bluuebunny/crossref_metadata_2025 huggingface dataset:
Usefulness of Measuring Thiopurine Metabolites in Children with Inflammatory Bowel Disease and Autoimmunological Hepatitis, Treated with Azathioprine
Authors: Bąk-Drabik Katarzyna , Adamczyk Piotr , Duda-Wrońska Justyna , Dąbrowska-Piechota Dominika , Jarzumbek Anna , Kwiecień Jarosław
Published: June 2021

Introduction. Thiopurines, such as azathioprine (AZA) and 6-mercaptopurine (6-MP), are immunomodulatory agents, used for the maintenance of remission in children with inflammatory bowel disease (IBD)—Crohn’s disease (CD) and ulcerative colitis (UC), as well as with autoimmunological hepatitis (AIH). Measurements of thiopurine metabolites may allow identifying patients at risk for toxicity and nonadherence. It can also provide an explanation for the ineffectiveness of the treatment, observed in some patients. Patients and Methods. A retrospective analysis was carried out of sixty-eight patients (thirty-six patients with CD, eighteen with UC, and fourteen with AIH), treated with AZA. Thiopurine metabolites, 6-thioguanine nucleotide (6-TGN) and 6-methylmercaptopurine (6-MMP), were assayed by high-performance liquid chromatography (HPLC), and the AZA dose was adjusted when 6-TGN concentration was known. Result. Only twenty-five (41%) children had therapeutic 6-TGN concentrations, ten (16%) subjects had suboptimal 6-TGN concentrations, and twenty-six subjects (43%) had 6-TGN concentrations above the recommended therapeutic range. 6-MMP was not above the therapeutic range in any case. Seven subjects revealed undetectable 6-TGN and 6-MMP levels, indicating nonadherence. The mean AZA dose after the 6-TGN concentration-related adjustment did not differ, in comparison to the initial dose, either in IBD or AIH groups. The mean AZA dose was lower in AIH than in IBD. The subjects with an optimal 6-TGN level presented with a higher ratio of remission (88%) than the under- or overdosed patients (60% and 69%), respectively (

Chi
−
square
 
test
=
3.87

,

p
<
0.05

). Conclusion. Timely measurements of thiopurine metabolites can be a useful tool to identify nonadherent patients before a decision is taken to switch to another drug. We may also spot the patients who receive either too low or too high doses, compensating dose deviations in an appropriate way. The patients with optimal 6-TGN levels presented a higher percentage of remission than the under- or overdosed patients. In most patients, both initial and adjusted AZA doses, lower than suggested in guidelines, appeared to be sufficient to maintain remission.
The Effect of 3D Printing Metal Materials on Osteoporosis Treatment
Authors: Wang Bing , Feng Chuwen , Pan Jianyu , Zhou Shuoyan , Sun Zhongren , Shao Yuming , Qu Yuanyuan , Bao Shengyong , Li Yang , Yang Tiansong
Published: June 2021

3D printing has been in use for a long time and has continued to contribute to breakthroughs in the fields of clinical, physical, and rehabilitation medicine. In order to evaluate the role of 3D printing technology in treating spinal disorders, this paper presents a systematic review of the relevant literature. 3D printing is described in terms of its adjunctive function in various stages of spinal surgery and assistance in osteoporosis treatment. A review of metal 3D printed materials and applications of the technology is also provided.
E2A Modulates Stemness, Metastasis, and Therapeutic Resistance of Breast Cancer
Authors: López-Menéndez Celia , Vázquez-Naharro Alberto , Santos Vanesa , Dubus Pierre , Santamaría Patricia G. , Martínez-Ramírez Ángel , Portillo Francisco , Moreno-Bueno Gema , Faraldo Marisa M. , Cano Amparo
Published: June 2021

