Search results for: features extraction

Authors: Jamerson Felipe Pereira Lima, Jeane Cecília Bezerra de Melo

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The difficulty and cost related to obtaining of protein tertiary structure information through experimental methods, such as X-ray crystallography or NMR spectroscopy, helped raising the development of computational methods to do so. An approach used in these last is prediction of tridimensional structure based in the residue chain, however, this has been proved an NP-hard problem, due to the complexity of this process, explained by the Levinthal paradox. An alternative solution is the prediction of intermediary structures, such as the secondary structure of the protein. Artificial Intelligence methods, such as Bayesian statistics, artificial neural networks (ANN), support vector machines (SVM), among others, were used to predict protein secondary structure. Due to its good results, artificial neural networks have been used as a standard method to predict protein secondary structure. Recent published methods that use this technique, in general, achieved a Q3 accuracy between 75% and 83%, whereas the theoretical accuracy limit for protein prediction is 88%. Alternatively, to achieve better results, support vector machines prediction methods have been developed. The statistical evaluation of methods that use different AI techniques, such as ANNs and SVMs, for example, is not a trivial problem, since different training sets, validation techniques, as well as other variables can influence the behavior of a prediction method. In this study, we propose a prediction method based on artificial neural networks, which is then compared with a selected SVM method. The chosen SVM protein secondary structure prediction method is the one proposed by Huang in his work Extracting Physico chemical Features to Predict Protein Secondary Structure (2013). The developed ANN method has the same training and testing process that was used by Huang to validate his method, which comprises the use of the CB513 protein data set and three-fold cross-validation, so that the comparative analysis of the results can be made comparing directly the statistical results of each method.

Keywords: artificial neural networks, protein secondary structure, protein structure prediction, support vector machines

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Authors: Sakeena Naikwadi, K. Jagaluraiah Sannapapamma

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Vetiveria zizanioides is an aromatic grass and traditionally been used in aromatherapy and ayurvedic medicine. Vetiver root is multi-functional biopolymer and has highly aromatic, antimicrobial, UV blocking, antioxidant properties suitable for textile finishing. The vetiver root (Gulabi) powder of different concentration (2, 4, 6,8 percent) were extracted by aqueous and solvent methods subjected to bioassay for antimicrobial efficiency and GCMS spectral analysis. The organic cotton fabric was finished with vetiver root extract (8 percent) by exhaust and pad dry cure methods. The finished fabric was assessed for functional properties viz., UV protective factor, antimicrobial efficiency and aroma intensity. The results revealed that Ethanol extraction showed a greater zone of inhibition compared to aqueous extract in root powder. Among the concentrations, 8 percent root extract in ethanol showed a greater zone of inhibition against gram-positive organism S. aureus and gram-negative organism E. coli. The major compounds present in vetiver root extracts were diethyl pathalate with greater percentage (87.73 %) followed by 7- Isopropyl dimethyl carboxylic acid (4.05 %), 2-butanone 4-trimethyle cyclohexen (1.21 %), phenanthrene carboxylic acid (1.03 %), naphthalene pentanoic acid (0.99 %), 1-phenanthrene carboxylic acid and 1 cyclohexenone 2-methyl oxobuty (0.89 %). The sample finished by pad dry cure method exhibited better UV protection even after 10th wash as compared to exhaust method. Vetiver extract treated samples exhibited maximum zone of inhibition against S. aureus than the E. coli organism. The vetiver root extract treated organic cotton fabric through pad dry cure method possessed good antimicrobial activity against S. aureus and E. coli even after 20th washes compared to vetiver root extract treated by exhaust method. The olfactory analysis was carried out by 30 panels of members and opined that vetiver root extract treated fabric has very good and pleasant aroma with better tactile properties that provide cooling, soothing effect and enhances the mood of the wearer. Vetiver root extract finished organic cotton fabric possessed aroma, antimicrobial and UV properties which are aptly suitable for medical and healthcare textiles viz., wound dressing, bandage gauze, surgical cloths, baby diapers and sanitary napkins. It can be used as after finishing agent for variegated garments and made-ups and can be replaced with commercial after finishing agents.

Keywords: antimicrobial, olfactory analysis, UV protection factor, vetiver root extract

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Authors: Ya-Mei Chen

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Online crowdsourced translation, an innovative translation practice brought by Web 2.0 technologies and the democratization of information, has become increasingly popular in the Internet era. Carried out by grass-root internet users, crowdsourced translation contains fundamentally different features from its off-line traditional counterpart, such as voluntary participation and parallel collaboration. To better understand such a participatory and collaborative nature, this paper will use the online Chinese translation project of Global Voices as a case study to investigate the following issues: (1) the changes in volunteer translators’ and reviewers’ motivations for participation, (2) translators’ and reviewers’ use of translation strategies and (3) the correlations of translators’ and reviewers’ motivations and strategies with the organizational mission, the translation style guide, the translator-reviewer interaction, the mediation of the translation platform and various types of capital within the translation field. With an aim to systematically explore the above three issues, this paper will collect both quantitative and qualitative data and then draw upon Engestrom’s activity theory and Bourdieu’s field theory as a theoretical framework to analyze the data in question. An online anonymous questionnaire will be conducted to obtain the quantitative data. The questionnaire will contain questions related to volunteer translators’ and reviewers’ backgrounds, participation motivations, translation strategies and mutual relations as well as the operation of the translation platform. Concerning the qualitative data, they will come from (1) a comparative study between some English news texts published on Global Voices and their Chinese translations, (2) an analysis of the online discussion forum associated with Global Voices’ Chinese translation project and (3) the information about the project’s translation mission and guidelines. It is hoped that this research, through a detailed sociological analysis of a cause-driven crowdsourced translation project, can enable translation researchers and practitioners to adequately meet the translation challenges appearing in the digital age.

