Search results for: multi-agent double deep Q-network (MADDQN)

Authors: Galuh Asri Bestari, Hajar Shofiyya

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Double burden of malnutrition (DBM) has been a global problem in these last decades occurs in both developed and developing countries. Overweight in adults and stunting among preschool children have dramatically increased and become the main problems of malnutrition that should be solved immediately since they are directly related with the health status and productivity. Reformulation of food product by using the local sea resources called terasi shrimp (Acetes indicus) has a potential possibility in facing the DBM. A study was carried out in Indonesia to determine the acceptability of terasi shrimp powder through sensory evaluation. Terasi shrimps were processed into powder form through sun drying and pounding methods. The powder form was directly added in food as alternative seasonings and tested among stunted and normal preschool children. Meanwhile, a further processing method is given to the shrimp powder tested in overweight and normal-weighed adults. The shrimp powder was mixed with sago flour and formed into balls, then steamed for 15-20 minutes, and finally served as alternative snacks. Based on the sensory evaluation, the shrimp powder has a good acceptance in taste (54%), shape (60%), and color properties (63%), while the shrimp balls has a good acceptance in size (65%), shape (50%), color (48%), taste (40%), and texture (36%). Terasi shrimp powder can be stored for a month in room temperature. In addition, carried out chemical analysis revealed that terasi shrimp (Acetes indicus) has higher percentage of protein, calcium, and iron than other animal sources, but conversely contains zero sodium and very low percentage of fat. Terasi shrimp’s shell also contains a substance called chitosan which acts by forming gels in the intestinal tract to entrap lipids, thus interfering with their absorption. After going through some processing methods, the shrimp powder and balls did not show any significant changes in their nutrient contents. So that, terasi shrimp powder is good to be consumed not only by overweight adults, but also by children to support their optimum growth. Intervention of terasi shrimp powder should be implemented step by step from national up to global governance program to face the DBM.

Keywords: Acetes indicus, alternative food, double burden of malnutrition, sensory evaluation

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Authors: Niloufar Yadgari

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GANs are a potent form of deep learning models that have found success in various fields. They are part of the larger group of generative techniques, which aim to produce authentic data using a probabilistic model that learns distributions from actual samples. In clinical settings, GANs have demonstrated improved abilities in capturing spatially intricate, nonlinear, and possibly subtle disease impacts in contrast to conventional generative techniques. This review critically evaluates the current research on how GANs are being used in imaging studies of different neurological conditions like Alzheimer's disease, brain tumors, aging of the brain, and multiple sclerosis. We offer a clear explanation of different GAN techniques for each use case in neuroimaging and delve into the key hurdles, unanswered queries, and potential advancements in utilizing GANs in this field. Our goal is to connect advanced deep learning techniques with neurology studies, showcasing how GANs can assist in clinical decision-making and enhance our comprehension of the structural and functional aspects of brain disorders.

Keywords: GAN, pathology, generative adversarial network, neuro imaging

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Authors: Gabor Varga, Krisztina Karadi, Zoltan Konya, Akos Kukovecz, Pal Sipos, Istvan Palinko

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Nickel can efficiently replace palladium in the Heck, Suzuki and Negishi reactions. This study focuses on the synthesis and catalytic application of Ni(II)-containing layered double hydroxides (LDHs) and layered triple hydroxides (LTHs). Our goals were to incorporate Ni(II) ions among the layers of LDHs or LTHs, or binding it to their surface or building it into their layers in such a way that their catalytic activities are maintained or even increased. The LDHs and LTHs were prepared by the co-precipitation method using ethylene glycol as co-solvent. In several cases, post-synthetic modifications (e.g., thermal treatment) were performed. After optimizing the synthesis conditions, the composites displayed good crystallinity and were free of byproducts. The success of the syntheses and the post-synthetic modifications was confirmed by relevant characterization methods (XRD, SEM, SEM-EDX and combined IR techniques). Catalytic activities of the produced and well-characterized solids were investigated through the Heck reaction. The composites behaved as efficient, recyclable catalysts in the Heck reaction between 4-bromoanisole and styrene. Through varying the reaction parameters, we were able to obtain acceptable conversions under mild conditions. Our study highlights the possibility of the application of Ni(II)-containing composites as efficient catalysts in coupling reactions.

Keywords: layered double hydroxide, layered triple hydroxide, heterogeneous catalysis, heck reaction

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Authors: Ming-Jen Huang, Chun-Fang Huang, Chiching Wei

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With the recent advance of the deep neural network, we observe new applications of NLP (natural language processing) and CV (computer vision) powered by deep neural networks for processing business documents. However, creating a real-world document processing system needs to integrate several NLP and CV tasks, rather than treating them separately. There is a need to have a unified approach for processing documents containing textual and graphical elements with rich formats, diverse layout arrangement, and distinct semantics. In this paper, a framework that fulfills this unified approach is presented. The framework includes a representation model definition for holding the information generated by various tasks and specifications defining the coordination between these tasks. The framework is a blueprint for building a system that can process documents with rich formats, styles, and multiple types of elements. The flexible and lightweight design of the framework can help build a system for diverse business scenarios, such as contract monitoring and reviewing.

