Inconsistency Problem Research Articles

Threatening Patch Attacks on Object Detection in Optical Remote Sensing Images

Advanced Patch Attacks (PAs) on object detection in natural images have pointed out the great safety vulnerability in methods based on deep neural networks. However, little attention has been paid to this topic in Optical Remote Sensing Images (O-RSIs). To this end, we focus on this research, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., PAs on object detection in O-RSIs, and propose a more Threatening PA without the scarification of the visual quality, dubbed TPA. Specifically, to address the problem of inconsistency between local and global landscapes in existing patch selection schemes, we propose leveraging the First-Order Difference (FOD) of the objective function before and after masking to select the sub-patches to be attacked. Further, considering the problem of gradient inundation when applying existing coordinate-based loss to PAs directly, we design an IoU-based objective function specific for PAs, dubbed Bounding box Drifting Loss (BDL), which pushes the detected bounding boxes far from the initial ones until there are no intersections between them. Finally, on two widely used benchmarks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., DIOR and DOTA, comprehensive evaluations of our TPA with four typical detectors (Faster R-CNN, FCOS, RetinaNet, and YOLO-v4) witness its remarkable effectiveness. To the best of our knowledge, this is the first attempt to study the PAs on object detection in O-RSIs, and we hope this work can get our readers interested in studying this topic.

Improvement of information support for personnel security systems of the regional economy

The article presents research materials on testing the hypothesis about the priority of principles of relevance and coordination when forming the composition of indicators included in social reporting of companies and statistical books by region. To test the hypothesis general scientific methods of analysis and synthesis, statistical methods for processing data on the use of indicators characterizing the human resources of the region and the company, and the secondary analysis of the results of studies published by Russian scientists on social reporting were used. The lack of proposals on overcoming problems of inconsistency of indicators characterizing human resources of systems at different levels, the region and the company, predetermined the need to study this topic. The authors systematized indicators of the level of human resources of companies and the region, which made it possible to analyze their comparability. The results of the analysis determined both priority principles for formation of the information base on human resources and proposals for its formation. Implementation of the proposals will contribute to the creation of a more informative and coordinated information system on the state of human resources, which is of practical importance in organizing personnel security in the region and in companies.

Deep Video Prior for Video Consistency and Propagation.

Applying an image processing algorithm independently to each video frame often leads to temporal inconsistency in the resulting video. To address this issue, we present a novel and general approach for blind video temporal consistency. Our method is only trained on a pair of original and processed videos directly instead of a large dataset. Unlike most previous methods that enforce temporal consistency with optical flow, we show that temporal consistency can be achieved by training a convolutional network on a video with Deep Video Prior (DVP). Moreover, a carefully designed iteratively reweighted training strategy is proposed to address the challenging multimodal inconsistency problem. We demonstrate the effectiveness of our approach on 7 computer vision tasks on videos. Extensive quantitative and perceptual experiments show that our approach obtains superior performance than state-of-the-art methods on blind video temporal consistency. We further extend DVP to video propagation and demonstrate its effectiveness in propagating three different types of information (color, artistic style, and object segmentation). A progressive propagation strategy with pseudo labels is also proposed to enhance DVP's performance on video propagation. Our source codes are publicly available at https://github.com/ChenyangLEI/deep-video-prior.