Cancer stem cells (CSC) are considered responsible for tumor initiation, therapeutic resistance, and metastasis. A comprehensive knowledge of the mechanisms governing the acquisition and maintenance of cancer stemness is crucial for the development of new therapeutic approaches in oncology. E2A basic helix–loop–helix (bHLH) transcription factors are associated with epithelial–mesenchymal transition (EMT) and tumor progression, but knowledge of their functional contributions to cancer biology is still limited. Using a combination of in vivo and in vitro analyses in a novel PyMT-E2A conditional knockout mouse model and derived primary tumor cell lines, we report here an essential role of E2A in stemness, metastasis, and therapeutic resistance in breast cancer. Targeted deletion of E2A in the mammary gland impaired tumor-initiating ability and dedifferentiation potential and severely compromised metastatic competence of PyMT-driven mammary tumors. Mechanistic studies in PyMT-derived cell lines indicated that E2A actions are mediated by the upregulation of Snai1 transcription. Importantly, high E2A and SNAIL1 expression occurred in aggressive human basal-like breast carcinomas, highlighting the relevance of the E2A–Snail1 axis in metastatic breast cancer. In addition, E2A factors contributed to the maintenance of genomic integrity and resistance to PARP inhibitors in PyMT and human triple-negative breast cancer cells. Collectively, these results support the potential for E2A transcription factors as novel targets worthy of translational consideration in breast cancer.

Significance:

These findings identify key functions of E2A factors in breast cancer cell stemness, metastasis, and drug resistance, supporting a therapeutic vulnerability to targeting E2A proteins in breast cancer.
ENO1 Promotes Lung Cancer Metastasis via HGFR and WNT Signaling–Driven Epithelial-to-Mesenchymal Transition
Authors: Li Hsin-Jung , Ke Feng-Yi , Lin Chia-Ching , Lu Mei-Yi , Kuo Yi-Huei , Wang Yi-Ping , Liang Kang-Hao , Lin Shin-Chang , Chang Ya-Hsuan , Chen Hsuan-Yu , Yang Pan-Chyr , Wu Han-Chung
Published: June 2021

ENO1 (α-enolase) expression is significantly correlated with reduced survival and poor prognosis in many cancer types, including lung cancer. However, the function of ENO1 in carcinogenesis remains elusive. In this study, we found that high expression of ENO1 is present in metastatic lung cancer cell lines and malignant tumors and is associated with poor overall survival of patients with lung cancer. Knockdown of ENO1 decreased cancer cell proliferation and invasiveness, whereas overexpression of ENO1 enhanced these processes. Moreover, ENO1 expression promoted tumor growth in orthotopic models and enhanced lung tumor metastasis in tail-vein injection models. These effects were mediated by upregulation of mesenchymal markers N-cadherin and vimentin and the epithelial-to-mesenchymal transition regulator SLUG, along with concurrent downregulation of E-cadherin. Mechanistically, ENO1 interacted with hepatocyte growth factor receptor (HGFR) and activated HGFR and Wnt signaling via increased phosphorylation of HGFR and the Wnt coreceptor LRP5/6. Activation of these signaling axes decreased GSK3β activity via Src–PI3K–AKT signaling and inactivation of the β-catenin destruction complex to ultimately upregulate SLUG and β-catenin. In addition, we generated a chimeric anti-ENO1 mAb (chENO1-22) that can decrease cancer cell proliferation and invasion. chENO1-22 attenuated cancer cell invasion by inhibiting ENO1-mediated GSK3β inactivation to promote SLUG protein ubiquitination and degradation. Moreover, chENO1-22 prevented lung tumor metastasis and prolonged survival in animal models. Taken together, these findings illuminate the molecular mechanisms underlying the function of ENO1 in lung cancer metastasis and support the therapeutic potential of a novel antibody targeting ENO1 for treating lung cancer.

Significance:

This study shows that ENO1 promotes lung cancer metastasis via HGFR and WNT signaling and introduces a novel anti-ENO1 antibody for potential therapeutic use in lung cancer.
CDK6 Is a Therapeutic Target in Myelofibrosis
Authors: Dutta Avik , Nath Dipmoy , Yang Yue , Le Bao T. , Mohi Golam
Published: June 2021

