A Dimension-Adaptive Multi-Index Monte Carlo Method Applied to a Model of a Heat Exchanger

We present an adaptive version of the Multi-Index Monte Carlo method, introduced by Haji-Ali, Nobile and Tempone (2016), for simulating PDEs with coefficients that are random fields. A classical technique for sampling from these random fields is the Karhunen-Lo\`eve expansion. Our adaptive algorithm is based on the adaptive algorithm used in sparse grid cubature as introduced by Gerstner and Griebel (2003), and automatically chooses the number of terms needed in this expansion, as well as the required spatial discretizations of the PDE model. We apply the method to a simplified model of a heat exchanger with random insulator material, where the stochastic characteristics are modeled as a lognormal random field, and we show consistent computational savings

Synthesis and Characterization of New Azo-Schiff Bases and Study Biological Activity of Some These Compounds

Abstract: Aso derivative (3) was prepared by coupling reaction between diazonium salt (2) and salicylaldehyde. Aso-schiff bases 4(a-h) have been synthesized by the condensation (3) with different aromatic amines. The completion of reactions was checked by TLC. The prepared azo compounds were identified by IR and 1HNMR spectroscopy and elemental analysis. The second part of this work includes studding the effect of the some bacteria

Aceites esenciales de plantas aromáticas egipcias como novedosos agentes anticancerígenos y antioxidantes en líneas celulares de cáncer humano

Inhibitors of tumor growth using extracts from aromatic plants are rapidly emerging as important new drug candidates for cancer therapy. The cytotoxicity and in vitro anticancer evaluation of the essential oils from thyme, juniper and clove has been assessed against five different human cancer cell lines (liver HepG2, breast MCF-7, prostate PC3, colon HCT116 and lung A549). A GC/MS analysis revealed that α-pinene, thymol and eugenol are the major components of Egyptian juniper, thyme and clove oils with concentrations of 31.19%, 79.15% and 82.71%, respectively. Strong antioxidant profiles of all the oils are revealed in vitro by DPPH and β-carotene bleaching assays. The results showed that clove oil was similarly potent to the reference drug, doxorubicin in prostate, colon and lung cell lines. Thyme oil was more effective than the doxorubicin in breast and lung cell lines while juniper oil was more effective than the doxorubicin in all the tested cancer cell lines except prostate cancer. In conclusion, the essential oils from Egyptian aromatic plants can be used as good candidates for novel therapeutic strategies for cancer as they possess significant anticancer activity.Los inhibidores de crecimiento de tumores usando extractos de plantas aromáticas están emergiendo con rapidez como nuevos e importantes medicamentos para el tratamiento del cáncer. La citotoxicidad y la acción anticancerígena in vitro de aceites esenciales de tomillo, enebro y clavo han sido evaluadas en cinco líneas celulares de cáncer humano (hígado HepG2, mama MCF-7, próstata PC3, colon HCT116 y pulmón A549). Los análisis de GC/MS mostraron que α-pineno, timol y eugenol son los principales componentes de los aceites egipcios de enebro, tomillo y clavo, con concentraciones de 31,19%, 79,15% y 82,71%, respectivamente. Se demuestra, mediante ensayos in vitro de blanqueo de DPPH y β-caroteno, el enérgico perfil antioxidante de todos los aceites. Los resultados mostraron que el aceite de clavo fue similar de potente al fármaco de referencia, doxorrubicina en las líneas celulares de próstata, colon y pulmón. El aceite de tomillo fue más efectivo que la doxorrubicina en las líneas celulares de mama y de pulmón, mientras que el aceite de enebro fue más eficaz que la doxorrubicina en todas las líneas celulares de cáncer ensayados, excepto en la de cáncer de próstata. En conclusión, los aceites esenciales de plantas aromáticas egipcias se pueden utilizar como buenos candidatos para nuevas estrategias terapéuticas para el cáncer al poseer una significativa actividad anticancerígena

