Search results for: deep brain stimulation

Authors: Mona Hejazi, Ali Motie Nasrabadi

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Epilepsy is a persistent neurological disorder that affects more than 50 million people worldwide. Hence, there is a necessity to introduce an efficient prediction model for making a correct diagnosis of the epileptic seizure and accurate prediction of its type. In this study we consider how the Effective Connectivity (EC) patterns obtained from intracranial Electroencephalographic (EEG) recordings reveal information about the dynamics of the epileptic brain and can be used to predict imminent seizures, as this will enable the patients (and caregivers) to take appropriate precautions. We use this definition because we believe that effective connectivity near seizures begin to change, so we can predict seizures according to this feature. Results are reported on the standard Freiburg EEG dataset which contains data from 21 patients suffering from medically intractable focal epilepsy. Six channels of EEG from each patients are considered and effective connectivity using Directed Transfer Function (DTF) and Granger Causality (GC) methods is estimated. We concentrate on effective connectivity standard deviation over time and feature changes in five brain frequency sub-bands (Alpha, Beta, Theta, Delta, and Gamma) are compared. The performance obtained for the proposed scheme in predicting seizures is: average prediction time is 50 minutes before seizure onset, the maximum sensitivity is approximate ~80% and the false positive rate is 0.33 FP/h. DTF method is more acceptable to predict epileptic seizures and generally we can observe that the greater results are in gamma and beta sub-bands. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices.

Keywords: effective connectivity, Granger causality, directed transfer function, epilepsy seizure prediction, EEG

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Authors: Morteza Karami, Soheila Dayani

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Deep Soil Mixing (DSM) is a soil improvement technique that involves mechanically mixing the soil with a binder material to improve its strength, stiffness, and durability. This technique is typically used in geotechnical engineering applications where weak or unstable soil conditions exist, such as in building foundations, embankment support, or ground improvement projects. In this study, the settlement of the foundation on the improved soil using the wet DSM technique has been analyzed for a case study. Before DSM production, the initial soil mixture has been determined based on the laboratory tests and then, the proper mix designs have been optimized based on the pilot scale tests. The results show that the spacing and depth of the DSM columns depend on the soil properties, the intended loading conditions, and other factors such as the available space and equipment limitations. Moreover, monitoring instruments installed in the pilot area verify that the settlement of the foundation has been placed in an acceptable range to ensure that the soil mixture is providing the required strength and stiffness to support the structure or load. As an important result, if the DSM columns touch or penetrate into the stiff soil layer, the settlement of the foundation can be significantly decreased. Furthermore, the DSM columns should be allowed to cure sufficiently before placing any significant loads on the structure to prevent excessive deformation or settlement.

Keywords: deep soil mixing, soil mixture, settlement, instrumentation, curing age

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Authors: Abhimanyu Thakur, Youngjin Lee

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Glioma is a lethal brain cancer whose early diagnosis and prognosis are limited due to the dearth of a suitable technique for its early detection. Current approaches, including magnetic resonance imaging (MRI), computed tomography (CT), and invasive biopsy for the diagnosis of this lethal disease, hold several limitations, demanding an alternative method. Recently, extracellular vesicles (EVs) have been used in numerous biomarker studies, majorly exosomes and microvesicles (MVs), which are found in most of the cells and biofluids, including blood, cerebrospinal fluid (CSF), and urine. Remarkably, glioma cells (GMs) release a high number of EVs, which are found to cross the blood-brain-barrier (BBB) and impersonate the constituents of parent GMs including protein, and lncRNA; however, biophysical properties of EVs have not been explored yet as a biomarker for glioma. We isolated EVs from cell culture conditioned medium of GMs and regular primary culture, blood, and urine of wild-type (WT)- and glioma mouse models, and characterized by nano tracking analyzer, transmission electron microscopy, immunogold-EM, and differential light scanning. Next, we measured the biophysical parameters of GMs-EVs by using atomic force microscopy. Further, the functional constituents of EVs were examined by FTIR and Raman spectroscopy. Exosomes and MVs-derived from GMs, blood, and urine showed distinction biophysical parameters (roughness, adhesion force, and stiffness) and different from that of regular primary glial cells, WT-blood, and -urine, which can be attributed to the characteristic functional constituents. Therefore, biophysical features can be potential diagnostic biomarkers for glioma.

Keywords: glioma, extracellular vesicles, exosomes, microvesicles, biophysical properties

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Authors: Shweta Verma

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Background: Epilepsy is a chronic non-communicable central nervous system (CNS) disorder which affects a large population of all ages. Different classes of drugs are used for the treatment of this neurological disorder, but due to augmented drug resistance and side effects, these drugs become incompetent. Therefore, we design the synthesis of ten new derivatives of Phenytoin. The moiety of Phenytoin was hybridized with different phenols by using three step approach. The synthesized molecules were then investigated for different physicochemical parameters, such as Log P values using diverse software programs and to predict the potential to cross the blood-brain barrier. Objective: The Phenytoin derivatives were designed, synthesized, and characterized to meet the structural necessities indispensable for antiepileptic activity. Method: Firstly, the chloroacetylation of the 5,5-diphenyl hydantoin was carried out, and then various substituted phenols were added to it. The synthesized compounds were characterized and evaluated for antianxiety activity by elevated plus maze method and antiepileptic activity by using subcutaneous pentylenetetrazole (scPTZ) and maximal electroshock (MES) models and neurotoxicity. Result: The number of derivatives of 5,5-diphenyl hydantoin was developed and optimized. The number of parameters was optimized which reveal that the compound containing chloro group such as C3 and C6 showed imperative potential when compared with the standard drug Diazepam. Other compounds containing nitro and methyl group were also found to possess activity. Conclusion: It was summarized that the new compounds of 5,5-diphenyl hydantoin derivatives were synthesized. The results of the data show that the compound containing chloro group is more potent for CNS activity. The new compounds have the probability of being optimized further to engender new scaffolds to treat various CNS disorders.