Myelofibrosis (myelofibrosis) is a deadly blood neoplasia with the worst prognosis among myeloproliferative neoplasms (MPN). The JAK2 inhibitors ruxolitinib and fedratinib have been approved for treatment of myelofibrosis, but they do not offer significant improvement of bone marrow fibrosis. CDK6 expression is significantly elevated in MPN/myelofibrosis hematopoietic progenitor cells. In this study, we investigated the efficacy of CDK4/6 inhibitor palbociclib alone or in combination with ruxolitinib in Jak2V617F and MPLW515L murine models of myelofibrosis. Treatment with palbociclib alone significantly reduced leukocytosis and splenomegaly and inhibited bone marrow fibrosis in Jak2V617F and MPLW515L mouse models of myelofibrosis. Combined treatment of palbociclib and ruxolitinib resulted in normalization of peripheral blood leukocyte counts, marked reduction of spleen size, and abrogation of bone marrow fibrosis in murine models of myelofibrosis. Palbociclib treatment also preferentially inhibited Jak2V617F mutant hematopoietic progenitors in mice. Mechanistically, treatment with palbociclib or depletion of CDK6 inhibited Aurora kinase, NF-κB, and TGFβ signaling pathways in Jak2V617F mutant hematopoietic cells and attenuated expression of fibrotic markers in the bone marrow. Overall, these data suggest that palbociclib in combination with ruxolitinib may have therapeutic potential for treatment of myelofibrosis and support the clinical investigation of this drug combination in patients with myelofibrosis.

Significance:

These findings demonstrate that CDK6 inhibitor palbociclib in combination with ruxolitinib ameliorates myelofibrosis, suggesting this drug combination could be an effective therapeutic strategy against this devastating blood disorder.
From Hypokalemic Crisis to Sjogren’s Syndrome: A Case Report and Literature Review
Authors: Mbengue Mansour , Ouanekpone Cedric , Diagne Seynabou , Niang Abdou
Published: June 2021

Renal involvement occurs in approximately 5% of patients with Sjögren’s syndrome (SS). We reported the case of a 20-year-old African woman who was received for paralysis of 4 limbs secondary to hypokalemia. The diagnosis of renal tubular acidosis type 1 complicated by hypokalemia was retained. In the etiologic research of renal tubular acidosis type 1, primary SS was retained. The patient received symptomatic treatment based on potassium chloride, sodium bicarbonate, hydration, and a low protein diet. In terms of etiological treatment, she was put on corticosteroid and hydroxychloroquine. The outcome was favorable with correction of acidosis and hypokalemia.
Lymphocytosis with Smudge Cells Is Not Equivalent to Chronic Lymphocytic Leukemia
Authors: Susman David , Price Russell , Kotchetkov Rouslan
Published: June 2021

Chronic lymphocytic leukemia (CLL) often presents with lymphocytosis and smudge cells (SCs) on routine peripheral blood (PB) tests. In some cases, these findings are assumed to be sufficient to diagnose CLL. We present a 54-year-old male who was referred for further management of progressing CLL. At the initial presentation, he looked unwell and had diffuse lymphadenopathy and splenomegaly. Blood work showed normocytic anemia (hemoglobin 72 g/L), thrombocytopenia (platelet count 74 × 109/L), leukocytosis (white blood cell count 135.5 × 109/L) including lymphocytosis (130.1 × 109/L), and the presence of SCs on a PB smear. Additional workup including flow cytometry (FC), bone marrow biopsy, and lymph node biopsy led to a diagnosis of leukemic stage of advanced-stage mantle cell lymphoma. Although lymphocytosis with SCs is more frequently and in higher quantities seen in CLL they are not pathognomonic and can be present in a variety of lymphoproliferative disorders. Additional diagnostic examination of cell morphology and FC to assess clonality and determine the immunotype of lymphocytes are required to establish an accurate diagnosis and determine appropriate further management of the specific disease type.
Lipid Core Burden Index Assessed by Near-Infrared Spectroscopy of Symptomatic Carotid Plaques: Association with Magnetic Resonance T1-Weighted Imaging
Authors: Nakagawa Ichiro , Kotsugi Masashi , Park HunSoo , Yokoyama Shohei , Furuta Takanori , Nakase Kenta , Okamoto Ai , Myouchin Kaoru , Yamada Syuichi , Nakase Hiroyuki
Published: June 2021