Keywords: crowdsourced translation, global voices, motivation, translation strategies

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Authors: Divya Ramaseshan

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The indigenous beauty of a space supported by its local context is unmatchable. India, known to be a hub for varied cultural significance, has one of the best uses of vernacularism. This paper focuses on the traditional houses present in Thirumayilai, Mylapore, one of the oldest and most populous cities in Chennai. The Mylapore houses are known for their Agraharam style with thinnai, courtyard, and sloping roof characteristics. These homes had a combined influence of Indian, Islamic as well as Neo-classical architecture in their design. The design of the houses reflects the lives of Brahmin communities which have almost vanished from sight now. According to the growing demands of local residents as well as urbanization, many houses have been renovated. Some of those structures have been conserved in certain streets showcasing their historical identity. Other structures have either been demolished or redesigned based on people’s needs. Those structures have been identified and studied to understand the comparative features that have been changed. Many of those were in direct relevance to the city’s climate, family size, socializing habits, and local materials. Being a temple town, Mylapore has contour variations sloping towards various water bodies. These factors have been considered for building homes as well. The study aims to list down the possible design guidelines that could be effective in today’s construction field. The pros and cons are analyzed, and the respective methodologies are framed. Our modern construction technologies have brought in the best visual aesthetics in a short frame of time, but the serene touch of teak wood, walking through paved stones, daydreaming in the sunlit courtyards, and chitchatting in porticos are always cherished. Architects around the world are trying hard to achieve such appreciated design elements in upcoming projects with the best use of modern technology. This will also improvise people’s mental health in the comfort of their homes.

Keywords: Agraharam, Mylapore, traditional, vernacularism

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Authors: André Furtado, Rafael Mineiro, Isabel Gonçalves, Cecília Santos, Telma Quintela

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The development of therapies for central nervous system (CNS) disorders is one of the biggest challenges of current pharmacology, given the unique features of brain barriers, which limit drug delivery. Efflux transporters (ABC transporters) expressed at the blood-cerebrospinal fluid barrier (BCSFB), are the main obstacles for the delivery of therapeutic compounds into the CNS, compromising the effective treatment of brain cancer, brain metastasis from peripheral cancers, or even neurodegenerative disorders. It is thus extremely important to understand the regulation of these transporters for reducing their expression while treating a brain disorder or choosing the most appropriate conditions for drug administration. Based on the fact that the BCSFB have fine-tuned biological rhythms, studying the circadian variation of drug transport processes is critical for choosing the most appropriate time of the day for drug administration. In our study, using an in vitro model of the BCSFB, we characterized the circadian transport profile of methotrexate (MTX) and donepezil (DNPZ), two drugs involved in the treatment of cancer and Alzheimer’s Disease symptoms, respectively. We found that MTX is transported across the basal and apical membranes of the BCSFB in a circadian way. The circadian pattern of an ABC transporter, Abcc4, might be partially responsible for MTX circadian transport. Furthermore, regarding the DNPZ transport study, we observed that the regulation of Abcg2 expression by the circadian rhythm will impact the circadian-dependent transport of DNPZ across the BCSFB. Overall, our results will contribute to the current knowledge on brain pharmacoresistance at the BCSFB by disclosing how circadian rhythms control drug delivery to the brain, setting the grounds for a potential application of chronotherapy to brain diseases to enhance the efficacy of medications and minimize their side effects.

Keywords: blood-cerebrospinal fluid barrier, ABC transporters, drug transport, chronotherapy

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Authors: Alireza Hashemi

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In recent decades, language teaching methodology has undergone significant changes due to the advent of computers and the growth of educational software. French language education has also benefited from these developments, and various software has been produced to facilitate the learning of this language. However, the question arises whether these software programs meet the educational needs of Iranian learners, particularly in Semnan Province. The aim of this study is to evaluate the efficiency and effectiveness of French language educational software for learners in Semnan Province, considering educational, cultural, and technical criteria. In this study, content analysis and performance evaluation methods were used to examine the educational software ‘Français Facile’. This software was evaluated based on criteria such as teaching methods, cultural compatibility, and technical features. To collect data, standardized questionnaires and semi-structured interviews with learners in Semnan Province were used. Additionally, the SPSS statistical software was employed for quantitative data analysis, and the thematic analysis method was used for qualitative data. The results indicated that the ‘Français Facile’ software has strengths such as providing diverse educational content and an interactive learning environment. However, some weaknesses include the lack of alignment of educational content with the learning culture of learners in Semnan Province and technical issues in software execution. Statistical data showed that 65% of learners were satisfied with the educational content, but 55% reported issues related to cultural alignment with their needs. This study indicates that to enhance the efficiency of French language educational software, there is a need to localize educational content and improve technical infrastructure. Producing locally adapted educational software can improve the quality of language learning and increase the motivation of learners in Semnan Province. This research emphasizes the importance of understanding the cultural and educational needs of learners in the development of educational software and recommends that developers of educational software pay special attention to these aspects.

Keywords: educational software, French language, Iran, learners in Semnan province

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Authors: Murat Emre, Zeynep Tufekcioglu

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Objective: We describe a patient with late onset phenylketonuria which presented with rapidly progressive dementia and parkinsonism that were reversible after management. Background: Phenylketonuria is an autosomal recessive disorder due to mutations in the phenylalanine hydroxlase gene. It normally presents in childhood, in rare cases, however, it may have its onset in adulthood and may mimic other neurological disorders. Case description: A previously normal functioning, 59 year old man was admitted for blurred vision, cognitive impairment and gait difficulty which emerged over the past eight months. In neurological examination he had brisk reflexes, slow gait and left-dominant parkinsonism. Mini-mental state examination score was 25/30, neuropsychological testing revealed a dysexecutive syndrome with constructional apraxia and simultanagnosia. In cranial MRI there were bilateral diffuse hyper-intense lesions in parietal and occipital white matter with no significant atrophy. Electroencephalography showed diffuse slowing with predominance of teta waves. In cerebrospinal fluid examination protein level was slightly elevated (61mg/dL), oligoclonal bands were negative. Electromyography was normal. Routine laboratory examinations for rapidly progressive dementia and parkinsonism were also normal. Serum amino acid levels were determined to explore metabolic leukodystrophies and phenylalanine level was found to be highly elevated (1075 µmol/L) with normal tyrosine (61,20 µmol/L). His cognitive impairment and parkinsonian symptoms improved following three months of phenylalanine restricted diet. Conclusions: Late onset phenylketonuria is a rare, potentially reversible cause of rapidly progressive parkinsonism with dementia. It should be considered in the differential diagnosis of patients with suspicious features.