Keywords: phenytoin, parameters, CNS activity, blood-brain barrier, Log P, CNS active

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Authors: Geetanjali Sharma

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Guillain Barre’ syndrome (GBS) is an auto-immune mediated demyelination poly-radiculo-neuropathy. Clinical features include progressive symmetrical ascending muscle weakness of more than two limbs, areflexia with or without sensory, autonomic and brainstem abnormalities, the purpose of this study was to determine subclinical neurological changes of CNS with GBS and to establish the presence of central demyelination in GBS. The study was prospective and conducted in the Department of Physiology, Pt. B. D. Sharma Post-graduate Institute of Medical Sciences, University of Health Sciences, Rohtak, Haryana, India to find out early central demyelination in clinically diagnosed patients of GBS. These patients were referred from the department of Medicine of our Institute to our department for electro-diagnostic evaluation. The study group comprised of 40 subjects (20 clinically diagnosed GBS patients and 20 healthy individuals as controls) aged between 6-65 years. Brain Stem evoked Potential (BAEP) were done in both groups using RMS EMG EP mark II machine. BAEP parameters included the latencies of waves I to IV, inter peak latencies I-III, III-IV & I-V. Statistically significant increase in absolute peak and inter peak latencies in the GBS group as compared with control group was noted. Results of evoked potential reflect impairment of auditory pathways probably due to focal demyelination in Schwann cell derived myelin sheaths that cover the extramedullary portion of auditory nerves. Early detection of the sub-clinical abnormalities is important as timely intervention reduces morbidity.

Keywords: brainstem, demyelination, evoked potential, Guillain Barre’

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Authors: Kamyar Moradi

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Background: There is mixed evidence regarding the association between recreational cannabinoid use and memory performance. One of the major reasons for the present controversy is different cannabinoid use-related covariates that influence the cognitive status of an individual. Adjustment of these confounding variables provides accurate insight into the real effects of cannabinoid use on memory status. In this study, we sought to investigate the association between recent recreational cannabinoid use and memory performance while correcting the model for other possible covariates such as demographic characteristics and duration, and amount of cannabinoid use. Methods: Cannabinoid users were assigned to two groups based on the results of THC urine drug screen test (THC+ group: n = 110, THC- group: n = 410). THC urine drug screen test has a high sensitivity and specificity in detecting cannabinoid use in the last 3-4 weeks. The memory domain of NIH Toolbox battery and brain MRI volumetric measures were compared between the groups while adjusting for confounding variables. Results: After Benjamini-Hochberg p-value correction, the performance in all of the measured memory outcomes, including vocabulary comprehension, episodic memory, executive function/cognitive flexibility, processing speed, reading skill, working memory, and fluid cognition, were significantly weaker in THC+ group (p values less than 0.05). Also, volume of gray matter, left supramarginal, right precuneus, right inferior/middle temporal, right hippocampus, left entorhinal, and right pars orbitalis regions were significantly smaller in THC+ group. Conclusions: this study provides evidence regarding the acute effect of recreational cannabis use on memory performance. Further studies are warranted to confirm the results.

Keywords: brain MRI, cannabis, memory, recreational use, THC urine test

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Authors: René A. Sørensen, Søren Skovsen, Peter Christiansen, Henrik Karstoft

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Autonomous agricultural machines act in stochastic surroundings and therefore, must be able to perceive the surroundings in real time. This perception can be achieved using image sensors combined with advanced machine learning, in particular Deep Learning. Deep convolutional neural networks excel in labeling and perceiving color images and since the cost of high-quality RGB-cameras is low, the hardware cost of good perception depends heavily on memory and computation power. This paper investigates the possibility of designing lightweight convolutional neural networks for semantic segmentation (pixel wise classification) with reduced hardware requirements, to allow for embedded usage in autonomous agricultural machines. Using compression techniques, a lightweight convolutional neural network is designed to perform real-time semantic segmentation on an embedded platform. The network is trained on two large datasets, ImageNet and Pascal Context, to recognize up to 400 individual classes. The 400 classes are remapped into agricultural superclasses (e.g. human, animal, sky, road, field, shelterbelt and obstacle) and the ability to provide accurate real-time perception of agricultural surroundings is studied. The network is applied to the case of autonomous grass mowing using the NVIDIA Tegra X1 embedded platform. Feeding case-specific images to the network results in a fully segmented map of the superclasses in the image. As the network is still being designed and optimized, only a qualitative analysis of the method is complete at the abstract submission deadline. Proceeding this deadline, the finalized design is quantitatively evaluated on 20 annotated grass mowing images. Lightweight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show competitive performance with regards to accuracy and speed. It is feasible to provide cost-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: autonomous agricultural machines, deep learning, safety, visual perception

