Search results for: feature combination

Authors: Meng-Chyi Wu, Bonn Lin, Jyun-Hao Liao, Chein-Ju Chen, Yu-Cheng Jhuang, Mau-Phon Houng, Fang-Hsing Wang, Min-Chu Liu, Cheng-Fu Yang, Cheng-Shong Hong

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Since the use of wireless communications has become critical nowadays, the available RF spectrum has become limited. Ultraviolet (UV) communication system can alleviate the spectrum constraint making UV communication system a potential alternative to future communication demands. Also, UV links can provide faster communication rate and can be used in combination with existing RF communication links, providing new communications diversity with higher user capacity. The UV region of electromagnetic spectrum has been of interest to detector, imaging and communication technologies because the stratospheric ozone layer effectively absorbs some solar UV radiation from reaching the earth surface. The wavebands where most of UV radiation is absorbed by the ozone are commonly known as the solar blind region. By operating in UV-C band (200-280 nm) the communication system can minimize the transmission power consumption since it will have less radiation noise. UV communication uses the UV ray as the medium. Electric signal is carried on this band after being modulated and then be transmitted within the atmosphere as channel. Though the background noise of UV-C communication is very low owing to the solar-blind feature, it leads to a large propagation loss. The 370 nm UV provides a much lower propagation loss than that the UV-C does and the recent device technology for UV source on this band is more mature. The fabricated 370 nm AlGaN light-emitting diodes (LEDs) with an aperture size of 45 m exhibit a modulation bandwidth of 165 MHz at 30 mA and a high power of 7 W/cm2 at 230 A/cm2. In order to solve the problem of low power in single UV LED, a UV LED array is presented in.

Keywords: ultraviolet (UV) communication, light-emitting diodes (LEDs), modulation bandwidth, LED array, 370 nm

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Authors: Zineb Abidi, Marc-Arthur Diaye

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There exists a large empirical literature concerning the relationship between corporate social responsibility (CSR) and corporate financial performance. This literature, however, applies mainly to large corporations and/or listed firms. To the best of our knowledge, the question of whether meeting CSR requirements impacts the financial performance of small and medium-sized unlisted SMEs has not so far been analyzed. This paper aims to analyze, for the first time, the effect of CSR on the financial performance of SMEs. Using an original database including 5,257 French SMEs, we show that adopting CSR practices has a positive but weak effect on a firm’s financial performance. To develop this further, we analyzed CSR practices interactions assessing the best combination of CSR components that positively influence SME financial performance. Our results show that French SMEs benefit more from their pro-social behavior when they choose a combination of CSR components best adapted to their individual characteristics.

Keywords: corporate social responsibility, financial performance, unlisted firms, SMEs

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Authors: Mahdi Almaky

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The thermostabilization of High Density Polyethylene (HDPE) is realized through the action of primary antioxidant such as phenolic antioxidants and secondary antioxidants as aryl phosphates. The efficiency of two secondary antioxidants, commercially named Irgafos 168 and Weston 399, was investigated using different physical, mechanical, spectroscopic, and calorimetric methods. The effect of both antioxidants on the processing stability and long term stability of HDPE produced in Ras Lanuf oil and gas processing Company were measured and compared. The combination of Irgafos 168 with Irganox 1010, as used in smaller concentration, results in a synergetic effect against thermo-oxidation and protect better than the combination of Weston 399 with Irganox 1010 against the colour change at processing temperature and during long term oxidation process.

Keywords: thermostabilization, high density polyethylene, primary antioxidant, phenolic antioxidant, Irgafos 168, Irganox 1010, Weston 399

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Authors: Indira Sinha

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According to the Oxford Advanced Learner's English Dictionary of Current English, environment is the complex of physical, chemical and biotic factors that act upon an organism or an ecological community and ultimately determines its form and survival. It is defined as conditions and circumstances which are affecting people's lives. The meaning of environmental degradation is the degradation of the environment through depletion of resources such as air, water and soil and the destruction of ecosystems and extinction of wildlife. Globalization is a significant feature of recent world history. The aim of this phenomenon is to integrate societies, economies and cultures through a common link of trading policies, technology and communication. Undoubtedly it has opened up the world economy at a very high speed but at the same time it has an adverse impact on the environment. The purpose of the present study is to investigate the impact of globalization on the environmental conditions. An overview of what the forces of globalization have in store for the environment with constructing large number of industries and destroying large forests lands will be given in this paper. The forces of globalization have created many serious environmental problems like high temperature, extinction of many species of plant and animal and outlet of poisonous chemicals from industries. The revelation of this study is that in case of developing economics these problems are more critical. In developing countries like India many factories are built with less environmental regulations, while developed economies maintain positive environmental practices. The present study is a micro level study which aims to employ a combination of theoretical, descriptive, empirical and analytical approach in addition to the time tested case method.