Introduction: Vulnerable plaques are a strong predictor of cerebrovascular ischemic events, and high lipid core plaques (LCPs) are associated with an increased risk of embolic infarcts during carotid artery stenting (CAS). Recent developments in magnetic resonance (MR) plaque imaging have enabled noninvasive assessment of carotid plaque vulnerability, and the lipid component and intraplaque hemorrhage (IPH) are visible as high signal intensity areas on T1-weighted MR images. Recently, catheter-based near-infrared spectroscopy (NIRS) has been shown to accurately distinguish LCPs without IPH. This study aimed to determine whether the results of assessment of high LCPs by catheter-based NIRS correlate with the results of MR plaque imaging. Methods: We recruited 82 consecutive symptomatic carotid artery stenosis patients who were treated with CAS under NIRS and MR plaque assessment. Maximum lipid core burden index (max-LCBI) at minimal luminal areas (MLA), defined as max-LCBIMLA, and max-LCBI for any 4-mm segment in a target lesion, defined as max-LCBIAREA, were assessed by NIRS. Correlations were investigated between max-LCBI and MR T1-weighted plaque signal intensity ratio (T1W-SIR) and MR time-of-flight signal intensity ratio (TOF-SIR) in the same regions as assessed by NIRS. Results: Both T1W-SIRMLA and T1W-SIRAREA were significantly lower in the high LCP group (max-LCBI >504, p < 0.001 for both), while TOF-SIRMLA and TOF-SIRAREA were significantly higher in the high LCP group (p < 0.001 and p = 0.004, respectively). A significant linear correlation was present between max-LCBIMLA and both TIW-SIRMLA and TOF-SIRMLA (r = −0.610 and 0.452, respectively, p < 0.0001 for both). Furthermore, logistic regression analysis revealed that T1W-SIRMLA and TOF-SIRMLA were significantly associated with a high LCP assessed by NIRS (OR, 44.19 and 0.43; 95% CI: 6.55–298.19 and 0.19–0.96; p < 0.001 and = 0.039, respectively). Conclusions: A high LCP assessed by NIRS correlates with the signal intensity ratio of MR imaging in symptomatic patients with unstable carotid plaques.
Reliability assessment for electrical power generation system based on advanced Markov process combined with blocks diagram
Authors: Tawfiq A. A. , El-Raouf M. Osama abed , El-Gawad A. A. , Farahat M. A.
Published: June 2021

This paper presents the power generation system reliability assessment using an advanced Markov process combined with blocks diagram technique. The effectiveness of the suggested methodology is based on HL-I of IEEE_EPS_24_bus. The proposed method achieved the generation reliability and availability of an electrical power system using the Markov chain which based on the operational transition from state to state which represented in matrix. The proposed methodology has been presented for reliability performance evaluation of IEEE_EPS_24_bus. MATLAB code is developed using Markov chain construction. The transition between probability states is represented using changing the failure and repair rates. The reduced number of generation system are used with Markov process to assess the availability, unavailability, and reliability for the generation system. Additionally, the proposed technique calculates the frequency, time duration of states, the probability of generation capacity state which get out of service or remained in service for each state of failure, and reliability indices. A considerable improvement in reliability indices is found with using blocks diagram technique which is used to reduce the infinity number of transition states and assess the system reliability. The proposed technique succeeded at achieving accurate and faster reliability for the power system.
Expert system application for reactive power compensation in isolated electric power systems
Authors: Kirgizov A. K. , Dmitriev S. A. , Safaraliev M. Kh. , Pavlyuchenko D. A. , Ghulomzoda A. H. , Ahyoev J. S.
Published: June 2021

Effective electricity use can be an option which enables to achieve significant economy while generating and transmitting of electricity. One of the most important things is to improve the electricity quality through reactive power correction up to optimum values. The current article presents the solution to compensate the reactive power in the distribution networks, in GornoBadakhshan Autonomous Oblast (GBAO) with the use of the advanced technologies based on the data collection within real time. The article describes the methodology of fuzzy logic application and bio-heuristic algorithms for the suggested solution effectiveness to be determined. Fuzzy logic application to specify the node priority for compensating devices based on the linguistic matrix power loss and voltage gives the possibility to the expert to take appropriate solutions for compensating devices installation location to be determined. The appropriate (correct) determination of the compensating devices installation location in the electric power system ensures the effective regulation of the reactive power with the least economic costs. Optimization problems related to the active power loss minimization are solved as well as the cost minimization with compensating devices to ensure the values tan(φ) not exceeding 0.35 through reducing multi-objective problem to the single-objective one using linear convolution.
Economic load dispatch solutions considering multiple fuels for thermal units and generation cost of wind turbines
Authors: Doan Anh Tuan , Viet Dinh Thanh , Duong Minh Quan
Published: June 2021

