Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning in Archaeological Remote Sensing: Automated Qanat Detection in the Kurdistan Region of Iraq

In this paper, we report the results of our work on automated detection of qanat shafts on the Cold War-era CORONA Satellite Imagery. The increasing quantity of air and space-borne imagery available to archaeologists and the advances in computational science have created an emerging interest in automated archaeological detection. Traditional pattern recognition methods proved to have limited applicability for archaeological prospection, for a variety of reasons, including a high rate of false positives. Since 2012, however, a breakthrough has been made in the field of image recognition through deep learning. We have tested the application of deep convolutional neural networks (CNNs) for automated remote sensing detection of archaeological features. Our case study is the qanat systems of the Erbil Plain in the Kurdistan Region of Iraq. The signature of the underground qanat systems on the remote sensing data are the semi-circular openings of their vertical shafts. We choose to focus on qanat shafts because they are promising targets for pattern recognition and because the richness and the extent of the qanat landscapes cannot be properly captured across vast territories without automated techniques. Our project is the first effort to use automated techniques on historic satellite imagery that takes advantage of neither the spectral imagery resolution nor very high (sub-meter) spatial resolution.
Digitale Zwillinge modellieren und verstehen: Eine Fallstudie zum problembasierten und forschenden Lernen (Modeling and Understanding Digital Twins: A Case Study of Problem-Based and Exploratory Learning)

Mit der Digitalisierung von Produkten, Produktionsprozessen und Dienstleistungen nimmt deren inhärente Komplexität zu. Diese beruht auf dem Umstand, dass parallel zum realen physischen Produkt eine virtuelle Instanz existiert, in der mittels Algorithmen Optimierungen gefunden, oder durch die reale Artefakte und Prozesse verknüpft und aufeinander abgestimmt werden. In diesem Zusammenhang spricht man dann auch von Cyber-Physical Systems. Deren virtueller Anteil ist der digitale Zwilling zur Realität. Die Modellierung digitaler Zwillinge ist eine anspruchsvolle Tätigkeit, da nicht nur deren statische Struktur sondern auch ihr dynamisches Verhalten abzubilden ist. Hinzu kommt, dass virtuelle Zwillinge oftmals unterschiedlichste Anwendungsdomänen umfassen: So reicht ein End to End gedachter Auftragsabwicklungsprozess von der Geschäftsprozessebene bis zur Produktionsebene und anschließend wieder zurück auf die Ebene der Geschäftsprozesse. Als abstrahierende Modellierungssprache können Petri-Netze hier einen signifikanten Beitrag leisten, unterschiedliche Anwendungsdomänen und Abstraktionsebenen miteinander zu verbinden. Um dies erlebbar zu machen, wurde eine Fallstudie entwickelt, bei der Studierende geleitet durch das Konzept des problembasierten bzw. forschenden Lernens mit einem webbasierten Petri-Netz-Werkzeug Abläufe modellieren, simulieren und in einer Produktionsstrecke ausführen. Es wird zudem dargestellt, wie diese Methode einen erweiterten Kompetenzerwerb jenseits des reinen Wissenserwerbs ermöglicht.
Fabrication of Bioactive Zirconia by Doubled Sandblasting Process and Incorporation of Apatite Nuclei

In this study, we aimed to introduce bioactivity to bioinert zirconia by performing sandblasting process and subsequently depositing apatite nuclei (AN), which are fine particles of calcium phosphate precipitated by raising pH of SBF, on the surface of the zirconia samples. By soaking the AN treated zirconia samples in SBF, hydroxyapatite formation was observed in 1 day and high hydroxyapatite-forming ability was attained.
The New Potential of Deep Convective Clouds as a Calibration Target for a Geostationary UV/VIS Hyperspectral Spectrometer

As one of geostationary earth orbit constellation for environmental monitoring over the next decade, the Geostationary Environment Monitoring Spectrometer (GEMS) has been designed to observe the Asia-Pacific region to provide information on atmospheric chemicals, aerosols, and cloud properties. In order to continuously monitor sensor performance after its launch in early 2020, we suggest in this paper deep convective clouds (DCCs) as a possible target for the vicarious calibration of the GEMS, the first ultraviolet and visible hyperspectral sensor onboard a geostationary satellite. The Tropospheric Monitoring Instrument (TROPOMI) and the Ozone Monitoring Instrument (OMI) are used as a proxy for GEMS, and a conventional DCC-detection approach applying a thermal threshold test is used for DCC detection based on collocations with the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. DCCs are frequently detected over the GEMS observation area at an average of over 200 pixels within a single observation scene. Considering the spatial resolution of the GEMS (3.5 × 8 km2), which is similar to the TROPOMI and its temporal resolution (eight times a day), the availability of DCCs is expected to be sufficient for the vicarious calibration of the GEMS. Inspection of the DCC reflectivity spectra estimated from OMI and TROPOMI data also shows promising results. The estimated DCC spectra are in good agreement within a known uncertainty range with comparable spectral features even with different observation geometries and sensor characteristics. When DCC detection is improved further by applying both visible and infrared tests, the variability of DCC reflectivity from TROPOMI data is reduced from 10% to 5%. This is mainly due to the efficient screening out of cold, thin cirrus clouds in the visible test and of bright, warm clouds in the infrared test. Precise DCC detection is also expected to contribute to the accurate characterization of cloud reflectivity, which will be investigated further in future research.
Scale specified single shot multibox detector

