Erratum to: `Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia` [Expert Systems with Applications 38 (2011) 8208`8219]

This note is to point out and correct an error in Sezer et al. (2011). İn the paper (Sezer et al. 2011), the authors mention “ANFIS model has not been used for landslide susceptibility mapping previously”. This statement must be corrected as “The ANFIS model has been applied in landslide susceptibility mapping previously by Pradhan, Sezer, Gokceoglu, and Buchroithner (2010) in a different study area namely Cameron Highlands, Malaysia.

Landslide susceptibility mapping of Cekmece area (Istanbul, Turkey) by conditional probability

International audienceAs a result of industrialization, throughout the world, the cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul. Today, the population of Istanbul is over 10 millions. Depending on this rapid urbanization, new suitable areas for settlements and engineering structures are necessary. For this reason, the Cekmece area, west of the Istanbul metropolitan area, is selected as the study area, because the landslides are frequent in this area. The purpose of the present study is to produce landslide susceptibility map of the selected area by conditional probability approach. For this purpose, a landslide database was constructed by both air ? photography and field studies. 19.2% of the selected study area is covered by landslides. Mainly, the landslides described in the area are generally located in the lithologies including the permeable sandstone layers and impermeable layers such as claystone, siltstone and mudstone layers. When considering this finding, it is possible to say that one of the main conditioning factors of the landslides in the study area is lithology. In addition to lithology, many landslide conditioning factors are considered during the landslide susceptibility analyses. As a result of the analyses, the class of 5?10° of slope, the class of 180?225 of aspect, the class of 25?50 of altitude, Danisment formation of the lithological units, the slope units of geomorphology, the class of 800?1000 m of distance from faults (DFF), the class of 75?100 m of distance from drainage (DFD) pattern, the class of 0?10m of distance from roads (DFR) and the class of low or impermeable unit of relative permeability map have the higher probability values than the other classes. When compared with the produced landslide susceptibility map, most of the landslides identified in the study area are found to be located in the most (54%) and moderate (40%) susceptible zones. This assessment is also supported by the performance analysis applied at end of the study. As a consequence, the landslide susceptibility map produced herein has a valuable tool for the planning purposes

Character Generation through Self-Supervised Vectorization

The prevalent approach in self-supervised image generation is to operate on pixel level representations. While this approach can produce high quality images, it cannot benefit from the simplicity and innate quality of vectorization. Here we present a drawing agent that operates on stroke-level representation of images. At each time step, the agent first assesses the current canvas and decides whether to stop or keep drawing. When a 'draw' decision is made, the agent outputs a program indicating the stroke to be drawn. As a result, it produces a final raster image by drawing the strokes on a canvas, using a minimal number of strokes and dynamically deciding when to stop. We train our agent through reinforcement learning on MNIST and Omniglot datasets for unconditional generation and parsing (reconstruction) tasks. We utilize our parsing agent for exemplar generation and type conditioned concept generation in Omniglot challenge without any further training. We present successful results on all three generation tasks and the parsing task. Crucially, we do not need any stroke-level or vector supervision; we only use raster images for training

6 FEBRUARY 2023 KAHRAMANMARAŞ – TÜRKİYE EARTHQUAKES: A GENERAL OVERVIEW

On 6 February 2023, two major earthquakes occurred in the East Anatolian Fault Zone (EAFZ) of Türkiye. The EAFZ forms the east border of the Anatolian Plate. The magnitude 7.7 and 7.5 Kahramanmaraş – Türkiye Earthquakes that struck southern Türkiye and resulted in common destruction in 11 provinces in the region. Total 6 main fault segments of the EAFZ ruptured during the earthquake sequence on 6 February 2023, and approximately 400 km surface rupture occurred. The life losses are reported as over 50000, and approximately 300,000 buildings were collapsed or severely damaged. In addition to damages on the buildings and the infrastructures, liquefactions, landslides, rockfalls, and rock avalanches were also observed during the earthquake sequence. The purpose of this study is to present a general overview on the 6 February 2023 Earthquake sequence

A multi-level multi-label text classification dataset of 19th century Ottoman and Russian literary and critical texts

This paper introduces a multi-level, multi-label text classification dataset comprising over 3000 documents. The dataset features literary and critical texts from 19th-century Ottoman Turkish and Russian. It is the first study to apply large language models (LLMs) to this dataset, sourced from prominent literary periodicals of the era. The texts have been meticulously organized and labeled. This was done according to a taxonomic framework that takes into account both their structural and semantic attributes. Articles are categorized and tagged with bibliometric metadata by human experts. We present baseline classification results using a classical bag-of-words (BoW) naive Bayes model and three modern LLMs: multilingual BERT, Falcon, and Llama-v2. We found that in certain cases, Bag of Words (BoW) outperforms Large Language Models (LLMs), emphasizing the need for additional research, especially in low-resource language settings. This dataset is expected to be a valuable resource for researchers in natural language processing and machine learning, especially for historical and low-resource languages. The dataset is publicly available^1

The milk IGF-2 level is positively correlated with milk yield in Anatolian water buffaloes