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Authors: Rita Phillips, Mark Burgess, Maga Berlinski

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Although the positive impact of religiosity on well-being, health, and life-coping abilities is well known, up to date research has failed to provide scientific evidence for the relationship reasons. Therefore the present study took a qualitative approach by examining how religiosity interacts in coping with emotionally distressful situations, for which wedding preparations are an example. Wedding preparations, related to the experience of ambiguous emotions, can be the reason for phases of high distress. Although being per-se religious ceremonies, they are also socially-scripted and characterized by people’s striving for personally meaningful celebrations. The negotiation of these many influences can evoke conflicts. To reveal components of religiosity which contribute to stress-resolution, eight biographic-narrative interviews with recently married spouses were conducted. Participants were from different nationalities and Catholic deep-belief communities in order to determine factors independent from national-culture and social-subgroup. The audio-tape recorded, transcribed and translated interviews were analyzed by Interpretative Phenomenological Analysis. Opposing previous research on wedding-related conflicts but in-line with the quantitative account on the relation between stress-resilience and religiosity, the present study found participants reporting very low levels of distress and ambiguity. Although similar areas of potential conflicts were revealed, deep-belief Christians seemed to handle them in a different way. Participants freed themselves from own and others’ rigor mundane expectations by their spiritual preparation and the focus on a divine instance. This evoked a feeling of perceived closeness to God and of unconditional love, resulting in acceptance of oneself and others. Through relativizing mundane goods, participants perceived absolute freedom. Thus belief did not supplement coping strategies, previously defined in the literature, but substituted them. The paper implies that in explaining the connection between stress-resilience and religiosity, one’s perception and experience of unconditional love might outweigh other social or personal factors. However, further qualitative investigations are needed to fully explain the phenomenon.

Keywords: deep-belief, religiosity, resilience, wedding

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Authors: Mojtaba Heydarizad

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Andarokh basin is one of the main karstic regions in Khorasan Razavi province NE Iran. This basin is part of Kopeh-Dagh mega zone extending from Caspian Sea in the east to northern Afghanistan in the west. This basin is covered by Mozdooran Formation, Ngr evaporative formation and quaternary alluvium deposits in descending order of age. Mozdooran carbonate formation is notably karstified. The main surface karstic features in Mozdooran formation are Groove karren, Cleft karren, Rain pit, Rill karren, Tritt karren, Kamintza, Domes, and Table karren. In addition to surface features, deep karstic feature Andarokh Cave also exists in the region. Studying Ca, Mg, Mn, Sr, Fe concentration and Sr/Mn ratio in Mozdooran formation samples with distance to main faults and joints system using PCA analyses demonstrates intense meteoric digenesis role in controlling carbonate rock geochemistry. The karst evaluation in Andarokh basin varies from early stages 'deep seated karst' in Mesozoic to mature karstic system 'Exhumed karst' in quaternary period. Andarokh cave (the main cave in Andarokh basin) is rudimentary branch work consists of three passages of A, B and C and two entrances Andarokh and Sky.

Keywords: Andarokh basin, Andarokh cave, geochemical analyses, karst evaluation

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Authors: Tanmay Bisen, Aastha Shayla

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This study introduces an advanced approach to tree canopy detection in urban environments and a regional analysis of Northern and Central Kolkata that delves into the intricate relationship between urban development and environmental conservation. Leveraging high-resolution drone imagery from diverse urban green spaces in Kolkata, we fine-tuned the deep forest model to enhance its precision and accuracy. Our results, characterized by an impressive Intersection over Union (IoU) score of 0.90 and a mean average precision (mAP) of 0.87, underscore the model's robustness in detecting and classifying tree crowns amidst the complexities of aerial imagery. This research not only emphasizes the importance of model customization for specific datasets but also highlights the potential of drone-based remote sensing in urban forestry studies. The study investigates the spatial distribution, density, and environmental impact of trees in Northern and Central Kolkata. The findings underscore the significance of urban green spaces in met-ropolitan cities, emphasizing the need for sustainable urban planning that integrates green infrastructure for ecological balance and human well-being.

Keywords: urban greenery, advanced spatial distribution analysis, drone imagery, deep learning, tree detection

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Authors: Avyarthana Dey, Naren P. Rao, Arpitha Jacob, Chaitra V. Hiremath, Shivarama Varambally, Ganesan Venkatasubramanian, Rose Dawn Bharath, Bangalore N. Gangadhar

