 1135843). S.R.D. reports unrelated support that includes grants from the NHMRC Australia, personal fees for educational activities from Besins Healthcare, Abbott Chile, BioFemme and Pfizer Australia, personal Advisory Board/consultancy fees from Theramex, Abbott Laboratories, Astellas, Mayne Pharmaceuticals, Roche Diagnostics, Lawley Pharmaceuticals and Que Oncology and has received institutional grant funding from Que Oncology and Ovoca research. The other authors declare no conflicts of interest. N/A.

Distributed Probabilistic Fuzzy Rule Mining for Clinical Decision Making

INTRODUCTION: With the growing size, complexity, and distributivity of databases, efficiency and scalability have become highly desirable attributes of data mining algorithms in decision support systems. OBJECTIVES: This study aims for a computational framework for clinical decision support systems that can handle inconsistent dataset while also being interpretable and scalable. METHODS: This paper proposes a Distributed Probabilistic Fuzzy Rule Mining (DPFRM) algorithm that extracts probabilistic fuzzy rules from numerical data using a self-organizing multi-agent approach. This agent-based method provides better scalability and fewer rules through agent interactions and rule-sharing. RESULTS: The performance of the proposed approach is investigated on several UCI medical datasets. The DPFRM is also used for predicting the mortality rate of burn patients. Statistical analysis confirms that the DPFRM significantly improves burn mortality prediction by at least 3%. Also, the training time is improved by 17% if implemented by a parallel computer. However, this speedup decreases with increased distributivity, due to the added communication overhead. CONCLUSION: The proposed approach can improve the accuracy of decision making by better handling of inconsistencies within the datasets. Furthermore, noise sensitivity analysis demonstrates that the DPFRM deteriorates more robustly as the noise levels increase.

Reporting "Reasonable Suspicion" of Child Abuse - A Survey among Physicians and Psychotherapists in Germany

There is evidence that health care professionals are uncertain about the legal framework when suspicion of child abuse or neglect is raised. This could result in inconsistent handling of such cases, putting children at risk in further danger. The present study was intended to provide an empirical basis for examining the knowledge of the legal framework among health care professionals. A survey of child and youth physicians, child surgeons, child and adolescent psychiatrists as well as psychotherapists working in Germany was carried out to obtain information on the general conditions. In all occupational groups, a majority of respondents feel insecure about the application of the legal framework on particular cases. Only a minority can correctly reflect the legal regulations of the Federal Child Protection Act ("Bundeskinderschutzgesetz"). Experience with child abuse cases doubled the odds to correctly understanding legal frameworks. Having attended training courses showed no impact. There is little knowledge of the legal framework in child protection. There is a need to improve training and provide low-threshold counselling services, especially for professionals with little experience in child protection cases.

Fixing Design Inconsistencies of Polymorphic Methods using Swarm Intelligence

Background: Modern industry is heavily dependent on software. The complexity of designing and developing software is a serious engineering issue. With the growing size of software systems and increase in complexity, inconsistencies arise in software design and intelligent techniques are required to detect and fix inconsistencies. Aim: Current industrial practice of manually detecting inconsistencies is time consuming, error prone and incomplete. Inconsistencies arising as a result of polymorphic object interactions are hard to trace. We propose an approach to detect and fix inconsistencies in polymorphic method invocations in sequence models. Method: A novel intelligent approach based on self regulating particle swarm optimization to solve the inconsistency during software system design is presented. Inconsistency handling is modelled as an optimization problem that uses a maximizing fitness function. The proposed approach also identifies the changes required in the design diagrams to fix the inconsistencies. Result: The method is evaluated on different software design models involving static and dynamic polymorphism and inconsistencies are detected and resolved. Conclusion: Ensuring consistency of design is highly essential to develop quality software and solves a major design issue for practitioners. In addition, our approach helps to reduce the time and cost of developing software.