Detecting objects at vastly different scales is a fundamental challenge in computer vision. To solve this, some approaches (e.g. TridentNet) investigate the effect of receptive fields, whereas other approaches (e.g. SNIP, SNIPER) are based on the image pyramid strategy. In this study, a novel single-shot based detector, called scale specified single-shot multibox detector (4SD) is proposed. It aims to predict objects of a specific scale range separately by using feature maps of different sizes. First, a parallel multi-branch architecture with feature maps of different sizes is generated by scale specific inference module. Then, the authors propose a scale specific training scheme to specialise each branch by sampling object instances of proper scales for training. Results are shown on both PASCAL VOC and COCO detection. The proposed method can achieve a mean average precision of 83.1% on PASCAL VOC 2007, and 36.9% on MS-COCO at a speed of 28 frames per second, which is superior to most single-stage detectors.
Fire Resistance of Reinforced Concrete Corrosion-Damaged Columns of the "Standard" Fire

The article describes the features of the effect of corrosion of reinforcement on the bearing capacity of reinforced concrete columns in a "standard" fire. On the basis of the standard calculation method, the fire resistance of the column was estimated under a four-sided fire effect taking into account the different duration of the fire. The study examined the operation of the column in a corrosive environment, it was assumed that the initiation of corrosion of concrete and reinforcement will occur after 10 years of exploitation. It was found that the destruction of concrete protective layer 25 mm thick in a medium aggressive environment will occur after 25 years, and the diameter of the reinforcement during this period will decrease by 20%. To compare the results, a reinforced concrete column with a section of 400x400mm was calculated under the influence of a “standard” fire under normal operating conditions and taking into account work in a corrosive environment. The results of heat engineering calculations are presented, where the temperature changes in the reinforcement depending on the heating time and reduction of the protective layer thickness, as well as the change in the diameter of the reinforcement and its effect on the bearing capacity are shown. It has been established that reducing the cross-sectional area of the working reinforcement and reducing the cross-sectional dimensions of the column due to the occurring corrosion processes leads to a decrease in the fire resistance limit on the loss of bearing capacity by 58%.
The Effect of Plasma Pretreatment of Dyeability of Silk with Acid Dye

Plasma technology provides an effective way to modify the surface of polymeric materials and thus improve the physicochemical properties of this material. This study was a preliminary study in exploring the application of plasma technology in the surface modification of silk fiber. Experimental results revealed that the dyeability of silk fabric with acid dye was improved after plasma treatment.
Neural PD Controller for an Unmanned Aerial Vehicle Trained with Extended Kalman Filter

Flying robots have gained great interest because of their numerous applications. For this reason, the control of Unmanned Aerial Vehicles (UAVs) is one of the most important challenges in mobile robotics. These kinds of robots are commonly controlled with Proportional-Integral-Derivative (PID) controllers; however, traditional linear controllers have limitations when controlling highly nonlinear and uncertain systems such as UAVs. In this paper, a control scheme for the pose of a quadrotor is presented. The scheme presented has the behavior of a PD controller and it is based on a Multilayer Perceptron trained with an Extended Kalman Filter. The Neural Network is trained online in order to ensure adaptation to changes in the presence of dynamics and uncertainties. The control scheme is tested in real time experiments in order to show its effectiveness.
Cell Adhesion Ability of α-Tricalcium Phosphate Films Formed on Titanium Substrates by an Er:YAG Laser Deposition Method: Imprecations for Management of Peri-Implant Inflammation

Dental implants have been widely used to restore missing or damaged teeth. Peri-implantitis is one of the main causes of the implant loss. However, a consensus concerning clinically successful peri-implantitis treatment has not been reached at present. We have newly proposed a treatment method for the peri-implantitis by applying the intraoral laser ablation technique to induce a surface modification of dental implants. In the present study, we investigated the cell adhesion ability of α-tricalcium phosphate (α-TCP) films formed on titanium substrates by an Erbium-Doped:Yttrium, Aluminum, and Garnet (Er:YAG) laser deposition method. Cell adhesion was enhanced by the α-TCP films attached firmly to the substrates using the Er:YAG laser deposition method. This intraoral laser deposition technique can be used to recover the osteointegration ability which was lost by the treatment procedures for peri-implantitis.
Phase Transformation of Nanocrystalline Diamond Films: Effect of Methane Concentration