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Objective: Impaired facial emotion recognition is widely reported in schizophrenia. Neuropeptide oxytocin is known to modulate brain regions involved in facial emotion recognition, namely amygdala, in healthy volunteers. However, its effect on facial emotion recognition deficits seen in schizophrenia is not well explored. In this study, we examined the effect of intranasal OXT on processing facial emotions and its neural correlates in patients with schizophrenia. Method: 12 male patients (age= 31.08±7.61 years, education= 14.50±2.20 years) participated in this single-blind, counterbalanced functional magnetic resonance imaging (fMRI) study. All participants underwent three fMRI scans; one at baseline, one each after single dose 24IU intranasal OXT and intranasal placebo. The order of administration of OXT and placebo were counterbalanced and subject was blind to the drug administered. Participants performed a facial emotion recognition task presented in a block design with six alternating blocks of faces and shapes. The faces depicted happy, angry or fearful emotions. The images were preprocessed and analyzed using SPM 12. First level contrasts comparing recognition of emotions and shapes were modelled at individual subject level. A group level analysis was performed using the contrasts generated at the first level to compare the effects of intranasal OXT and placebo. The results were thresholded at uncorrected p < 0.001 with a cluster size of 6 voxels. Neuropeptide oxytocin is known to modulate brain regions involved in facial emotion recognition, namely amygdala, in healthy volunteers. Results: Compared to placebo, intranasal OXT attenuated activity in inferior temporal, fusiform and parahippocampal gyri (BA 20), premotor cortex (BA 6), middle frontal gyrus (BA 10) and anterior cingulate gyrus (BA 24) and enhanced activity in the middle occipital gyrus (BA 18), inferior occipital gyrus (BA 19), and superior temporal gyrus (BA 22). There were no significant differences between the conditions on the accuracy scores of emotion recognition between baseline (77.3±18.38), oxytocin (82.63 ± 10.92) or Placebo (76.62 ± 22.67). Conclusion: Our results provide further evidence to the modulatory effect of oxytocin in patients with schizophrenia. Single dose oxytocin resulted in significant changes in activity of brain regions involved in emotion processing. Future studies need to examine the effectiveness of long-term treatment with OXT for emotion recognition deficits in patients with schizophrenia.

Keywords: recognition, functional connectivity, oxytocin, schizophrenia, social cognition

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Authors: Yao-Hong Tsai

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Due to the sensor technology, video surveillance has become the main way for security control in every big city in the world. Surveillance is usually used by governments for intelligence gathering, the prevention of crime, the protection of a process, person, group or object, or the investigation of crime. Many surveillance systems based on computer vision technology have been developed in recent years. Moving target tracking is the most common task for Unmanned Aerial Vehicle (UAV) to find and track objects of interest in mobile aerial surveillance for civilian applications. The paper is focused on vision-based collision avoidance for UAVs by recurrent neural networks. First, images from cameras on UAV were fused based on deep convolutional neural network. Then, a recurrent neural network was constructed to obtain high-level image features for object tracking and extracting low-level image features for noise reducing. The system distributed the calculation of the whole system to local and cloud platform to efficiently perform object detection, tracking and collision avoidance based on multiple UAVs. The experiments on several challenging datasets showed that the proposed algorithm outperforms the state-of-the-art methods.

Keywords: unmanned aerial vehicle, object tracking, deep learning, collision avoidance

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Authors: K. Shaji Mon, P. R. John Sreenidhi

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In deep-submicrometer CMOS processes, time-domain resolution of a digital signal is becoming higher than voltage resolution of analog signals. This claim is nowadays pushing toward a new circuit design paradigm in which the traditional analog signal processing is expected to be progressively substituted by the processing of times in the digital domain. Within this novel paradigm, digitally controlled delay lines (DCDL) should play the role of digital-to-analog converters in traditional, analog-intensive, circuits. Digital delay locked loops are highly prevalent in integrated systems.The proposed paper addresses the glitches present in delay circuits along with area,power dissipation and signal integrity.The digitally controlled delay lines(DCDL) under study have been designed in a 90 nm CMOS technology 6 layer metal Copper Strained SiGe Low K Dielectric. Simulation and synthesis results show that the novel circuits exhibit no glitches for dual output coarse DCDL with less power dissipation and consumes less area compared to the glitch free NAND based DCDL.

Keywords: glitch free, NAND-based DCDL, CMOS, deep-submicrometer

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Authors: Majid Habib Khan, Jinwei Zhao, Xinhong Hei, Liu Jiedong, Rana Shahzad Noor, Muhammad Imran

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Deep learning is increasingly employed in image classification, yet the scarcity and high cost of labeled data for training remain a challenge. Limited samples often lead to overfitting due to biased sample distribution. This paper introduces a dynamic distribution calibration method for few-shot learning. Initially, base and new class samples undergo normalization to mitigate disparate feature magnitudes. A pre-trained model then extracts feature vectors from both classes. The method dynamically selects distribution characteristics from base classes (both adjacent and remote) in the embedding space, using a threshold value approach for new class samples. Given the propensity of similar classes to share feature distributions like mean and variance, this research assumes a Gaussian distribution for feature vectors. Subsequently, distributional features of new class samples are calibrated using a corrected hyperparameter, derived from the distribution features of both adjacent and distant base classes. This calibration augments the new class sample set. The technique demonstrates significant improvements, with up to 4% accuracy gains in few-shot classification challenges, as evidenced by tests on miniImagenet and CUB datasets.