Keywords: document processing, framework, formal definition, machine learning

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Authors: Halimatu S. Abdullahi, Ray E. Sheriff, Fatima Mahieddine

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Agriculture is essential to the continuous existence of human life as they directly depend on it for the production of food. The exponential rise in population calls for a rapid increase in food with the application of technology to reduce the laborious work and maximize production. Technology can aid/improve agriculture in several ways through pre-planning and post-harvest by the use of computer vision technology through image processing to determine the soil nutrient composition, right amount, right time, right place application of farm input resources like fertilizers, herbicides, water, weed detection, early detection of pest and diseases etc. This is precision agriculture which is thought to be solution required to achieve our goals. There has been significant improvement in the area of image processing and data processing which has being a major challenge. A database of images is collected through remote sensing, analyzed and a model is developed to determine the right treatment plans for different crop types and different regions. Features of images from vegetations need to be extracted, classified, segmented and finally fed into the model. Different techniques have been applied to the processes from the use of neural network, support vector machine, fuzzy logic approach and recently, the most effective approach generating excellent results using the deep learning approach of convolution neural network for image classifications. Deep Convolution neural network is used to determine soil nutrients required in a plantation for maximum production. The experimental results on the developed model yielded results with an average accuracy of 99.58%.

Keywords: convolution, feature extraction, image analysis, validation, precision agriculture

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Authors: Hossam Abdelrahman, Sama Rostom, Reem Yassein, Yara Mohamed, Salma Salah, Nour Awny

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In today’s environment, people are becoming increasingly interested in their appearance. However, they are afraid of their unknown appearance after a plastic surgery or treatment. Accidents, burns and genetic problems such as bowing of body parts of people have a negative impact on their mental health with their appearance and this makes them feel uncomfortable and underestimated. The approach presents a revolutionary deep learning-based image inpainting method that analyses the various picture structures and corrects damaged images. In this study, A model is proposed based on the in-painting of medical images with Stable Diffusion Inpainting method. Reconstructing missing and damaged sections of an image is known as image inpainting is a key progress facilitated by deep neural networks. The system uses the input of the user of an image to indicate a problem, the system will then modify the image and output the fixed image, facilitating for the patient to see the final result.

Keywords: generative adversarial network, large mask inpainting, stable diffusion inpainting, plastic surgery

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Authors: Yaxin Bi

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Accurate prediction and understanding of the evolution mechanisms of earthquakes remain challenging in the fields of geology, geophysics, and seismology. This study leverages Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), a generative model tailored to time-series data, for generating synthetic time series data based on Swarm satellite data, which will be used for detecting seismic anomalies. LSTMs demonstrated commendable predictive performance in generating synthetic data across multiple countries. In contrast, the GAN models struggled to generate synthetic data, often producing non-informative values, although they were able to capture the data distribution of the time series. These findings highlight both the promise and challenges associated with applying deep learning techniques to generate synthetic data, underscoring the potential of deep learning in generating synthetic electromagnetic satellite data.

Keywords: LSTM, GAN, earthquake, synthetic data, generative AI, seismic precursors

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Authors: S. Chandrakaran, Aarthy D.

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Abstract: Under reamed piles are piles which are of different types like bored cast in-situ pile or bored compaction concrete piles where one or more bulbs are provided. In this paper, the design procedure of under reamed pile by both experimental study and numerical study using PLAXIS 3D Foundation software was studied. The soil chosen for study was M Sand. The Single and double under reamed pile modelling was made using mild steel. The pile load test experiment was conducted in the laboratory and the ultimate compression load for 25 mm settlement on single and double under reamed pile was observed and finally the result was compared with conventional pile (pile without bulb). The parametric influence on under reamed pile was studied by varying the geometrical parameters like diameter of bulbs, spacing between bulbs, position of bulbs and number of bulbs. The results of the numerical model showed that when the diameter of bulb D u =2.5D, the ultimate compression load for an under-reamed pile with a single bulb increased by 55 % compared to a pile without a bulb. It was observed that when the spacing between the bulbs was S=6D u with three different positions of bulb from bottom of pile as D u , 2D u and 3D u , the ultimate compression load increased by 88%, 94% and 73 % respectively, compared to the ultimate compression load for 25 mm settlement on conventional pile and if spacing was more than 6D u , ultimate compression load for 25 mm settlement started to decrease. It was observed that when the bucket length was more than 2D u , the ultimate compression

Keywords: load capcity, under remed bulb . sand, model study, sand

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Authors: Gianni Jacucci

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Epistemology traditionally finds its place within human research philosophies and methodologies. Artificial intelligence methods pose challenges, particularly given the unresolved relationship between AI and pivotal concepts in social arenas such as hermeneutics and accountability. We begin by examining the essential criteria governing scientific rigor in the human sciences. We revisit the three foundational philosophies underpinning qualitative research methods: empiricism, hermeneutics, and phenomenology. We elucidate the distinct attributes, merits, and vulnerabilities inherent in the methodologies they inspire. The integration of AI, e.g., deep learning algorithms, sparks an interest in evaluating these criteria against the diverse forms of AI architectures. For instance, Interpreted AI could be viewed as a hermeneutic approach, relying on a priori interpretations, while straight AI may be perceived as a descriptive phenomenological approach, processing original and uncontaminated data. This paper serves as groundwork for such explorations, offering preliminary reflections to lay the foundation and outline the initial landscape.