Keywords: globalization, trade policies, environmental degradation, developing economies, large industries

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Authors: Elkedrouci Lotfi, Diao Bo, Pang Sen, Li Yi

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Deterioration of reinforced concrete structures is a serious concern in the construction engineering, largely due to chloride induced corrosion of reinforcement. Chloride penetration is markedly influenced by one or several major factors at the same time such as cuing in combination with different crack widths which have spectacular effect on reinforced concrete structures. This research presents the results of an experimental investigation involving reinforced concrete beams with three different crack widths ranging from 0 to 0.2mm, curing temperatures of 20°C or 40°C and water-to-cement of 0.5. Chloride content profiles were determined under non-steady state diffusion at 20°C. Based on the obtained results, higher chloride content was obtained under condition of high curing temperature in combination with large crack more than 0.1mm and there are no significant differences between narrow crack width (less than 0.1 mm) and beams without crack (0mm).

Keywords: crack width, high curing temperature, rapid chloride migration, reinforced concrete beam

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Authors: Chih Jou Hsiao, Chung Ming Lo, Li Chun Hsieh

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Brain tumor is not the cancer having high incidence rate, but its high mortality rate and poor prognosis still make it as a big concern. On clinical examination, the grading of brain tumors depends on pathological features. However, there are some weak points of histopathological analysis which can cause misgrading. For example, the interpretations can be various without a well-known definition. Furthermore, the heterogeneity of malignant tumors is a challenge to extract meaningful tissues under surgical biopsy. With the development of magnetic resonance imaging (MRI), tumor grading can be accomplished by a noninvasive procedure. To improve the diagnostic accuracy further, this study proposed a computer-aided diagnosis (CAD) system based on MRI features to provide suggestions of tumor grading. Gliomas are the most common type of malignant brain tumors (about 70%). This study collected 34 glioblastomas (GBMs) and 73 lower-grade gliomas (LGGs) from The Cancer Imaging Archive. After defining the region-of-interests in MRI images, multiple quantitative morphological features such as region perimeter, region area, compactness, the mean and standard deviation of the normalized radial length, and moment features were extracted from the tumors for classification. As results, two of five morphological features and three of four image moment features achieved p values of <0.001, and the remaining moment feature had p value <0.05. Performance of the CAD system using the combination of all features achieved the accuracy of 83.18% in classifying the gliomas into LGG and GBM. The sensitivity is 70.59% and the specificity is 89.04%. The proposed system can become a second viewer on clinical examinations for radiologists.

Keywords: brain tumor, computer-aided diagnosis, gliomas, magnetic resonance imaging

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Authors: Shafiullah Soomro

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Active contour is one of the image segmentation techniques and its goal is to capture required object boundaries within an image. In this paper, we propose a novel image segmentation method by using an active contour method based on anisotropic diffusion feature enhancement technique. The traditional active contour methods use only pixel information to perform segmentation, which produces inaccurate results when an image has some noise or complex background. We use Perona and Malik diffusion scheme for feature enhancement, which sharpens the object boundaries and blurs the background variations. Our main contribution is the formulation of a new SPF (signed pressure force) function, which uses global intensity information across the regions. By minimizing an energy function using partial differential framework the proposed method captures semantically meaningful boundaries instead of catching uninterested regions. Finally, we use a Gaussian kernel which eliminates the problem of reinitialization in level set function. We use several synthetic and real images from different modalities to validate the performance of the proposed method. In the experimental section, we have found the proposed method performance is better qualitatively and quantitatively and yield results with higher accuracy compared to other state-of-the-art methods.

Keywords: active contours, anisotropic diffusion, level-set, partial differential equations

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Authors: Cangir Uyarlar, Ismail Bayram, Ibrahim Sadi Cetingul, Mustafa Kabu, Eyup Eren Gultepe

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This study was conducted to determine the effects of organic chromium and organic chromium+propylene glycol on milk yield and some blood parameters related with liver fatty acid metabolism in early lactation dairy cows. Thirty multiparous Holstein dairy cows were used as study material. Cows assigned to three groups as control (C), chromium (Cr) and chromium+propylene glycol (CP). Live weight, parity and body condition score were used as covariates for statistical analyses. The study began at calving and finished at 3 weeks after calving. All cows were consumed same diet. Organic chromium and organic chromium+propylene glycol were orally administrated to cows in treatment groups shortly after the morning milking. Blood samples were collected from all cows on 0 (calving), 3rd, 6th, 9th, 12th, 15th, 18th, 21th days after calving. Then, samples were analyzed for BHBA (Betahydroxybutiric acids), NEFA (Non Esterified Fatty Acids), urea, total protein (TP) and glucose concentrations. Weekly milk yields were calculated from daily milk data on farm. Organic chromium treatment had no significant differences on serum biochemical parameters and milk yields. However, administration of organic chromium and propylene glycol combination decreased serum urea and total protein concentration, helped to protection from subclinical metabolic diseases via decreasing serum NEFA and BHBA concentrations. Also, this combination decreased serum glucose levels of cows. Neither only chromium nor chromium and propylene glycol combination did not affect milk yield throughout the study. These findings were suggested that orally administrations of chromium and propylene glycol combination improved liver glucose and fatty acid metabolism, decreased serum parameters which are representing subclinical diseases in early lactation dairy cows.