Keywords: dementia, neurology, Phenylketonuria, rapidly progressive parkinsonism

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Authors: Zoltan Sebestyen, Eszter Barta-Rajnai, Emma Jakab, Zsuzsanna Czegeny

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Extraction of the energy content of lignocellulosic biomass is one of the possible pathways to reduce the greenhouse gas emission derived from the burning of the fossil fuels. The application of the bioenergy can mitigate the energy dependency of a country from the foreign natural gas and the petroleum. The diversity of the plant materials makes difficult the utilization of the raw biomass in power plants. This problem can be overcome by the application of thermochemical techniques. Pyrolysis is the thermal decomposition of the raw materials under inert atmosphere at high temperatures, which produces pyrolysis gas, biooil and charcoal. The energy content of these products can be exploited by further utilization. The differences in the chemical and physical properties of the raw biomass materials can be reduced by the use of torrefaction. Torrefaction is a promising mild thermal pretreatment method performed at temperatures between 200 and 300 °C in an inert atmosphere. The goal of the pretreatment from a chemical point of view is the removal of water and the acidic groups of hemicelluloses or the whole hemicellulose fraction with minor degradation of cellulose and lignin in the biomass. Thus, the stability of biomass against biodegradation increases, while its energy density increases. The volume of the raw materials decreases so the expenses of the transportation and the storage are reduced as well. Biooil is the major product during pyrolysis and an important by-product during torrefaction of biomass. The composition of biooil mostly depends on the quality of the raw materials and the applied temperature. In this work, thermoanalytical techniques have been used to study the qualitative and quantitative composition of the pyrolysis and torrefaction oils of a woody (black locust) and two herbaceous samples (rape straw and wheat straw). The biooil contains C5 and C6 anhydrosugar molecules, as well as aromatic compounds originating from hemicellulose, cellulose, and lignin, respectively. In this study, special emphasis was placed on the formation of the lignin monomeric products. The structure of the lignin fraction is different in the wood and in the herbaceous plants. According to the thermoanalytical studies the decomposition of lignin starts above 200 °C and ends at about 500 °C. The lignin monomers are present among the components of the torrefaction oil even at relatively low temperatures. We established that the concentration and the composition of the lignin products vary significantly with the applied temperature indicating that different decomposition mechanisms dominate at low and high temperatures. The evolutions of decomposition products as well as the thermal stability of the samples were measured by thermogravimetry/mass spectrometry (TG/MS). The differences in the structure of the lignin products of woody and herbaceous samples were characterized by the method of pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). As a statistical method, principal component analysis (PCA) has been used to find correlation between the composition of lignin products of the biooil and the applied temperatures.

Keywords: pyrolysis, torrefaction, biooil, lignin

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Authors: Ramuel Spirituel Mattathiah A. San Juan, Neil Ambasing

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Background: Reversal of vision metamorphopsia is a transient form of metamorphopsia described as an upside-down alteration of the visual field in the coronal plane. Patients would describe objects, such as cups, upside down, but the tea would not spill, and people would walk on their heads. It is extremely rare as a stable finding, lasting days or weeks. We report a case wherein this type of metamorphopsia occurred only in written words and lasted for six months. Objective: To the best of our knowledge, we report the first rare occurrence of reversal of vision metamorphopsia described as inverted words as the sole initial presentation of an underlying stroke. Case Presentation: We report a 59-year-old male with poorly controlled hypertension and diabetes mellitus who presented with a 3-day history of difficulty reading, described as the words were turned upside down as if the words were inverted horizontally then with the progression of deficits such as right homonymous hemianopia and achromatopsia, prosopagnosia. Cranial magnetic resonance imaging (MRI) revealed an acute infarct on the left posterior cerebral artery territory. Follow-up after six months revealed improvement of the visual field cut but with the persistence of the higher cortical function deficits. Conclusion: We report the first rare occurrence of metamorphopsia described as purely inverted words as the sole initial presentation of an underlying stroke. The differential diagnoses of a patient presenting with text reversal metamorphopsia should include stroke in the occipitotemporal areas. It further expands the landscape of metamorphopsias due to its exclusivity to written words and prolonged duration. Knowing these clinical features will help identify the lesion locus and improve subsequent stroke care, especially in time-bound management like intravenous thrombolysis.

Keywords: rare presentation, text reversal metamorphopsia, ischemic stroke, stroke

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Authors: Atefeh Abedini, Fatemeh Razavi, Mihan Pourabdollah Toutkaboni, Hossein Mehravaran, Arda Kiani

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In countries with the highest prevalence of tuberculosis, such as Iran, the differentiation of malignant tumors from non-malignant is very important. In this study, which was conducted for the first time among the Iranian population, the utility of the ultrasonographic morphological characteristics in patients undergoing EBUS was used to distinguish the non-malignant versus malignant lymph nodes. The morphological characteristics of lymph nodes, which consist of size, shape, vascular pattern, echogenicity, margin, coagulation necrosis sign, calcification, and central hilar structure, were obtained during Endobronchial Ultrasound-Guided Trans-Bronchial Needle Aspiration and were compared with the final pathology results. During this study period, a total of 253 lymph nodes were evaluated in 93 cases. Round shape, non-hilar vascular pattern, heterogeneous echogenicity, hyperechogenicity, distinct margin, and the presence of necrosis sign were significantly higher in malignant nodes. On the other hand, the presence of calcification and also central hilar structure were significantly higher in the benign nodes (p-value ˂ 0.05). Multivariate logistic regression showed that size>1 cm, heterogeneous echogenicity, hyperechogenicity, the presence of necrosis signs and, the absence of central hilar structure are independent predictive factors for malignancy. The accuracy of each of the aforementioned factors is 42.29 %, 71.54 %, 71.90 %, 73.51 %, and 65.61 %, respectively. Of 74 malignant lymph nodes, 100% had at least one of these independent factors. According to our results, the morphological characteristics of lymph nodes based on Endobronchial Ultrasound-Guided Trans-Bronchial Needle Aspiration can play a role in the prediction of malignancy.