Keywords: artificial intelligence, deep learning, epistemology, qualitative research, methodology, hermeneutics, accountability

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Authors: Shahab Uddin, Kazi M. Uddin, Hamamah Sadiqa

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The paper deals with the Banghabandhu Multipurpose Bridge (BMB), the 11th longest bridge in the world was constructed in 1998 aimed at contributing to promote economic development in Bangladesh. In recent years, however, the high incidence of traffic accidents and injuries at the bridge sites looms as a great safety concern. Investigation into the derailment of nine bogies out of thirteen of Dinajpur-bound intercity train ‘Drutajan Express ’were derailed and inclined on the Banghabandhu Multipurpose Bridge on 28 April 2014. The train accident in Bridge will be deep concern for both structural safety of bridge and people than other vehicles accident. In this study we analyzed the disaster probability of the Banghabandhu Multipurpose Bridge for accidents by checking the fitness of Bridge structure. We found that train accident impact is more risky than other vehicles accidents. We also found that socio-economic impact on Bangladesh will be deep concerned.

Keywords: train accident, derailment, disaster, socio-economic

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Authors: E. Oke, A. O. Ladokun, J. O. Daramola, O. M. Onagbesan

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There has been a growing interest on the effects of access to pasture on poultry health status. However, there is a paucity of data on the relative benefits of grass and legume pastures. An experiment was conducted to determine the effects of rearing systems {deep litter system (DL), deep litter with access to legumes (LP) or grass (GP) pastures} haematology and serum chemistry of ISA Brown layers. The study involved the use of two hundred and forty 12 weeks old pullets. The birds were reared until 60 weeks of age. Eighty birds were assigned to each treatment; each treatment had four replicates of 20 birds each. Blood samples (2.5 ml) were collected from the wing vein of two birds per replicate and serum chemistry and haematological parameters were determined. The results showed that there were no significant differences between treatments in all the parameters considered at 18 weeks of age. At 24 weeks old, the percentage of heterophyl (HET) in DL and LP were similar but higher than that of GP. The ratio of H:L was higher (P<0.05) in DL than those of LP and GP while LP and GP were comparable. At week 38 of age, the percentage of PCV in the birds in LP and GP were similar but the birds in DL had significantly lower level than that of GP. In the early production phase, serum total protein of the birds in LP was similar to that of GP but higher (P<0.05) than that of DL. At the peak production phase (week 38), the total protein in GP and DL were similar but significantly lower than that of LP. The albumin level in LP was greater (P<0.05) than GP but similar to that of DL. In the late production phase, the total protein in LP was significantly higher than that of DL but similar to that of GP. It was concluded that rearing chickens in either grass or legume pasture did not have deleterious effects on the health of laying chickens but improved some parameters including blood protein and HET/lymphocyte.

Keywords: rearing systems, stylosanthes, cynodon serum chemistry, haematology, hen

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Authors: M. L. Yeung

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With the surge of divorce rate and male cancer onset/death rates, the phenomenon of divorced wives in the facing cancer death of their ex-husbands is not uncommon in Hong Kong. Yet, there is a dearth of study on the experiences of bereaved-divorced wives in the Hong Kong cultural context. This project fills the knowledge gap by conducting a qualitative study for having interviewed four bereaved ex-wives, who returned to ex-husbands’ end-of-life caregiving and eventually grieved for the ex-spousal’s death. From the perspectives of attachment theory and disenfranchised grief in the Hong Kong cultural context, a ‘double-loss’ experience is found in which interviewees suffer from the first loss of divorce and the second loss of ex-husbands’ death. Traumatic childhood experiences, attachment needs, role ambiguity, unresolved emotions and unrecognized grief are found significant in their lived experiences which alert the ‘double-loss’ is worthy of attention. Extending a family-centered end-of-life and bereavement care services to divorced couples is called for, in which validation on the attachment needs, ex-couple reconciliation, and acknowledgement on the disenfranchised grief are essential for social work practice on this group of clienteles specifically in Hong Kong cultural context.

Keywords: changing family, disenfranchised grief, divorce, ex-spousal death, marriage

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

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Authors: Marius Sebastian Secula, Mihaela Darie, Gabriela Carja

