Search results for: neural smith predictor

Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

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To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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Authors: Mia Bahar, Özlem Bozkurt

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Obsessive-compulsive disorder (OCD) is a typical, persistent, and long-lasting mental health condition in which a person experiences uncontrollable, recurrent thoughts (or "obsessions") and/or activities (or "compulsions") that they feel compelled to engage in repeatedly. Obsessive-compulsive disorder is both underdiagnosed and undertreated. It frequently manifests in a variety of medical settings and is persistent, expensive, and burdensome. Obsessive-compulsive neurosis was long believed to be a condition that offered valuable insight into the inner workings of the unconscious mind. Obsessive-compulsive disorder is now recognized as a prime example of a neuropsychiatric condition susceptible to particular pharmacotherapeutic and psychotherapy therapies and mediated by pathology in particular neural circuits. An obsessive-compulsive disorder which is called OCD, usually has two components, one cognitive and the other behavioral, although either can occur alone. Obsessions are often repetitive and intrusive thoughts that invade consciousness. These obsessions are incredibly hard to control or dismiss. People who have OCD often engage in rituals to reduce anxiety associated with intrusive thoughts. Once the ritual is formed, the person may feel extreme relief and be free from anxiety until the thoughts of contamination intrude once again. These thoughts are strengthened through a manifestation of negative reinforcement because they allow the person to avoid anxiety and obscurity. These thoughts are described as autogenous, meaning they most likely come from nowhere. These unwelcome thoughts are related to actions which we can describe as Thought Action Fusion. The thought becomes equated with an action, such as if they refuse to perform the ritual, something bad might happen, and so people perform the ritual to escape the intrusive thought. In almost all cases of OCD, the person's life gets extremely disturbed by compulsions and obsessions. Studies show OCD is an estimated 1.1% prevalence, making it a challenging issue with high co-morbidities with other issues like depressive episodes, panic disorders, and specific phobias. The first to reveal brain anomalies in OCD were numerous CT investigations, although the results were inconsistent. A few studies have focused on the orbitofrontal cortex (OFC), anterior cingulate gyrus (AC), and thalamus, structures also implicated in the pathophysiology of OCD by functional neuroimaging studies, but few have found consistent results. However, some studies have found abnormalities in the basal ganglion. There have also been some discussions that OCD might be genetic. OCD has been linked to families in studies of family aggregation, and findings from twin studies show that this relationship is somewhat influenced by genetic variables. Some Research has shown that OCD is a heritable, polygenic condition that can result from de novo harmful mutations as well as common and unusual variants. Numerous studies have also presented solid evidence in favor of a significant additive genetic component to OCD risk, with distinct OCD symptom dimensions showing both common and individual genetic risks.

Keywords: compulsions, obsessions, neuropsychiatric, genetic

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Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi

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Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.

Keywords: agriculture, computer vision, data science, geospatial technology

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Authors: Rasha Waheeb, Bjorn Andersen, Rafa Shakir

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Purpose The purpose of this study is to avoid delays that occur in emergency reconstruction projects especially in post disaster circumstances whether if they were natural or manmade due to their particular national and humanitarian importance. We presented a theoretical and practical concepts for projects management in the field of construction industry that deal with a range of global and local trails. This study aimed to identify the factors of effective delay in construction projects in Iraq that affect the time and the specific quality cost, and find the best solutions to address delays and solve the problem by setting parameters to restore balance in this study. 30 projects were selected in different areas of construction were selected as a sample for this study. Design/methodology/approach This study discusses the reconstruction strategies and delay in time and cost caused by different delay factors in some selected projects in Iraq (Baghdad as a case study).A case study approach was adopted, with thirty construction projects selected from the Baghdad region, of different types and sizes. Project participants from the case projects provided data about the projects through a data collection instrument distributed through a survey. Mixed approach and methods were applied in this study. Mathematical data analysis was used to construct models to predict delay in time and cost of projects before they started. The artificial neural networks analysis was selected as a mathematical approach. These models were mainly to help decision makers in construction project to find solutions to these delays before they cause any inefficiency in the project being implemented and to strike the obstacles thoroughly to develop this industry in Iraq. This approach was practiced using the data collected through survey and questionnaire data collection as information form. Findings The most important delay factors identified leading to schedule overruns were contractor failure, redesigning of designs/plans and change orders, security issues, selection of low-price bids, weather factors, and owner failures. Some of these are quite in line with findings from similar studies in other countries/regions, but some are unique to the Iraqi project sample, such as security issues and low-price bid selection. Originality/value we selected ANN’s analysis first because ANN’s was rarely used in project management , and never been used in Iraq to finding solutions for problems in construction industry. Also, this methodology can be used in complicated problems when there is no interpretation or solution for a problem. In some cases statistical analysis was conducted and in some cases the problem is not following a linear equation or there was a weak correlation, thus we suggested using the ANN’s because it is used for nonlinear problems to find the relationship between input and output data and that was really supportive.

Keywords: construction projects, delay factors, emergency reconstruction, innovation ANN, post disasters, project management

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Authors: André Augusto Ceballos Melo

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Design to facilitate the recognition of congruent prosthetic movements, context-to-motion translations guided by image, verbal prompt, users nonverbal communication such as facial expressions, gestures, paralinguistics, scene context, and object recognition contributes to this process though it can also be applied to other tasks, such as walking, Prosthetic limbs as assistive technology through gestures, sound codes, signs, facial, body expressions, and scene context The context-to-motion model is a machine learning approach that is designed to improve the control and dexterity of prosthetic limbs. It works by using sensory input from the prosthetic limb to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. This can help to improve the performance of the prosthetic limb and make it easier for the user to perform a wide range of tasks. There are several key benefits to using the context-to-motion model for prosthetic limb control. First, it can help to improve the naturalness and smoothness of prosthetic limb movements, which can make them more comfortable and easier to use for the user. Second, it can help to improve the accuracy and precision of prosthetic limb movements, which can be particularly useful for tasks that require fine motor control. Finally, the context-to-motion model can be trained using a variety of different sensory inputs, which makes it adaptable to a wide range of prosthetic limb designs and environments. Stable diffusion is a machine learning method that can be used to improve the control and stability of movements in robotic and prosthetic systems. It works by using sensory feedback to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. One key aspect of stable diffusion is that it is designed to be robust to noise and uncertainty in the sensory feedback. This means that it can continue to produce stable, smooth movements even when the sensory data is noisy or unreliable. To implement stable diffusion in a robotic or prosthetic system, it is typically necessary to first collect a dataset of examples of the desired movements. This dataset can then be used to train a machine learning model to predict the appropriate control inputs for a given set of sensory observations. Once the model has been trained, it can be used to control the robotic or prosthetic system in real-time. The model receives sensory input from the system and uses it to generate control signals that drive the motors or actuators responsible for moving the system. Overall, the use of the context-to-motion model has the potential to significantly improve the dexterity and performance of prosthetic limbs, making them more useful and effective for a wide range of users Hand Gesture Body Language Influence Communication to social interaction, offering a possibility for users to maximize their quality of life, social interaction, and gesture communication.