Keywords: deep learning, computer vision, image classification, few-shot learning, threshold

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Authors: Skyler Kim

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An early diagnosis of leukemia has always been a challenge to doctors and hematologists. On a worldwide basis, it was reported that there were approximately 350,000 new cases in 2012, and diagnosing leukemia was time-consuming and inefficient because of an endemic shortage of flow cytometry equipment in current clinical practice. As the number of medical diagnosis tools increased and a large volume of high-quality data was produced, there was an urgent need for more advanced data analysis methods. One of these methods was the AI approach. This approach has become a major trend in recent years, and several research groups have been working on developing these diagnostic models. However, designing and implementing a leukemia diagnostic system in real clinical environments based on a deep learning approach with larger sets remains complex. Leukemia is a major hematological malignancy that results in mortality and morbidity throughout different ages. We decided to select acute lymphocytic leukemia to develop our diagnostic system since acute lymphocytic leukemia is the most common type of leukemia, accounting for 74% of all children diagnosed with leukemia. The results from this development work can be applied to all other types of leukemia. To develop our model, the Kaggle dataset was used, which consists of 15135 total images, 8491 of these are images of abnormal cells, and 5398 images are normal. In this paper, we design and implement a leukemia diagnostic system in a real clinical environment based on deep learning approaches with larger sets. The proposed diagnostic system has the function of detecting and classifying leukemia. Different from other AI approaches, we explore hybrid architectures to improve the current performance. First, we developed two independent convolutional neural network models: VGG19 and ResNet50. Then, using both VGG19 and ResNet50, we developed a hybrid deep learning architecture employing transfer learning techniques to extract features from each input image. In our approach, fusing the features from specific abstraction layers can be deemed as auxiliary features and lead to further improvement of the classification accuracy. In this approach, features extracted from the lower levels are combined into higher dimension feature maps to help improve the discriminative capability of intermediate features and also overcome the problem of network gradient vanishing or exploding. By comparing VGG19 and ResNet50 and the proposed hybrid model, we concluded that the hybrid model had a significant advantage in accuracy. The detailed results of each model’s performance and their pros and cons will be presented in the conference.

Keywords: acute lymphoblastic leukemia, hybrid model, leukemia diagnostic system, machine learning

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Authors: Bassant M. M. Ibrahim, Doha H. Abou Baker, Ahmed Abd Elghafour

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Excessive exposure to heavy metals contributes to the occurrence of deleterious health problems that affect vital organs like the brain, liver, kidneys, and heart. The use of natural products that have antioxidant capabilities may contribute to the protection of these organs. In the present study, the essential oil of summer savory (Satureja hortensis) was used to evaluate its protective effects against lead acetate induced damaging effect on rats’ vital organs, due to its high contents of carvacrol, y-terpinene, and p-cymene. Forty female Wister Albino rats were classified into five equal groups, the 1st served as normal group, the 2nd served as positive control group was given lead acetate (60 mg/kg) intra-peritoneal (IP), the third to fifth groups were treated with calcium disodium (EDTA) as chelating agent and summer savory essential oil in doses of (50 and 100mg/kg) respectively. All treatments were given IP concomitant with lead acetate for ten successive days. At the end of the experiment duration electrocardiogram (ECG), an open field test for the evaluation of psychological state, rotarod test as for the evaluation of locomotor coordination ability as well as anti-inflammatory and oxidative stress biomarkers in serum and histopathology of vital organs were performed. The investigations in this study show that the protective effect of high dose of summer savory essential oil is more than the low dose and that the essential oil of summer savory is a promising agent that can contribute to the protection of vital organs against the hazardous damaging effects of lead acetate.

Keywords: brain, heart, kidneys, lead acetate, liver, protective, summer savory

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Authors: M. Draman, S. Z. Muhamad Yassin, M. S. Alias, Z. Lambak, M. I. Zulkifli, S. N. Padhi, K. N. Baharim, F. Maskuriy, A. I. A. Rahim

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The performance of Malay automatic speech recognition (ASR) system for the call centre environment is presented. The system utilizes Kaldi toolkit as the platform to the entire library and algorithm used in performing the ASR task. The acoustic model implemented in this system uses a deep neural network (DNN) method to model the acoustic signal and the standard (n-gram) model for language modelling. With 80 hours of training data from the call centre recordings, the ASR system can achieve 72% of accuracy that corresponds to 28% of word error rate (WER). The testing was done using 20 hours of audio data. Despite the implementation of DNN, the system shows a low accuracy owing to the varieties of noises, accent and dialect that typically occurs in Malaysian call centre environment. This significant variation of speakers is reflected by the large standard deviation of the average word error rate (WERav) (i.e., ~ 10%). It is observed that the lowest WER (13.8%) was obtained from recording sample with a standard Malay dialect (central Malaysia) of native speaker as compared to 49% of the sample with the highest WER that contains conversation of the speaker that uses non-standard Malay dialect.

Keywords: conversational speech recognition, deep neural network, Malay language, speech recognition

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Authors: Khansa Hayat

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Organizational stress is one of the vital phenomena which is having its roots deep down in has deep roots in management, psychology, and organizational behavior research. In the meanwhile, keeping its focus on the positive strength of humans rather than the traditional negativity oriented research, positive psychology has emerged as a separate branch of organizational behavior. The current study investigates the interactive effects of Challenge and hindrance stressors and core Self Evaluations (CSE’s) of the individual on job performances including the in-role performance and extra role performances. The study also aims to investigate the supporting/buffering role of the human dispositions (i.e., self esteem, self efficacy, locus of control and emotional stability). The results show that Challenge stressors have a significant positive effect on in role performance and extra role performance of the individual. The findings of the study indicate that Core Self evaluations strengthen the relationship between challenge stressors and in role performance of the individual. In case of Hindrance Stressors the Core self Evaluations lessen the negative impact of Hindrance stressors and they let the individual perform at a better and normal position even when the Hindrance stressors are high. The relationship and implication of conservation of resource theory are also discussed. The limitations, future research directions and implications of the study are also discussed.