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Persistent organic pollutant discharged by various industries or urban regions into the aquatic ecosystems represent a serious threat to fauna and human health. The endocrine disrupting compounds are known to have toxic effects even at very low values of concentration. The anti-inflammatory agent Ibuprofen is an endocrine disrupting compound and is considered as model pollutant in the present study. The use of light energy to accomplish the latest requirements concerning wastewater discharge demands highly-performant and robust photo-catalysts. Many efforts have been paid to obtain efficient photo-responsive materials. Among the promising photo-catalysts, layered double hydroxides (LDHs) attracted significant consideration especially due to their composition flexibility, high surface area and tailored redox features. This work presents Fe(II) self-supported on ZnMeLDHs (Me =Al3+, Fe3+) as novel efficient photo-catalysts for Fenton-like catalysis. The co-precipitation method was used to prepare ZnAlLDH, ZnFeAlLDH and ZnCrLDH (Zn2+/Me3+ = 2 molar ratio). Fe(II) was self-supported on the LDHs matrices by using the reconstruction method, at two different values of weight concentration. X-ray diffraction (XRD), thermogravimetric analysis (TG/DTG), Fourier transform infrared (FTIR) and transmission electron microscopy (TEM) were used to investigate the structural, textural, and micromorphology of the catalysts. The Fe(II)/ZnMeLDHs nano-hybrids were tested for the degradation of a model pharmaceutical agent, the anti-inflammatory agent ibuprofen, by photocatalysis and photo-Fenton catalysis, respectively. The results point out that the embedment Fe(II) into ZnFeAlLDH and ZnCrLDH lead to a slight enhancement of ibuprofen degradation by light irradiation, whereas in case of ZnAlLDH, the degradation process is relatively low. A remarkable enhancement of ibuprofen degradation was found in the case of Fe(II)/ZnMeLDHs by photo-Fenton process. Acknowledgements: This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI, project number PN-II-RU-TE-2014-4-0405.

Keywords: layered double hydroxide, heterogeneous Fenton, micropollutant, photocatalysis

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Authors: Vijaya Yuvaram Singh V M, Kameshwar Rao J V

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The challenge with development of neural network based methods for medical is the availability of data. Anatomical landmark detection in the medical domain is a process to find points on the x-ray scan report of the patient. Most of the time this task is done manually by trained professionals as it requires precision and domain knowledge. Traditionally object detection based methods are used for landmark detection. Here, we utilize reinforcement learning and query based method to train a single agent capable of detecting multiple landmarks. A deep Q network agent is trained to detect single and multiple landmarks present on hip and shoulder from x-ray scan of a patient. Here a single agent is trained to find multiple landmark making it superior to having individual agents per landmark. For the initial study, five images of different patients are used as the environment and tested the agents performance on two unseen images.

Keywords: reinforcement learning, medical landmark detection, multi target detection, deep neural network

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Authors: Pingping Zhang, Yanpeng Cai, Jianlong Wang

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Stormwater storage tank (SST) is a popular low impact development technology for reducing stormwater runoff in the construction of sponge city. At present, it is difficult to perform the automatic control of SST for reducing peak flow. In this paper, fuzzy control was introduced into the peak control of SST to improve the efficiency of reducing stormwater runoff. Firstly, the design of SST was investigated. A catchment area and a return period were assumed, a SST model was manufactured, and then the storage capacity of the SST was verified. Secondly, the control parameters of the SST based on reducing stormwater runoff were analyzed, and a schematic diagram of real-time control (RTC) system based on peak control SST was established. Finally, fuzzy control system of a double input (flow and water level) and double output (inlet and outlet valve) was designed. The results showed that 1) under the different return periods (one year, three years, five years), the SST had the effect of delayed peak control and storage by increasing the detention time, 2) rainfall, pipeline flow, the influent time and the water level in the SST could be used as RTC parameters, and 3) the response curves of flow velocity and water level fluctuated very little and reached equilibrium in a short time. The combination of online monitoring and fuzzy control was feasible to control the SST automatically. This paper provides a theoretical reference for reducing stormwater runoff and improving the operation efficiency of SST.

Keywords: stormwater runoff, stormwater storage tank, real-time control, fuzzy control

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Authors: Louis Botha

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Since the presentation of South Africa’s inclusive education strategy in Education White Paper 6 in 2001, very little has been accomplished in terms of its implementation. The failure to achieve the goals set by this policy document is related to teachers lacking confidence and knowledge about how to enact inclusive education, as well as challenges of inflexible curricula, limited resources in overcrowded classrooms, and so forth. This paper presents a socio-cultural approach to addressing these challenges of implementing inclusive education in the South African context. It takes its departure from the view that inclusive education has been adequately theorized and conceptualized in terms of its philosophical and ethical principles, especially in South African policy and debates. What is missing, however, are carefully theorized, practically implementable research interventions which can address the concerns mentioned above. Drawing on socio-cultural principles of learning and development and on cultural-historical activity theory (CHAT) in particular, this paper argues for the use of formative interventions which introduce appropriately constructed mediational artifacts that have the potential to initiate inclusive practices and pedagogies within South African schools and classrooms. It makes use of Vygotsky’s concept of double stimulation to show how the proposed artifacts could instigate forms of transformative agency which promote the adoption of inclusive cultures of learning and teaching.