Keywords: stable diffusion, neural interface, smart prosthetic, augmenting

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Authors: Sijie Chen, Xiaofang Chen, Juan Wang, Yingying Zhang, Yu Hong, Wanyu Zhuang, Xinxin Huang, Ping Ou, Longsheng Huang

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Purpose: Autistic symptom improvement can be observed in children treated with acupuncture, but the mechanism is still being explored. In the present study, we used scalp acupuncture to treat autism rat model, and then their improvement in the abnormal behaviors and specific mechanisms behind were revealed by detecting animal behaviors, analyzing the RNA sequencing of the prefrontal cortex(PFC), and observing the ultrastructure of PFC neurons under the transmission electron microscope. Methods: On gestational day 12.5, Wistar rats were given valproic acid (VPA) by intraperitoneal injection, and their offspring were considered to be reliable rat models of autism. They were randomized to VPA or VPA-acupuncture group (n=8). Offspring of Wistar pregnant rats that were simultaneously injected with saline were randomly selected as the wild-type group (WT). VPA_acupuncture group rats received acupuncture intervention at 23 days of age for 4 weeks, and the other two groups followed without intervention. After the intervention, all experimental rats underwent behavioral tests. Immediately afterward, they were euthanized by cervical dislocation, and their prefrontal cortex was isolated for RNA sequencing and transmission electron microscopy. Results: The main results are as follows: 1. Animal behavioural tests: VPA group rats showed more anxiety-like behaviour and repetitive, stereotyped behaviour than WT group rats. While VPA group rats showed less spatial exploration ability, activity level, social interaction, and social novelty preference than WT group rats. It was gratifying to observe that acupuncture indeed improved these abnormal behaviors of autism rat model. 2. RNA-sequencing: The three groups of rats differed in the expression and enrichment pathways of multiple genes related to synaptic function, neural signal transduction, and circadian rhythm regulation. Our experiments indicated that acupuncture can alleviate the major symptoms of ASD by improving these neurological abnormalities. 3. Under the transmission electron microscopy, several lysosomes and mitochondrial structural abnormalities were observed in the prefrontal neurons of VPA group rats, which were manifested as atrophy of the mitochondrial membran, blurring or disappearance of the mitochondrial cristae, and even vacuolization. Moreover, the number of synapses and synaptic vesicles was relatively small. Conversely, the mitochondrial structure of rats in the WT group and VPA_acupuncture was normal, and the number of synapses and synaptic vesicles was relatively large. Conclusion: Acupuncture effectively improved the abnormal behaviors of autism rat model and the ultrastructure of the PFC neurons, which might worked by improving their abnormal synaptic function, synaptic plasticity and promoting neuronal signal transduction.

Keywords: autism spectrum disorder, acupuncture, animal behavior, RNA sequencing, transmission electron microscope

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Authors: Brittany Richardson, Ying Wang

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For physical therapy, exercise prescription, athlete training, and regular fitness training, it is crucial to perform health assessments or fitness assessments periodically. An accurate assessment is propitious for tracking recovery progress, preventing potential injury and making long-range training plans. Assessments include necessary measurements, height, weight, blood pressure, heart rate, body fat, etc. and advanced evaluation, muscle group strength, stability-mobility, and movement evaluation, etc. In the current standard assessment procedures, the accuracy of assessments, especially advanced evaluations, largely depends on the experience of physicians, coaches, and personal trainers. And it is challenging to track clients’ progress in the current assessment. Unlike the tradition assessment, in this paper, we present a deep learning based face recognition algorithm for accurate, comprehensive and trackable assessment. Based on the result from our assessment, physicians, coaches, and personal trainers are able to adjust the training targets and methods. The system categorizes the difficulty levels of the current activity for the client or user, furthermore make more comprehensive assessments based on tracking muscle group over time using a designed landmark detection method. The system also includes the function of grading and correcting the form of the clients during exercise. Experienced coaches and personal trainer can tell the clients' limit based on their facial expression and muscle group movements, even during the first several sessions. Similar to this, using a convolution neural network, the system is trained with people’s facial expression to differentiate challenge levels for clients. It uses landmark detection for subtle changes in muscle groups movements. It measures the proximal mobility of the hips and thoracic spine, the proximal stability of the scapulothoracic region and distal mobility of the glenohumeral joint, as well as distal mobility, and its effect on the kinetic chain. This system integrates data from other fitness assistant devices, including but not limited to Apple Watch, Fitbit, etc. for a improved training and testing performance. The system itself doesn’t require history data for an individual client, but the history data of a client can be used to create a more effective exercise plan. In order to validate the performance of the proposed work, an experimental design is presented. The results show that the proposed work contributes towards improving the quality of exercise plan, execution, progress tracking, and performance.

Keywords: exercise prescription, facial recognition, landmark detection, fitness assessments

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Authors: Anna Slupik, Anna Mosiolek, Sebastian Wojtowicz, Dariusz Bialoszewski

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Introduction: Whole-body vibration (WBV) uses high frequency mechanical stimuli generated by a vibration plate and transmitted through bone, muscle and connective tissues to the whole body. Research has shown that long-term vibration-plate training improves neuromuscular facilitation, especially in afferent neural pathways, responsible for the conduction of vibration and proprioceptive stimuli, muscle function, balance and proprioception. Some researchers suggest that the vibration stimulus briefly inhibits the conduction of afferent signals from proprioceptors and can interfere with the maintenance of body balance. The aim of this study was to evaluate the influence of a single set of exercises associated with whole-body vibration on the joint position sense and body balance. Material and methods: The study enrolled 55 people aged 19-24 years. These individuals were randomly divided into a test group (30 persons) and a control group (25 persons). Both groups performed the same set of exercises on a vibration plate. The following vibration parameters: frequency of 20Hz and amplitude of 3mm, were used in the test group. The control group performed exercises on the vibration plate while it was off. All participants were instructed to perform six dynamic exercises lasting 30 seconds each with a 60-second period of rest between them. The exercises involved large muscle groups of the trunk, pelvis and lower limbs. Measurements were carried out before and immediately after exercise. Joint position sense (JPS) was measured in the knee joint for the starting position at 45° in an open kinematic chain. JPS error was measured using a digital inclinometer. Balance was assessed in a standing position with both feet on the ground with the eyes open and closed (each test lasting 30 sec). Balance was assessed using Matscan with FootMat 7.0 SAM software. The surface of the ellipse of confidence and front-back as well as right-left swing were measured to assess balance. Statistical analysis was performed using Statistica 10.0 PL software. Results: There were no significant differences between the groups, both before and after the exercise (p> 0.05). JPS did not change in both the test (10.7° vs. 8.4°) and control groups (9.0° vs. 8.4°). No significant differences were shown in any of the test parameters during balance tests with the eyes open or closed in both the test and control groups (p> 0.05). Conclusions. 1. Deterioration in proprioception or balance was not observed immediately after the vibration stimulus. This suggests that vibration-induced blockage of proprioceptive stimuli conduction can have only a short-lasting effect that occurs only as long as a vibration stimulus is present. 2. Short-term use of vibration in treatment does not impair proprioception and seems to be safe for patients with proprioceptive impairment. 3. These results need to be supplemented with an assessment of proprioception during the application of vibration stimuli. Additionally, the impact of vibration parameters used in the exercises should be evaluated.

Keywords: balance, joint position sense, proprioception, whole body vibration

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Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Kseniya Gladun