Keywords: chromium, early lactation dairy cows, propylene glycol, milk yield

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Authors: H. D. Duong, J. I. Rhee

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In this work, gold nanoparticles in star shape (called gold nanostars, GNS) were synthesized and coated by N-(3-aminopropyl) methacrylamide hydrochloride (PA) and mercaptopropionic acid (MPA) for functionalizing their surface by amine and carboxyl groups and then investigated for ROS production. The GNS with big size and multi-tips seem to be superior in singlet oxygen production as compared with that of small GNS and less tips. However, the functioned GNS in small size could also enhance efficiency of singlet oxygen production about double as compared with that of the intact GNS. In combination with methylene blue (MB+), the functioned GNS could enhance the singlet oxygen production of MB+ after 1h of LED750 irradiation and no difference between small size and big size in this reaction was observed. In combination with 5-aminolevulinic acid (ALA), only GNS coated PA could enhance the singlet oxygen production of ALA and the small size of GNS coated PA was a little higher effect than that of the bigger size. However, GNS coated MPA with small size had strong effect on hydroxyl radical production of ALA.

Keywords: 5-aminolevulinic acid, gold nanostars, methylene blue, ROS production

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Authors: Sharma Aseem, Dhurat Rachita, Pawar Varsha, Khalse Manisha

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Introduction: Androgenetic alopecia (AGA) is a prevalent condition characterized by progressive hair thinning driven by genetic and androgen-related factors. The current FDA-approved treatments include oral finasteride and topical minoxidil, though many patients seek combination therapies to enhance results. This study aims to evaluate the effectiveness of a combination therapy involving Minoxidil, Finasteride, and Capixyl™ based on feedback from dermatologists. Methodology: A survey, validated by experts, was distributed to 29 leading dermatologists across India (in Tier 1 and 2 cities). The survey examined real-world clinical experiences, focusing on patient outcomes and the overall effectiveness of the mentioned formulation. Results: Among the surveyed dermatologists, 41.4% identified women aged 35-40 as the most frequently diagnosed with female pattern hair loss. The combination therapy with Minoxidil, Finasteride, and Capixyl™ was utilized by 34.5% of dermatologists for over 60 patients per month. The majority highlighted the benefits of this combination therapy, which acts via multiple mechanisms, such as vasodilation and dihydrotestosterone (DHT) receptor blockade, resulting in improved hair regrowth. Additionally, patients demonstrated better clinical outcomes, enhanced compliance, and fewer side effects. Demographically, younger patients, particularly those with AGA for less than 10 years, responded more positively to the treatment. Early intervention led to quicker and more significant results. Overall satisfaction among dermatologists was high, with 86.2% expressing positive feedback on the therapy. In terms of treatment outcomes, 51.7% of dermatologists observed visible results within 4-6 months, while 34.5% noticed a significant reduction in hair fall within 8-12 weeks. Improvements in scalp health were reported by 48.3%, and 51.7% saw an increased hair density within 3-4 months. Despite mild side effects such as scalp irritation, dryness, flaking, and occasional issues like folliculitis, headaches, itching, and redness, patient satisfaction remained high. Dermatologists reported that 93.1% of patients experienced faster and better hair regrowth with Capixyl™ compared to Minoxidil alone. Suggestions for improving the formulation included incorporating peptides like Saw Palmetto and enhancing product packaging to better meet patient needs. Discussion: The combination of Minoxidil, Finasteride, and Capixyl™ yielded positive clinical outcomes, especially in improving hair density, scalp health, and overall patient satisfaction. Dermatologists found that Capixyl™ peptides enhanced the therapeutic effect, promoting hair regrowth and improving compliance. While side effects were generally mild, there were suggestions to further improve the formulation by adding additional peptides like Saw Palmetto. Conclusion: The combination of Minoxidil and Finasteride fortified with Capixyl™ presents a promising therapeutic option for managing AGA. Dermatologists reported significant improvements in hair density, scalp health, and patient satisfaction. With its favorable efficacy and manageable side effects, this formulation proves to be a valuable addition to the treatment landscape for AGA.