A Novel Adaptive Kalman Filter Based on Credibility Measure

It is quite often that the theoretic model used in the Kalman filtering may not be sufficiently accurate for practical applications, due to the fact that the covariances of noises are not exactly known. Our previous work reveals that in such scenario the filter calculated mean square errors (FMSE) and the true mean square errors (TMSE) become inconsistent, while FMSE and TMSE are consistent in the Kalman filter with accurate models. This can lead to low credibility of state estimation regardless of using Kalman filters or adaptive Kalman filters. Obviously, it is important to study the inconsistency issue since it is vital to understand the quantitative influence induced by the inaccurate models. Aiming at this, the concept of credibility is adopted to discuss the inconsistency problem in this paper. In order to formulate the degree of the credibility, a trust factor is constructed based on the FMSE and the TMSE. However, the trust factor can not be directly computed since the TMSE cannot be found for practical applications. Based on the definition of trust factor, the estimation of the trust factor is successfully modifled to online estimation of the TMSE. More importantly, a necessary and sufficient condition is found, which turns out to be the basis for better design of Kalman filters with high performance. Accordingly, beyond trust factor estimation with Sage-Husa technique (TFE-SHT), three novel trust factor estimation methods, which are directly numerical solving method (TFE-DNS), the particle swarm optimization method (PSO) and expectation max-imization-particle swarm optimization method (EM-PSO) are proposed. The analysis and simulation results both show that the proposed TFE-DNS is better than the TFE-SHT for the case of single unknown noise covariance. Meanwhile, the proposed EM-PSO performs completely better than the EM and PSO on the estimation of the credibility degree and state when both noise covariances should be estimated online.

Continuing Education Through Modes of Learning in the Height of Pandemic

The COVID-19 pandemic shocked the whole world and changed the educational landscape. To develop responsive solutions and continue with its mandate, University adheres to the Flexible Learning Modality (FLM). Behind the tedious preparation, it is essential to investigate the effectiveness of its implementation. This study aims to identify further experiences and problems encountered in implementing Flexible Learning. The study utilized a convergent design mixed-method research. The research instrument was developed based on the guidelines for implementing FLM at the University. The result shows that the guidelines were mostly implemented, although there are hindrances in the full implementation. There was an inconsistent internet connection and problems acquiring advanced technology for learning. However, it is notable in the parents' comments that they are thankful to the University for finding ways for their children to continue their education amidst the pandemic.

ORGANIZATIONAL AND TECHNOLOGICAL PROBLEMS OF INTELLIGENT INTERACTION OF ROAD AND SEA TRANSPORT

This article is devoted to the issues and related problems of intelligent interaction of road and sea transport in the organization of multimodal international transportation. Since, today, the bulk of transportation is carried out with the participation of two or more types of vehicles, a multimodal delivery scheme is in most cases the only possible one when sending goods to another country, as well as to achieve an optimal ratio of quality, price and time. However, there is a problem of inconsistency in the technological process, due to the lack of development of the information management system. The article deals with the main problems of vehicle interaction, shortcomings in the process of intellectual interaction of elements of the transportation process, and their impact on the organization of transportation in the context of a conditional enterprise. Insufficient attention is paid to resolving issues of interaction and eliminating the causes of its inefficiency, while any complex system, which is undoubtedly a transport and logistics system, taking into account the requirements and security conditions imposed on it, in order to optimize, automate processes and smoothly organize work and interaction between individual elements of the system, it is necessary to detailed ordering and systematization of operations and actions that occur in the processes of this system. The mainlimiting factor for successful interaction is the lack of an intelligent management model in the organization of the transport process.

Non-equilibrium strategy for enhancing thermoelectric properties and improving stability of AgSbTe2

Inorganic thermoelectrics have progressed in leaps and bounds in the recent years. This is largely driven by the advancements in both physical understanding coupled with structural properties. In particular, p-type AgSbTe2 has recently emerged as one of the best thermoelectric materials for low and medium temperature applications. Nevertheless, it suffers from longstanding stability and inconsistency problems, which results in n-type Ag2Te precipitates and drastic deterioration in performance. In this work, we trace the origin of the variability of thermoelectric properties of AgSbTe2 found in literatures to the cooling rate during synthesis. Furthermore, we demonstrate a non-equilibrium annealing strategy to achieve consistent properties. Ultimately, a peak zT of 1.15 at 623 K was achieved for optimally annealed and quenched pristine AgSbTe2. Importantly, in the absence of dopant to fully stabilize the AgSbTe2 phase, we propose limiting its application to the vicinity of room temperature for cooling, and above 633 K for waste heat harvesting.