Keywords: challenge-hindrance stressors, core self evaluations, in-role performance, extra-role performance

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

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Authors: Maria Del Carmen Millan-Linares, Ana Lemus Conejo, Rocio Toscano, Alvaro Villanueva, Francisco Millan, Justo Pedroche, Sergio Montserrat-De La Paz

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GPETAFLR (Glycine-Proline-Glutamine-Threonine-Alanine-Phenylalanine-Leucine-Arginine) is a peptide isolated from Lupinus angustifolius L. protein hydrolysate (LPH). Herein, the effect of this peptide was investigated in two different models of neuroinflammation: in the immortalized murine microglia cell line BV-2 and in a high-fat-diet-induced obesity mouse model. Methods and Results: Effects of GPETAFLR on neuroinflammation were evaluated by RT-qPCR, flow cytometry, and ELISA techniques. In BV-2 microglial cells, Lipopolysaccharides (LPS) enhanced the release of pro-inflammatory cytokines (TNF-α, IL-1β, and IL-6) whereas GPETAFLR decreased pro-inflammatory cytokine levels and increased the release of the anti-inflammatory cytokine IL-10 in BV2 microglial cells. M1 (CCR7 and iNOS) and M2 (Arg-1 and Ym-1) polarization markers results showed how the GPETAFLR octapeptide was able to decrease M1 polarization marker expression and increase the M2 polarization marker expression compared to LPS. Animal model results indicate that GPETAFLR has an immunomodulatory capacity, both decreasing pro-inflammatory cytokine IL-6 and increasing the anti-inflammatory cytokine IL-10 in brain tissue. Polarization markers in the brain tissue were also modulated by GPETAFLR that decreased the pro-inflammatory expression (M1) and increased the anti-inflammatory expression (M2). Conclusion: Our results suggest that GPETAFLR isolated from LPH has significant potential for management of neuroinflammatory conditions and offer benefits derived from the consumption of Lupinus angustifolius L. in the prevention of neuroinflammatory-related diseases.

Keywords: GPETAFLR peptide, BV-2 cell line, neuroinflammation, cytokines, high-fat-diet

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Authors: Fatima Zohra Ibn Majdoub Hassani, Ivan Lavandera, Joseph Kreit

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This study explored the effects of different organic solvents, temperature, and the amount of glycerol on the alcohol dehydrogenase (ADH)-catalysed stereoselective reduction of different ketones. These conversions were then analyzed by gas chromatography. It was found that when the amount of deep eutectic solvents (DES) increases, it can improve the stereoselectivity of the enzyme although reducing its ability to convert the substrate into the corresponding alcohol. Moreover, glycerol was found to have a strong stabilizing effect on the ADH from Ralstonia sp. (E. coli/ RasADH). In the case of organic solvents, it was observed that the best conversions into the alcohols were achieved with DMSO and hexane. It was also observed that temperature decreased the ability of the enzyme to convert the substrates into the products and also affected the selectivity. In addition to that, the recycling of DES up to three times gave good conversions and enantiomeric excess results and glycerol showed a positive effect in the stability of various ADHs. Using RasADH, a good conversion and enantiomeric excess into the S-alcohol were obtained. It was found that an enhancement of the temperature disabled the stabilizing effect of glycerol and decreased the stereoselectivity of the enzyme. However, for other ADHs a temperature increase had an opposite positive effect, especially with ADH-T from Thermoanaerobium sp. One of the objectives of this study was to see the effect of cofactors such as NAD(P) on the biocatlysis activities of ADHs.

Keywords: alcohol dehydrogenases, DES, gas chromatography, RasADH

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Authors: Aragorn Joaquin Pineda Dela Cruz

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Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps.

Keywords: intelligent transportation system, vehicle detection, traffic analysis, deep learning, machine learning, computer vision, traffic prediction

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Authors: Lei Hou, Xinli Du, Nikolaos Boulgouris

Abstract:

A cochlear implant, referred to as a CI, is a small electronic device that can provide direct electrical stimulation to the auditory nerve. During cochlear implant surgery, atraumatic electrode array insertion is considered to be a crucial step. However, during implantation, the mechanical behaviour of an electrode array inside the cochlea is not known. The behaviour of an electrode array inside of the cochlea is hardly identified by regular methods. In this study, a CI electrode array capacitive sensor system is proposed. It is able to automatically determine the array state as a result of the capacitance variations. Instead of applying sensors to the electrode array, the capacitance information from the electrodes will be gathered and analysed. Results reveal that this sensing method is capable of recognising different states when fed into a pre-shaped model.