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Tactile stimulation of a dorsal side of the wrist can have a strong impact on our attitude toward physical objects such as pleasant and unpleasant impact. This study explored different aspects of tactile perception to investigate atypical touch sensitivity in children with autism spectrum disorder (ASD). This study included 40 children with ASD and 40 healthy children aged 5 to 9 years. We recorded rsEEG (sampling rate of 250 Hz) during 20 min using EEG amplifier “Encephalan” (Medicom MTD, Taganrog, Russian Federation) with 19 AgCl electrodes placed according to the International 10–20 System. The electrodes placed on the left, and right mastoids served as joint references under unipolar montage. The registration of EEG v19 assignments was carried out: frontal (Fp1-Fp2; F3-F4), temporal anterior (T3-T4), temporal posterior (T5-T6), parietal (P3-P4), occipital (O1-O2). Subjects were passively touched by 4 types of tactile stimuli on the left wrist. Our stimuli were presented with a velocity of about 3–5 cm per sec. The stimuli materials and procedure were chosen for being the most "pleasant," "rough," "prickly" and "recognizable". Type of tactile stimulation: Soft cosmetic brush - "pleasant" , Rough shoe brush - "rough", Wartenberg pin wheel roller - "prickly", and the cognitive tactile stimulation included letters by finger (most of the patient’s name ) "recognizable". To designate the moments of the stimuli onset-offset, we marked the moment when the moment of the touch began and ended; the stimulation was manual, and synchronization was not precise enough for event-related measures. EEG epochs were cleaned from eye movements by ICA-based algorithm in EEGLAB plugin for MatLab 7.11.0 (Mathwork Inc.). Muscle artifacts were cut out by manual data inspection. The response to tactile stimuli was significantly different in the group of children with ASD and healthy children, which was also depended on type of tactile stimuli and the severity of ASD. Amplitude of Alpha rhythm increased in parietal region to response for only pleasant stimulus, for another type of stimulus ("rough," "thorny", "recognizable") distinction of amplitude was not observed. Correlation dimension D2 was higher in healthy children compared to children with ASD (main effect ANOVA). In ASD group D2 was lower for pleasant and unpleasant compared to the background in the right parietal area. Hilbert transform changes in the frequency of the theta rhythm found only for a rough tactile stimulation compared with healthy participants only in the right parietal area. Children with autism spectrum disorders and healthy children were responded to tactile stimulation differently with specific frequency distribution alpha and theta band in the right parietal area. Thus, our data supports the hypothesis that rsEEG may serve as a sensitive index of altered neural activity caused by ASD. Children with autism have difficulty in distinguishing the emotional stimuli ("pleasant," "rough," "prickly" and "recognizable").

Keywords: autism, tactile stimulation, Hilbert transform, pediatric electroencephalography

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Authors: Robert Caulk

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A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

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Authors: Yiea Funk Te, Daniel Mueller, Irena Pletikosa Cvijikj

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Small and medium enterprises (SMEs) play an important role in the economy of many countries. When the overall world economy is considered, SMEs represent 95% of all businesses in the world, accounting for 66% of the total employment. Existing studies show that the current business environment is characterized as highly turbulent and strongly influenced by modern information and communication technologies, thus forcing SMEs to experience more severe challenges in maintaining their existence and expanding their business. To support SMEs at improving their competitiveness, researchers recently turned their focus on applying data mining techniques to build risk and growth prediction models. However, data used to assess risk and growth indicators is primarily obtained via questionnaires, which is very laborious and time-consuming, or is provided by financial institutes, thus highly sensitive to privacy issues. Recently, web mining (WM) has emerged as a new approach towards obtaining valuable insights in the business world. WM enables automatic and large scale collection and analysis of potentially valuable data from various online platforms, including companies’ websites. While WM methods have been frequently studied to anticipate growth of sales volume for e-commerce platforms, their application for assessment of SME risk and growth indicators is still scarce. Considering that a vast proportion of SMEs own a website, WM bears a great potential in revealing valuable information hidden in SME websites, which can further be used to understand SME risk and growth indicators, as well as to enhance current SME risk and growth prediction models. This study aims at developing an automated system to collect business-relevant data from the Web and predict future growth trends of SMEs by means of WM and data mining techniques. The envisioned system should serve as an 'early recognition system' for future growth opportunities. In an initial step, we examine how structured and semi-structured Web data in governmental or SME websites can be used to explain the success of SMEs. WM methods are applied to extract Web data in a form of additional input features for the growth prediction model. The data on SMEs provided by a large Swiss insurance company is used as ground truth data (i.e. growth-labeled data) to train the growth prediction model. Different machine learning classification algorithms such as the Support Vector Machine, Random Forest and Artificial Neural Network are applied and compared, with the goal to optimize the prediction performance. The results are compared to those from previous studies, in order to assess the contribution of growth indicators retrieved from the Web for increasing the predictive power of the model.

Keywords: data mining, SME growth, success factors, web mining

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Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

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Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

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Authors: Felice Elefant, Akanksha Bhatnaghar, Keegan Krick, Elizabeth Heller

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Context: The severity of Alzheimer’s Disease (AD) progression involves an interplay of genetics, age, and environmental factors orchestrated by histone acetyltransferase (HAT) mediated neuroepigenetic mechanisms. While disruption of Tip60 HAT action in neural gene control is implicated in AD, alternative mechanisms underlying Tip60 function remain unexplored. Altered RNA splicing has recently been highlighted as a widespread hallmark in the AD transcriptome that is implicated in the disease. Research Aim: The aim of this study was to identify a novel RNA binding/splicing function for Tip60 in human hippocampus and impaired in brains from AD fly models and AD patients. Methodology/Analysis: The authors used RNA immunoprecipitation using RNA isolated from 200 pooled wild type Drosophila brains for each of the 3 biological replicates. To identify Tip60’s RNA targets, they performed genome sequencing (DNB-SequencingTM technology, BGI genomics) on 3 replicates for Input RNA and RNA IPs by Tip60. Findings: The authors' transcriptomic analysis of RNA bound to Tip60 by Tip60-RNA immunoprecipitation (RIP) revealed Tip60 RNA targets enriched for critical neuronal processes implicated in AD. Remarkably, 79% of Tip60’s RNA targets overlap with its chromatin gene targets, supporting a model by which Tip60 orchestrates bi-level transcriptional regulation at both the chromatin and RNA level, a function unprecedented for any HAT to date. Since RNA splicing occurs co-transcriptionally and splicing defects are implicated in AD, the authors investigated whether Tip60-RNA targeting modulates splicing decisions and if this function is altered in AD. Replicate multivariate analysis of transcript splicing (rMATS) analysis of RNA-Seq data sets from wild-type and AD fly brains revealed a multitude of mammalian-like AS defects. Strikingly, over half of these altered RNAs were bonafide Tip60-RNA targets enriched for in the AD-gene curated database, with some AS alterations prevented against by increasing Tip60 in fly brain. Importantly, human orthologs of several Tip60-modulated spliced genes in Drosophila are well characterized aberrantly spliced genes in human AD brains, implicating disruption of Tip60’s splicing function in AD pathogenesis. Theoretical Importance: The authors' findings support a novel RNA interaction and splicing regulatory function for Tip60 that may underlie AS impairments that hallmark AD etiology. Data Collection: The authors collected data from RNA immunoprecipitation experiments using RNA isolated from 200 pooled wild type Drosophila brains for each of the 3 biological replicates. They also performed genome sequencing (DNBSequencingTM technology, BGI genomics) on 3 replicates for Input RNA and RNA IPs by Tip60. Questions: The question addressed by this study was whether Tip60 has a novel RNA binding/splicing function in human hippocampus and whether this function is impaired in brains from AD fly models and AD patients. Conclusions: The authors' findings support a novel RNA interaction and splicing regulatory function for Tip60 that may underlie AS impairments that hallmark AD etiology.