A Semi-Supervised Method for PatchMatch Multi-View Stereo with Sparse Points

Recently, the deep-learning-based PatchMatch method has been rapidly developed in 3D reconstruction, based on which boundary regions are filled with other parts that most closely match edge parts, but limited PatchMatch data hinder the generalization of the method to unknown settings. If various large-scale PatchMatch datasets are generated, the process would require considerable time and resources when performing neighborhood point-matching calculations using random iterative algorithms. To solve this issue, we first propose a new, sparse, semi-supervised stereo-matching framework called SGT-PatchMatchNet, which can reconstruct reliable 3D structures with a small number of 3D points using the ground truth of surface frame values. Secondly, in order to solve the problem of the luminosity inconsistency of some pixels in other views, a photometric similar-point loss function is proposed to improve the performance of 3D reconstruction, which causes the neighborhood information to project the depth value of the predicted depth to meet the same 3D coordinates. Finally, in order to solve the problem of the edge blurring of the depth map obtained using the network model, we propose a robust-point consistency loss function to improve the integrity and robustness of the occlusion and edge areas. The experimental results show that the proposed method not only has good visual effects and performance indicators but can also effectively reduce the amount of computation and improve the calculation time.

Memahami Al-Ghazzali: Jawaban atas Kritik Skeptisisme dan Inkonsistensi

Al-Ghazali is one of the greatest Muslim scholars. He is known as Hujjah al-Islām because of his success in defending Islamic orthodoxy in the face of heretical groups. However, Al-Ghazali is also not free from controversy. Among these criticisms is that Al-Ghazali had experienced a period of skepticism and inconsistent thinking. This research attempts to analyze Al-Ghazali's works and secondary books that explain Al-Ghazali's thoughts. This study uses a qualitative approach to the type of literature research. The topic of this research is the controversy surrounding skepticism and accusations of inconsistency in Al-Ghazali's thought. The steps in this study include topic selection, information exploration, focus determination, data collection, data presentation, and report preparation. The results of this study suggest that Al-Ghazali's skeptical period was not a problem because it happened in a short time and he did not produce any papers. The problem of Al-Ghazali's inconsistency arises because of the many works attributed to him but not actually Al-Ghazali's work or because of the different readers intended by Al-Ghazali in his different books, as stated by Dunya. In conclusion, this study argues that a comprehensive reading of Al-Ghazali's work is needed to provide a complete picture of Al-Ghazali's figure and the extent to which his works are devoted.

Data-centric annotation analysis for plant disease detection: Strategy, consistency, and performance.

Object detection models have become the current tool of choice for plant disease detection in precision agriculture. Most existing research improved the performance by ameliorating networks and optimizing the loss function. However, because of the vast influence of data annotation quality and the cost of annotation, the data-centric part of a project also needs more investigation. We should further consider the relationship between data annotation strategies, annotation quality, and the model's performance. In this paper, a systematic strategy with four annotation strategies for plant disease detection is proposed: local, semi-global, global, and symptom-adaptive annotation. Labels with different annotation strategies will result in distinct models' performance, and their contrasts are remarkable. An interpretability study of the annotation strategy is conducted by using class activation maps. In addition, we define five types of inconsistencies in the annotation process and investigate the severity of the impact of inconsistent labels on model's performance. Finally, we discuss the problem of label inconsistency during data augmentation. Overall, this data-centric quantitative analysis helps us to understand the significance of annotation strategies, which provides practitioners a way to obtain higher performance and reduce annotation costs on plant disease detection. Our work encourages researchers to pay more attention to annotation consistency and the essential issues of annotation strategy. The code will be released at: https://github.com/JiuqingDong/PlantDiseaseDetection_Yolov5 .