Keywords: cochlear implant, electrode, hearing preservation, insertion force, capacitive sensing

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Authors: Mehrdad Shafiei Dizaji, Hoda Azari

Abstract:

The research explores the pioneering integration of Physics-Informed Neural Networks (PINNs) into the domain of Ground-Penetrating Radar (GPR) data prediction, akin to advancements in medical imaging for tracking tumor progression in the human body. This research presents a detailed development framework for a specialized PINN model proficient at interpreting and forecasting GPR data, much like how medical imaging models predict tumor behavior. By harnessing the synergy between deep learning algorithms and the physical laws governing subsurface structures—or, in medical terms, human tissues—the model effectively embeds the physics of electromagnetic wave propagation into its architecture. This ensures that predictions not only align with fundamental physical principles but also mirror the precision needed in medical diagnostics for detecting and monitoring tumors. The suggested deep learning structure comprises three components: a CNN, a spatial feature channel attention (SFCA) mechanism, and ConvLSTM, along with temporal feature frame attention (TFFA) modules. The attention mechanism computes channel attention and temporal attention weights using self-adaptation, thereby fine-tuning the visual and temporal feature responses to extract the most pertinent and significant visual and temporal features. By integrating physics directly into the neural network, our model has shown enhanced accuracy in forecasting GPR data. This improvement is vital for conducting effective assessments of bridge deck conditions and other evaluations related to civil infrastructure. The use of Physics-Informed Neural Networks (PINNs) has demonstrated the potential to transform the field of Non-Destructive Evaluation (NDE) by enhancing the precision of infrastructure deterioration predictions. Moreover, it offers a deeper insight into the fundamental mechanisms of deterioration, viewed through the prism of physics-based models.

Keywords: physics-informed neural networks, deep learning, ground-penetrating radar (GPR), NDE, ConvLSTM, physics, data driven

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Authors: Awan A. Zaima, Tanvieer Ayesha, Mirshahi Shahsoltan, Pocard Marc, Mirshahi Massoud

Abstract:

Brain-derived neurotrophic factor (BDNF) is described as a factor helping to support the survival of existing neurons by involving the growth and differentiation of new neurons and synapses. Cancer diagnosis impacts the mental health, and in consequences, depression arise eventually hinders recovery and disrupts the quality of life and surviving chances of patients. The focus of this study is to hint upon a prospective biomarker as a promising diagnostic tool for an early indicator/predictor of depression prevalence in cancer patients for better care and treatment options. The study aims to analyze peripheral biomarkers from neuro immune axis (BDNF, IL21 as a NK cell activator) using co-relation approach. Samples were obtained from random non cancer candidates and advanced peritoneum carcinomatosis patients with 25% pseudomyxoma, 21% Colon cancer,19% stomach cancer, 10% ovarian cancer, 8% appendices cancer, and 10% other area of peritoneum cancer patients. Both groups of the study were categorized by gender and age, with a range of 18 to 86 years old. Biomarkers were analyzed in collected plasma by performing multiplex sandwich ELISA system. Data were subjected to statistical analysis for the assessment of the correlation. Our results demonstrate that BNDF and IL 21 down regulated significantly in patient groupas compared to non-cancer candidates (ratio of patients/normalis 2.57 for BNDF and 1.32 for IL21). This preliminary investigation suggested that the neuro immune biomarkers are down regulated in carcinomatosis patients and can be associated with cancer expansion and cancer genesis. Further studies on larger cohort are necessary to validate this hypothesis.

Keywords: biomarkers, depression, peritoneum carcinoma, BNDF, IL21

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Authors: John E. Muñoz, Jose F. Lopez, David S. Lopez

Abstract:

Attention Deficit Hyperactivity Disorder (ADHD) is a disorder that affects 1 out of 5 Colombian children, converting into a real public health problem in the country. Conventional treatments such as medication and neuropsychological therapy have been proved to be insufficient in order to decrease high incidence levels of ADHD in the principal Colombian cities. This work demonstrates a design and development of a videogame that uses a brain computer interface not only to serve as an input device but also as a tool to monitor neurophysiologic signal. The video game named “The Harvest Challenge” puts a cultural scene of a Colombian coffee grower in its context, where a player can use his/her avatar in three mini games created in order to reinforce four fundamental aspects: i) waiting ability, ii) planning ability, iii) ability to follow instructions and iv) ability to achieve objectives. The details of this collaborative designing process of the multimedia tool according to the exact clinic necessities and the description of interaction proposals are presented through the mental stages of attention and relaxation. The final videogame is presented as a tool for sustained attention training in children with ADHD using as an action mechanism the neuromodulation of Beta and Theta waves through an electrode located in the central part of the front lobe of the brain. The processing of an electroencephalographic signal is produced automatically inside the videogame allowing to generate a report of the theta/beta ratio evolution - a biological marker, which has been demonstrated to be a sufficient measure to discriminate of children with deficit and without.