Keywords: androgenetic alopecia, combination therapy, minoxidil, finasteride, capixyl

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Authors: Mawloud Mosbah, Bachir Boucheham

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We present here the results for a comparative study of some techniques, available in the literature, related to the relevance feedback mechanism in the case of a short-term learning. Only one method among those considered here is belonging to the data mining field which is the K-Nearest Neighbours Algorithm (KNN) while the rest of the methods is related purely to the information retrieval field and they fall under the purview of the following three major axes: Shifting query, Feature Weighting and the optimization of the parameters of similarity metric. As a contribution, and in addition to the comparative purpose, we propose a new version of the KNN algorithm referred to as an incremental KNN which is distinct from the original version in the sense that besides the influence of the seeds, the rate of the actual target image is influenced also by the images already rated. The results presented here have been obtained after experiments conducted on the Wang database for one iteration and utilizing colour moments on the RGB space. This compact descriptor, Colour Moments, is adequate for the efficiency purposes needed in the case of interactive systems. The results obtained allow us to claim that the proposed algorithm proves good results; it even outperforms a wide range of techniques available in the literature.

Keywords: CBIR, category search, relevance feedback, query point movement, standard Rocchio’s formula, adaptive shifting query, feature weighting, original KNN, incremental KNN

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Authors: A. Falsafi, M. Dadkhah, S. Shahidi

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The destruction and defeat of drill pipes and drill rigs in oil wells often combined with a combination of shear modulus II and III. In such a situation, the strength and load bearing capacity of the drill are evaluated based on the principles of fracture mechanics and crack growth criteria. In this paper, using the three-dimensional stress equations around the Turkish frontier, the relations of the tense-tense criterion (MTS) are extracted for the loading of the combined II and III modulus. It is shown that in crisp deflection under loading of combination II and III, the level of fracture is characterized by two different angles: the longitudinal angle of deflection θ and the angle of the deflection of the alpha. Based on the relationships obtained from the MTS criterion, the failure criteria, the longitudinal angle of the theta failure and the lateral angle of the failure of the alpha are presented. Also, the role of Poisson's coefficient on these parameters is investigated in these graphs.

Keywords: most tangential tension criterion, longitudinal angle of failure, side angle of fracture, drills crack

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Authors: Youngmin Kim, Jae-Hwan Ahn, Seog-Ku Kim, Hye-Cheol Oh, Bokjin Lee, Hee-Jun Kang

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The high energy consuming processes such as advanced oxidation and reverse osmosis are used as a reuse process. This study aims at developing an energy efficient reuse process by combination of nanofiltration (NF) and capacitive deionization processes (CDI) processes. Lab scale experiments were conducted by using effluents from a wastewater treatment plant located at Koyang city in Korea. Commercial NF membrane (NE4040-70, Toray Ltd.) and CDI module (E40, Siontech INC.) were tested in series. The pollutant removal efficiencies were evaluated on the basis of Korean water quality criteria for water reuse. In addition, the energy consumptions were also calculated. As a result, the hybrid process showed lower energy consumption than conventional reverse osmosis process even though its effluent did meet the Korean standard. Consequently, this study suggests that the hybrid process is feasible for the energy efficient water reuse.

Keywords: capacitive deionization, energy efficient process, nanofiltration, water reuse

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Authors: Farhad Alizadeh, Alireza Faregh Gharamaleki, Mojtaba Jalilzadeh, Houshang Gholami, Ali Akhoundzadeh

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This study presented hybrid pre-processing approach along with a conceptual model to enhance the accuracy of river discharge prediction. In order to achieve this goal, Ensemble Empirical Mode Decomposition algorithm (EEMD), Discrete Wavelet Transform (DWT) and Mutual Information (MI) were employed as a hybrid pre-processing approach conjugated to Least Square Support Vector Machine (LSSVM). A conceptual strategy namely multi-station model was developed to forecast the Souris River discharge more accurately. The strategy used herein was capable of covering uncertainties and complexities of river discharge modeling. DWT and EEMD was coupled, and the feature selection was performed for decomposed sub-series using MI to be employed in multi-station model. In the proposed feature selection method, some useless sub-series were omitted to achieve better performance. Results approved efficiency of the proposed DWT-EEMD-MI approach to improve accuracy of multi-station modeling strategies.

Keywords: river stage-discharge process, LSSVM, discrete wavelet transform, Ensemble Empirical Decomposition Mode, multi-station modeling

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Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: Yasmin M. Attia, Hanan S. El-Abhar, Mahmoud M. Al Marzabani, Samia A. Shouman

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Background: Tamoxifen (TAM) is the most commonly used hormone therapy for the treatment of early and metastatic breast cancer. Although it significantly decreases the tumor recurrence rate and provides an overall benefit, as much as 20–30% of women still relapse during or after long-term therapy. 3-Bromopyruvate (3-BP) is a promising agent with impressive antitumor effects in several models of animal tumors and cell lines. Aim: This study was designed to investigate the combined effect of (TAM) and (3-BP) in breast cancer cells and to explore their molecular interaction via assessment of apoptotic, angiogenic, and metastatic markers. Methods: In vitro cytotoxicity study was carried out for both compounds to determine the combination regimen producing a synergistic effect and mechanistic pathways were studied using RT-PCR and western techniques. Moreover, the anti-oncolytic and anti-angiogenic potentials were assessed in mice bearing solid Ehrlich carcinoma (SEC). Results: The combined treatment significantly increased the expressions and protein levels of caspase 7, 9, and 3 and decreased of angiogenic markers VEGF, HIF-1α, and HK2 compared to cells treated with either drug individually. However, there were no significant changes in MMP-2 and MMP-9 protein levels. Interestingly, the in vivo results supported the in vitro findings; there was a decrease in the tumor volume and VEFG using immunohistochemistry in the combination-treated groups compared to either TAM or 3-BP treated one. Conclusion: 3-BP synergizes the cytotoxic effect of TAM by increasing apoptosis and decreasing angiogenesis which makes this combination a promising regimen to be applied clinically.