A Short Survey on Inconsistency Handling in Ontology-Mediated Query Answering

This paper provides a concise overview of the literature on inconsistency handling for ontology-mediated query answering, a topic which has grown into an active area of research over the last decade. The focus of this survey is on the case where errors are localized in the data (i.e., the ontology is deemed reliable) and where inconsistency-tolerant semantics are employed with the aim of obtaining meaningful information from inconsistent knowledge bases.

Surface Electromyography Normalization Affects the Interpretation of Muscle Activity and Coactivation in Children With Cerebral Palsy During Walking.

Investigating muscle activity and coactivation with surface electromyography (sEMG) gives insight into pathological muscle function during activities like walking in people with neuromuscular impairments, such as children with cerebral palsy (CP). There is large variation in the amount of coactivation reported during walking in children with CP, possibly due to the inconsistent handling of sEMG and in calculating the coactivation index. The aim of this study was to evaluate how different approaches of handling sEMG may affect the interpretation of muscle activity and coactivation, by looking at both absolute and normalized sEMG. Twenty-three ambulatory children with CP and 11 typically developing (TD) children participated. We conducted a three-dimensional gait analysis (3DGA) with concurrent sEMG measurements of tibialis anterior, soleus, gastrocnemius medialis, rectus femoris, and hamstring medialis. They walked barefoot at a self-selected, comfortable speed back and forth a 7-m walkway. The gait cycle extracted from the 3DGA was divided into six phases, and for each phase, root mean square sEMG amplitude was calculated (sEMG-RMS-abs), and also normalized to peak amplitude of the linear envelope (50-ms running RMS window) during the gait cycle (sEMG-RMS-norm). The coactivation index was calculated using sEMG-RMS-abs and sEMG-RMS-norm values and by using two different indices. Differences between TD children's legs and the affected legs of children with CP were tested with a mixed model. The between-subject muscle activity variability was more evenly distributed using sEMG-RMS-norm; however, potential physiological variability was eliminated as a result of normalization. Differences between groups in one gait phase using sEMG-RMS-abs showed opposite differences in another phase using sEMG-RMS-norm for three of the five muscles investigated. The CP group showed an increased coactivation index in two out of three muscle pairs using sEMG-RMS-abs and in all three muscle pairs using sEMG-RMS-norm. These results were independent of index calculation method. Moreover, the increased coactivation indices could be explained by either reduced agonist activity or increased antagonist activity. Thus, differences in muscle activity and coactivation index between the groups change after normalization. However, because we do not know the truth, we cannot conclude whether to normalize and recommend incorporating both.

On the measure of conflicts: an argumentation-based framework

An important issue in the management of knowledge-based systems is the handling of inconsistency. This problem has recently been attracting a lot of attention from Artificial Intelligence community. When inconsistency occurs in a knowledge base, there are mainly two ways to deal with it; we either resolve it or accept inconsistency and cope with it. This paper tackles the problem of evaluating the amount of contradiction in propositional knowledge bases, and provides a new measure of conflict based on deductive argumentation theory. Measuring the degree of conflict of a knowledge base can help us to deal with inconsistencies. Several semantic- and syntax-based approaches have been proposed separately. Given the pivotal role of argumentation in representing and handling inconsistency, in this paper, we use logical argumentation as a way to compute the inconsistency measure for propositional formulae. We show using the complete argumentation tree that our family of inconsistency measures is able to localise the conflict of a formula following its context and allows us to distinguish between formulae. We extend our measure to quantify the degree of inconsistency of a set of formulae and give a general formulation of the inconsistency using some logical properties. We also provide a general formulation of our method in order to quantify the conflict of the whole knowledge base. Finally, we address the problem of restoring consistency using inconsistency measures.

The Effect of Gentle Handling on Depressive-Like Behavior in Adult Male Mice: Considerations for Human and Rodent Interactions in the Laboratory.

