A Comprehensive Review of Intrusion Detection and Prevention Systems against Single Flood Attacks in SIP-Based Systems

Voice over Internet Protocol (VoIP) is a recent voice communication technology and due to its variety of calling capabilities, the system is expected to fuel the market value even further in the next five years. However, there are serious concerns since VoIP systems are frequently been attacked. According to recent security alliance reports, malicious activities have increased largely during the current pandemic against VoIP and other vulnerable networks. This hence implies that existing models are not sufficiently reliable since most of them do not have a hundred percent detection rate. In this paper, a review of our most recent Intrusion Detection & Prevention Systems (IDPS) developed is proposed together with a comparative analysis. The final work consisted of ten models which addressed flood intentional attacks to mitigate VoIP attacks. The methodological approaches of the studies included the quantitative and scientific paradigms, for which several instruments (comparative analysis and experiments) were used. Six prevention models were developed using three sorting methods combined with either a modified galloping algorithm or an extended quadratic algorithm. The seventh IDPS was designed by improving an existing genetic algorithm (e-GAP) and the eighth model is a novel deep learning method known as the Closest Adjacent Neighbour (CAN). Finally, for a better comparative analysis of AI-based algorithms, a Deep Analysis of the Intruder Tracing (DAIT) model using a bottom-up approach was developed to address the issues of processing time, effectiveness, and efficiency which were challenges when addressing very large datasets of incoming messages. This novel method prevented intruders to access a system without authorization and avoided any anomaly filtering at the firewall with a minimum processing time. Results revealed that the DAIT and the e-GAP models are very efficient and gave better results when benchmarking with models. These two models obtained an F-score of 98.83%, a detection rate of 100%, a false rate of 0%, an accuracy of 98.7%, and finally a processing time per message of 0.092 ms and 0.094 ms respectively. When comparing with previous models in the literature from which it is specified that detection rates obtained are 95.5% and falsepositive alarm of around 1.8%, except for one recent machine learning-based model having a detection rate of 100% and a processing time of 0.53 ms, the DAIT and the e-GAP models give better results.

Mortality prediction in chronic interstitial lung diseases (ILDs): ILD-GAP (gender age physiology) and (the new modified classification) TNMC-ILD-GAP

Introduction: The interstitial lung disease-gender age physiology (ILD-GAP) model has been proposed on the lines of GAP-idiopathic pulmonary fibrosis (IPF) to predict mortality in chronic ILDs, other than IPF. We replaced diffusion capacity of lung for carbon monoxide (DLCO) with the easy to perform 6-minute walk distance (6 MWD) in the ILD-GAP and proposed the new modified classification (TNMC)-ILD-GAP model. Our aim was to demonstrate the usefulness of the TNMC-ILD-GAP as a predictor of mortality in all chronic ILDs and compare the results with the ILD-GAP. Methods: It was a retrospective observational study conducted at a tertiary care centre. Baseline characteristics of the patients among different ILD groups were analysed. A 3-year follow-up was recorded. The results were compared with the original ILDGAP model. Chi-square and paired t-test was used for comparison of qualitative and quantitative data, respectively. Results: Mean ages were 62.34 (±9.85), 57.9 (±11.6), 46.1 (±14.6) and 55.5 (±14.7) in IPF, non-specific interstitial pneumonitis (NSIP), connective tissue disease (CTD) and unclassifiable groups, respectively. There were 50%, 18%, 26% and 55% males in the four categories respectively. Mean 6 MWD was 227 m, 302 m, 342 m, and 332 m. Mean PaO2 was 64 mmHg, 70 mmHg, 75 mmHg, 69 mmHg. Mean forced vital capacity (FVC) (percentage predicted) was 52, 49, 51 and 54. Most of the patients had comorbidities, among which pulmonary hypertension was the most common. Mortality with ILD-GAP model and TNMC-ILD-GAP showed concordant results as the difference in mortality predicted by ILD-GAP and the TNMC-ILD-GAP models was statistically insignificant (P > 0.050). Conclusion: 6 MWD can serve as a reliable replacement for DLCO in the ILD-GAP model, especially in resource limited settings.