Keywords: cultural-historical activity theory, double stimulation, formative interventions, transformative agency

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Authors: Hadjer Sadoune, Liza Lamini, Scherazade Krim, Amel Djouadi, Rachida Rihani

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Deep neural networks are highly regarded for their accuracy in predicting intricate fermentation processes. Their ability to learn from a large amount of datasets through artificial intelligence makes them particularly effective models. The primary obstacle in improving the performance of these models is to carefully choose the suitable hyperparameters, including the neural network architecture (number of hidden layers and hidden units), activation function, optimizer, learning rate, and other relevant factors. This study predicts biogas production from real wastewater treatment plant data using a sophisticated approach: hybrid Bayesian optimization with a tree-structured Parzen estimator (BO-TPE) for an optimised deep neural network (DNN) model. The plant utilizes an Upflow Anaerobic Sludge Blanket (UASB) digester that treats industrial wastewater from soft drinks and breweries. The digester has a working volume of 1574 m3 and a total volume of 1914 m3. Its internal diameter and height were 19 and 7.14 m, respectively. The data preprocessing was conducted with meticulous attention to preserving data quality while avoiding data reduction. Three normalization techniques were applied to the pre-processed data (MinMaxScaler, RobustScaler and StandardScaler) and compared with the Non-Normalized data. The RobustScaler approach has strong predictive ability for estimating the volume of biogas produced. The highest predicted biogas volume was 2236.105 Nm³/d, with coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values of 0.712, 164.610, and 223.429, respectively.

Keywords: anaerobic digestion, biogas production, deep neural network, hybrid bo-tpe, hyperparameters tuning

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Authors: Danilo A. F. Fontes, Magaly A. M.Lyra, Maria L. C. Moura, Leslie R. M. Ferraz, Salvana P. M. Costa, Amanda C. Q. M. Vieira, Larissa A. Rolim, Giovanna C. R. M. Schver, Ping I. Lee, Severino Alves-Júnior, José L. Soares-Sobrinho, Pedro J. Rolim-Neto

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Efavirenz (EFV) is a first-choice drug in antiretroviral therapy with high efficacy in the treatment of infection by Human Immunodeficiency Virus, which causes Acquired Immune Deficiency Syndrome (AIDS). EFV has low solubility in water resulting in a decrease in the dissolution rate and, consequently, in its bioavailability. Among the technological alternatives to increase solubility, the Lamellar Double Hydroxides (LDH) have been applied in the development of systems with poorly water-soluble drugs. The use of analytical techniques such as X-Ray Diffraction (XRD), Infrared Spectroscopy (IR) and Differential Scanning Calorimetry (DSC) allowed the elucidation of drug interaction with the lamellar compounds. The objective of this work was to characterize and develop the binary systems with EFV and LDH in order to increase the solubility of the drug. The LDH-CaAl was synthesized by the method of co-precipitation from salt solutions of calcium nitrate and aluminum nitrate in basic medium. The systems EFV-LDH and their physical mixtures (PM) were obtained at different concentrations (5-60% of EFV) using the solvent technique described by Takahashi & Yamaguchi (1991). The characterization of the systems and the PM’s was performed by XRD techniques, IR, DSC and dissolution test under non-sink conditions. The results showed improvements in the solubility of EFV when associated with LDH, due to a possible change in its crystal structure and formation of an amorphous material. From the DSC results, one could see that the endothermic peak at 173°C, temperature that correspond to the melting process of EFZ in the crystal form, was present in the PM results. For the EFZ-LDH systems (with 5, 10 and 30% of drug loading), this peak was not observed. XRD profiles of the PM showed well-defined peaks for EFV. Analyzing the XRD patterns of the systems, it was found that the XRD profiles of all the systems showed complete attenuation of the characteristic peaks of the crystalline form of EFZ. The IR technique showed that, in the results of the PM, there was the appearance of one band and overlap of other bands, while the IR results of the systems with 5, 10 and 30% drug loading showed the disappearance of bands and a few others with reduced intensity. The dissolution test under non-sink conditions showed that systems with 5, 10 and 30% drug loading promoted a great increase in the solubility of EFV, but the system with 10% of drug loading was the only one that could keep substantial amount of drug in solution at different pHs.

Keywords: Efavirenz, Lamellar Double Hydroxides, Pharmaceutical Techonology, Solubility

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Authors: Khaled M. Ali, Ayman A. Mostafa, Soliman M. Soliman

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Objectives: To describe the most common clinical and endoscopic findings associated with complicated corneal ulcers in cats, and to determine the short-term outcomes after surgical treatment of these cats. Animals Eighteen client-owned cats of different breeds (52 females and 28 males), ranging in age from 3 months to 6 years, with corneal ulcers. Procedures: Cats were clinically evaluated to initially determine the concurrent corneal abnormalities. Endoscopic examination was performed to determine the anterior and posterior segments abnormalities. Superficial and deep stromal ulcers were treated using conjunctival flap. Corneal sequestrum was treated by partial keratectomy and conjunctival flap. Anterior synechia was treated via peripheral iridectomy and separation of the adhesion between the iris and the inner cornea. Symblepharon was treated by removal of the adhered conjunctival membrane from the cornea. Incurable endophthalmitis was treated surgically by extirpation. Short-term outcomes after surgical managements of selected corneal abnormalities were then assessed clinically and endoscopically. Results: Deep stromal ulcer with descemetocele, endophthalmitis, symblepharon, corneal sequestration and anterior synechia with secondary glaucoma and corneal scarring were the most common complications of corneal ulcer. FHV-1 was a common etiologic factor of corneal ulceration. Persistent corneal scars of varying shape and size developed in cats with deep stromal ulcer, anterior synechia, and corneal sequestration. Conclusions: Domestic shorthaired and Persian cats were the most predisposed breeds to FHV-1 infection and subsequent corneal ulceration. Immediate management of patients with corneal ulcer would prevent serious complications. No age or sex predisposition to complicated corneal ulceration in cats.