Ultra-nanocrystalline diamond films were prepared by a microwave plasma-enhanced chemical vapor deposition reactor using CH4/H2 gas mixture with a power as low as 650 W. The effects of CH4 concentration on nanostructure of the films and gas-phase species in plasma were investigated. The CH4 concentrations of 1.5%, 3.0%, 3.5%, and 4.0% were used and balanced with H2 to a total flow rate of 200 sccm. Morphology and composition of the films were characterized by SEM, Raman spectroscopy and Auger spectroscopy. The gas-phase species and electron density in the plasma were explored by optical emission spectroscopy and plasma-impedance measurement. The increasing CH4 concentration from 1.5% to 4.0% increased C2Hx species and decreased electron density. Phase of the film transform from nano- into ultranano- diamond phase but the growth rate revealingly decreased from 300 to 210 nm/h. Raman spectra indicate the higher CH4 concentration promted phase of the film transiton from NCD to UNCD. While Auger spectra revealed that UNCD film deposited with 4.0%CH4 was composed of 90.52% diamond phase but only 9.48% of graphite phase. The relation between phase transformation of the films and growth mechnism controlled by gas-phase species in the plasma will be dissused.
Crime Detection with ICA and Artificial Intelligent Approach

The Rise of Crime in Malaysia reported that violent crimes comprised only 10% of reported crimes each year and the majority of crimes, 90%, were classified as property crimes. However, the ratio of police to population is 3.6 officers to 1,000 citizens in Malaysia. This lack of manpower sources ratios alone are not a comprehensive afford of crime fighting capabilities. Thus, we proposed an Artificial Intelligent Techniques to determine the behaviour of the burglar with Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA) and k Nearest Neighbor (k-NN) Classifier. This system provided a good justification as a monitoring supplementary tool for the Malaysian police arm forced.
Open Set Forests

—Current ensemble classiﬁers use closed set decisions for each individual classiﬁer in the ensemble. This applies to random forests, where each decision tree is a closed set classiﬁer. In this article we propose using statistical extreme value theory to determine the relevance of the input to each decision tree in a random forest. this allows us to make open set decisions for the forest as a whole by looking at the average similarity between each tree and the given datapoint. We found that it is difﬁcult to preserve the closed set accuracy when working with open set data in a random forest. However, it is possible to preserve the open set accuracy as unknown classes are added to testing.
Temporal Word Embeddings for Dynamic User Profiling in Twitter

The research described in this paper focused on exploring the domain of user profiling, a nascent and contentious technology which has been steadily attracting increased interest from the research community as its potential for providing personalised digital services is realised. An extensive review of related literature revealed that limited research has been conducted into how temporal aspects of users can be captured using user profiling techniques. This, coupled with the notable lack of research into the use of word embedding techniques to capture temporal variances in language, revealed an opportunity to extend the Random Indexing word embedding technique such that the interests of users could be modelled based on their use of language. To achieve this, this work concerned itself with extending an existing implementation of Temporal Random Indexing to model Twitter users across multiple granularities of time based on their use of language. The product of this is a novel technique for temporal user profiling, where a set of vectors is used to describe the evolution of a Twitter user’s interests over time through their use of language. The vectors produced were evaluated against a temporal implementation of another state-of-the-art word embedding technique, the Word2Vec Dynamic Independent Skip-gram model, where it was found that Temporal Random Indexing outperformed Word2Vec in the generation of temporal user profiles.
Collective Action in An Embedded Choice Model: A Study of Fishing Cooperatives in Calcutta

In recent years, collective action to reduce the impact of poverty has been attempted through membership based organisations, of which co-operatives are an important classical form. The participation of target groups in such organisations can be explained using the rational choice model in terms of opportunity costs of collective action. However, this approach ignores the context in which agents are situated. The rationality of agents is bounded, with actions depending upon perceived opportunity costs. The roots of such perceptions are embedded in the history, politics, and the socio-cultural and economic background of agents. This implies that collective action should be analysed in an embedded choice model. In this paper, labour supply to the collective action arena is analysed using an optimisation model embedded in the historic and economic context of the communities. Studies of two fishermen's co-operative in Calcutta, India, provide the back drop of the study.
Challenges and Opportunities of Business Models in Sustainable Transitions: Evidence from Solar Energy Niche Development in Lebanon