In this paper, economic load dispatch (ELD) problem is solved by applying a suggested improved particle swarm optimization (IPSO) for reaching the lowest total power generation cost from wind farms (WFs) and thermal units (TUs). The suggested IPSO is the modified version of Particle swarm optimization (PSO) by changing velocity and position updates. The five best solutions are employed to replace the so-far best position of each particle in velocity update mechanism and the five best solutions are used to replace previous position of each particle in position update. In addition, constriction factor is also used in the suggested IPSO. PSO, constriction factor-based PSO (CFPSO) and bat optimization algorithm (BOA) are also run for comparisons. Two systems are used to run the four methods. The first system is comprised of nine TUs with multiple fuels and one wind farm. The second system is comprised of eight TUs with multiple fuels and two WFs. From the comparisons of results, IPSO is much more powerful than three others and it can find optimal power generation with the lowest total power generation cost.
Early fever detection on COVID-19 infection using thermoelectric module generators
Authors: Sanin-Villa Daniel , Monsalve-Cifuentes Oscar D. , Del Rio Jorge Sierra
Published: June 2021

In 2020 the COVID-19 pandemic has suddenly stopped society and changed human interaction. In this work, a thermoelectric generator wearable device for early fever detection symptoms is presented as a possible solution to avoid higher propagation of this disease. To identify a possible fever symptom, numerical and parametric simulations are developed using a highquality-refined hexahedral mesh. At first, a 2-pair-leg thermoelectric module has undergone simulations to establish temperature conditions, open-circuit voltage, and power output generation; and secondly, these previous results are extrapolated for a larger thermoelectric module containing 28 pair-leg of N-P type material. The numerical study shows that a maximum value of electrical power of 60.70 mW was reached for 28-pair-leg N-P type thermocouples under a constant temperature difference of 20 K.
Improvements in space radiation-tolerant FPGA implementation of land surface temperature-split window algorithm
Authors: El Makhloufi Assaad , Chekroun Nisrine , Tagmouti Noha , El Adib Samir , Raissouni Naoufal
Published: June 2021

The trend in satellite remote sensing assignments has continuously been concerning using hardware devices with more flexibility, smaller size, and higher computational power. Therefore, field programmable gate arrays (FPGA) technology is often used by the developers of the scientific community and equipment for carrying out different satellite remote sensing algorithms. This article explains hardware implementation of land surface temperature split window (LST-SW) algorithm based on the FPGA. To get a high-speed process and real-time application, VHSIC hardware description language (VHDL) was employed to design the LST-SW algorithm. The paper presents the benefits of the used Virtex-4QV of radiation tolerant series FPGA. The experimental results revealed that the suggested implementation of the algorithm using Virtex4QV achieved higher throughput of 435.392 Mbps, and faster processing time with value of 2.95 ms. Furthermore, a comparison between the proposed implementation and existing work demonstrated that the proposed implementation has better performance in terms of area utilization; 1.17% reduction in number of Slice used and 1.06% reduction in of LUTs. Moreover, the significant advantage of area utilization would be the none use of block RAMs comparing to existing work using three blocks RAMs. Finally, comparison results show improvements using the proposed implementation with rates of 2.28% higher frequency, 3.66 x higher throughput, and 1.19% faster processing time.
Applying calcium fluoride and silica particles: A solution to improve color homogeneity of pc-WLEDS
Authors: Dang Huu Phuc , Loan Nguyen Thi Phuong , Nguyen Thanh Tung , Ho Sang Dang
Published: June 2021

This article focuses on enhancing the lighting efficiency of pc-WLEDs, a new and advanced lighting solution that has received lots of attention. To adapt to the demand of modern lighting, the lighting performance of pc-WLEDs must be improved, especially the color homogeneity and luminous flux, two of the most important quality indicators of pc-WLEDs. Through experiments, this article proposes using the scattering enhancement particles (SEPs) such as CaF2 and SiO2 with yellow phosphor Y3Al5O12:Ce3+ in pc-WLEDs configuration. The pc-WLEDs model is created by using the LightTools program and set at 8500 K correlated color temperature, while the experimental results yielded from this simulation will be verified by Mie-scattering theory. The information from this article reveals the scattering coefficients of SEPs at 455 nm and 595 nm wavelengths. Moreover, it is confirmed that the employment of CaF2 is effective in promoting the color but may damage the luminous efficiency if the concentration is too high while the SEP material, SiO2, exhibits high luminous efficiency at all concentration.
Extended of TEA: A 256 bits block cipher algorithm for image encryption
Authors: Shimal Abeer F. , Helal Baydaa H. , Hashim Ashwaq T.
Published: June 2021