Keywords: cats, complicated corneal ulceration, clinical, endoscopic diagnosis, FHV-1

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Authors: Marius Sebastian Secula, Mihaela Darie, Gabriela Carja

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Ibuprofen is a non-steroidal anti-inflammatory drug (NSAIDs) and is among the most frequently detected pharmaceuticals in environmental samples and among the most widespread drug in the world. Its concentration in the environment is reported to be between 10 and 160 ng L-1. In order to improve the abatement efficiency of this compound for water source prevention and reclamation, the development of innovative technologies is mandatory. AOPs (advanced oxidation processes) are known as highly efficient towards the oxidation of organic pollutants. Among the promising combined treatments, photo-Fenton processes using layered double hydroxides (LDHs) attracted significant consideration especially due to their composition flexibility, high surface area and tailored redox features. This work presents the self-supported Fe, Mn or Ti on ZnFeAl LDHs obtained by co-precipitation followed by reconstruction method as novel efficient photo-catalysts for Fenton-like catalysis. Fe, Mn or Ti/ZnFeAl LDHs nano-hybrids were tested for the degradation of a model pharmaceutical agent, the anti-inflammatory agent ibuprofen, by photocatalysis and photo-Fenton catalysis, respectively, by means of a lab-scale system consisting of a batch reactor equipped with an UV lamp (17 W). The present study presents comparatively the degradation of Ibuprofen in aqueous solution UV light irradiation using four different types of LDHs. The newly prepared Ti/ZnFeAl 4:1 catalyst results in the best degradation performance. After 60 minutes of light irradiation, the Ibuprofen removal efficiency reaches 95%. The slowest degradation of Ibuprofen solution occurs in case of Fe/ZnFeAl 4:1 LDH, (67% removal efficiency after 60 minutes of process). Evolution of Ibuprofen degradation during the photo Fenton process is also studied using Ti/ZnFeAl 2:1 and 4:1 LDHs in the presence and absence of H2O2. It is found that after 60 min the use of Ti/ZnFeAl 4:1 LDH in presence of 100 mg/L H2O2 leads to the fastest degradation of Ibuprofen molecule. After 120 min, both catalysts Ti/ZnFeAl 4:1 and 2:1 result in the same value of removal efficiency (98%). In the absence of H2O2, Ibuprofen degradation reaches only 73% removal efficiency after 120 min of degradation process. Acknowledgements: This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI, project number PN-II-RU-TE-2014-4-0405.

Keywords: layered double hydroxide, advanced oxidation process, micropollutant, heterogeneous Fenton

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Shanxiong Chen, Xu Han, Xiaolong Wang, Hui Ma

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Yi is an ethnic group mainly living in mainland China, with its own spoken and written language systems, after development of thousands of years. Ancient Yi is one of the six ancient languages in the world, which keeps a record of the history of the Yi people and offers documents valuable for research into human civilization. Recognition of the characters in ancient Yi helps to transform the documents into an electronic form, making their storage and spreading convenient. Due to historical and regional limitations, research on recognition of ancient characters is still inadequate. Thus, deep learning technology was applied to the recognition of such characters. Five models were developed on the basis of the four-layer convolutional neural network (CNN). Alpha-Beta divergence was taken as a penalty term to re-encode output neurons of the five models. Two fully connected layers fulfilled the compression of the features. Finally, at the softmax layer, the orthographic features of ancient Yi characters were re-evaluated, their probability distributions were obtained, and characters with features of the highest probability were recognized. Tests conducted show that the method has achieved higher precision compared with the traditional CNN model for handwriting recognition of the ancient Yi.

Keywords: recognition, CNN, Yi character, divergence

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Authors: Bessaha Hassiba, Bouraada Mohamed

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Layered double hydroxide with Zn/(Al+Fe) molar ratio of 3:1 was synthesized by co-precipitation method and their calcined product was obtained by heating treatment of ZAF-HT at 500°C. The calcined and uncalcined materials were used to remove weak acid dyes: indigo carmine (IC) and green bezanyl-F2B (F2B) in aqueous solution. The synthesized materials were characterized by XRD, SEM, FTIR and TG/DTA analysis confirming the formation of pure layered structure of ZAF-HT, the destruction of the original structure after calcination and the intercalation of the dyes molecules. Moreover, the interlayer distance increases from 7.645 Å in ZAF-HT to 19.102 Å after the dyes sorption. The dose of the adsorbents was chosen 0.5 g/l while the initial concentrations were 250 and 750 mg/l for indigo carmine and green bezanyl-F2B respectively. The sorption experiments were carried out at ambient temperature and without adjusting the initial solution pH (pHi = 6.10 for IC and pHi = 5.01 for F2B). In addition, the maximum adsorption capacities obtained by ZAF-HT and CZAF for both dyes followed the order: CZAF-F2B (1501.4 mg.g-1) > CZAF-IC (617.3 mg.g-1) > ZAF-HT-IC (41.4 mg.g-1) > ZAF-HT-F2B (28.9 mg.g-1). The removal of indigo carmine and green bezanyl-F2B by ZAF-HT was due to the anion exchange and/or the adsorption on the surface. By using the calcined material (CZAF), the removal of the dyes was based on a particular property, called ‘memory effect’. CZAF recover the pristine structure in the presence anionic molecules such as acid dyes where they occupy the interlayer space. The sorption process was spontaneous in nature and followed pseudo-second-order. The isotherms showed that the removal of IC and F2B by ZAF-HT and CZAF were consistent with Langmiur model.