An efficient intrusion detection model based on hybridization of artificial bee colony and dragonfly algorithms for training multilayer perceptrons

One of the most persistent challenges concerning network security is to build a model capable of detecting intrusions in network systems. The issue has been extensively addressed in uncountable researches and using various techniques, of which a commonly used technique is that based on detecting intrusions in contrast to normal network traffic and the classification of network packets as either normal or abnormal. However, the problem of improving the accuracy and efficiency of classification models remains open and yet to be resolved. This study proposes a new binary classification model for intrusion detection, based on hybridization of Artificial Bee Colony algorithm (ABC) and Dragonfly algorithm (DA) for training an artificial neural network (ANN) in order to increase the classification accuracy rate for malicious and non-malicious traffic in networks. At first the model selects the suitable biases and weights utilizing a hybrid (ABC) and (DA). Next, the neural network is retrained using these ideal values in order for the intrusion detection model to be able to recognize new attacks. Ten other metaheuristic algorithms were adapted to train the neural network and their performances were compared with that of the proposed model. In addition, four types of intrusion detection evaluation datasets were applied to evaluate the proposed model in comparison to the others. The results of our experiments have demonstrated a significant improvement in inefficient network intrusion detection over other classification methods

Deep Pipeline Architecture for Fast Fractal Color Image Compression Utilizing Inter-Color Correlation

Fractal compression technique is a well-known technique that encodes an image by mapping the image into itself and this requires performing a massive and repetitive search. Thus, the encoding time is too long, which is the main problem of the fractal algorithm. To reduce the encoding time, several hardware implementations have been developed. However, they are generally developed for grayscale images, and using them to encode colour images leads to doubling the encoding time 3× at least. Therefore, in this paper, new high-speed hardware architecture is proposed for encoding RGB images in a short time. Unlike the conventional approach of encoding the colour components similarly and individually as a grayscale image, the proposed method encodes two of the colour components by mapping them directly to the most correlated component with a searchless encoding scheme, while the third component is encoded with a search-based scheme. This results in reducing the encoding time and also in increasing the compression rate. The parallel and deep-pipelining approaches have been utilized to improve the processing time significantly. Furthermore, to reduce the memory access to the half, the image is partitioned in such a way that half of the matching operations utilize the same data fetched for processing the other half of the matching operations. Consequently, the proposed architecture can encode a 1024×1024 RGB image within a minimal time of 12.2 ms, and a compression ratio of 46.5. Accordingly, the proposed architecture is further superior to the state-of-the-art architectures.©2022 The Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/fi=vertaisarvioitu|en=peerReviewed

Test suite generation based on hybrid flower pollination algorithm and hill climbing

One of the common application of search-based software testing (SBST) is generating test cases for all objectives characterized by a scope model (e.g. articulations, mutants, branches). The application of meta-heuristic algorithms in t-way tests generation, as an example of SBST, has as of late gotten to be predominant. Thus, numerous valuable meta-heuristic algorithms have been created on the premise of the usage of t-way techniques (where t shows the interaction quality). T-way testing technique is a sampling technique to produce an optimum test suite in a systematic manner. In other words, is to generate a smaller test suite size that can be used for testing the software in less time and coast. Here, all t-way techniques generate the test suite with the aim to cover every possible combination produced by the interacting inputs or parameters. All possible t-combinations of the system's components must be covered at least once. Besides, the purpose of the t-way testing technique is to overcome exhaustive testing. Studies reported that there is no single strategy that appears to be superior in all configurations considered. In this research paper, we propose a new software t-way testing tool based on hybrid Flower Pollination Algorithm and Hill Climbing for generating test suite generation, called FPA-HC strategy can be used for generating smaller test suite size. The FPA-HC evaluated against the existing t-way strategies including the original FPA. Experimental results have shown promising results as FPA-HC can produce very competitive results comparing with existing t-way strategies