Keywords: tamoxifen, 3-bromopyruvate, breast cancer, cytotoxicity, angiogenesis

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Authors: M. Ghasemi-Varnamkhasti, S. H. Yoosefian, A. Mohammad-Razdari

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This study investigated the effects of Ag nanoparticle polyethylene film and active modified atmosphere on the postharvest quality of tomatoes stored at 6 ºC. The atmosphere composition used in the packaging was 7% O₂ + 7% CO₂ + 86% N₂, and synthetic air (control). The variables measured were weight loss, firmness, color and respiration rate over 21 days. The results showed that the combination of Ag nanoparticle polyethylene film and modified atmosphere could extend the shelf life of tomatoes to 21 days and could influence the postharvest quality of tomatoes. Also, existence of Ag nanoparticles caused preventing from increasing weight loss, a*, b*, Chroma, Hue angle and reducing firmness and L*. As well as, tomatoes at Ag nanoparticle polyethylene films had lower respiration rate than Polyethylene and paper bags to 13.27% and 23.50%, respectively. The combination of Ag nanoparticle polyethylene film and active modified atmosphere was effective with regard to delaying maturity during the storage period, and preserving the quality of tomatoes.

Keywords: ag nanoparticles, modified atmosphere, polyethylene film, tomato

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Authors: Lubna Azmi, Ila Shukla, Shyam Sundar Gupta, Padam Kant, C. V. Rao

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Elvitegravir is the mainstay of anti-HIV combination therapy in most endemic countries presently. However, it cannot be used alone owing to its long onset time of action. 2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxychromen-4-one (Quercetin: QU) is a polyphenolic compound obtained from Argeria speciosa Linn (Family: Convolvulaceae), an anti-HIV candidate. In the present study, a sensitive, simple and rapid high-performance liquid chromatography coupled with positive ion electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) method was developed for the simultaneous determination elvitegravir and Quercetin, in rat plasma. The method was linear over a range of 0.2–500 ng/ml. All validation parameters met the acceptance criteria according to regulatory guidelines. LC–MS/MS method for determination of Elvitegravir and Quercetin was developed and validated. Results show the potential of drug–drug interaction upon co-administration this marketed drugs and plant derived secondary metabolite.

Keywords: anti-HIV resistance, extraction, HPLC-ESI-MS-MS, validation

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Authors: Payam Najafi, Behnam Ebrahimi, Hamid Montazerolghaem, Safoura Akbari-Alavijeh, Rasoul Tarkesh Esfahani

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The combination of work and study has been recently gained lots of attention due to the crucial demand of industries to skillfully trained youth. Nevertheless, the distance between university and industry makes this combination challenging. According to the OECD (2012), in most countries, there is a limited link between students’ field of study and their area of work while studying. On the other hand, high unemployment rates among the specialized workforce, which is common in developing countries, highlights the need to strengthen this relationship. Innovative Center of Isfahan Chamber of Commerce has defined a project called 'POUYESH', which helps students to find related work opportunities to their field of study as well as supporting industries to supply their needed workforce. The present research is sought to explore the effect of the running project as a model of combining work and study on the university-industry linkage.

Keywords: work and study, university-industry linkage, POUYESH project, field of study

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Authors: William Sigvardsson, Christoffer Alenius, Jenny Lindblom, Andreas Johansson, Marcus Sandberg