Keywords: acid dyes, adsorption, calcination, layered double hydroxides

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Authors: Simon Guerin-Marthe, Marie Violay

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Thermal Pressurization (TP) has been proposed as a key mechanism involved in the weakening of faults during dynamic ruptures. Theoretical and numerical studies clearly show how frictional heating can lead to an increase in pore fluid pressure due to the rapid slip along faults occurring during earthquakes. In addition, recent laboratory studies have evidenced local pore pressure or local temperature variation during rotary shear tests, which are consistent with TP theoretical and numerical models. The aim of this study is to complement previous ones by measuring both local pore pressure and local temperature variations in the vicinity of a water-saturated calcite gouge layer subjected to a controlled slip velocity in direct double shear configuration. Laboratory investigation of TP process is crucial in order to understand the conditions at which it is likely to become a dominant mechanism controlling dynamic friction. It is also important in order to understand the timing and magnitude of temperature and pore pressure variations, to help understanding when it is negligible, and how it competes with other rather strengthening-mechanisms such as dilatancy, which can occur during rock failure. Here we present unique direct measurements of temperature and pressure variations during high-speed friction experiments under various load point velocities and show the timing of these variations relatively to the slip event.

Keywords: thermal pressurization, double-shear test, high-speed friction, dilatancy

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Authors: M. Nadeem, Y. K. Chang, C. Diallo, U. Venkatadri, P. Havard, T. Nguyen-Quang

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Uniform distribution of agro-chemicals is highly important because there is a significant loss of agro-chemicals, for example from pesticide, during spraying due to non-uniformity of droplet and off-target drift. Improving the efficiency of spray pattern for different cropping systems would reduce energy, costs and to minimize environmental pollution. In this paper, we examine the water jet patterns in order to study the performance and uniformity of water distribution during the spraying process. We present a method to quantify the water amount from a sprayer jet by using the Particle Image Velocimetry (PIV) system. The results of the study will be used to optimize sprayer or nozzles design for chemical application. For this study, ten sets of images were acquired by using the following PIV system settings: double frame mode, trigger rate is 4 Hz, and time between pulsed signals is 500 µs. Each set of images contained different numbers of double-framed images: 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 at eight different pressures 25, 50, 75, 100, 125, 150, 175 and 200 kPa. The PIV images obtained were analysed using custom-made image processing software for droplets and volume calculations. The results showed good agreement of both manual and PIV measurements and suggested that the PIV technique coupled with image processing can be used for a precise quantification of flow through nozzles. The results also revealed that the method of measuring fluid flow through PIV is reliable and accurate for sprayer patterns.

Keywords: image processing, PIV, quantifying the water volume from nozzle, spraying pattern

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Authors: Catarina Isabel Ramos Pereira, Jorge Machado, Begona Alonso Criado, Maria João Santos

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Introduction: Gait disorders are one of the symptoms that have severe implications on the quality of life in Parkinson's disease (PD). Currently, there is no therapy to reverse or treat this condition. None of the drugs used in conventional medical treatment is entirely efficient, and all have a high incidence of side effects. Acupuncture treatment is believed to improve motor ability, but there is still little scientific evidence in individuals with PD. Aim: The aim of the study is to investigate the acute effect of acupuncture on gait parameters in Parkinson's disease. Methods: This is a randomized and controlled crossover study. The same individual patient was part of both the experimental (real acupuncture) and control group (false acupuncture/sham), and the sequence was randomized. Gait parameters were measured at two different moments, before and after treatment, using four force platforms as well as the collection of 3D markers positions taken by 11 cameras. Images were quantitatively analyzed using Qualisys Track Manager software that let us extract data related to the quality of gait and balance. Seven patients with the diagnosis of Parkinson's disease were included in the study. Results: Statistically significant differences were found in gait speed (p = 0.016), gait cadence (p = 0.006), support base width (p = 0.0001), medio-lateral oscillation (p = 0.017), left-right step length (p = 0.0002), and stride length: right-right (p = 0.0000) and left-left (p = 0.0018), time of left support phase (p = 0.029), right support phase (p = 0.025) and double support phase (p = 0.015), between the initial and final moments for the experimental group. Differences in right-left stride length were found for both groups. Conclusion: Our results show that acupuncture could enhance gait in Parkinson's disease patients. Deep research involving a larger number of volunteers should be accomplished to validate these encouraging findings.