This paper introduces an effective image encryption approach that merges a chaotic map and polynomial with a block cipher. According to this scheme, there are three levels of encryption. In the first level, pixel positions of the image are scuffled into blocks randomly based on a chaotic map. In the second level, the polynomials are constructed by taking N unused pixels from the permuted blocks as polynomial coefficients. Finally, the third level a proposed secret-key block cipher called extended of tiny encryption algorithm (ETEA) is used. The proposed ETEA algorithm increased the block size from 64-bit to 256-bit by using F-function in type three Feistel network design. The key schedule generation is very straightforward through admixture the entire major subjects in the identical manner for every round. The proposed ETEA algorithm is word-oriented, where wholly internal operations are executed on words of 32 bits. So, it is possible to efficiently implement the proposed algorithm on smart cards. The results of the experimental demonstration that the proposed encryption algorithm for all methods are efficient and have high security features through statistical analysis using histograms, correlation, entropy, randomness tests, and the avalanche effect.
Lifetime centric load balancing mechanism in wireless sensor network based IoT environment
Authors: Veerabadrappa Veerabadrappa , Marikannan Booma Poolan
Published: June 2021

Wireless sensor network (WSN) is a vital form of the underlying technology of the internet of things (IoT); WSN comprises several energy-constrained sensor nodes to monitor various physical parameters. Moreover, due to the energy constraint, load balancing plays a vital role considering the wireless sensor network as battery power. Although several clustering algorithms have been proposed for providing energy efficiency, there are chances of uneven load balancing and this causes the reduction in network lifetime as there exists inequality within the network. These scenarios occur due to the short lifetime of the cluster head. These cluster head (CH) are prime responsible for all the activity as it is also responsible for intra-cluster and inter-cluster communications. In this research work, a mechanism named lifetime centric load balancing mechanism (LCLBM) is developed that focuses on CH-selection, network design, and optimal CH distribution. Furthermore, under LCLBM, assistant cluster head (ACH) for balancing the load is developed. LCLBM is evaluated by considering the important metrics, such as energy consumption, communication overhead, number of failed nodes, and one-way delay. Further, evaluation is carried out by comparing with ES-Leach method, through the comparative analysis it is observed that the proposed model outperforms the existing model.
Blockchain based voting system for Jordan parliament elections
Authors: Malkawi Mohammad , Yassein Muneer Bani , Bataineh Asmaa
Published: June 2021

Covid-19 pandemic has stressed more than any-time before the necessity for conducting election processes in an electronic manner, where voters can cast their votes remotely with complete security, privacy, and trust. The different voting schema in different countries makes it very difficult to utilize a one fits all system. This paper presents a blockchain based voting system (BBVS) applied to the Parliamentary elections system in the country of Jordan. The proposed system is a private and centralized blockchain implemented in a simulated environment. The proposed BBVS system implements a hierarchical voting process, where a voter casts votes at two levels, one for a group, and the second for distinct members within the group. This paper provides a novel blockchain based e-Voting system, which proves to be transparent and yet secure. This paper utilizes synthetic voter benchmarks to measure the performance, accuracy and integrity of the election process. This research introduced and implemented new algorithms and methods to maintain acceptable performance both at the time of creating the blockchain(s) for voters and candidates as well as at the time of casting votes by voters.
Improving radiologists’ and orthopedists’ QoE in diagnosing lumbar disk herniation using 3D modeling
Authors: Abu Alasal Sanaa , Alsmirat Mohammad , Al-Mnayyis Asma’a , Bani baker Qanita , Al-Ayyoub Mahmoud
Published: June 2021