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This article covers a study focusing on a subarctic greenhouse located in Nikkala, Sweden. Through a visit and the creation of a CFD model, the study investigates the differences in energy demand with high pressure sodium (HPS) lights and light emitting diode (LED) lights in combination with an air-carried and water-carried heating system accordingly. Through an IDA ICE model, the impact of insulating the parts of the greenhouse without active cultivation was also investigated. This, with the purpose of comparing the current system in the greenhouse to state-of-the-art alternatives and evaluating if an investment in either a water-carried heating system in combination with LED lights and insulating the non-cultivating parts of the greenhouse could be considered profitable. Operating a greenhouse in the harsh subarctic climate found in the northern parts of Sweden is not an easy task and especially if the operation is year-round. With an average temperature of under -5 °C from November through January, efficient growing techniques are a must to ensure a profitable business. Today the most crucial parts of a greenhouse are the heating system, lighting system, dehumidifying measures, as well as thermal screen, and the impact of a poorly designed system in a sub-arctic could be devastating as the margins are slim. The greenhouse studied uses a pellet burner to power their air- carried heating system which is used. The simulations found the resulting savings amounted to just under 14 800 SEK monthly or 18 % of the total cost of energy by implementing the water-carrying heating system in combination with the LED lamps. Given this, a payback period of 3-9 years could be expected given different scenarios, including specific time periods, financial aids, and the resale price of the current system. The insulation of the non-cultivating parts of the greenhouse was found to have possible savings of 25 300 SEK annually or 46 % of the current heat demand resulting in a payback period of just over 1-2 years. Given the possible energy savings, a reduction in emitted CO2 equivalents of almost 1,9 tonnes could be achieved annually. It was concluded that relatively inexpensive investments in modern greenhouse equipment could make a significant contribution to reducing the energy consumption of the greenhouse resulting in a more competitive business environment for sub-arctic greenhouse owners. New parts of the greenhouse should be built with the water-carried heating system in combination with state-of-the-art LED lights, and all parts which are not housing active cultivation should be insulated. If the greenhouse in Nikkala is eligible for financial aid or finds a resale value in the current system, an investment should be made in a new water-carried heating system in combination with LED lights.

Keywords: energy efficiency, sub-arctic greenhouses, energy measures, greenhouse climate control, greenhouse technology, CFD

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Authors: Mehrdad Shafiei Dizaji, Hoda Azari

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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: Fei Gao, Xinyang Dong, Yisu Ge, Shufang Lu, Libo Weng

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Vehicle color recognition plays an important role in traffic accident investigation. However, due to the influence of illumination, weather, and noise, vehicle color recognition still faces challenges. In this paper, a vehicle color classification network based on spatial cluster loss and channel attention mechanism (SCNet) is proposed for vehicle color recognition. A channel attention module is applied to extract the features of vehicle color representative regions and reduce the weight of nonrepresentative color regions in the channel. The proposed loss function, called spatial clustering loss (SC-loss), consists of two channel-specific components, such as a concentration component and a diversity component. The concentration component forces all feature channels belonging to the same class to be concentrated through the channel cluster. The diversity components impose additional constraints on the channels through the mean distance coefficient, making them mutually exclusive in spatial dimensions. In the comparison experiments, the proposed method can achieve state-of-the-art performance on the public datasets, VCD, and VeRi, which are 96.1% and 96.2%, respectively. In addition, the ablation experiment further proves that SC-loss can effectively improve the accuracy of vehicle color recognition.

Keywords: feature extraction, convolutional neural networks, intelligent transportation, vehicle color recognition

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Authors: Ponghyung Lee

Abstract:

This study seeks to investigate the variations occurring to Korean consonantal variations center around laryngeal features of the concerned sounds, to the exclusion of others. Our fundamental premise is that the weak contrast associated with concerned segments might be held accountable for the oscillation of the status quo of the concerned consonants. What is more, we assume that an array of notions as a measure of communicative efficiency of linguistic units would be significantly influential on triggering those variations. To this end, we have tried to compute the surprisal, entropic contribution, and relative contrastiveness associated with Korean obstruent consonants. What we found therein is that the Information-theoretic perspective is compelling enough to lend support our approach to a considerable extent. That is, the variant realizations, chronologically and stylistically, prove to be profoundly affected by a set of Information-theoretic factors enumerated above. When it comes to the biblical proper names, we use Georgetown University CQP Web-Bible corpora. From the 8 texts (4 from Old Testament and 4 from New Testament) among the total 64 texts, we extracted 199 samples. We address the issue of laryngeal feature variations associated with Korean obstruent consonants under the presumption that the variations stem from the weak contrast among the triad manifestations of laryngeal features. The variants emerge from diverse sources in chronological and stylistic senses: Christianity biblical texts, ordinary casual speech, the shift of loanword adaptation over time, and ideophones. For the purpose of discussing what they are really like from the perspective of Information Theory, it is necessary to closely look at the data. Among them, the massive changes occurring to loanword adaptation of proper nouns during the centennial history of Korean Christianity draw our special attention. We searched 199 types of initially capitalized words among 45,528-word tokens, which account for around 5% of total 901,701-word tokens (12,786-word types) from Georgetown University CQP Web-Bible corpora. We focus on the shift of the laryngeal features incorporated into word-initial consonants, which are available through the two distinct versions of Korean Bible: one came out in the 1960s for the Protestants, and the other was published in the 1990s for the Catholic Church. Of these proper names, we have closely traced the adaptation of plain obstruents, e. g. /b, d, g, s, ʤ/ in the sources. The results show that as much as 41% of the extracted proper names show variations; 37% in terms of aspiration, and 4% in terms of tensing. This study set out in an effort to shed light on the question: to what extent can we attribute the variations occurring to the laryngeal features associated with Korean obstruent consonants to the communicative aspects of linguistic activities? In this vein, the concerted effects of the triad, of surprisal, entropic contribution, and relative contrastiveness can be credited with the ups and downs in the feature specification, despite being contentiousness on the role of surprisal to some extent.