Accuracy of fluoroscopic examination in the treatment of Bennett’s fracture

Background Restoration of joint congruity is an important factor for the prevention of subsequent arthritis in patients with Bennett’s fracture. Surgical treatment of Bennett’s fracture is thus generally recommended for displaced intra-articular fractures to the proximal aspect of the thumb metacarpal. Fluoroscopic examination is used to evaluate the adequacy of closed reduction after pinning of Bennett’s fracture. The purpose of this study was to determine the accuracy of fluoroscopy to determine the reduction of Bennett’s fractures. Methods A model was created, to mimic a Bennett’s fracture utilizing ten fresh-frozen cadaveric hands. An oblique cut was made in the proximal aspect of the thumb metacarpal using an oscillating saw. The small oblique fragment involved 1/4–1/3 of the joint surface was then shifted in position creating a step-off or gap at the fracture site. An anatomical reduction model, gap models (1 mm, 2 mm, 3 mm), and step-off models (1 mm, 2 mm, 3 mm) were created using percutaneous fixation with two 1.0 mm Kirschner wires for each cadaveric hand. Fluoroscopic assessment then took place and was reviewed by 2 attending hand surgeons blinded to the actual position. Their estimated fluoroscopic position was then compared to the actual displacement. Results The step-off and gap on fluoroscopic examination showed a significant difference compared to the step-off and gap from direct visualization. The frequency of underestimation for the 3 mm displacement models from the fluoroscopic examination was 60%. The frequency for overestimated was 9% for the models in which displacement was within 2 mm (0, 1, 2 mm). Conclusions The assessment of articular gap and step-off using PA (postero-anterior), AP (antero-posterior), and lateral view of fluoroscopic examination is not accurate as compared to the examination by direct visualization. Surgeons need to be aware that PA, AP and lateral view of fluoroscopic examination alone may not be sufficient to judge the final position of a reduced Bennett’s fracture. Other methods such as live fluoroscopy in multiple different planes, 3-dimensional fluoroscopy or arthroscopic examination should be considered.

Mathematical modeling of the mosaic of forest systems

In the article a small review of the main mathematical models describing the mosaic dynamics of forest communities is given: catastrophic model, the Tuzinkevich model, the Hahyama model and gap-models.

Approaches to Initial Gap Modeling in Final Aircraft Assembly Simulation

Variation simulation of final aircraft assembly concerning compliance and contact interaction of parts requires the specialized approaches to setting and generation of initial gaps between the joined parts. The initial gap reflects all assembly deviations for the joining parts. In this paper two models for the generation of the initial gap samples are considered: the random field model and the mode-based model. The random field model represents the initial gap as a random field with defined properties. The mode-based model decomposes the initial gap into a series of natural modes of the parts. Typically for the real assembly processes, there is not enough accessible information about initial gaps. If the measurements are not included in the production process, then the number of measured samples can be small. The goal of this paper is to compare how considered initial gap models cope with the small number of given measurements. The models are examined on the example of wing-to-fuselage assembly process. For the considered process both initial gap models are trained on different amounts of measurements, and generated gaps are applied for the variation simulations. The comparison with measured gaps allows to determine which method is the most suitable during variation simulation with limited measurement data. The performed study shows that the mode-based approach is more accurate for initial gap modeling in case of the small number of available measurements.

Two decades of creating drought tolerant maize and underpinning prediction technologies in the US corn-belt: Review and perspectives on the future of crop design

Over the last decade, society witnessed the largest expansion of agricultural land planted with drought tolerant (DT) maize (Zea mays L.) Dedicated efforts to drought breeding led to development of DT maize. Here we show that after two decades of sustained breeding efforts the rate of crop improvement under drought is in the range 1.0-1.6% yr−1, which is higher than rates (0.7% yr−1) reported prior to drought breeding. Prediction technologies that leverage biological understanding and statistical learning to improve upon the quantitative genetics framework will further accelerate genetic gain. A review of published and unpublished analyses conducted on data including 138 breeding populations and 93 environments between 2009 and 2019 demonstrated an average prediction skill (r) improvement around 0.2. These methods applied to pre-commercial stages showed accuracies higher that current statistical approaches (0.85 vs. 0.70). Improvement in hybrid and management choice can increase water productivity. Digital gap analyses are applicable at field scale suggesting the possibility of transition from evaluating hybrids to designing genotype x management (GxM) technologies for target cropping systems in drought prone areas. Due to the biocomplexity of drought, research and development efforts should be sustained to advance knowledge and iteratively improve models.HighlightCrop improvement rate in maize increased after implementation of drought breeding efforts. Harnessing crop, quantitative genetics and gap models will enable the transition from genetic evaluation to crop design.