Keywords: EBUS-TBNA, malignancy, nodal characteristics, pathology

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Authors: Amna J. Ghith, Khaled Abu Zid, Khairia Youssef, Nasser Saad

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Backgrounds: The multikinase and vascular endothelial growth factor (VEGF) receptor inhibitors interrupt the pathway by which angiogenesis becomes established and promulgated, resulting in the inadequate nourishment of metastatic disease. VEGFR-2 has been the principal target of anti-angiogenic therapies. We disclose the new thieno pyrimidines as inhibitors of VEGFR-2 designed by a molecular modeling approach with increased synergistic activity and decreased side effects. Purpose: 2-substituted thieno pyrimidines are designed and synthesized with anticipated anticancer activity based on its in silico molecular docking study that supports the initial pharmacophoric hypothesis with a same binding mode of interaction at the ATP-binding site of VEGFR-2 (PDB 2QU5) with high docking score. Methods: A series of compounds were designed using discovery studio 4.1/CDOCKER with a rational that mimic the pharmacophoric features present in the reported active compounds that targeted VEGFR-2. An in silico ADMET study was also performed to validate the bioavailability of the newly designed compounds. Results: The Compounds to be synthesized showed interaction energy comparable to or within the range of the benzimidazole inhibitor ligand when docked with VEGFR-2. ADMET study showed comparable results most of the compounds showed absorption within (95-99) zone varying according to different substitutions attached to thieno pyrimidine ring system. Conclusions: A series of 2-subsituted thienopyrimidines are to be synthesized with anticipated anticancer activity and according to docking study structure requirement for the design of VEGFR-2 inhibitors which can act as powerful anticancer agents.

Keywords: docking, discovery studio 4.1/CDOCKER, heterocycles based anticancer agents, 2-subsituted thienopyrimidines

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Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

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Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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Authors: Awadhesh Kishore

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The goal of the study was to determine how different non-metallic nutrients are partitioned from feed in various physiological contexts and how buffer addition in ruminant nutrition affects these processes. Six lactating crossbred dairy cows were selected and divided into three groups on the basis of their phenotypic and productive features (374±14 kg LW). Two treatments, T1 and T2, were randomly assigned to one animal from each group. Animals under T1 and T2 were moved to T2 and T1, respectively, after 30 days. T2 was the only group to receive buffers containing magnesium oxide and sodium bicarbonate at 0.0 and 0.01% of LW (the real amounts are equivalent to 75.3±4.0 and 30 7.7±2.0 g/d, respectively). T1 was used as the control. Wheat straw and berseem were part of the base diet, whereas wheat grain and mustard cake were part of the concentrate mixture. Following a 21-day feeding period, metabolic and milk production trials were carried out for seven consecutive days. The Kearl equation used the urine's calorific value to determine its volume. Chemical analyses were performed to determine the levels of nitrogen, carbohydrates, calories, and phosphorus in samples of feed, waste, buffer, mineral mixture, water, feces, urine, and milk that were collected. The information was analyzed statistically. Notable results included decreased nitrogen and carbohydrate partitioning to feces from feed, while increased calorie partitioning to milk and body storage, and increased carbohydrate partitioning to body storage. Phosphorus balance was significantly better in T2. The application of buffers in ruminant diets was found to increase the output of calories in milk, as well as the number of calories and carbohydrates stored in the body, while decreasing the amount of nitrogen in faeces. As a result, it may be advised to introduce buffers to feed crossbred dairy cattle.

Keywords: cattle, Magnesium oxide, non-metallic nutrients, partitioning, Sodium bicarbonate

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Authors: Ana Paula Mousinho, Ronaldo D. Mansano, Nelson Ordonez

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Carbon nanotubes are one of the main elements in nanotechnologies; their applications are in microelectronics, nano-electronics devices (photonics, spintronic), chemical sensors, structural material and currently in clean energy devices (supercapacitors and fuel cells). The use of magnetic particle decorated carbon nanotubes increases the applications in magnetic devices, magnetic memory, and magnetic oriented drug delivery. In this work, single-walled carbon nanotubes (CNTs) forest decorated with chromium were deposited at room temperature by high-density plasma chemical vapor deposition (HDPCVD) system. The CNTs forest was obtained using pure methane plasmas and chromium, as precursor material (seed) and for decorating the CNTs. Magnetron sputtering deposited the chromium on silicon wafers before the CNTs' growth. Scanning electron microscopy, atomic force microscopy, micro-Raman spectroscopy, and X-ray diffraction characterized the single-walled CNTs forest decorated with chromium. In general, the CNTs' spectra show a unique emission band, but due to the presence of the chromium, the spectra obtained in this work showed many bands that are related to the CNTs with different diameters. The CNTs obtained by the HDPCVD system are highly aligned and showed metallic features, and they can be used as photonic material, due to the unique structural and electrical properties. The results of this work proved the possibility of obtaining the controlled deposition of aligned single-walled CNTs forest films decorated with chromium by high-density plasma chemical vapor deposition system.

Keywords: CNTs forest, high density plasma deposition, high-aligned CNTs, nanomaterials

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Authors: Jaiden Xuan Schraut, Leon Liu, Yiqiao Yin

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Computer vision in medical diagnosis has achieved a high level of success in diagnosing diseases with high accuracy. However, conventional classifiers that produce an image to-label result provides insufficient information for medical professionals to judge and raise concerns over the trust and reliability of a model with results that cannot be explained. In order to gain local insight into cancerous regions, separate tasks such as imaging segmentation need to be implemented to aid the doctors in treating patients, which doubles the training time and costs which renders the diagnosis system inefficient and difficult to be accepted by the public. To tackle this issue and drive AI-first medical solutions further, this paper proposes a multi-output network that follows a U-Net architecture for image segmentation output and features an additional convolutional neural networks (CNN) module for auxiliary classification output. Class activation maps are a method of providing insight into a convolutional neural network’s feature maps that leads to its classification but in the case of lung diseases, the region of interest is enhanced by U-net-assisted Class Activation Map (CAM) visualization. Therefore, our proposed model combines image segmentation models and classifiers to crop out only the lung region of a chest X-ray’s class activation map to provide a visualization that improves the explainability and is able to generate classification results simultaneously which builds trust for AI-led diagnosis systems. The proposed U-Net model achieves 97.61% accuracy and a dice coefficient of 0.97 on testing data from the COVID-QU-Ex Dataset which includes both diseased and healthy lungs.