The aim of this study was to investigate the relationship between milk insulin-like growth factor-2 (IGF-2) concentration and milk yield in Anatolian water buffaloes. This study was conducted on milk samples from 80 Anatolian water buffaloes. Milk samples collected from buffaloes were evaluated for subclinical mastitis. For this purpose, the California mastitis test and somatic cell count were performed in milk samples taken from four different mammary lobes of buffaloes. The milk IGF-2 level was determined by an enzyme-linked immunosorbent assay kit. Milk IGF-2 concentration ranged from 21.6 ng/ml - 63.2 ng/ml in Anatolian water buffaloes. The IGF-2 concentration in Anatolian water buffaloes milk was 40.1±8.5 ng/ml. Milk IGF-2 was positively correlated with milk yield (r2=0.941, P<0.01). Our results showed that milk IGF-2 concentration was associated with milk yield in Anatolian buffaloes. These findings show that locally synthesized IGF-2 can affect milk yield. This study contributes to the understanding of the composition of buffalo milk, which has an important value in human nutrition. It is recommended to confirm the results of similar measurements in milk from other animal species used for human consumption

MIMO Systems with Reconfigurable Antennas: Joint Channel Estimation and Mode Selection

Reconfigurable antennas (RAs) are a promising technology to enhance the capacity and coverage of wireless communication systems. However, RA systems have two major challenges: (i) High computational complexity of mode selection, and (ii) High overhead of channel estimation for all modes. In this paper, we develop a low-complexity iterative mode selection algorithm for data transmission in an RA-MIMO system. Furthermore, we study channel estimation of an RA multi-user MIMO system. However, given the coherence time, it is challenging to estimate channels of all modes. We propose a mode selection scheme to select a subset of modes, train channels for the selected subset, and predict channels for the remaining modes. In addition, we propose a prediction scheme based on pattern correlation between modes. Representative simulation results demonstrate the system's channel estimation error and achievable sum-rate for various selected modes and different signal-to-noise ratios (SNRs)

Machine Learning-based Methods for Reconfigurable Antenna Mode Selection in MIMO Systems

MIMO technology has enabled spatial multiple access and has provided a higher system spectral efficiency (SE). However, this technology has some drawbacks, such as the high number of RF chains that increases complexity in the system. One of the solutions to this problem can be to employ reconfigurable antennas (RAs) that can support different radiation patterns during transmission to provide similar performance with fewer RF chains. In this regard, the system aims to maximize the SE with respect to optimum beamforming design and RA mode selection. Due to the non-convexity of this problem, we propose machine learning-based methods for RA antenna mode selection in both dynamic and static scenarios. In the static scenario, we present how to solve the RA mode selection problem, an integer optimization problem in nature, via deep convolutional neural networks (DCNN). A Multi-Armed-bandit (MAB) consisting of offline and online training is employed for the dynamic RA state selection. For the proposed MAB, the computational complexity of the optimization problem is reduced. Finally, the proposed methods in both dynamic and static scenarios are compared with exhaustive search and random selection methods

Evaluation and selection of indicators for land degradation and desertification monitoring : types of degradation, causes, and implications for management

International audienceIndicator-based approaches are often used to monitor land degradation and desertification from the global to the very local scale. However, there is still little agreement on which indicators may best reflect both status and trends of these phenomena. In this study, various processes of land degradation and desertification have been analyzed in 17 study sites around the world using a wide set of biophysical and socioeconomic indicators. The database described earlier in this issue by Kosmas and others (Environ Manage, 2013) for defining desertification risk was further analyzed to define the most important indicators related to the following degradation processes: water erosion in various land uses, tillage erosion, soil salinization, water stress, forest fires, and overgrazing.

MULTI-HAZARD SUSCEPTIBILITY ASSESSMENT WITH HYBRID MACHINE LEARNING METHODS FOR TUT REGION (ADIYAMAN, TURKIYE)

Recent Kahramanmaras earthquakes (Mw 7.7 and 7.6) occurred on 6 February 2023 have shown the importance of site selection for settlements and infrastructure considering the fact that multiple hazards may affect the same area and even interact with each other. The Kahramanmaras earthquakes triggered several landslides, which also increased the level of destruction. Here, we implemented a multi-hazard susceptibility assessment approach for Tut town of Golbasi, Adiyaman and its surroundings. Over 600 landslides were triggered in the area by the earthquakes. In addition, the region is prone to flooding and a devastating one occurred on March 15, 2023 after heavy rains. In this study, we employed co-seismic landslide inventory for landslide susceptibility assessment with random forest. Regarding flood susceptibility, a modified analytical hierarchical process was utilized based on expert opinion on factor importance. The earthquake hazard probability distribution was obtained from a distance-based interpolation of Arias intensity values. We utilized Mamdani Fuzzy Inference System for producing a multi-hazard susceptibility map from univariate maps of earthquake, landslide and flood. The result shows that the selected methods for each type of susceptibility map was suitable and the output of the study can be utilized for the site selection in Tut region, which is a crucial subject due to the need of new construction sites after the earthquakes