Although business models presumably have a prominent role in socio-technical change remarkably little research has been conducted in this domain, more particularly in the context of developing economies. In this paper, we tap into this knowledge gap and study business model components and the challenges they face. We argue that the market value of renewable energy technologies will increase when new business models are implemented to overcome financial and institutional challenges. We complement concepts of the business model literature with the insights from the sustainable transitions literature. This paper addresses two research questions: What are the challenges of business and financial models in the transition towards decentralized solar energy driven systems? And what are the promising opportunities for new business models in a developing country context? To answer these research questions, we use a case study research design focusing on niche market development of solar thermal and solar photovoltaic (PV) technology in Lebanon. Data collection involved analysis of relevant text documents and expert interviews with 30 informants across different groups of stakeholders. Data analysis involved qualitative interpretation of collected data against concepts from the business model and Strategic Niche Management literatures. The results show that business model challenges initially were highly dependent on donor aid, which contributed to the launch and network creation of niche markets. Later, a shift to micro-finance and business startup models was observed, which showed promising development. Knowledge transfer and community empowerment were found to play an important role in developing new business models that involve consumers more closely. As this development is expected to take place more often, we expect that new opportunity pathways will develop in developing economies like Lebanon.
C9orf72 arginine-rich dipeptide repeat proteins disrupt karyopherin-mediated nuclear import

Disruption of nucleocytoplasmic transport is increasingly implicated in the pathogenesis of neurodegenerative diseases, including ALS caused by a C9orf72 hexanucleotide repeat expansion. However, the mechanism(s) remain unclear. Karyopherins, including importin β and its cargo adaptors, have been shown to co-precipitate with the C9orf72 arginine-containing dipeptide repeat proteins (R-DPRs), poly-glycine arginine (GR) and poly-proline arginine (PR), and are protective in genetic modifier screens. Here, we show that R-DPRs interact with importin β, disrupt its cargo loading, and inhibit nuclear import of importin β, importin α/β, and transportin cargoes in permeabilized mouse neurons and HeLa cells, in a manner that can be rescued by RNA. Although R-DPRs induce widespread protein aggregation in this in vitro system, transport disruption is not due to nucleocytoplasmic transport protein sequestration, nor blockade of the phenylalanine-glycine (FG)-rich nuclear pore complex. Our results support a model in which R-DPRs interfere with cargo loading on karyopherins.
Up-regulation of MicroRNAs-21 and -223 in a Sprague-Dawley Rat Model of Traumatic Spinal Cord Injury

In this experimental animal study, we examined alterations in the degree of transcription of two microRNAs (miRs)—miR-21 and -223—in a Sprague-Dawley (SD) rat model of traumatic spinal cord injury (TSCI). Depending on the volume of the balloon catheter (V), a total of 75 male SD rats were divided into the three experimental groups: the sham group (n = 25; V = 0 μL), the mild group (n = 25; V = 20 μL), and the severe group (n = 25; V = 50 μL). Successful induction of TSCI was confirmed on both locomotor rating scale at 4 h and 1, 3 and 7 days post-lesion and histopathologic examinations. Then, RNA isolation and quantitative polymerase chain reaction (PCR) were performed. No differences in the level of miR-21 expression were found at the first time point studied (4 h post-lesion) between the three experimental groups, whereas such differences were significant at all the other time points (p < 0.05). Moreover, there were significant alterations in the level of miR-223 expression at all time points studied through all the experimental groups (p < 0.05). Furthermore, locomotor rating scale scores had a linear relationship with the level of miR-21 expression (R2 = 0.4363, Y = 1.661X + 3.096) and that of miR-223 one (R2 = 0.9104, Y = 0.8385X + 2.328). Taken together, we conclude that up-regulation of miR-21 and -223 might be closely associated with progression and the early course of TSCI, respectively.
Association between Occupational Characteristics and Overweight and Obesity among Working Korean Women: The 2010–2015 Korea National Health and Nutrition Examination Survey

Associations between several occupational characteristics and obesity are not fully elucidated in Korean working populations, especially in females. This study investigated associations between occupational characteristics and overweight/obesity among Korean women. Data on 2090 female workers (the mean age was 38.8 ± 0.2 years), extracted from Korean National Health and Nutrition Examination Surveys in 2010–2015, were analyzed and showed that 6.8% of subjects were underweight, 50.8% had normal weight, 20.1% were overweight, and 22.2% were individuals with obesity. Multiple regression analysis was performed to examine associations between occupational characteristics and overweight/obesity, after controlling for demographic, behavioral, and health-related characteristics. The reference group was normal weight. Working hours were strongly associated with overweight/obesity. The odds ratio (OR) of obesity in women who worked for ≥60 h per week was 2.68 (95% confidence interval: [CI] 2.13–3.36) compared with those who worked for <40 h. Night/shift workers were 1.21 times (95% confidence interval: [CI] 1.01–1.45) more likely to experience obesity than day or evening workers. In conclusion, obesity rates increase among female workers with longer working hours and those who work at night or in shifts. Occupational characteristics should be considered in the prevention of obesity among working women.