Keywords: multi-output network model, U-net, class activation map, image classification, medical imaging analysis

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Authors: Rafael do Amaral Teodoro, Leandro Augusto da Silva

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Originated from graphite, graphene is a two-dimensional (2D) material that promises to revolutionize technology in many different areas, such as energy, telecommunications, civil construction, aviation, textile, and medicine. This is possible because its structure, formed by carbon bonds, provides desirable optical, thermal, and mechanical characteristics that are interesting to multiple areas of the market. Thus, several research and development centers are studying different manufacturing methods and material applications of graphene, which are often compromised by the scarcity of more agile and accurate methodologies to characterize the material – that is to determine its composition, shape, size, and the number of layers and crystals. To engage in this search, this study proposes a computational methodology that applies deep learning to identify graphene oxide crystals in order to characterize samples by crystal sizes. To achieve this, a fully convolutional neural network called U-net has been trained to segment SEM graphene oxide images. The segmentation generated by the U-net is fine-tuned with a standard deviation technique by classes, which allows crystals to be distinguished with different labels through an object delimitation algorithm. As a next step, the characteristics of the position, area, perimeter, and lateral measures of each detected crystal are extracted from the images. This information generates a database with the dimensions of the crystals that compose the samples. Finally, graphs are automatically created showing the frequency distributions by area size and perimeter of the crystals. This methodological process resulted in a high capacity of segmentation of graphene oxide crystals, presenting accuracy and F-score equal to 95% and 94%, respectively, over the test set. Such performance demonstrates a high generalization capacity of the method in crystal segmentation, since its performance considers significant changes in image extraction quality. The measurement of non-overlapping crystals presented an average error of 6% for the different measurement metrics, thus suggesting that the model provides a high-performance measurement for non-overlapping segmentations. For overlapping crystals, however, a limitation of the model was identified. To overcome this limitation, it is important to ensure that the samples to be analyzed are properly prepared. This will minimize crystal overlap in the SEM image acquisition and guarantee a lower error in the measurements without greater efforts for data handling. All in all, the method developed is a time optimizer with a high measurement value, considering that it is capable of measuring hundreds of graphene oxide crystals in seconds, saving weeks of manual work.

Keywords: characterization, graphene oxide, nanomaterials, U-net, deep learning

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Authors: Hang Yang, Jichao Li, Kewei Yang, Tianyang Lei

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Multivariate time series anomaly detection is a significant research topic in the field of data mining, encompassing a wide range of applications across various industrial sectors such as traffic roads, financial logistics, and corporate production. The inherent spatial dependencies and temporal characteristics present in multivariate time series introduce challenges to the anomaly detection task. Previous studies have typically been based on the assumption that all variables belong to the same spatial hierarchy, neglecting the multi-level spatial relationships. To address this challenge, this paper proposes a multi-scale spatial and unified temporal feature fusion network, denoted as MSUT-Net, for multivariate time series anomaly detection. The proposed model employs a multi-level modeling approach, incorporating both temporal and spatial modules. The spatial module is designed to capture the spatial characteristics of multivariate time series data, utilizing an adaptive graph structure learning model to identify the multi-level spatial relationships between data variables and their attributes. The temporal module consists of a unified temporal processing module, which is tasked with capturing the temporal features of multivariate time series. This module is capable of simultaneously identifying temporal dependencies among different variables. Extensive testing on multiple publicly available datasets confirms that MSUT-Net achieves superior performance on the majority of datasets. Our method is able to model and accurately detect systems data with multi-level spatial relationships from a spatial-temporal perspective, providing a novel perspective for anomaly detection analysis.

Keywords: data mining, industrial system, multivariate time series, anomaly detection

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Authors: Svetlana S. Saveysheva, Victoria E. Vasilenko

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The purpose of the research was to study the emotional and personal characteristics of preschool children in relation to the characteristics of child-parent interaction and deviant parental attitudes. The study involved 172 mothers and 172 children (85 boys and 87 girls) aged 4,5 to 7 years (mean age 6 years) living in St. Petersburg, Russia. Methods used were, demographic questionnaire, projective drawing method 'House-Tree-Man', Test of anxiety (Temml, Dorki, Amen), technique of studying self-esteem 'Ladder', expert evaluation of sociability and aggressiveness, questionnaire for children-parent emotional interaction (E.I. Zaharova) and questionnaire 'Analysis of family relationships' (E.G. Eidemiller, V.V. Yustitsky). Results. The greatest number of links with personal characteristics have received such parental deviant attitudes as overprotection and characteristics of authoritarian style (prohibitions, sanctions). If the mother has such peculiarities of the parental relationship, the child is characterized by lower self-esteem, increased anxiety, distrust of themselves and hostility. Children have more pronounced manifestations of aggression in a conniving and unstable style of parenting. The sensitivity of the mother is positively associated with children’s self-esteem. Unconditional acceptance of the child, the predominance of a positive emotional background, orientation to the state of the child during interaction promote the development of communication skills and reduce of aggressiveness. But the excessive closeness of the mother with the child can make it difficult to develop the communicative skills. Conclusions. The greatest influence on emotional and personal characteristics is provided by such features of the parental relation as overprotection, characteristics of authoritarian style, underdevelopment of the sphere of parental feelings, sensitivity of mother and behavioral manifestations of emotional interaction. Research is supported by RFBR №18-013-00990.