Environmental factors play a significant role in well-being of laboratory animals. Regulations and guidelines recommend, if not require, that stressors such as bright lighting, smells, and noises are eliminated or reduced to maximize animal well-being. A factor that is often overlooked is handling and how researchers interact with their animals. Researchers, lab assistants, and husbandry staff in animal facilities may use inconsistent handling methods when interacting with rodents, but humans should be considered a part of the animal's social environment. This study examined the effects of different handling techniques on depressive-like behavior, measured by the Porsolt forced swim test, in adult C57BL/6J male mice. The same two researchers handled the mice in a gentle, aggressive, or minimal (control) fashion over approximately two weeks prior to testing. The results demonstrated a beneficial effect of gentle handling: gentle handling reduced swimming immobility in the forced swim test compared to mice that were aggressively or minimally handled. We argue that gentle handling, rather than methodical handling, can foster a better relationship between the handlers and rodents. Although handling is not standardized across labs, consistent gentle handling allows for less challenging behavioral testing, better data collection, and overall improved animal welfare.

Systematic Review of Software Behavioral Model Consistency Checking

In software development, models are often used to represent multiple views of the same system. Such models need to be properly related to each other in order to provide a consistent description of the developed system. Models may contain contradictory system specifications, for instance, when they evolve independently. Therefore, it is very crucial to ensure that models conform to each other. In this context, we focus on consistency checking of behavior models. Several techniques and approaches have been proposed in the existing literature to support behavioral model consistency checking. This article presents a Systematic Literature Review (SLR) that was carried out to obtain an overview of the various consistency concepts, problems, and solutions proposed regarding behavior models. In our study, the identification and selection of the primary studies was based on a well-planned search strategy. The search process identified a total of 1770 studies, out of which 96 have been thoroughly analyzed according to our predefined SLR protocol. The SLR aims to highlight the state-of-the-art of software behavior model consistency checking and identify potential gaps for future research. Based on research topics in selected studies, we have identified seven main categories: targeted software models, types of consistency checking, consistency checking techniques, inconsistency handling, type of study and evaluation, automation support, and practical impact. The findings of the systematic review also reveal suggestions for future research, such as improving the quality of study design and conducting evaluations, and application of research outcomes in industrial settings. For this purpose, appropriate strategy for inconsistency handling, better tool support for consistency checking and/or development tool integration should be considered in future studies.

On an MCS-based inconsistency measure

An important problem in knowledge-based systems is inconsistency handling. This problem has recently been attracting a lot of attention in AI community. In this paper, we tackle the problem of evaluating the amount of conflicts in knowledge bases, and provide a new fine grained inconsistency measure, denoted IMCC, based on maximal consistent sets (MCSes). The main idea consists in quantifying the inconsistency of a knowledge base by considering that all its consistent pieces of information are possible. Furthermore, we provide an epistemic interpretation of our inconsistency measure using the multimodal logic S5. Then, we show that IMCC satisfies several state-of-the-art postulates. Moreover, we provide an encoding in integer linear programming for computing our inconsistency measure, which is defined from the set of MCSes. We also propose a Partial Max-SAT encoding, which allows us to avoid the computation of the MCSes. Finally, we provide a comparison between IMCC and two related existing inconsistency measures.

Traffic fatality trends in four continents based micro level data for three decades

Traffic fatality data for four continents, namely Asia, Africa, Europe and North America, are analysed and modelled here. The analysis is based on gathered traffic death frequencies and rates for over three decades considering 176 countries within the four continents. The predicted values for the coming decade, based on best regression fits, are also analysed. The data sums to over 28,000 records. Official publications, well-known databases, journal papers, official web-sites and personal correspondance were manily employed in collecting the necessary data. While fatlity and vehicular data for the developed countries and few other less developed countries take no time to compile; those for most of the other countries are highly time consuming and distracting, due to the presence of conflicting data, to be processed. It took over three years to gather such data. Such large gathered data for such long period of time are yet not observed in the literature using the common means. Moreover, while future trends of traffic fatalities in Europe and North America are very common and widely presented; that in Asia and Africa are very scarce and lack accuracy due to lack of long-term data. The results indicate that traffic fatalities in Asia and Africa raised from 153 thousands and 46 thousands during 1980 to 384 thousands and 90 thousands deaths during 2011, in respective order; that in Europe and North America dropped from 97 thousands and 57 thousands deaths to 39 thousands and 33 thousands, respectively. It is also intresting to mention that the traffic fatalities in the Arab world sharply increased from 22,145 during 1980 to 37,736 during 2011. It is expected to pass 45 thousands deaths during 2021. The traffic fatalities in the four continents will add up to just 790 thousands considering a flat rate of adjustment factor for underreporting. This however raises serious question regarding underreporting of the official records. Nevertheless, while the roadway deaths in Europe and North America is expected to drop by around 40 and 20%, respectively by the year 2025, according to the models developed here, that in Asia and Africa is expected to increase by around 15 and 70%, respectively. The fatality rates per population followed a very similar pattern, but steeper, of the fatality frequency. There are many contributory factors leading to such high traffic deaths in Asia and Africa. These include, among many other factors, lack of measurable long term safety plans on real ground, inconsistent handling of traffic safety strategies, poor involvement of NGO’s, poor coordination between various stake holders, poor research involvement in the traffic safety crises and limited post-accident rehabilitation centres. Selected