This article studies and analyzes the use of 3D models, built from magnetic resonance imaging (MRI) axial scans of the lumbar intervertebral disk, that are needed for the diagnosis of disk herniation. We study the possibility of assisting radiologists and orthopedists and increasing their quality of experience (QoE) during the diagnosis process. The main aim is to build a 3D model for the desired area of interest and ask the specialists to consider the 3D models in the diagnosis process instead of considering multiple axial MRI scans. We further propose an automated framework to diagnose the lumber disk herniation using the constructed 3D models. We evaluate the effectiveness of increasing the specialists QoE by conducting a questionnaire on 14 specialists with different experiences ranging from residents to consultants. We then evaluate the effectiveness of the automated diagnosis framework by training it with a set of 83 cases and then testing it on an unseen test set. The results show that the the use of 3D models increases doctors QoE and the automated framework gets 90% of diagnosis accuracy.
Role of artificial intelligence in cloud computing, IoT and SDN: Reliability and scalability issues
Authors: Belgaum Mohammad Riyaz , Alansari Zainab , Musa Shahrulniza , Mansoor Alam Muhammad , Mazliham M. S.
Published: June 2021

Information technology fields are now more dominated by artificial intelligence, as it is playing a key role in terms of providing better services. The inherent strengths of artificial intelligence are driving the companies into a modern, decisive, secure, and insight-driven arena to address the current and future challenges. The key technologies like cloud, internet of things (IoT), and software-defined networking (SDN) are emerging as future applications and rendering benefits to the society. Integrating artificial intelligence with these innovations with scalability brings beneficiaries to the next level of efficiency. Data generated from the heterogeneous devices are received, exchanged, stored, managed, and analyzed to automate and improve the performance of the overall system and be more reliable. Although these new technologies are not free of their limitations, nevertheless, the synthesis of technologies has been challenged and has put forth many challenges in terms of scalability and reliability. Therefore, this paper discusses the role of artificial intelligence (AI) along with issues and opportunities confronting all communities for incorporating the integration of these technologies in terms of reliability and scalability. This paper puts forward the future directions related to scalability and reliability concerns during the integration of the above-mentioned technologies and enable the researchers to address the current research gaps.
Source side pre-ordering using recurrent neural networks for English-Myanmar machine translation
Authors: Nyein May Kyi , Soe Khin Mar
Published: June 2021

Word reordering has remained one of the challenging problems for machine translation when translating between language pairs with different word orders e.g. English and Myanmar. Without reordering between these languages, a source sentence may be translated directly with similar word order and translation can not be meaningful. Myanmar is a subject-objectverb (SOV) language and an effective reordering is essential for translation. In this paper, we applied a pre-ordering approach using recurrent neural networks to pre-order words of the source Myanmar sentence into target English’s word order. This neural pre-ordering model is automatically derived from parallel word-aligned data with syntactic and lexical features based on dependency parse trees of the source sentences. This can generate arbitrary permutations that may be non-local on the sentence and can be combined into English-Myanmar machine translation. We exploited the model to reorder English sentences into Myanmar-like word order as a preprocessing stage for machine translation, obtaining improvements quality comparable to baseline rule-based pre-ordering approach on asian language treebank (ALT) corpus.
State regulation of the IoT in the Russian Federation: Fundamentals and challenges
Authors: Anna Zharova , Vladimir Elin
Published: June 2021

The purpose of this section is to study the problems with implementing technical and legal regulations for the development of public administration functions in the Russian Federation when using the internet of things (IoT). The introduction is based on an analysis of regulatory legal acts and presents the main strategic directions for the development of public administration functions in the Russian federation when using IoT. State reports, scientific literature, a system of technical and legal regulation are analyzed, and the main problems of implementing the IoT that impede the achievement of effective public administration are studied. The Russian practice of using IoT in various economic areas is investigated. Based on an analysis of the mechanisms for ensuring data safety of information technology users in the Russian federation, problems were investigated, such as the collecting data through IoT, including publicly available personal data in order to profile human activities, and creating of a digital twin of a person. The social constraints for introducing distributed registry technologies are users' distrust in the field of data privacy protection and mathematical algorithms that are used to establish trust in a digital environment instead of trusted centralized intermediaries; these problems were also analyzed. The Russian approach was analyzed in comparison to European experience in this field. To ensure information security and the possibility of its distribution, the IoT is revealed.
MQTT-PRESENT: Approach to secure internet of things applications using MQTT protocol
Authors: Sahmi Imane , Abdellaoui Abderrahim , Mazri Tomader , Hmina Nabil
Published: June 2021