Keywords: characteristics of personality, child-parent interaction, children, deviant parental attitudes

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Authors: Nada Alghali

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Translanguaging pedagogy suggests a new perspective in language education relating to multilingualism; multilingual learners have one linguistic repertoire and not two or more separate language systems (García and Wei, 2014). When learners translanguage, they are able to draw on all their language features in a flexible and integrated way (Otheguy, García, & Reid, 2015). In the Foreign Language Classroom, however, the tendency to use the target language only is still advocated as a pedagogy. This study attempts to enable learners in the English as a foreign language classroom to draw on their full linguistic repertoire through collaborative reading lessons. In observations prior to this study, in a classroom where English only policy prevails, learners still used their first language in group discussions yet were constrained at times by the teacher’s language policies. Through strategically enabling translanguaging in reading lessons (Celic and Seltzer, 2011), this study has revealed that learners showed creative ways of language use for learning and reflected positively on thisexperience. This case study enabled two groups in two different proficiency level classrooms who are learning English as a foreign language in their first year at University in Saudi Arabia. Learners in the two groups wereobserved over six weeks and wereasked to reflect their learning every week. The same learners were also interviewed at the end of translanguaging weeks after completing a modified model of the learning reflection (Ash and Clayton, 2009). This study positions translanguaging as collaborative and agentive within a sociocultural framework of learning, positioning translanguaging as a resource for learning as well as a process of learning. Translanguaging learning episodes are elicited from classroom observations, artefacts, interviews, reflections, and focus groups, where they are analysed qualitatively following the sociocultural discourse analysis (Fairclough &Wodak, 1997; Mercer, 2004). Initial outcomes suggest functions of translanguaging in collaborative reading tasks and recommendations for a collaborative translanguaging pedagogy approach in the EFL classroom.

Keywords: translanguaging, EFL, sociocultural theory, discourse analysis

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Authors: Y. Trantafyllou, C. Alexandraki

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Music interpretation accounts to the way musicians shape their performance by deliberately deviating from composers’ intentions, which are commonly communicated via some form of music transcription, such as a music score. For transcribed and non-improvised music, music expression is manifested by introducing subtle deviations in tempo, dynamics and articulation during the evolution of performance. This paper presents an application, named FlameCens, which, given two recordings of the same piece of music, presumably performed by different musicians, allow visualising deviations in tempo and dynamics during playback. The application may also compare a certain performance to the music score of that piece (i.e. MIDI file), which may be thought of as an expression-neutral representation of that piece, hence depicting the expressive queues employed by certain performers. FlameCens uses the Dynamic Time Warping algorithm to compare two audio sequences, based on CENS (Chroma Energy distribution Normalized Statistics) audio features. Expressive deviations are illustrated in a moving flame, which is generated by an animation of particles. The length of the flame is mapped to deviations in dynamics, while the slope of the flame is mapped to tempo deviations so that faster tempo changes the slope to the right and slower tempo changes the slope to the left. Constant slope signifies no tempo deviation. The detected deviations in tempo and dynamics can be additionally recorded in a text file, which allows for offline investigation. Moreover, in the case of monophonic music, the color of particles is used to convey the pitch of the notes during performance. FlameCens has been implemented in Python and it is openly available via GitHub. The application has been experimentally validated for different music genres including classical, contemporary, jazz and popular music. These experiments revealed that FlameCens can be a valuable tool for music specialists (i.e. musicians or musicologists) to investigate the expressive performance strategies employed by different musicians, as well as for music audience to enhance their listening experience.

Keywords: audio synchronization, computational music analysis, expressive music performance, information visualization

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Authors: A. Degale Desta, Tamirat Kebamo

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Background: Recent advances in computer vision and pattern recognition have significantly improved activity recognition through video analysis, particularly with the application of Deep Convolutional Neural Networks (CNNs). One-stage detectors now enable efficient video-based recognition by simultaneously predicting object categories and locations. Such advancements are highly relevant in educational settings where CCTV surveillance could automatically monitor academic activities, enhancing security and classroom management. However, current datasets and recognition systems lack the specific focus on campus environments necessary for practical application in these settings.Objective: This study aims to address this gap by developing a dataset and testing an automated activity recognition system specifically tailored for educational campuses. The EthioCAD dataset was created to capture various classroom activities and teacher-student interactions, facilitating reliable recognition of academic activities using deep learning models. Method: EthioCAD, a novel video-based dataset, was created with a design science research approach to encompass teacher-student interactions across three domains and 18 distinct classroom activities. Using the Roboflow AI framework, the data was processed, with 4.224 KB of frames and 33.485 MB of images managed for frame extraction, labeling, and organization. The Ultralytics YOLOv8 model was then implemented within Google Colab to evaluate the dataset’s effectiveness, achieving high mean Average Precision (mAP) scores. Results: The YOLOv8 model demonstrated robust activity recognition within campus-like settings, achieving an mAP50 of 90.2% and an mAP50-95 of 78.6%. These results highlight the potential of EthioCAD, combined with YOLOv8, to provide reliable detection and classification of classroom activities, supporting automated surveillance needs on educational campuses. Discussion: The high performance of YOLOv8 on the EthioCAD dataset suggests that automated activity recognition for surveillance is feasible within educational environments. This system addresses current limitations in campus-specific data and tools, offering a tailored solution for academic monitoring that could enhance the effectiveness of CCTV systems in these settings. Conclusion: The EthioCAD dataset, alongside the YOLOv8 model, provides a promising framework for automated campus activity recognition. This approach lays the groundwork for future advancements in CCTV-based educational surveillance systems, enabling more refined and reliable monitoring of classroom activities.

Keywords: deep CNN, EthioCAD, deep learning, YOLOv8, activity recognition

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Authors: Luisito L. Lacatan, Gian Carlo J. Bergonia, Felipe C. Buado III, Gerald L. Gono, Ron Mark V. Ortil, Calvin A. Yap

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This paper features the development of a Mobile Self-sustaining Electricity Generator for water distillation process with MCU- based wireless controller & indicator designed to solve the problem of scarcity of clean water. It is a fact that pure water is precious nowadays and its value is more precious to those who do not have or enjoy it. There are many water filtration products in existence today. However, none of these products fully satisfies the needs of families needing clean drinking water. All of the following products require either large sums of money or extensive maintenance, and some products do not even come with a guarantee of potable water. The proposed project was designed to alleviate the problem of scarcity of potable water in the country and part of the purpose was also to identify the problem or loopholes of the project such as the distance and speed required to produce electricity using a wheel and alternator, the required time for the heating element to heat up, the capacity of the battery to maintain the heat of the heating element and the time required for the boiler to produce a clean and potable water. The project has three parts. The first part included the researchers’ effort to plan every part of the project from the conversion of mechanical energy to electrical energy, from purifying water to potable drinking water to the controller and indicator of the project using microcontroller unit (MCU). This included identifying the problem encountered and any possible solution to prevent and avoid errors. Gathering and reviewing related studies about the project helped the researcher reduce and prevent any problems before they could be encountered. It also included the price and quantity of materials used to control the budget.