Keywords: acupuncture, traditional Chinese medicine, Parkinson's disease, gait

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Authors: Luther Ollier, Sylvie Thiria, Anastase Charantonis, Carlos E. Mejia, Michel Crépon

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Sea Surface Height Anomaly (SLA) is a signature of the sub-mesoscale dynamics of the upper ocean. Sea Surface Temperature (SST) is driven by these dynamics and can be used to improve the spatial interpolation of SLA fields. In this study, we focused on the temporal evolution of SLA fields. We explored the capacity of deep learning (DL) methods to predict short-term SLA fields using SST fields. We used simulated daily SLA and SST data from the Mercator Global Analysis and Forecasting System, with a resolution of (1/12)◦ in the North Atlantic Ocean (26.5-44.42◦N, -64.25–41.83◦E), covering the period from 1993 to 2019. Using a slightly modified image-to-image convolutional DL architecture, we demonstrated that SST is a relevant variable for controlling the SLA prediction. With a learning process inspired by the teaching-forcing method, we managed to improve the SLA forecast at five days by using the SST fields as additional information. We obtained predictions of a 12 cm (20 cm) error of SLA evolution for scales smaller than mesoscales and at time scales of 5 days (20 days), respectively. Moreover, the information provided by the SST allows us to limit the SLA error to 16 cm at 20 days when learning the trajectory.

Keywords: deep-learning, altimetry, sea surface temperature, forecast

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Authors: Kouassi Louis Kra

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The nearshore deep-water gravity flow deposits in the Northern steep slope of Dongying depression, Bohai Bay basin, have been acknowledged as important reservoirs in the rift lacustrine basin. These deep strata term as coarse clastic sediment, deposit at the root of the slope have complex depositional processes and involve wide diagenetic events which made high-quality reservoir prediction to be complex. Based on the integrated study of seismic interpretation, sedimentary analysis, petrography, cores samples, wireline logging data, 3D seismic and lithological data, the reservoir formation mechanism deciphered. The Geoframe software was used to analyze 3-D seismic data to interpret the stratigraphy and build a sequence stratigraphic framework. Thin section identification, point counts were performed to assess the reservoir characteristics. The software PetroMod 1D of Schlumberger was utilized for the simulation of burial history. CL and SEM analysis were performed to reveal diagenesis sequences. Backscattered electron (BSE) images were recorded for definition of the textural relationships between diagenetic phases. The result showed that the nearshore steep slope deposits mainly consist of conglomerate, gravel sandstone, pebbly sandstone and fine sandstone interbedded with mudstone. The reservoir is characterized by low-porosity and ultra-low permeability. The diagenesis reactions include compaction, precipitation of calcite, dolomite, kaolinite, quartz cement and dissolution of feldspars and rock fragment. The main types of reservoir space are primary intergranular pores, residual intergranular pores, intergranular dissolved pores, intergranular dissolved pores, and fractures. There are three obvious anomalous high-porosity zones in the reservoir. Overpressure and early hydrocarbon filling are the main reason for abnormal secondary pores development. Sedimentary facies control the formation of high-quality reservoir, oil and gas filling preserves secondary pores from late carbonate cementation.

Keywords: Bohai Bay, Dongying Sag, deep strata, formation mechanism, high-quality reservoir

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Authors: Hai Zhou, Wenzhi Zhao

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Water is one of the most important factors limiting plant growth and development in desert ecosystems. In order to understand how desert shrubs cope with variation in water sources over time, it is important to understand plant–water relations in desert-oasis ecotone. We selected the typical desert shrubs: Nitraria sibirica, Calligonum mongolicum and Haloxylon ammodendron of 5-, 10-, 20- and 40-year old as the research species, to study the seasonal variation of plant water sources and response to precipitation in the desert-oasis ecotone of Linze, Northwestern China. We examined stable isotopic ratios of oxygen (δ18O) in stem water of desert shrubs as well as in precipitation, groundwater, and soil water in different soil layers and seasons to determine water sources for the shrubs. We found that the N. sibirica and H. ammodendron of 5-, 10-year old showed significant seasonal variation characteristics of δ18O value of stem water and water sources. However, the C. mongolicum and 20- and 40-year H. ammodendron main water sources were from deep soil water and groundwater, and less response to precipitation pulse. After 22.4 mm precipitation, the contribution of shallow soil water (0-50cm) to the use of N. sibirica increased from 6.7% to 36.5%; the C. mongolicum rarely use precipitation that were about 58.29% and 23.51%, absorbed from the deep soil water and groundwater; the contribution of precipitation to use of H. ammodendron had significantly differences among the four ages. The H. ammodendron of 5- and 10-year old about 86.3% and 42.5% water sources absorbed from the shallow soil water after precipitation. However, the contribution to 20- and 40-year old plant was less than 15%. So, the precipitation was one of the main water sources for desert shrubs, but the species showed different water utilization. We conclude that the main water source of the N. sibirica and H. ammodendron of 5-, 10-year was soil water recharged by precipitation, but the deeply rooted H. ammodendron of 20‐ and 40‐year‐old and the C. mongolicum have the ability to exploit a deep and reliable water source.

Keywords: water use pattern, water resource, stable isotope, seasonal change, precipitation pulse

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