Keywords: Alzheimer's disease, cognition, aging, neuroepigenetics

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Authors: Elham Bagheri, Yalda Mohsenzadeh

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Image memorability refers to the phenomenon where certain images are more likely to be remembered by humans than others. It is a quantifiable and intrinsic attribute of an image. Understanding how visual perception and memory interact is important in both cognitive science and artificial intelligence. It reveals the complex processes that support human cognition and helps to improve machine learning algorithms by mimicking the brain's efficient data processing and storage mechanisms. To explore the computational underpinnings of image memorability, this study examines the relationship between an image's reconstruction error, distinctiveness in latent space, and its memorability score. A trained autoencoder is used to replicate human-like memorability assessment inspired by the visual memory game employed in memorability estimations. This study leverages a VGG-based autoencoder that is pre-trained on the vast ImageNet dataset, enabling it to recognize patterns and features that are common to a wide and diverse range of images. An empirical analysis is conducted using the MemCat dataset, which includes 10,000 images from five broad categories: animals, sports, food, landscapes, and vehicles, along with their corresponding memorability scores. The memorability score assigned to each image represents the probability of that image being remembered by participants after a single exposure. The autoencoder is finetuned for one epoch with a batch size of one, attempting to create a scenario similar to human memorability experiments where memorability is quantified by the likelihood of an image being remembered after being seen only once. The reconstruction error, which is quantified as the difference between the original and reconstructed images, serves as a measure of how well the autoencoder has learned to represent the data. The reconstruction error of each image, the error reduction, and its distinctiveness in latent space are calculated and correlated with the memorability score. Distinctiveness is measured as the Euclidean distance between each image's latent representation and its nearest neighbor within the autoencoder's latent space. Different structural and perceptual loss functions are considered to quantify the reconstruction error. The results indicate that there is a strong correlation between the reconstruction error and the distinctiveness of images and their memorability scores. This suggests that images with more unique distinct features that challenge the autoencoder's compressive capacities are inherently more memorable. There is also a negative correlation between the reduction in reconstruction error compared to the autoencoder pre-trained on ImageNet, which suggests that highly memorable images are harder to reconstruct, probably due to having features that are more difficult to learn by the autoencoder. These insights suggest a new pathway for evaluating image memorability, which could potentially impact industries reliant on visual content and mark a step forward in merging the fields of artificial intelligence and cognitive science. The current research opens avenues for utilizing neural representations as instruments for understanding and predicting visual memory.

Keywords: autoencoder, computational vision, image memorability, image reconstruction, memory retention, reconstruction error, visual perception

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Authors: Koteswara Rao Pagolu, Raghava Rao Tamanam

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The present study was aimed to find out the prevalence of DD's in Visakhapatnam, one of the north coastal districts of Andhra Pradesh, India during a span of five years. A cross-sectional investigation was held at District early intervention center (DEIC), Visakhapatnam from 2016 to 2020. To identify the pattern and trend of different DD's including seasonal variations, a retrospective analysis of the health center's inpatient database for the past 5 years was done. Male and female children aged 2 months-18 years are included in the study with the prior permission of the concerned medical officer. The screening tool developed by the Ministry of health and family welfare, India, was used for the study. Among 26,423 cases, children with birth defects are 962, 2229 with deficiencies, 7516 with diseases, and 15716 with disabilities were admitted during the study period. From birth defects, congenital deafness occurred in large numbers with 22.66%, and neural tube defect observed in a small number of cases with 0.83% during the period. From the side of deficiencies, severe acute malnutrition has mostly occurred (66.80 %) and a small number of children were affected with goiter (1.70%). Among the diseases, dental carriers (67.97%) are mostly found and these cases were at peak during the years 2016 and 2019. From disabilities, children with vision impairment (20.55%) have mostly approached the center. Over the past 5 years, the admission rate of down's syndrome and congenital deafness cases showed a rising trend up to 2019 and then declined. Hearing impairment, motor delay, and learning disorder showed a steep rise and gradual decline trend, whereas severe anemia, vitamin-D deficiency, otitis media, reactive airway disease, and attention deficit hyperactivity disorder showed a declining trend. However, congenital heart diseases, dental caries, and vision impairment admission rates showed a zigzag pattern over the past 5 years. This center had inadequate diagnostic facilities related to genetic disease management. For advanced confirmation, the cases are referred to a district government hospital or private diagnostic laboratories in the city for genetic tests. Information regarding the overall burden and pattern of admissions in the health center is obtained by the review of DEIC records. Through this study, it is observed that the incidence of birth defects, as well as genetic disease burden, is high in the Visakhapatnam district. Hence there is a need for strengthening of management services for these diseases in this region.

Keywords: child health screening, developmental delays, district early intervention center, genetic disease management, infrastructural facility, Visakhapatnam district

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Authors: Sara Rejeb, Catherine Duveau, Tabea Rebafka

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To monitor the production process of turbofan aircraft engines, multiple measurements of various geometrical parameters are systematically recorded on manufactured parts. Engine parts are subject to extremely high standards as they can impact the performance of the engine. Therefore, it is essential to analyze these databases to better understand the influence of the different parameters on the engine's performance. Self-organizing maps are unsupervised neural networks which achieve two tasks simultaneously: they visualize high-dimensional data by projection onto a 2-dimensional map and provide clustering of the data. This technique has become very popular for data exploration since it provides easily interpretable results and a meaningful global view of the data. As such, self-organizing maps are usually applied to aircraft engine condition monitoring. As databases in this field are huge and complex, they naturally contain multiple missing entries for various reasons. The classical Kohonen algorithm to compute self-organizing maps is conceived for complete data only. A naive approach to deal with partially observed data consists in deleting items or variables with missing entries. However, this requires a sufficient number of complete individuals to be fairly representative of the population; otherwise, deletion leads to a considerable loss of information. Moreover, deletion can also induce bias in the analysis results. Alternatively, one can first apply a common imputation method to create a complete dataset and then apply the Kohonen algorithm. However, the choice of the imputation method may have a strong impact on the resulting self-organizing map. Our approach is to address simultaneously the two problems of computing a self-organizing map and imputing missing values, as these tasks are not independent. In this work, we propose an extension of self-organizing maps for partially observed data, referred to as missSOM. First, we introduce a criterion to be optimized, that aims at defining simultaneously the best self-organizing map and the best imputations for the missing entries. As such, missSOM is also an imputation method for missing values. To minimize the criterion, we propose an iterative algorithm that alternates the learning of a self-organizing map and the imputation of missing values. Moreover, we develop an accelerated version of the algorithm by entwining the iterations of the Kohonen algorithm with the updates of the imputed values. This method is efficiently implemented in R and will soon be released on CRAN. Compared to the standard Kohonen algorithm, it does not come with any additional cost in terms of computing time. Numerical experiments illustrate that missSOM performs well in terms of both clustering and imputation compared to the state of the art. In particular, it turns out that missSOM is robust to the missingness mechanism, which is in contrast to many imputation methods that are appropriate for only a single mechanism. This is an important property of missSOM as, in practice, the missingness mechanism is often unknown. An application to measurements on one type of part is also provided and shows the practical interest of missSOM.

Keywords: imputation method of missing data, partially observed data, robustness to missingness mechanism, self-organizing maps

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Authors: Vivek Mahadev, Manoj Kumar, Neelu Mathur, Brahm Dutt Pandey

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Perimeter guarding and protection of critical installations require prompt intrusion detection and assessment to take effective countermeasures. Currently, visual and electronic surveillance are the primary methods used for perimeter guarding. These methods can be costly and complicated, requiring careful planning according to the location and terrain. Moreover, these methods often struggle to detect stealthy and camouflaged insurgents. The object of the present work is to devise a surveillance technique using seismic sensors that overcomes the limitations of existing systems. The aim is to improve intrusion detection, assessment, and characterization by utilizing seismic sensors. Most of the similar systems have only two types of intrusion detection capability viz., human or vehicle. In our work we could even categorize further to identify types of intrusion activity such as walking, running, group walking, fence jumping, tunnel digging and vehicular movements. A virtual fence of 60 meters at GCNEP, Bahadurgarh, Haryana, India, was created by installing four underground geophones at a distance of 15 meters each. The signals received from these geophones are then processed to find unique seismic signatures called features. Various feature optimization and selection methodologies, such as LightGBM, Boruta, Random Forest, Logistics, Recursive Feature Elimination, Chi-2 and Pearson Ratio were used to identify the best features for training the machine learning models. The trained models were developed using algorithms such as supervised support vector machine (SVM) classifier, kNN, Decision Tree, Logistic Regression, Naïve Bayes, and Artificial Neural Networks. These models were then used to predict the category of events, employing weighted ensemble voting to analyze and combine their results. The models were trained with 1940 training events and results were evaluated with 831 test events. It was observed that using the weighted ensemble voting increased the efficiency of predictions. In this study we successfully developed and deployed the virtual fence using geophones. Since these sensors are passive, do not radiate any energy and are installed underground, it is impossible for intruders to locate and nullify them. Their flexibility, quick and easy installation, low costs, hidden deployment and unattended surveillance make such systems especially suitable for critical installations and remote facilities with difficult terrain. This work demonstrates the potential of utilizing seismic sensors for creating better perimeter guarding and protection systems using multiple machine learning models in weighted ensemble voting. In this study the virtual fence achieved an intruder detection efficiency of over 97%.