Macroeconomic determinants of external indebtedness of Ethiopia: ARDL approach to co-integration

Although Ethiopia is one of the Heavily Indebted Poor Countries (HIPC), there is a lack of empirical studies about the determinants of its external indebtedness. This paper aims to fill this gap by examining the macroeconomic determinants of the external indebtedness of Ethiopia between 1981 and 2016, using the two- and three-gap models as a theoretical framework and an autoregressive distributed lag bound testing approach. The result shows that in the long run, the savings-investment gap, trade deficit, fiscal deficit, and debt service have a positive and significant impact on external indebtedness. However, the growth rate of gross domestic product, trade openness, and inflation negatively and significantly affect the external indebtedness of the country. These results coincide with the predictions of the two- and three-gap models of the theoretical framework. The study argues that appropriate macroeconomic, social, and supply-side policies are essential to reducing the external indebtedness of Ethiopia.

A Spatial–Temporal Analysis of Cultural and Creative Industries with Micro-Geographic Disaggregation

Recent years have witnessed growing interest in studying the spatial distribution of cultural and creative industries (CCI), both for their contribution to economic development and for their impact on spatial planning and remodeling of urban structure. However, spatial interdependence and diffusion of agglomeration economies have not been explained enough so far, due to the use of aggregate spatial units. This paper examines CCI sector location patterns in Spain from a spatial–temporal perspective, using micro-geographic data and considering a new and hitherto unused territorial unit, districts, an intermediate demarcation between municipalities and provinces or regions. We used a geographic information system (GIS) analysis and spatial econometric techniques to study territorial distribution and spatial dependences. Results show that CCI are mainly concentrated in metropolitan areas, with spillover effects in adjacent districts, reflecting a non-contemporary spatial dependence process, whilst large territories are devoid of these effects. This reveals a new source of regional disparities, as CCI seems to follow technology gap models, triggering greater and more intense spatial imbalances wherever it appears. Policy implications regarding accountability resources and institutional coordination must be deduced.

Beyond forest succession: a gap model to study ecosystem functioning and tree community composition under climate change

ABSTRACTClimate change impacts forest functioning and dynamics, and large uncertainties remain regarding the interactions between species composition, demographic processes, and environmental drivers. There are few robust tools available to link these processes, which precludes accurate projections and recommendations for long-term forest management. Forest gap-models present a balance between complexity and generality and are widely used in predictive forest ecology. However, their relevance to tackle questions about the links between species composition, climate and forest functioning is unclear. In this regard, demonstrating the ability of gap-models to predict the growth of forest stands at the annual time scale – representing a sensitive and integrated signal of tree functioning and mortality risk - appears as a fundamental step.In this study, we aimed at assessing the ability of a gap-model to accurately predict forest growth in the short-term and potential community composition in the long-term, across a wide range of species and environmental conditions. To do so, we present the gap-model ForCEEPS, calibrated using an original parameterization procedure for the main tree species in France. ForCEEPS was shown to satisfactorily predict forest annual growth (averaged over a few years) at the plot level from mountain to Mediterranean climates, regardless the species. Such an accuracy was not gained at the cost of losing precision for long-term predictions, as the model showed a strong ability to predict potential community composition along a gradient of sites with contrasted conditions. The mechanistic relevance of ForCEEPS parameterization was explored by showing the congruence between the values of key model parameter and species functional traits. We further showed that accounting for the spatial configuration of crowns within forest stands, the effects of climatic constraints and the variability of shade tolerances in the species community are all crucial to better predict short-term productivity with gap-models.The dual ability of predicting short-term functioning and long-term community composition, as well as the balance between generality and realism (i.e., predicting accuracy) of the new generation of gap-models may open great perspectives for the exploration of the biodiversity-ecosystem functioning relationships, species coexistence mechanisms, and the impacts of climate change on forest ecosystems.