Urban river water quality monitoring based on self-optimizing machine learning method using multi-source remote sensing data

Urban rivers are complex ecosystems that directly determine the living environment of human beings. Monitoring the urban river water quality indexes is a challenge in water quality evaluation. The purpose of this study was to propose a multi-source remote sensing water quality inversion method based on a small number of samples to solve the problem of scale inconsistency among multi-source remote sensing data, so as to achieve large-scale and efficient inversion of urban river water quality. Since there is a very important problem that the complex nonlinear relationships must be solved between simple ground point data and remote sensing data in water quality inversion, a novel self-optimizing machine learning monitoring method is proposed, which can automatically find the optimal parameters of the model from a small number of samples, and reduce the training time. Meanwhile, in order to strengthen the correlation between water quality parameters and remote sensing data, the feature enhancement method was used for generating the input data. Moreover, to solve the problem of the multi-source data quantity and quality, the spatial mapping method was used to achieve consistency in the water quality information since these data have different nonlinear characteristics. The experimental results show that for unmanned aerial vehicle (UAV) images, the R2 of chlorophyll a (Chla), turbidity (TUB), and ammonia nitrogen (NH3-N) can reached 0.917, 0.877 and 0.846, respectively. Using a satellite image, the R2 of Chla, TUB, and NH3-N can reach 0.827, 0.679 and 0.779, respectively. This method provides a new way to realize the integration of air-space-ground monitoring of urban inland rivers in the future.

FRCR: Raft Consensus Scheme Based on Semi Asynchronous Federal Reconstruction

Today, Raft’s distributed cluster scale is growing rapidly and cluster throughput is declining. The Raft consensus algorithm needs to be continuously optimized to adapt to a complex and changeable application environment. To solve the above problems, we propose a federal reconstruction Committee Raft consensus algorithm FRCR. Based on the Federation reconstruction technology, the algorithm trains, updates and evaluates the model of the characteristic data set of the Raft node, runs the model to get the nodes with better performance, constructs the committee mechanism, and improves the quality and speed of the election. We also design a semi asynchronous buffer mechanism and a strategy to resist malicious node attacks to solve the inconsistency and security problems of federation aggregation. Finally, seven aspects of FRCR are tested and analyzed to verify the effectiveness of FRCR in the consensus cluster.

Agrarian potential of Ukraine under post-Maidan crisis and armed conflict: factors and patterns of regional unevenness

Problem Statement. Regional socio-economic development is characterized by diversity and multifacetedness and is stipulated with different impacts of factors. Ukraine has a pronounced spatial asymmetry of socio-economic development of regions; therefore, it is an important case for study of various components of regional unevenness. In the present context of post-Maidan crisis and armed conflict, study of uneven development of agrarian potential in Ukraine is becoming increasingly important, whereas it is important to assess and monitor the agrarian potential of regions for economic and food security of the country and regions. Research Methodology. The research focuses on assessment of the agrarian potential development of Ukraine’s regions in 2015 and 2018, rather than on an empirical comparison of the agrarian potential development of regions before and during the post-Maidan crisis and armed conflict. A hierarchical model for assessing the development of the agrarian potential of Ukraine's regions has been elaborated. Based on the factor analysis results, the individual influence of each factor on the development of the agrarian potential of regions was determined. Using the method of ranking and Kohonen Self-Organizing Mapping, the changes in the uneven development of the agrarian potential of Ukraine’s regions during the post-Maidan crisis and armed conflict period were investigated, and the trajectories of the development of this potential were revealed. This paper aims to identify the regional patterns of agrarian potential development and determine the factors that influenced its regional unevenness during the post-Maidan crisis and armed conflict. Results. The research results prove a spatial asymmetry and an increase in regional divergence in the agrarian potential development. The impact of socio-economic and production factors was revealed to grow and the impact of natural resources and macroeconomic factors on the agrarian potential development of the regions was revealed to weaken. The trajectories of agrarian potential development of the regions are determined; they are typified in nine types of trajectories of the agrarian potential development of the regions of Ukraine during the post-Maidan crisis and armed conflict. Recommendations are given on the regional policy in the agrarian sector of Ukraine in order to mitigate the uneven development of agrarian potential in the regions. Given that Ukraine is facing the problem of inconsistency of the institutional environment with the needs of agropotential development, it is very important to implement the institutional transformations, which should focus on harmonization of state and regional policy for the agrarian sector with EU principles.