Keywords: geophone, seismic perimeter surveillance, machine learning, weighted ensemble method

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Authors: Aleksandra Jonas, Aleksandra Gronowska, Maciej Ścigacz, Szymon Jadczak

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Exact Data is a smart tool which helps with meaningful marketing content creation. It helps marketers achieve this by analyzing the text of an advertisement before and after its publication on social media sites like Facebook or Instagram. In our research we focus on four areas of natural language processing (NLP): grammar correction, sentiment analysis, irony detection and advertisement interpretation. Our research has identified a considerable lack of NLP tools for the Polish language, which specifically aid online marketers. In light of this, our research team has set out to create a robust and versatile NLP tool for the Polish language. The primary objective of our research is to develop a tool that can perform a range of language processing tasks in this language, such as sentiment analysis, text classification, text correction and text interpretation. Our team has been working diligently to create a tool that is accurate, reliable, and adaptable to the specific linguistic features of Polish, and that can provide valuable insights for a wide range of marketers needs. In addition to the Polish language version, we are also developing an English version of the tool, which will enable us to expand the reach and impact of our research to a wider audience. Another area of focus in our research involves tackling the challenge of the limited availability of linguistically diverse corpora for non-English languages, which presents a significant barrier in the development of NLP applications. One approach we have been pursuing is the translation of existing English corpora, which would enable us to use the wealth of linguistic resources available in English for other languages. Furthermore, we are looking into other methods, such as gathering language samples from social media platforms. By analyzing the language used in social media posts, we can collect a wide range of data that reflects the unique linguistic characteristics of specific regions and communities, which can then be used to enhance the accuracy and performance of NLP algorithms for non-English languages. In doing so, we hope to broaden the scope and capabilities of NLP applications. Our research focuses on several key NLP techniques including sentiment analysis, text classification, text interpretation and text correction. To ensure that we can achieve the best possible performance for these techniques, we are evaluating and comparing different approaches and strategies for implementing them. We are exploring a range of different methods, including transformers and convolutional neural networks (CNNs), to determine which ones are most effective for different types of NLP tasks. By analyzing the strengths and weaknesses of each approach, we can identify the most effective techniques for specific use cases, and further enhance the performance of our tool. Our research aims to create a tool, which can provide a comprehensive analysis of advertising effectiveness, allowing marketers to identify areas for improvement and optimize their advertising strategies. The results of this study suggest that a smart tool for advertisement analysis can provide valuable insights for businesses seeking to create effective advertising campaigns.

Keywords: NLP, AI, IT, language, marketing, analysis

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Authors: Azza A. Ali, Abeer I. Abd El-Fattah, Shaimaa S. Hussein, Hanan A. Abd El-Samea, Karema Abu-Elfotuh

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Background: Aluminium (Al) represents an environmental risk factor. Exposure to high levels of Al causes neurotoxic effects and different diseases. Vinpocetine is widely used to improve cognitive functions, it possesses memory-protective and memory-enhancing properties and has the ability to increase cerebral blood flow and glucose uptake. Cocoa bean represents a rich source of iron as well as a potent antioxidant. It can protect from the impact of free radicals, reduces stress as well as depression and promotes better memory and concentration. Wheatgrass is primarily used as a concentrated source of nutrients. It contains vitamins, minerals, carbohydrates, amino acids and possesses antioxidant and anti-inflammatory activities. Coenzyme Q10 (CoQ10) is an intracellular antioxidant and mitochondrial membrane stabilizer. It is effective in improving cognitive disorders and has been used as anti-aging. Zinc is a structural element of many proteins and signaling messenger that is released by neural activity at many central excitatory synapses. Objective: To study the role of some nutrients and drugs as Vinpocetine, Cocoa, Wheatgrass, CoQ10 and Zinc against neurotoxicity induced by Al in rats as well as to compare between their potency in providing protection. Methods: Seven groups of rats were used and received daily for three weeks AlCl3 (70 mg/kg, IP) for Al-toxicity model groups except for the control group which received saline. All groups of Al-toxicity model except one group (non-treated) were co-administered orally together with AlCl3 the following treatments; Vinpocetine (20mg/kg), Cocoa powder (24mg/kg), Wheat grass (100mg/kg), CoQ10 (200mg/kg) or Zinc (32mg/kg). Biochemical changes in the rat brain as acetyl cholinesterase (ACHE), Aβ, brain derived neurotrophic factor (BDNF), inflammatory mediators (TNF-α, IL-1β), oxidative parameters (MDA, SOD, TAC) were estimated for all groups besides histopathological examinations in different brain regions. Results: Neurotoxicity and neurodegenerations in the rat brain after three weeks of Al exposure were indicated by the significant increase in Aβ, ACHE, MDA, TNF-α, IL-1β, DNA fragmentation together with the significant decrease in SOD, TAC, BDNF and confirmed by the histopathological changes in the brain. On the other hand, co-administration of each of Vinpocetine, Cocoa, Wheatgrass, CoQ10 or Zinc together with AlCl3 provided protection against hazards of neurotoxicity and neurodegenerations induced by Al, their protection were indicated by the decrease in Aβ, ACHE, MDA, TNF-α, IL-1β, DNA fragmentation together with the increase in SOD, TAC, BDNF and confirmed by the histopathological examinations of different brain regions. Vinpocetine and Cocoa showed the most pronounced protection while Zinc provided the least protective effects than the other used nutrients and drugs. Conclusion: Different degrees of protection from neurotoxicity and neuronal degenerations induced by Al could be achieved through the co-administration of some nutrients and drugs during its exposure. Vinpocetine and Cocoa provided the most protection than Wheat grass, CoQ10 or Zinc which showed the least protective effects.

Keywords: aluminum, neurotoxicity, vinpocetine, cocoa, wheat grass, coenzyme Q10, Zinc, rats

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Authors: Muhammad Ahnaf Zahin, Yaw Adu-Gyamfi

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Bus transit is a crucial component of transportation networks, especially in urban areas. Any intelligent transportation system must have accurate real-time information on bus travel times since it minimizes waiting times for passengers at different stations along a route, improves service reliability, and significantly optimizes travel patterns. Bus agencies must enhance the quality of their information service to serve their passengers better and draw in more travelers since people waiting at bus stops are frequently anxious about when the bus will arrive at their starting point and when it will reach their destination. For solving this issue, different models have been developed for predicting bus travel times recently, but most of them are focused on smaller road networks due to their relatively subpar performance in high-density urban areas on a vast network. This paper develops a deep learning-based architecture using a single-step multi-station forecasting approach to predict average bus travel times for numerous routes, stops, and trips on a large-scale network using heterogeneous bus transit data collected from the GTFS database. Over one week, data was gathered from multiple bus routes in Saint Louis, Missouri. In this study, Gated Recurrent Unit (GRU) neural network was followed to predict the mean vehicle travel times for different hours of the day for multiple stations along multiple routes. Historical time steps and prediction horizon were set up to 5 and 1, respectively, which means that five hours of historical average travel time data were used to predict average travel time for the following hour. The spatial and temporal information and the historical average travel times were captured from the dataset for model input parameters. As adjacency matrices for the spatial input parameters, the station distances and sequence numbers were used, and the time of day (hour) was considered for the temporal inputs. Other inputs, including volatility information such as standard deviation and variance of journey durations, were also included in the model to make it more robust. The model's performance was evaluated based on a metric called mean absolute percentage error (MAPE). The observed prediction errors for various routes, trips, and stations remained consistent throughout the day. The results showed that the developed model could predict travel times more accurately during peak traffic hours, having a MAPE of around 14%, and performed less accurately during the latter part of the day. In the context of a complicated transportation network in high-density urban areas, the model showed its applicability for real-time travel time prediction of public transportation and ensured the high quality of the predictions generated by the model.