Keywords: mobile, self – sustaining, electricity generator, water distillation, wireless battery indicator, wireless water level indicator

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Authors: Apostolos Kalatzis, Ashish Teotia, Vishnunarayan Girishan Prabhu, Laura Stanley

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Emotions are functional behaviors influenced by thoughts, stimuli, and other factors that induce neurophysiological changes in the human body. Understanding and classifying emotions are challenging as individuals have varying perceptions of their environments. Therefore, it is crucial that there are publicly available databases and virtual reality (VR) based environments that have been scientifically validated for assessing emotional classification. This study utilized two commercially available VR applications (Guided Meditation VR™ and Richie’s Plank Experience™) to induce acute stress and calm state among participants. Subjective and objective measures were collected to create a validated multimodal dataset and classification scheme for affective state classification. Participants’ subjective measures included the use of the Self-Assessment Manikin, emotional cards and 9 point Visual Analogue Scale for perceived stress, collected using a Virtual Reality Assessment Tool developed by our team. Participants’ objective measures included Electrocardiogram and Respiration data that were collected from 25 participants (15 M, 10 F, Mean = 22.28  4.92). The features extracted from these data included heart rate variability components and respiration rate, both of which were used to train two machine learning models. Subjective responses validated the efficacy of the VR applications in eliciting the two desired affective states; for classifying the affective states, a logistic regression (LR) and a support vector machine (SVM) with a linear kernel algorithm were developed. The LR outperformed the SVM and achieved 93.8%, 96.2%, 93.8% leave one subject out cross-validation accuracy, precision and recall, respectively. The VR assessment tool and data collected in this study are publicly available for other researchers.

Keywords: affective computing, biosignals, machine learning, stress database

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Authors: Norbert Tribl

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European contemporary scientific public opinion considers the concept of constitutional identity as a highlighted issue. Some scholars interpret the matter as the manifestation of a conflict of Europe. Nevertheless, constitutional identity is a bridge between the Member States and the EU rather than a river that will wash away the achievements of the integration. In accordance with the opinion of the author, the main problem of constitutional identity in Europe is the undetermined nature: the exact concept of constitutional identity has not been defined until now. However, this should be the first step to understand and use identity as a legal institution. Having regard to this undetermined nature, the legal-theoretical examination of constitutional identity is the main purpose of this study. The concept of constitutional identity appears in the Anglo-Saxon legal systems by a different approach than in the supranational system of European Integration. While the interpretation of legal institutions in conformity with the constitution is understood under it, the European concept is applied when possible conflicts arise between the legal system of the European supranational space and certain provisions of the national constitutions of the member states. The European concept of constitutional identity intends to offer input in determining the nature of the relationship between the constitutional provisions of the member states and the legal acts of the EU integration. In the EU system of multilevel constitutionalism, a long-standing central debate on integration surrounds the conflict between EU legal acts and the constitutional provisions of the member states. In spite of the fact that the Court of Justice of the European Union stated in Costa v. E.N.E.L. that the member states cannot refer to the provisions of their respective national constitutions against the integration. Based on the experience of more than 50 years since the above decision, and also in light of the Treaty of Lisbon, we now can clearly see that EU law has itself identified an obligation for the EU to protect the fundamental constitutional features of the Member States under Article 4 (2) of Treaty on European Union, by respecting the national identities of member states. In other words, the European concept intends to offer input for the determination of the nature of the relationship between the constitutional provisions of the member states and the legal acts of the EU integration.

Keywords: constitutional identity, EU law, European Integration, supranationalism

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Authors: Aguiar-Castillo Lidia, Perez-Jimenez Rafael

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The integration of gamification in educational settings aims to enhance student engagement and motivation through game design elements in learning activities. This paper introduces "Gamipulation," the subtle manipulation of students via gamification techniques serving hidden agendas without explicit consent. It highlights the need to distinguish between beneficial and exploitative uses of gamification in education, focusing on its potential to psychologically manipulate students for purposes misaligned with their best interests. Through a literature review and expert interviews, this study presents a conceptual framework outlining gamipulation's features. It examines ethical concerns like gradually introducing desired behaviors, using distraction to divert attention from significant learning objectives, immediacy of rewards fostering short-term engagement over long-term learning, infantilization of students, and exploitation of emotional responses over reflective thinking. Additionally, it discusses ethical issues in collecting and utilizing student data within gamified environments.  Key findings suggest that while gamification can enhance motivation and engagement, there's a fine line between ethical motivation and unethical manipulation. The study emphasizes the importance of transparency, respect for student autonomy, and alignment with educational values in gamified systems. It calls for educators and designers to be aware of gamification's manipulative potential and strive for ethical implementation that benefits students. In conclusion, this paper provides a framework for educators and researchers to understand and address gamipulation's ethical challenges. It encourages developing ethical guidelines and practices to ensure gamification in education remains a tool for positive engagement and learning rather than covert manipulation.

Keywords: gradualness, distraction, immediacy, infantilization, emotion

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Authors: Ma Cecilia Siva

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This study introduces a Bidirectional Encoder Representations from Transformers (BERT)-based machine learning model aimed at improving educational counseling by automating the process of recommending academic strands for students. The framework is designed to streamline and enhance the strand selection process by analyzing students' profiles and suggesting suitable academic paths based on their interests, strengths, and goals. Data was gathered from a sample of 200 grade 10 students, which included personal essays and survey responses relevant to strand alignment. After thorough preprocessing, the text data was tokenized, label-encoded, and input into a fine-tuned BERT model set up for multi-label classification. The model was optimized for balanced accuracy and computational efficiency, featuring a multi-category classification layer with sigmoid activation for independent strand predictions. Performance metrics showed an F1 score of 88%, indicating a well-balanced model with precision at 80% and recall at 100%, demonstrating its effectiveness in providing reliable recommendations while reducing irrelevant strand suggestions. To facilitate practical use, the final deployment phase created a recommendation framework that processes new student data through the trained model and generates personalized academic strand suggestions. This automated recommendation system presents a scalable solution for academic guidance, potentially enhancing student satisfaction and alignment with educational objectives. The study's findings indicate that expanding the data set, integrating additional features, and refining the model iteratively could improve the framework's accuracy and broaden its applicability in various educational contexts.