The Joint Method of Triple Attention and Novel Loss Function for Entity Relation Extraction in Small Data-Driven Computational Social Systems

With the development of the social Internet of Things (IoT) and multimedia communications, our daily lives in computational social systems have become more convenient; for example, we can share shopping experiences and ask questions of people in an ad hoc network. Relation extraction focuses on supervised learning with adequate training data, and it helps to understand the knowledge behind the observed information. However, if only some social data in an unknown area can be used, how to obtain the related knowledge and information is a key topic for supporting social intelligence. This article proposes the joint method of triple attention and novel loss function for entity relation extraction by few-shot learning in computational social systems. We consider using a prototypical network as the base model to acquire support set prototypes and to compare queries with the prototypes for classification. First, triple attention is employed to make the query instances and support set share interactive information in a global and instancewise manner, highlighting the important features. Second, we combine a weighted Euclidean distance function with a multilayer perceptron (MLP) to perform class matching, which maps the generated features to their proper classifications, emphasizing the prominent dimensions in the feature space and relieving data sparsity. Third, triplet loss and uniformity regularization are used to solve the inconsistency problem faced by the support set, where the features of the support set in the same class are often far apart in different characteristic dimensions. Finally, the experimental results demonstrate the improved performance of our model on the FewRel dataset.

A Curriculum Batching Strategy for Automatic ICD Coding with Deep Multi-Label Classification Models.

The International Classification of Diseases (ICD) has an important role in building applications for clinical medicine. Extremely large ICD coding label sets and imbalanced label distribution bring the problem of inconsistency between the local batch data distribution and the global training data distribution into the minibatch gradient descent (MBGD)-based training procedure for deep multi-label classification models for automatic ICD coding. The problem further leads to an overfitting issue. In order to improve the performance and generalization ability of the deep learning automatic ICD coding model, we proposed a simple and effective curriculum batching strategy in this paper for improving the MBGD-based training procedure. This strategy generates three batch sets offline through applying three predefined sampling algorithms. These batch sets satisfy a uniform data distribution, a shuffling data distribution and the original training data distribution, respectively, and the learning tasks corresponding to these batch sets range from simple to complex. Experiments show that, after replacing the original shuffling algorithm-based batching strategy with the proposed curriculum batching strategy, the performance of the three investigated deep multi-label classification models for automatic ICD coding all have dramatic improvements. At the same time, the models avoid the overfitting issue and all show better ability to learn the long-tailed label information. The performance is also better than a SOTA label set reconstruction model.

Improved Spatiotemporal Information Fusion Approach Based on Bayesian Decision Theory for Land Cover Classification

High-spatial-resolution (HSR) images and high-temporal-resolution (HTR) images have their unique advantages and can be replenished by each other effectively. For land cover classification, a series of spatiotemporal fusion algorithms were developed to acquire a high-resolution land cover map. The fusion processes focused on the single level, especially the pixel level, could ignore the different phenology changes and land cover changes. Based on Bayesian decision theory, this paper proposes a novel decision-level fusion for multisensor data to classify the land cover. The proposed Bayesian fusion (PBF) combines the classification accuracy of results and the class allocation uncertainty of classifiers in the estimation of conditional probability, which consider the detailed spectral information as well as the various phenology information. To deal with the scale inconsistency problem at the decision level, an object layer and an area factor are employed for unifying the spatial resolution of distinct images, which would be applied for evaluating the classification uncertainty related to the conditional probability inference. The approach was verified on two cases to obtain the HSR land cover maps, in comparison with the implementation of two single-source classification methods and the benchmark fusion methods. Analyses and comparisons of the different classification results showed that PBF outperformed the best performance. The overall accuracy of PBF for two cases rose by an average of 27.8% compared with two single-source classifications, and an average of 13.6% compared with two fusion classifications. This analysis indicated the validity of the proposed method for a large area of complex surfaces, demonstrating the high potential for land cover classification.