Keywords: gated recurrent unit, mean absolute percentage error, single-step forecasting, travel time prediction.

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Authors: Gu Sihao

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The coordinated development of "ecology-production-life" in cities has been highly concerned by the country, and the transformation development and sustainable development of resource-based cities have become a hot research topic at present. As an important part of China's resource-based cities, coal resource-based cities have the characteristics of large number and wide distribution. However, due to the adjustment of national energy structure and the gradual exhaustion of urban coal resources, the development vitality of coal resource-based cities is gradually reduced. In many studies, the deterioration of ecological environment in coal resource-based cities has become the main problem restricting their urban transformation and sustainable development due to the "emphasis on economy and neglect of ecology". Since the 18th National Congress of the Communist Party of China (CPC), the Central Government has been deepening territorial space planning and development. On the premise of optimizing territorial space development pattern, it has completed the demarcation of ecological protection red lines, carried out ecological zoning and ecosystem evaluation, which have become an important basis and scientific guarantee for ecological modernization and ecological civilization construction. Grasp the regional multiple ecosystem services is the precondition of the ecosystem management, and the relationship between the multiple ecosystem services study, ecosystem services cluster can identify the interactions between multiple ecosystem services, and on the basis of the characteristics of the clusters on regional ecological function zoning, to better Social-Ecological system management. Based on this cognition, this study optimizes the spatial function zoning of Gujiao, a coal resource-based city, in order to provide a new theoretical basis for its sustainable development. This study is based on the detailed analysis of characteristics and utilization of Gujiao city land space, using SOFM neural networks to identify local ecosystem service clusters, according to the cluster scope and function of ecological function zoning of space partition balance and coordination between different ecosystem services strength, establish a relationship between clusters and land use, and adjust the functions of territorial space within each zone. Then, according to the characteristics of coal resources city and national spatial function zoning characteristics, as the driving factors of land change, by cellular automata simulation program, such as simulation under different restoration strategy situation of urban future development trend, and provides relevant theories and technical methods for the "third-line" demarcations of Gujiao's territorial space planning, optimizes territorial space functions, and puts forward targeted strategies for the promotion of regional ecosystem services, providing theoretical support for the improvement of human well-being and sustainable development of resource-based cities.

Keywords: coal resource-based city, territorial spatial planning, ecosystem service cluster, gmop model, geosos-FLUS model, functional zoning optimization and upgrading

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Authors: Laura O’Halloran, Zhipeng Cao, Clare M. Kelly, Hugh Garavan, Robert Whelan

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Reaction time (RT) variability on cognitive tasks provides the index of the efficiency of executive control processes (e.g. attention and inhibitory control) and is considered to be a hallmark of clinical disorders, such as attention-deficit disorder (ADHD). Increased RT variability is associated with structural and functional brain differences in children and adults with various clinical disorders, as well as poorer task performance accuracy. Furthermore, the strength of functional connectivity across various brain networks, such as the negative relationship between the task-negative default mode network and task-positive attentional networks, has been found to reflect differences in RT variability. Although RT variability may provide an index of attentional efficiency, as well as being a useful indicator of neurological impairment, the brain substrates associated with RT variability remain relatively poorly defined, particularly in a healthy sample. Method: Firstly, we used the intra-individual coefficient of variation (ICV) as an index of RT variability from “Go” responses on the Stop Signal Task. We then examined the functional and structural neural correlates of ICV in a large sample of 14-year old healthy adolescents (n=1719). Of these, a subset had elevated symptoms of ADHD (n=80) and was compared to a matched non-symptomatic control group (n=80). The relationship between brain activity during successful and unsuccessful inhibitions and gray matter volume were compared with the ICV. A mediation analysis was conducted to examine if specific brain regions mediated the relationship between ADHD symptoms and ICV. Lastly, we looked at functional connectivity across various brain networks and quantified both positive and negative correlations during “Go” responses on the Stop Signal Task. Results: The brain data revealed that higher ICV was associated with increased structural and functional brain activation in the precentral gyrus in the whole sample and in adolescents with ADHD symptoms. Lower ICV was associated with lower activation in the anterior cingulate cortex (ACC) and medial frontal gyrus in the whole sample and in the control group. Furthermore, our results indicated that activation in the precentral gyrus (Broadman Area 4) mediated the relationship between ADHD symptoms and behavioural ICV. Conclusion: This is the first study first to investigate the functional and structural correlates of ICV collectively in a large adolescent sample. Our findings demonstrate a concurrent increase in brain structure and function within task-active prefrontal networks as a function of increased RT variability. Furthermore, structural and functional brain activation patterns in the ACC, and medial frontal gyrus plays a role-optimizing top-down control in order to maintain task performance. Our results also evidenced clear differences in brain morphometry between adolescents with symptoms of ADHD but without clinical diagnosis and typically developing controls. Our findings shed light on specific functional and structural brain regions that are implicated in ICV and yield insights into effective cognitive control in healthy individuals and in clinical groups.

Keywords: ADHD, fMRI, reaction-time variability, default mode, functional connectivity

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Authors: Ammar Alali, Mahmoud Abughaban, William Contreras Otalvora

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Optimizing the drilling process for cost and efficiency requires the optimization of the rate of penetration (ROP). ROP is the measurement of the speed at which the wellbore is created, in units of feet per hour. It is the primary indicator of measuring drilling efficiency. Maximization of the ROP can indicate fast and cost-efficient drilling operations; however, high ROPs may induce unintended events, which may lead to nonproductive time (NPT) and higher net costs. The proposed ROP optimization solution is a hybrid, data-driven system that aims to improve the drilling process, maximize the ROP, and minimize NPT. The system consists of two phases: (1) utilizing existing geological and drilling data to train the model prior, and (2) real-time adjustments of the controllable dynamic drilling parameters [weight on bit (WOB), rotary speed (RPM), and pump flow rate (GPM)] that direct influence on the ROP. During the first phase of the system, geological and historical drilling data are aggregated. After, the top-rated wells, as a function of high instance ROP, are distinguished. Those wells are filtered based on NPT incidents, and a cross-plot is generated for the controllable dynamic drilling parameters per ROP value. Subsequently, the parameter values (WOB, GPM, RPM) are calculated as a conditioned mean based on physical distance, following Inverse Distance Weighting (IDW) interpolation methodology. The first phase is concluded by producing a model of drilling best practices from the offset wells, prioritizing the optimum ROP value. This phase is performed before the commencing of drilling. Starting with the model produced in phase one, the second phase runs an automated drill-off test, delivering live adjustments in real-time. Those adjustments are made by directing the driller to deviate two of the controllable parameters (WOB and RPM) by a small percentage (0-5%), following the Constrained Random Search (CRS) methodology. These minor incremental variations will reveal new drilling conditions, not explored before through offset wells. The data is then consolidated into a heat-map, as a function of ROP. A more optimum ROP performance is identified through the heat-map and amended in the model. The validation process involved the selection of a planned well in an onshore oil field with hundreds of offset wells. The first phase model was built by utilizing the data points from the top-performing historical wells (20 wells). The model allows drillers to enhance decision-making by leveraging existing data and blending it with live data in real-time. An empirical relationship between controllable dynamic parameters and ROP was derived using Artificial Neural Networks (ANN). The adjustments resulted in improved ROP efficiency by over 20%, translating to at least 10% saving in drilling costs. The novelty of the proposed system lays is its ability to integrate historical data, calibrate based geological formations, and run real-time global optimization through CRS. Those factors position the system to work for any newly drilled well in a developing field event.