Keywords: tokenized, sigmoid activation, transformer, multi category classification

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Authors: Shahroz Khan, Erkan Gunpinar, Kemal Mart

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Recently parametric design techniques became a vital concept in the field of Computer Aided Design (CAD), which helps to provide sophisticated platform to the designer in order to automate the design process in efficient time. In these techniques, design process starts by parameterizing the important features of design models (typically the key dimensions), with the implementation of design constraints. The design constraints help to retain the overall shape of the model while modifying its parameters. However, the process of initializing an appropriate number of design parameters and constraints is the crucial part of parametric design techniques, especially for complex surface models such as yacht hull. This paper introduces a method to create complex surface models in favor of parametric design techniques, a method to define the right number of parameters and respective design constraints, and a system to implement design parameters in contract to design constraints schema. For this, in our proposed approach the design process starts by dividing the yacht hull into three sections. Each section consists of different shape lines, which form the overall shape of yacht hull. The shape lines are created using Cubic Bezier Curves, which allow larger design flexibility. Design parameters and constraints are defined on the shape lines in 3D design space to facilitate the designers for better and individual handling of parameters. Afterwards, shape modifiers are developed, which allow the modification of each parameter while satisfying the respective set of criteria and design constraints. Such as, geometric continuities should be maintained between the shape lines of the three sections, fairness of the hull surfaces should be preserved after modification and while design modification, effect of a single parameter should be negligible on other parameters. The constraints are defined individually on shape lines of each section and mutually between the shape lines of two connecting sections. In order to validate and visualize design results of our shape modifiers, a real time graphic interface is created.

Keywords: design parameter, design constraints, shape modifies, yacht hull

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Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

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Authors: Camilo Andres Agudelo-Velez, Lina María Martinez-Sanchez, Natalia Perilla-Hernandez, Maria De Los Angeles Rodriguez-Gazquez, Felipe Hernandez-Restrepo, Dayana Andrea Quintero-Moreno, Camilo Ruiz-Mejia, Isabel Cristina Ortiz-Trujillo, Monica Maria Zuluaga-Quintero

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Chronic obstructive pulmonary disease is common condition, characterized by a persistent blockage of airflow, partially reversible and progressive, that represents 5% of total deaths around the world, and it is expected to become the third leading cause of death by 2030. Objective: To establish the clinical and epidemiological profile of patients with chronic obstructive pulmonary disease in a medical institution from the city of Medellin, Colombia. Methods: A cross-sectional study was performed, with a sample of 50 patients with a diagnosis of chronic obstructive pulmonary disease in a private institution in Medellin, during 2015. The software SPSS vr. 20 was used for the statistical analysis. For the quantitative variables, averages, standard deviations, and maximun and minimun values were calculated, while for ordinal and nominal qualitative variables, proportions were estimated. Results: The average age was 73.5±9.3 years, 52% of the patients were women, 50% of them had retired, 46% ere married and 80% lived in the city of Medellín. The mean time of diagnosis was 7.8±1.3 years and 100% of the patients were treated at the internal medicine service. The most common clinical features were: 36% were classified as class D for the disease, 34% had a FEV1 <30%, 88% had a history of smoking and 52% had oxygen therapy at home. Conclusion: It was found that class D was the most common, and the majority of the patients had a history of smoking, indicating the need to strengthen promotion and prevention strategies in this regard.

Keywords: pulmonary disease, chronic obstructive, pulmonary medicine, oxygen inhalation therapy

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Authors: Valliyil Govindankutty

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Human civilizations across the globe stand testimony to water being one of the major interaction points with nature. The interactions with nature especially in drier areas revolve around water, be it harnessing, transporting, usage and management. Many ingenious ideas were born, nurtured and developed for harnessing, transporting, storing and distributing water through the areas in the drier parts of the world. Many methods of water extraction, collection and management could be found throughout the world, some of which are associated with efficient, sustained use of surface water, ground water and rain water. Karez is one such ingenious method of collection, transportation, storage and distribution of ground water. Most of the Karez systems in India were developed during reign of Muslim dynasties with ruling class descending from Persia or having influential connections and inviting expert engineers from there. Karez have strongly influenced the village socio-economic organisations due to multitude of uses they were brought into. These are masterpiece engineering structures to collect groundwater and direct it, through a subsurface gallery with a gradual slope, to surface canals that provide water to settlements and agricultural fields. This ingenious technology, karez was result of need for harnessing groundwater in arid areas like that of Bidar. The study views this traditional technology in historical perspective linked to sustainable utilization and management of groundwater and above all the immediate environment. The karez system is one of the best available demonstration of human ingenuity and adaptability to situations and locations of water scarcity. Bidar, capital of erstwhile Bahmani sultanate with a history of more than 700 years or more is one of the heritage cities of present Karnataka State. The unique water systems of Bidar along with other historic entities have been listed under World Heritage Watch List by World Monument Fund. The Historical or cultural landscape in Bidar is very closely associated to the natural resources of the region, Karez systems being one of the best examples. The Karez systems were the lifeline of Bidar’s historical period providing potable water, fulfilling domestic and irrigation needs, both within and outside the fort enclosures. These systems are still functional, but under great pressure and threat of rapid and unplanned urbanisation. The change in land use and fragmentation of land are already paving way for irreversible modification of the karez cultural and geographic landscape. The Paper discusses the significance of character defining elements of Naubad Karez Landscape, highlights the importance of conserving cultural heritage and presents a geographical approach to its revival.

Keywords: Karez, groundwater, traditional water harvesting, cultural heritage landscape, urban planning

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