A multi-scale interactive U-Net for pulmonary vessel segmentation method based on transfer learning

Pulmonary vessel segmentation is the key application of AI in lung disease diagnosis and surgical planning. Compared with manual labeling, automatic labeling of pulmonary vessels using an AI-based medical image segmentation method has the advantages of low cost, high accuracy, and efficiency, which is the development trend of medical images. In terms of pulmonary vessel segmentation, FCN and U-Net are the most widely used pulmonary vessel segmentation methods based on deep learning. However, the precision of pulmonary vessel segmentation, especially the small vessels, tends to be poor by such methods. Therefore, to solve the above problem, Multi-Scale Interactive U-Net (MSI-U-Net) is proposed. In MSI-U-Net, three decoder branches are used to extract small-scale, middle-scale and large-scale vessels respectively, which can improve the accuracy of small vessel segmentation by enhancing the representational ability of small vessels. In addition, to solve the problem of small vessel information loss caused by down-sampling, we introduce the attention mechanism into the skip-layer connection and propose a cross-layer aggregation module (CLA). Among the three decoder branches, a multi-scale information interaction strategy (MSIIS) is proposed based on transfer learning, which can effectively enhance the correlation of multi-scale vessels in lung CT images. In the training stage, we propose a scale-induced supervision strategy (SISS). This strategy uses the idea of fusion first and then supervision, which effectively solves the problem of inconsistency in multi-scale vessels classification, thereby reducing the segmentation errors. Finally, we use feature transmission instead of convolution parameter sharing to realize the multi-scale information interaction strategy, and propose an extension scheme called Multi-Level Cascade Interactive U-Net (MLCI-U-Net). The experimental results indicate that our MSI-U-Net and MLCI-U-Net have better performance than other state-of-the-art methods on pulmonary vessel segmentation. Specifically, the best Dice similarity coefficient (DSC), Sensitivity and Precision are obtained by the proposed methods to segment pulmonary vessels.

Requirements Conflict Detection and Resolution in AREM Using Intelligence System Approach

Requirements engineering (RE) is the process of defining user requirements that are used as the main reference in the system development process. The quality of the RE results is measured based on the consistency and completeness of the requirements. The collection of requirements from multiple stakeholders can cause requirements conflict and have an impact on the inconsistency and incompleteness of the resulting requirements model. In this study, a method for automatic conflict detection and resolution in the Automatic Requirements Engineering Model (AREM) was developed. AREM is a model that automates the process of elicitation, analysis, validation, and requirements specification. The requirement conflict detection method was developed using an intelligent agent approach combined with a Weighted Product approach. Meanwhile, Conflict resolution is made automatically using a rule-based model and clustering method. Testing the ability of the method to detect and resolve conflicting requirements was carried out through five data sets of requirements from five system development projects. Based on the test results, it is known that the system is able to produce a set of objects that have conflicts in the data requirements. For conflict resolution, experiments were conducted with five conflict resolution scenarios. The experimental results show that the method is able to resolve conflicts by producing the highest completeness value, but the results of conflict resolution also produce a number of soft goals. The success of the method in detecting and resolving conflicts in the model is able to overcome the problem of inconsistencies and incompleteness in the requirements model.