Keywords: drilling optimization, geological formations, machine learning, rate of penetration

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Authors: Vinod Kumar Tewari, Mazhar Husain, Hari Kishan Das Gupta

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Background: Primary or secondary brain death is also accompanied with vasospasm of the perforators other than tissue disruption & further exaggerates the anoxic damage, in the form of neuropraxia. In normal conditions the excitatory impulse propagates as anterograde neurotransmission (ANT) and at the level of synapse, glutamate activates NMDA receptors on postsynaptic membrane. Nitric oxide (NO) is produced by Nitric oxide Synthetase (NOS) in postsynaptic dendride or cell body and travels backwards across a chemical synapse to bind to the axon terminal of a presynaptic neuron for regulation of ANT this process is called as the retrograde neurotransmission (RNT). Thus the primary function of NO is RNT and the purpose of RNT is regulation of chemical neurotransmission at synapse. For this reason, RNT allows neural circuits to create feedback loops. The haem is the ligand binding site of NO receptor (sGC) at presynaptic membrane. The affinity of haem exhibits > 10,000-fold excess for NO than Oxygen (THE 10,000 FOLD EFFECT). In pathological conditions ANT, normal synaptic activity including RNT is absent. NO donors like sodium nitroprusside (SNP) releases NO by activating NOS at the level of postsynaptic area. NO now travels backwards across a chemical synapse to bind to the haem of NO receptor at axon terminal of a presynaptic neuron as in normal condition. NO now acts as impulse generator (at presynaptic membrane) thus bypasses the normal ANT. Also the arteriolar perforators are having Nitric Oxide Synthetase (NOS) at the adventitial side (outer border) on which sodium nitroprusside (SNP) acts; causing release of Nitric Oxide (NO) which vasodilates the perforators causing gush of blood in brain’s tissue and reversal of brain death. Objective: In brain death cases we only think for various transplantations but this study being a pilot study reverses some criteria of brain death by vasodilating the arteriolar perforators. To study the effect of intrathecal sodium nitroprusside (IT SNP) in cases of brain death in which: 1. Retrograde transmission = assessed by the hyperacute timings of reversal 2. The arteriolar perforator vasodilatation caused by NO and the maintenance of reversal of brain death reversal. Methods: 35 year old male, who became brain death after head injury and has not shown any signs of improvement after every maneuver for 6 hours, a single superfusion done by SNP via transoptic canal route for quadrigeminal cistern and cisternal puncture for IV ventricular with SNP done. Results: He showed spontaneous respiration (7 bouts) with TCD studies showing start of pulsations of various branches of common carotid arteries. Conclusions: In future we can give this SNP via transoptic canal route and in IV ventricle before declaring the body to be utilized for transplantations or dead or in broader way we can say that in near future it is possible to revert back from brain death or we have to modify our criterion.

Keywords: brain death, intrathecal sodium nitroprusside, TCD studies, perforators, vasodilatations, retrograde transmission, 10, 000 fold effect

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Authors: Jia Li, Xing Wei, Yuchen Hong, Yang Lu

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Detecting vehicle behavior has always been the focus of intelligent transportation, but with the explosive growth of the number of vehicles and the complexity of the road environment, the vehicle behavior videos captured by traditional surveillance have been unable to satisfy the study of vehicle behavior. The traditional method of manually labeling vehicle behavior is too time-consuming and labor-intensive, but the existing object detection and tracking algorithms have poor practicability and low behavioral location detection rate. This paper proposes a vehicle behavior detection algorithm based on the dual-stream convolution network and the multi-dimensional video dynamic detection network. In the videos, the straight-line behavior of the vehicle will default to the background behavior. The Changing lanes, turning and turning around are set as target behaviors. The purpose of this model is to automatically mark the target behavior of the vehicle from the untrimmed videos. First, the target behavior proposals in the long video are extracted through the dual-stream convolution network. The model uses a dual-stream convolutional network to generate a one-dimensional action score waveform, and then extract segments with scores above a given threshold M into preliminary vehicle behavior proposals. Second, the preliminary proposals are pruned and identified using the multi-dimensional video dynamic detection network. Referring to the hierarchical reinforcement learning, the multi-dimensional network includes a Timer module and a Spacer module, where the Timer module mines time information in the video stream and the Spacer module extracts spatial information in the video frame. The Timer and Spacer module are implemented by Long Short-Term Memory (LSTM) and start from an all-zero hidden state. The Timer module uses the Transformer mechanism to extract timing information from the video stream and extract features by linear mapping and other methods. Finally, the model fuses time information and spatial information and obtains the location and category of the behavior through the softmax layer. This paper uses recall and precision to measure the performance of the model. Extensive experiments show that based on the dataset of this paper, the proposed model has obvious advantages compared with the existing state-of-the-art behavior detection algorithms. When the Time Intersection over Union (TIoU) threshold is 0.5, the Average-Precision (MP) reaches 36.3% (the MP of baselines is 21.5%). In summary, this paper proposes a vehicle behavior detection model based on multi-dimensional dynamic detection network. This paper introduces spatial information and temporal information to extract vehicle behaviors in long videos. Experiments show that the proposed algorithm is advanced and accurate in-vehicle timing behavior detection. In the future, the focus will be on simultaneously detecting the timing behavior of multiple vehicles in complex traffic scenes (such as a busy street) while ensuring accuracy.

Keywords: vehicle behavior detection, convolutional neural network, long short-term memory, deep learning

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Authors: Gyu-Dong Kim, Wuk-Jae Yoo, Sang-Youl Lee

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Composite structures offer numerous advantages over conventional structural systems in the form of higher specific stiffness and strength, lower life-cycle costs, and benefits such as easy installation and improved safety. Recently, there has been a considerable increase in the use of composites in engineering applications and as wraps for seismic upgrading and repairs. However, these composites deteriorate with time because of outdated materials, excessive use, repetitive loading, climatic conditions, manufacturing errors, and deficiencies in inspection methods. In particular, damaged fibers in a composite result in significant degradation of structural performance. In order to reduce the failure probability of composites in service, techniques to assess the condition of the composites to prevent continual growth of fiber damage are required. Condition assessment technology and nondestructive evaluation (NDE) techniques have provided various solutions for the safety of structures by means of detecting damage or defects from static or dynamic responses induced by external loading. A variety of techniques based on detecting the changes in static or dynamic behavior of isotropic structures has been developed in the last two decades. These methods, based on analytical approaches, are limited in their capabilities in dealing with complex systems, primarily because of their limitations in handling different loading and boundary conditions. Recently, investigators have introduced direct search methods based on metaheuristics techniques and artificial intelligence, such as genetic algorithms (GA), simulated annealing (SA) methods, and neural networks (NN), and have promisingly applied these methods to the field of structural identification. Among them, GAs attract our attention because they do not require a considerable amount of data in advance in dealing with complex problems and can make a global solution search possible as opposed to classical gradient-based optimization techniques. In this study, we propose an alternative damage-detection technique that can determine the degraded stiffness distribution of vibrating laminated composites made of Glass Fiber-reinforced Polymer (GFRP). The proposed method uses a modified form of the bivariate Gaussian distribution function to detect degraded stiffness characteristics. In addition, this study presents a method to detect the fiber property variation of laminated composite plates from the micromechanical point of view. The finite element model is used to study free vibrations of laminated composite plates for fiber stiffness degradation. In order to solve the inverse problem using the combined method, this study uses only first mode shapes in a structure for the measured frequency data. In particular, this study focuses on the effect of the interaction among various parameters, such as fiber angles, layup sequences, and damage distributions, on fiber-stiffness damage detection.