Keywords: BCI, neuromodulation, ADHD, videogame, neurofeedback, theta/beta ratio

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Authors: Madhawa Basnayaka, Jouni Paltakari

Abstract:

Fully passive backscattering chipless RFID tags are an emerging wireless technology with low cost, higher reading distance, and fast automatic identification without human interference, unlike already available technologies like optical barcodes. The design optimization of chipless RFID tags is crucial as it requires replacing integrated chips found in conventional RFID tags with printed geometric designs. These designs enable data encoding and decoding through backscattered electromagnetic (EM) signatures. The applications of chipless RFID tags have been limited due to the constraints of data encoding capacity and the ability to design accurate yet efficient configurations. The traditional approach to accomplishing design parameters for a desired EM response involves iterative adjustment of design parameters and simulating until the desired EM spectrum is achieved. However, traditional numerical simulation methods encounter limitations in optimizing design parameters efficiently due to the speed and resource consumption. In this work, a deep learning neural network (DNN) is utilized to establish a correlation between the EM spectrum and the dimensional parameters of nested centric rings, specifically square and octagonal. The proposed bi-directional DNN has two simultaneously running neural networks, namely spectrum prediction and design parameters prediction. First, spectrum prediction DNN was trained to minimize mean square error (MSE). After the training process was completed, the spectrum prediction DNN was able to accurately predict the EM spectrum according to the input design parameters within a few seconds. Then, the trained spectrum prediction DNN was connected to the design parameters prediction DNN and trained two networks simultaneously. For the first time in chipless tag design, design parameters were predicted accurately after training bi-directional DNN for a desired EM spectrum. The model was evaluated using a randomly generated spectrum and the tag was manufactured using the predicted geometrical parameters. The manufactured tags were successfully tested in the laboratory. The amount of iterative computer simulations has been significantly decreased by this approach. Therefore, highly efficient but ultrafast bi-directional DNN models allow rapid and complicated chipless RFID tag designs.

Keywords: artificial intelligence, chipless RFID, deep learning, machine learning

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Authors: Yahia Massinissa, Melakhessou Med Akram, Mezahdia Mehdi, Marref Salah Eddine

Abstract:

Cytokines are natural proteins which act as true intercellular communication signals in immune and inflammatory responses. Reverse signaling pathways that activate cytokines help to regulate different functions at the target cell, causing its activation, its proliferation, the differentiation, its survival or death. It was shown that malfunctioning of the cytokine regulation, particularly over-expression, contributes to the onset and development of certain serious diseases such as chronic rheumatoid arthritis, Crohn's disease, psoriasis, lupus. The action mode of Kinoid® technology is based on the principle vaccine: The patient's immune system is activated so that it neutralizes itself and the factor responsible for the disease. When applied specifically to autoimmune diseases, therapeutic vaccination allows the body to neutralize cytokines (proteins) overproduced through a highly targeted stimulation of the immune system.

Keywords: cytokines, Kinoid tech, auto-immune diseases, vaccination

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Authors: M. Solairaju, Nithin J. Thomas, S. Ganesan

Abstract:

Each and every rotating part of a machine element consists of bearings within its structure. In particular, the rolling element bearings such as cylindrical roller bearing and deep groove ball bearings are frequently used. Improper handling, excessive loading, improper lubrication and sealing cause bearing damage. Hence health monitoring of bearings is an important aspect for radiation pattern of bearing vibration is computed using the dipole model. Sound pressure level for defect-free and race defect the prolonged life of machinery and auto motives. This paper presents modeling and analysis of Acoustic response of deep groove ball bearing with localized race defects. Most of the ball bearings, especially in machine tool spindles and high-speed applications are pre-loaded along an axial direction. The present study is carried out with axial preload. Based on the vibration response, the orbit motion of the inner race is studied, and it was found that the oscillation takes place predominantly in the axial direction. Simplified acoustic is estimated. Acoustic response shows a better indication in identifying the defective bearing. The computed sound signal is visualized in diagrammatic representation using Symmetrised Dot Pattern (SDP). SDP gives better visual distinction between the defective and defect-free bearing

Keywords: bearing, dipole, noise, sound

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Authors: Mitchell Stanton

Abstract:

Background: Endovascular management is well established as a mainstay treatment option for cerebral aneurysms. It is also well established that immediate post procedural imaging can be difficult to interpret due to the presence of contrast material. However, through the use of Dual-Energy computed tomography, it has become possible to differentiate contrast extravasation and intracranial haemorrhage. This case illustrates the importance of this technology following endovascular treatment of an unruptured cerebral aneurysm. Case Presentation: A 79-year-old female was found to have an unruptured large intracavernous ICA fusiform aneurysm on CT Brain Angiogram after presenting with acute ophthalmoplegia. This ophthalmoplegia was caused by mass effect from the aneurysm and subsequently the aneurysm was treated with an endovascular flow diverting stent. CT brain was performed post operatively due to a reduced level of consciousness and this showed diffuse subarachnoid hyperdensity of the left hemisphere. The use of Dual-Energy CT allowed accurate differentiation and illustrated diffuse contrast material extravasation, allowing patient to continue on dual-antiplatelets and therapeutic anticoagulation to reduce the risk of ischaemic injury post endovascular stent. Conclusion: Endovascular treatment options for management of intracranial aneurysms are constantly evolving. The use of Dual-Energy CT therefore has an integral role in accurately diagnosing any post-operative complications. Specifically, differentiating between subarachnoid haemorrhage and contrast extravasation is vital in these patients due to the significant consequences to their ongoing management in regards to continuation or cessation of antiplatelets or anticoagulation. With increasing access to this technology, its use should become standard practice in the post-operative investigation of these patients undergoing endovascular treatment.

Keywords: aneurysm, computed tomography, contrast extravasation, dual-energy CT, endovascular, subarachnoid haemorrhage

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