Keywords: entropic contribution, laryngeal feature variation, relative contrastiveness, surprisal

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Authors: Rajesh Ranjan, Marimuthu Palaniswami, A. A. Hashmi

Abstract:

With the changing lifestyle and environment around us, the prevalence of the critical and incurable disease has proliferated. One such condition is the neurological disorder which is rampant among the old age population and is increasing at an unstoppable rate. Most of the neurological disorder patients suffer from some movement disorder affecting the movement of their body parts. Tremor is the most common movement disorder which is prevalent in such patients that infect the upper or lower limbs or both extremities. The tremor symptoms are commonly visible in Parkinson’s disease patient, and it can also be a pure tremor (essential tremor). The patients suffering from tremor face enormous trouble in performing the daily activity, and they always need a caretaker for assistance. In the clinics, the assessment of tremor is done through a manual clinical rating task such as Unified Parkinson’s disease rating scale which is time taking and cumbersome. Neurologists have also affirmed a challenge in differentiating a Parkinsonian tremor with the pure tremor which is essential in providing an accurate diagnosis. Therefore, there is a need to develop a monitoring and assistive tool for the tremor patient that keep on checking their health condition by coordinating them with the clinicians and caretakers for early diagnosis and assistance in performing the daily activity. In our research, we focus on developing a system for automatic classification of tremor which can accurately differentiate the pure tremor from the Parkinsonian tremor using a wearable accelerometer-based device, so that adequate diagnosis can be provided to the correct patient. In this research, a study was conducted in the neuro-clinic to assess the upper wrist movement of the patient suffering from Pure (Essential) tremor and Parkinsonian tremor using a wearable accelerometer-based device. Four tasks were designed in accordance with Unified Parkinson’s disease motor rating scale which is used to assess the rest, postural, intentional and action tremor in such patient. Various features such as time-frequency domain, wavelet-based and fast-Fourier transform based cross-correlation were extracted from the tri-axial signal which was used as input feature vector space for the different supervised and unsupervised learning tools for quantification of severity of tremor. A minimum covariance maximum correlation energy comparison index was also developed which was used as the input feature for various classification tools for distinguishing the PT and ET tremor types. An automatic system for efficient classification of tremor was developed using feature extraction methods, and superior performance was achieved using K-nearest neighbors and Support Vector Machine classifiers respectively.

Keywords: machine learning approach for neurological disorder assessment, automatic classification of tremor types, feature extraction method for tremor classification, neurological movement disorder, parkinsonian tremor, essential tremor

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Authors: Radhika Jain, Sangeeta Goomer

Abstract:

In the present study an attempt has been made to re-engineer traditional wadi into wholesome ready-to-use cereal-pulse-based chunks rich in protein quality and fibre content. Chunks were made using extrusion-dehydration combination. Two formulations i.e., whole green gram dhal with instant oats and washed green gram dhal with whole oats were formulated. These chunks are versatile in nature as they can be easily incorporated in day-to-day home-made preparations such as pulao, potato curry and kadhi. Cereal-pulse ratio was calculated using NDpCal%. Limiting amino acids such as lysine, tryptophan, methionine, cysteine and threonine were calculated for maximum amino acid profile in cereal-pulse combination. Time-temperature combination for extrusion at 130^oC and dehydration at 65^oC for 7 hours and 15 minutes were standardized to obtain maximum protein and fibre content. Proximate analysis such as moisture, fat and ash content were analyzed. Protein content of formulation was 62.10% and 68.50% respectively. Fibre content of formulations was 2.99% and 2.45%, respectively. Using a 5-point hedonic scale, consumer preference trials of 102 consumers were conducted and analyzed. Evaluation of chunks prepared in potato curry, kadi and pulao showed preferences for colour 82%, 87%, 86%, texture and consistency 80%, 81%, 88%, flavour and aroma 74%, 82%, 86%, after taste 70%, 75%, 86% and overall acceptability 77%, 75%, 88% respectively. High temperature inactivates antinutritional compounds such as trypsin inhibitors, lectins, saponins etc. Hence, availability of protein content was increased. Developed products were palatable and easy to prepare.