Assessing Oral Intake Outcomes in Head and Neck Cancer Patients Treated with Definitive Radiation with or Without Chemotherapy

Background: Head and neck cancer treatment modalities can significantly impact functional outcomes of patients, especially oral intake (Brizel, et al N Engl J Med 1998; Kamal, et al Support Care Cancer 2019). Radiation therapy in particular has been associated with posttreatment xerostomia and dysphagia (Adelstein, et al J Clin Oncol 2003; Hutcheson, et al Cancer 2013) which can affect quality of life and impair weight gain, contributing to worse long-term outcomes (Payakachat, et al Head Neck, 2013). Early speech-language pathology intervention has shown to be effective in improving these functional outcomes in this population (Greco, et al Int J Radiat Oncol Biol Phys 2018). Objectives: The purpose of this study is to evaluate oral intake outcomes of patients undergoing definitive radiation therapy with or without chemotherapy for head and neck squamous cell carcinoma. Methods: A cohort of patients with stage III or IV squamous cell carcinoma of the oropharynx, larynx, and hypopharynx treated with definitive radiation therapy with or without chemotherapy were extracted from system database. Patients with evidence of distant metastases were excluded. Swallow function was assessed both pre- and post-treatment (within four months of treatment initiation or conclusion) with the Functional Oral Intake Scale (FOIS) (Crary et al, Arch Phys Med Rehabil, 2005) as measured by a Speech-Language Pathologist (SLP) involved in the patient\u27s care. Body mass index (BMI) was evaluated within four months of treatment initiation and one year after treatment completion. The use of enteral feeding at one-year post-treatment was also assessed. Data was analyzed with descriptive statistical methods, Wilcoxon sign rank tests, and [chi]2d tests. Results: The sample included 152 patients. Table 1 highlights patient baseline characteristics, tumor location, and treatment. FOIS scores decreased from pre-treatment to post-treatment, with 75% of patients having a FOIS of 7 at pre-treatment compared with only 13.8% at the post-treatment time point (Table 1). Median BMI also decreased from pre-treatment to one-year post-treatment (Table 2). At one-year post-treatment, 23.7% patients (n=33) required enteral feeding. Conclusions: Definitive radiation therapy with or without chemotherapy in the treatment of head and neck cancer is associated with impaired oral intake. Treatment is also associated with decreases in BMI and longer use of enteral feeding, which may reflect sequelae of impaired oral intake. These factors have a negative impact on quality of life and can lead to long-term morbidity. Integrative treatment plans would therefore benefit from speech-language pathology interventions throughout the treatment process

Assessing Oral Intake Outcomes in Head and Neck Cancer Patients Treated with Definitive Radiation with or Without Chemotherapy

Background: Head and neck cancer treatment modalities can significantly impact functional outcomes of patients, especially oral intake (Brizel, et al N Engl J Med 1998; Kamal, et al Support Care Cancer 2019). Radiation therapy in particular has been associated with posttreatment xerostomia and dysphagia (Adelstein, et al J Clin Oncol 2003; Hutcheson, et al Cancer 2013) which can affect quality of life and impair weight gain, contributing to worse long-term outcomes (Payakachat, et al Head Neck, 2013). Early speech-language pathology intervention has shown to be effective in improving these functional outcomes in this population (Greco, et al Int J Radiat Oncol Biol Phys 2018). Objectives: The purpose of this study is to evaluate oral intake outcomes of patients undergoing definitive radiation therapy with or without chemotherapy for head and neck squamous cell carcinoma. Methods: A cohort of patients with stage III or IV squamous cell carcinoma of the oropharynx, larynx, and hypopharynx treated with definitive radiation therapy with or without chemotherapy were extracted from system database. Patients with evidence of distant metastases were excluded. Swallow function was assessed both pre- and post-treatment (within four months of treatment initiation or conclusion) with the Functional Oral Intake Scale (FOIS) (Crary et al, Arch Phys Med Rehabil, 2005) as measured by a Speech-Language Pathologist (SLP) involved in the patient\u27s care. Body mass index (BMI) was evaluated within four months of treatment initiation and one year after treatment completion. The use of enteral feeding at one-year post-treatment was also assessed. Data was analyzed with descriptive statistical methods, Wilcoxon sign rank tests, and [chi]2d tests. Results: The sample included 152 patients. Table 1 highlights patient baseline characteristics, tumor location, and treatment. FOIS scores decreased from pre-treatment to post-treatment, with 75% of patients having a FOIS of 7 at pre-treatment compared with only 13.8% at the post-treatment time point (Table 1). Median BMI also decreased from pre-treatment to one-year post-treatment (Table 2). At one-year post-treatment, 23.7% patients (n=33) required enteral feeding. Conclusions: Definitive radiation therapy with or without chemotherapy in the treatment of head and neck cancer is associated with impaired oral intake. Treatment is also associated with decreases in BMI and longer use of enteral feeding, which may reflect sequelae of impaired oral intake. These factors have a negative impact on quality of life and can lead to long-term morbidity. Integrative treatment plans would therefore benefit from speech-language pathology interventions throughout the treatment process

Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm

Networks are strained by spam, which also overloads email servers and blocks mailboxes with unwanted messages and files. Setting the protective level for spam filtering might become even more crucial for email users when malicious steps are taken since they must deal with an increase in the number of valid communications being marked as spam. By finding patterns in email communications, spam detection systems (SDS) have been developed to keep track of spammers and filter email activity. SDS has also enhanced the tool for detecting spam by reducing the rate of false positives and increasing the accuracy of detection. The difficulty with spam classifiers is the abundance of features. The importance of feature selection (FS) comes from its role in directing the feature selection algorithm’s search for ways to improve the SDS’s classification performance and accuracy. As a means of enhancing the performance of the SDS, we use a wrapper technique in this study that is based on the multi-objective grasshopper optimization algorithm (MOGOA) for feature extraction and the recently revised EGOA algorithm for multilayer perceptron (MLP) training. The suggested system’s performance was verified using the SpamBase, SpamAssassin, and UK-2011 datasets. Our research showed that our novel approach outperformed a variety of established practices in the literature by as much as 97.5%, 98.3%, and 96.4% respectively.©2022 the Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

An adaptive opposition-based learning selection: the case for Jaya algorithm

Over the years, opposition-based Learning (OBL) technique has been proven to effectively enhance the convergence of meta-heuristic algorithms. The fact that OBL is able to give alternative candidate solutions in one or more opposite directions ensures good exploration and exploitation of the search space. In the last decade, many OBL techniques have been established in the literature including the Standard-OBL, General-OBL, Quasi Reflection-OBL, Centre-OBL and Optimal-OBL. Although proven useful, much existing adoption of OBL into meta-heuristic algorithms has been based on a single technique. If the search space contains many peaks with potentially many local optima, relying on a single OBL technique may not be sufficiently effective. In fact, if the peaks are close together, relying on a single OBL technique may not be able to prevent entrapment in local optima. Addressing this issue, assembling a sequence of OBL techniques into meta-heuristic algorithm can be useful to enhance the overall search performance. Based on a simple penalized and reward mechanism, the best performing OBL is rewarded to continue its execution in the next cycle, whilst poor performing one will miss cease its current turn. This paper presents a new adaptive approach of integrating more than one OBL techniques into Jaya Algorithm, termed OBL-JA. Unlike other adoptions of OBL which use one type of OBL, OBL-JA uses several OBLs and their selections will be based on each individual performance. Experimental results using the combinatorial testing problems as case study demonstrate that OBL-JA shows very competitive results against the existing works in term of the test suite size. The results also show that OBL-JA performs better than standard Jaya Algorithm in most of the tested cases due to its ability to adapt its behaviour based on the current performance feedback of the search process