TÜRKİYE’DE DEVLET VE SURİYELİ MÜLTECİLER: ÇELİŞKİLİ POLİTİKALAR VE ARAÇSAL RASYONELLİK

Amaç: Bu makalenin amacı Türkiye’nin Suriyelilere yönelik iç ve dış politika alanlarında ortaya çıkan çelişkili politikaları, devletin “araçsal rasyonellik” (instrumental rationalism) bakış açısından kaynaklanan politika tutarsızlığı teorik çerçevesi ile açıklamaktır. Yöntem: Araştırmanın yöntemi, politika belgelerinin ve internet haber sitelerinin sistematik ve kapsamlı bir nitel analizine dayanmaktadır. Makale süreç izleme (process tracing) metodu ile, “araçsal rasyonellik” teorik çerçevesinin Türkiye devletinin Suriyelilere yönelik politikalarını açıklamaktaki geçerliliğini test etmektedir. Bulgular: Bulgular son on senede Türkiye’de devletin Suriyelilere yönelik uyum üçüncü ülkeye yeniden yerleştirme ve geri gönderme politikalarını zaman içinde değişen - hatta çelişen - söylemlerle birbirlerini alternatifi olarak dile getirdiği yönündedir. Başlangıçtaki Suriye’deki krizin kısa süreceği ve gelen sığınmacıların ülkelerine geri döneceği beklentisi ile edinilen “misafir” bakış açısı; daha uzun süre kalacakları anlaşılınca, önce “geçici koruma” statüsü verilerek (2014), sonra da çok sınırlı da olsa “çalışma izni” ve “vatandaşlığına geçiş” sağlanarak (2016) nerdeyse onlar için bir entegrasyon sürecinin öngörülmesine evirilmiştir. 2017 yılından sonra ise devlet söyleminde, Suriyeliler için hızla “geri gönderme” eğiliminin yükselişine tanıklık edilmiştir. 2019 yılında bir yandan bu “geri gönderme” söylemlerinin somut politikalara doğru evirildiği, diğer yandan ise “11. Kalkınma Planı”(2019-23) ve “Uyum Strateji Belgesi ve Ulusal Eylem Planı” (2018-23) gibi temel politika belgelerinde Suriyelilerin entegrasyonu için somut planlamalar yapıldığı görülmüştür. Bu örneklerden de deneyimlendiği gibi Türkiye’de devlet sığınmacıların geleceğini çoğul bir dizi politika seçeneği ile uluslararası iltica rejiminde normatif kabul gören üç politika seçeneğine paralel olarak düzenleme eğiliminde olmuştur. Bu politika ve söylemlerdeki iç ve dış politik konjonktüre cevaben ortaya çıkan ani değişiklikler, göç politikalarındaki birbiri ile çelişen uyumsuzluğun, Türkiye’de devletin göç politikalarını araçsal bir rasyonalite ile şekillendirmesinden kaynaklandığını göstermektedir. Ana bulgu bu rasyonalitenin Türkiye’de devletin Suriyeli mülteciler üzerine olan politikalarında bir tutarlılık problemine yol açtığı yönündedir.Özgünlük: Makalenin özgün değeri, Türkiye’de devletin Suriyelilere yönelik ulaşılabilir kıldığı politika seçeneklerine yönelik eleştirel bir bakış açısı sunmasıdır. Türkiye’de devletin Suriyeli mültecilere yönelik, uyum ya da geri gönderme gibi mevcut tüm politika seçeneklerini ulaşılabilir kılma çabası, politika yapıcılar tarafından olumlu bir durum olarak ortaya konulmaktadır. Bu politika tutarsızlığının olası siyasi, toplumsal ve bireysel sonuçları bu makalede irdelenmiştir.

The distinct problems of physical inconsistency and of multivariate bias involved in the statistical adjustment of climate simulations

AbstractBias adjustment of numerical climate model simulations involves several arguments wherein the notion of physical inconsistency is referred to, either for rejecting the legitimacy of bias adjustment in general or for justifying the necessity of sophisticated multivariate techniques. However, this notion is often mishandled, in part because the literature generally proceeds without defining it. In this context, the central objective of this study is to clarify and illustrate the distinction between physical inconsistency and multivariate bias, by investigating the effect of bias adjustment on two different kinds of intervariable relationships, namely a physical constraint expected to hold at every step of a time series and statistical properties that emerge with potential bias over a climatic timescale. To this end, 18 alternative bias adjustment techniques are applied on 10 climate simulations at 12 sites over North America. Adjusted variables are temperature, pressure, relative humidity and specific humidity, linked by a thermodynamic constraint. The analysis suggests on the one hand that a clear instance of potential physical inconsistency can be avoided with either a univariate or a multivariate technique, if and only if the bias adjustment strategy explicitly considers the physical constraint to be preserved. On the other hand, it also suggests that sophisticated multivariate techniques alone are not complete adjustment strategies in presence of a physical constraint, as they cannot replace its explicit consideration. By involving common bias adjustment procedures with likely effects on diverse basic statistical properties, this study may also help guide climate information users in the determination of adequate bias adjustment strategies for their research purposes.