Keywords: extrusion, NDpCal%, protein quality, wadi

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Authors: Zhang Shuqi, Liu Dan

Abstract:

For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

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Authors: Hussein Alahmer, Amr Ahmed

Abstract:

Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, fuzzy c means, lesion detection, liver segmentation

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Authors: Vramori Mitra, Bornali Sarma, Arun K. Sarma

Abstract:

Recurrence is a ubiquitous feature of any real dynamical system. The states in phase space trajectory of a system have an inherent tendency to return to the same state or its close state after certain time laps. Recurrence quantification analysis technique, based on this fundamental feature of a dynamical system, detects evaluation of state under variation of control parameter of the system. The paper presents the investigation of nonlinear dynamical behavior of plasma floating potential fluctuations obtained by using a Langmuir probe in different magnetic field under the variation of discharge voltages. The main measures of recurrence quantification analysis are considered as determinism, linemax and entropy. The increment of the DET and linemax variables asserts that the predictability and periodicity of the system is increasing. The variable linemax indicates that the chaoticity is being diminished with the slump of magnetic field while increase of magnetic field enhancing the chaotic behavior. Fractal property of the plasma time series estimated by DFA technique (Detrended fluctuation analysis) reflects that long-range correlation of plasma fluctuations is decreasing while fractal dimension is increasing with the enhancement of magnetic field which corroborates the RQA analysis.

Keywords: detrended fluctuation analysis, chaos, phase space, recurrence

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Authors: Zhengkun Yi

Abstract:

Surface texture discrimination using artificial tactile sensors has attracted increasing attentions in the past decade as it can endow technical and robot systems with a key missing ability. However, as a major component of texture, roughness has rarely been explored. This paper presents an approach for tactile surface roughness discrimination, which includes two parts: (1) design and fabrication of a bioinspired artificial fingertip, and (2) tactile signal processing for tactile surface roughness discrimination. The bioinspired fingertip is comprised of two polydimethylsiloxane (PDMS) layers, a polymethyl methacrylate (PMMA) bar, and two perpendicular polyvinylidene difluoride (PVDF) film sensors. This artificial fingertip mimics human fingertips in three aspects: (1) Elastic properties of epidermis and dermis in human skin are replicated by the two PDMS layers with different stiffness, (2) The PMMA bar serves the role analogous to that of a bone, and (3) PVDF film sensors emulate Meissner’s corpuscles in terms of both location and response to the vibratory stimuli. Various extracted features and classification algorithms including support vector machines (SVM) and k-nearest neighbors (kNN) are examined for tactile surface roughness discrimination. Eight standard rough surfaces with roughness values (Ra) of 50 μm, 25 μm, 12.5 μm, 6.3 μm 3.2 μm, 1.6 μm, 0.8 μm, and 0.4 μm are explored. The highest classification accuracy of (82.6 ± 10.8) % can be achieved using solely one PVDF film sensor with kNN (k = 9) classifier and the standard deviation feature.

Keywords: bioinspired fingertip, classifier, feature extraction, roughness discrimination

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Authors: Sylwia K. Naliwajko, Renata Markiewicz-Zukowska, Justyna Moskwa, Krystyna Gromkowska-Kepka, Konrad Mielcarek, Patryk Nowakowski, Katarzyna Socha, Anna Puscion-Jakubik, Maria H. Borawska

Abstract:

Glioblastoma (grade IV WHO) is a rapidly progressive brain tumor with very high morbidity and mortality. The vast malignant gliomas are not curable despite the therapy (surgical, radiotherapy, chemotherapy) and patients seek alternative or complementary treatments. Patients often use cannabidiol (CBD) oil as an alternative therapy of glioblastoma. CBD is one of the cannabinoids, an active component of Cannabis sativa. THC (Δ9-tetrahydrocannabinol) can be addictive, and in many countries CBD oil without THC ( < 0,2%) is available. Propolis produced by bees from the resin collected from trees has antiglioma properties in vitro and can be used as a supplement in complementary therapy of gliomas. The aim of this study was to examine the influence of extract from CBD oil in combination with propolis extract on two glioblastoma cell lines. The MTT (Thiazolyl Blue Tetrazolium Bromide) test was used to determine the influence of CBD oil extract and polish propolis extract (PPE) on the viability of glioblastoma cell lines – U87MG and LN18. The cells were incubated (24, 48 and 72 h) with CBD oil extract and PPE. CBD extract was used in concentration 1, 1.5 and 3 µM and PPE in 30 µg/mL. The data were presented compared to the control. The statistical analysis was performed using Statistica v. 13.0 software. CBD oil extract in concentrations 1, 1.5 and 3 µM did not inhibit the viability of U87MG and LN18 cells (viability more than 90% cells compared to the control). There was no dose-response viability, and IC50 value was not recognized. PPE in the concentration of 30 µg/mL time-dependently inhibited the viability of U87MG and LN18 cell line (after 48 h the viability as a percent of the control was 59,7±6% and 57,8±7%, respectively). In a combination of CBD with PPE, the viability of the treated cells was similar to PPE used alone (58,2±7% and 56,5±9%, respectively). CBD oil extract did not show anti-glioma activity and in combination with PPE did not change the activity of PPE.

Keywords: anticancer, cannabidiol, cell line, glioblastoma

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