Keywords: stiffness detection, fiber damage, genetic algorithm, layup sequences

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Authors: Bruno Biagianti, Angela Tseng, Kathy Wannaviroj, Allison Corlett, Megan DuBois, Kyu Lee, Suma Jacob

Abstract:

Background: ASD is characterized by pervasive Sensory Processing Abnormalities (SPA) and social cognitive deficits that persist throughout the course of the illness and have been linked to functional abnormalities in specific neural systems that underlie the perception, processing, and representation of sensory information. SPA and social cognitive deficits are associated with difficulties in interpersonal relationships, poor development of social skills, reduced social interactions and lower academic performance. Importantly, they can hamper the effects of established evidence-based psychological treatments—including PEERS (Program for the Education and Enrichment of Relationship Skills), a parent/caregiver-assisted, 16-weeks social skills intervention—which nonetheless requires a functional brain capable of assimilating and retaining information and skills. As a matter of fact, some adolescents benefit from PEERS more than others, calling for strategies to increase treatment response rates. Objective: We will present interim data on CICADAS (Care Improving Cognition for ADolescents on the Autism Spectrum)—a clinician-assisted, closed-loop technology mobile application for adolescents with ASD. Via ten mobile assessments, CICADAS captures data on sensory processing abnormalities and associated cognitive deficits. These data populate a machine learning algorithm that tailors the delivery of ten neuroplasticity-based social cognitive training (NB-SCT) exercises targeting sensory processing abnormalities. Methods: In collaboration with the Autism Spectrum and Neurodevelopmental Disorders Clinic at the University of Minnesota, we conducted a fully remote, three-arm, randomized crossover trial with adolescents with ASD to document the acceptability of CICADAS and evaluate its potential as a stand-alone treatment or as a treatment enhancer of PEERS. Twenty-four adolescents with ASD (ages 11-18) have been initially randomized to 16 weeks of PEERS + CICADAS (Arm A) vs. 16 weeks of PEERS + computer games vs. 16 weeks of CICADAS alone (Arm C). After 16 weeks, the full battery of assessments has been remotely administered. Results: We have evaluated the acceptability of CICADAS by examining adherence rates, engagement patterns, and exit survey data. We found that: 1) CICADAS is able to serve as a treatment enhancer for PEERS, inducing greater improvements in sensory processing, cognition, symptom reduction, social skills and behaviors, as well as the quality of life compared to computer games; 2) the concurrent delivery of PEERS and CICADAS induces greater improvements in study outcomes compared to CICADAS only. Conclusion: While preliminary, our results indicate that the individualized assessment and treatment approach designed in CICADAS seems effective in inducing adaptive long-term learning about social-emotional events. CICADAS-induced enhancement of processing and cognition facilitates the application of PEERS skills in the environment of adolescents with ASD, thus improving their real-world functioning.

Keywords: ASD, social skills, cognitive training, mobile app

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Authors: Zhao Gao, Eran Edirisinghe

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The production of an accurate sketch of a suspect based on a verbal description obtained from a witness is an essential task for most criminal investigations. The criminal investigation system employs specifically trained professional artists to manually draw a facial image of the suspect according to the descriptions of an eyewitness for subsequent identification. Within the advancement of Deep Learning, Recurrent Neural Networks (RNN) have shown great promise in Natural Language Processing (NLP) tasks. Additionally, Generative Adversarial Networks (GAN) have also proven to be very effective in image generation. In this study, a trained GAN conditioned on textual features such as keywords automatically encoded from a verbal description of a human face using an RNN is used to generate photo-realistic facial images for criminal investigations. The intention of the proposed system is to map corresponding features into text generated from verbal descriptions. With this, it becomes possible to generate many reasonably accurate alternatives to which the witness can use to hopefully identify a suspect from. This reduces subjectivity in decision making both by the eyewitness and the artist while giving an opportunity for the witness to evaluate and reconsider decisions. Furthermore, the proposed approach benefits law enforcement agencies by reducing the time taken to physically draw each potential sketch, thus increasing response times and mitigating potentially malicious human intervention. With publically available 'CelebFaces Attributes Dataset' (CelebA) and additionally providing verbal description as training data, the proposed architecture is able to effectively produce facial structures from given text. Word Embeddings are learnt by applying the RNN architecture in order to perform semantic parsing, the output of which is fed into the GAN for synthesizing photo-realistic images. Rather than the grid search method, a metaheuristic search based on genetic algorithms is applied to evolve the network with the intent of achieving optimal hyperparameters in a fraction the time of a typical brute force approach. With the exception of the ‘CelebA’ training database, further novel test cases are supplied to the network for evaluation. Witness reports detailing criminals from Interpol or other law enforcement agencies are sampled on the network. Using the descriptions provided, samples are generated and compared with the ground truth images of a criminal in order to calculate the similarities. Two factors are used for performance evaluation: The Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). A high percentile output from this performance matrix should attribute to demonstrating the accuracy, in hope of proving that the proposed approach can be an effective tool for law enforcement agencies. The proposed approach to criminal facial image generation has potential to increase the ratio of criminal cases that can be ultimately resolved using eyewitness information gathering.

Keywords: RNN, GAN, NLP, facial composition, criminal investigation

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Authors: Ola Hall, Ibrahim Wahab, Thorsteinn Rognvaldsson, Mattias Ohlsson

Abstract:

The subfield of poverty and welfare estimation that applies machine learning tools and methods on satellite imagery is a nascent but rapidly growing one. This is in part driven by the sustainable development goal, whose overarching principle is that no region is left behind. Among other things, this requires that welfare levels can be accurately and rapidly estimated at different spatial scales and resolutions. Conventional tools of household surveys and interviews do not suffice in this regard. While they are useful for gaining a longitudinal understanding of the welfare levels of populations, they do not offer adequate spatial coverage for the accuracy that is needed, nor are their implementation sufficiently swift to gain an accurate insight into people and places. It is this void that satellite imagery fills. Previously, this was near-impossible to implement due to the sheer volume of data that needed processing. Recent advances in machine learning, especially the deep learning subtype, such as deep neural networks, have made this a rapidly growing area of scholarship. Despite their unprecedented levels of performance, such models lack transparency and explainability and thus have seen limited downstream applications as humans generally are apprehensive of techniques that are not inherently interpretable and trustworthy. While several studies have demonstrated the superhuman performance of AI models, none has directly compared the performance of such models and human readers in the domain of poverty studies. In the present study, we directly compare the performance of human readers and a DL model using different resolutions of satellite imagery to estimate the welfare levels of demographic and health survey clusters in Tanzania, using the wealth quintile ratings from the same survey as the ground truth data. The cluster-level imagery covers all 608 cluster locations, of which 428 were classified as rural. The imagery for the human readers was sourced from the Google Maps Platform at an ultra-high resolution of 0.6m per pixel at zoom level 18, while that of the machine learning model was sourced from the comparatively lower resolution Sentinel-2 10m per pixel data for the same cluster locations. Rank correlation coefficients of between 0.31 and 0.32 achieved by the human readers were much lower when compared to those attained by the machine learning model – 0.69-0.79. This superhuman performance by the model is even more significant given that it was trained on the relatively lower 10-meter resolution satellite data while the human readers estimated welfare levels from the higher 0.6m spatial resolution data from which key markers of poverty and slums – roofing and road quality – are discernible. It is important to note, however, that the human readers did not receive any training before ratings, and had this been done, their performance might have improved. The stellar performance of the model also comes with the inevitable shortfall relating to limited transparency and explainability. The findings have significant implications for attaining the objective of the current frontier of deep learning models in this domain of scholarship – eXplainable Artificial Intelligence through a collaborative rather than a comparative framework.

Keywords: poverty prediction, satellite imagery, human readers, machine learning, Tanzania

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