<sc>Cina</sc>: Suppressing the Detection of Unstable Context Inconsistency

Context-aware applications adapt their behavior based on contexts. Contexts can, however, be incorrect. A popular means to build dependable applications is to augment them with a set of constraints to govern the consistency of context values. These constraints are evaluated upon context changes to detect inconsistencies so that they can be timely handled. However, we observe that many context inconsistencies are unstable. They vanish by themselves and do not require handling. Such inconsistencies are detected due to misaligned sensor sampling or improper inconsistency detection scheduling. We call them un s table con t ext in consistencies (or STINs). STINs should be avoided to prevent unnecessary inconsistency handling and unstable behavioral adaptation to applications. In this article, we study STINs systematically, from examples to theoretical analysis, and present algorithms to suppress their detection. Our key insight is that only certain patterns of context changes can make a consistency constraint subject to the detection of STINs. We derive such patterns and proactively use them to suppress the detection of STINs. We implemented our idea and applied it to real-world applications. Experimental results confirmed its effectiveness in suppressing the detection of numerous STINs with negligible overhead, while preserving the detection of stable context inconsistencies that require inconsistency handling.

Investigation of fatal crash trends in the Arab world compared with Eu

Over three decades, vehicular and fatality related data are analysed and modelled for the Arab world and compared with 27 EU countries. The data comparison and modelling include vehicle development, death frequencies and death rates per inhabitants and vehicles. The modelling is based on least-squares regression fit considering around two-third of a million gathered data points for 45 considered countries. The gathered Arab countries’ data, as a whole, are thought to be presented for the first time; since no such data are yet observed in the literature. It is expected that an extra 117 million vehicles will be pumped into the global road network in a decade time from the earlier two regions. This sums to around 15% of the current global vehicle population. While such an increase looks very encouraging for the vehicles’ manufacturers, its environmental consequences and power consumption needs are surely not. While crash deaths in EU countries dropped from 74,876 during 1980 to 30,170 during 2011, that in the Arab countries increased from 22,145 victims to 37,736. The death record in the Arab countries is expected to further escalate to 54 thousand by the year 2022 based on mathematical models developed here. This represents around 45% increase since 2011. That for 27 EU countries is expected to be within 10 and 20 thousand deaths, which is less than half of the 2011 record. The crash death rates in the EU countries decreased continuously during the past three decades and will continue doing so during the coming decade. That in the Arab world will also continue dropping gently during the coming decade. Currently, the overall rates for the EU countries is half that of the average Arab countries. The gap between the two will increase with the time if no proper counter action is considered. The rates in the Arab countries is quite misleading because of the clear difference in the vehicle ownership rate and average vehicle-miles travelled between Arab and EU countries. While the deaths rates per population in the 15 EU countries are heading towards zero in a decade time; the rate in the Arab countries are expected to increase by 16%. Many factors contribute to such high rates of traffic deaths in the Arab countries, as lack of measurable long-term safety plans, inconsistent handling of traffic safety strategies, poor research involvement and limited post-accident rehabilitation centres.