The big challenge to raise for deploying the application's domain of the Internet of Things is security. As one of the popular messaging protocols in the IoT world, the message queue telemetry transport (MQTT) is designed for constrained devices and machine-to-machine communications, based on the publish-subscribe model, it offers a basic authentication using username and password. However, this authentication method might have a problem in terms of security and scalability. In this paper, we provide an analysis of the current research in the literature related to the security for the MQTT protocol, before we give a brief description of each algorithm used on our approach, to finally propose a new approach to secure this protocol based on AugPAKE algorithm and PRESENT encryption. This solution provides mutual authentication between the broker and their clients (publishers and subscribers), the confidentiality of the published message is protected twice, the integrity and non-repudiation of MQTT messages which is protected during the process of transmission.
The model influence of authoritarian parenting, extraversion personality, and conformity to bullying among students
Authors: Muhopilah Pipih , Tentama Fatwa , Yuzarion Yuzarion
Published: June 2021

The purpose of this study was to analyze the model of the influence of authoritarian parenting, extraversion personality, and conformity to bullying in students. The population in this study were all students of grade eight of X, Y, Z state middle school in Yogyakarta, consisting of 524 students. The sample in this study was 185 students. The sampling technique used in this study was cluster random sampling. The data were collected with the scales which were developed for each variable studied. Data analysis was performed by testing the outer and inner models using structural equation model (SEM) through the Smart Partial Least Square 3.2.8 program. The results of this study were the formation of the model of the influence of authoritarian parenting, extraversion personality, and conformity to bullying that was found to be fit with empirical data. Overall, the findings suggested that all variables under investigation (authoritarian parenting, extraversion personality, and conformity) had positive and a very significant influence on bullying. This model can be used as a valid reference in developing bullying variables.
The impact of collaborative model assisted by Google Classroom to improve students’ creative thinking skills
Authors: Jumadi Jumadi , Perdana Riki , Hariadi Muhammad Helmi , Warsono Warsono , Wahyudi Andi
Published: June 2021

Indonesian students' creative thinking skill is still at a low level. This study aimed to examine the effectiveness of project collaborative model assisted by Google Classroom (PjCM-GC) in improving students' creative thinking skills. This research explored the differences between students who learned through the demonstration model (DM), project collaborative model (PjCM), and project collaborative model assisted by Google Classroom (PjCM-GC). It was a quasi-experimental with pre and post-test design. The population of this research was students at Senior High School, Lombok Timur-Indonesia. Random sampling technique was employed in this study. The sample was 86 science students grade XI (15-16 ages) who studied in SMAN 1 Aikmel. The data analyzed by paired-sample t-test, comparative-descriptive analysis, and ANOVA mixed design using SPSS 24. The study showed that The PjCM-GC group had a significant difference in the level of creative thinking skills (sig .0000). The PjCM-GC was the most effective model to improve the skills with a gain score of .47 (medium). Learning with the PjCM-GC model can be an alternative for policymakers and teachers to solve the problems of creative thinking.
Implementation of performance assessment in STEM-based science learning to improve students’ habits of mind
Authors: Yakob M. , Hamdani H. , Sari R. P. , Haji A. G. , Nahadi N.
Published: June 2021

This study described the implementation of STEM-based science learning and performance assessment to the improvement of students’ thinking skill in Aceh, Indonesia. The purposive sampling technique was employed in this descriptive research. The respondents were 300 students at grade 11th academic year 2019/2020 which representing five public high schools in Aceh, Indonesia. The instruments used to measure the effectiveness of the implementation of STEM-based science learning were the rubric of habits of mind, observation sheets of performance assessments, and questionnaires. The finding presented the implementation of STEM-based science learning was able to habituate and develop students' habits of mind with an average score of 0.71 which included into high category. The average value of the overall STEM-based science learning implementation for the formation of habits of mind is 95.98% which categorized as good. It can be concluded that there is the success of researchers in implementing STEM-based science learning to form students' habits of mind.
The students’ career choice and job preparedness strategies: A social environmental perspective
Authors: Siddiky Md. Roknuzzaman , Akter Shahanaz
Published: June 2021

The study aimed to investigate the factors determining the students’ career choice and find out their job preparedness strategies. Moreover, the study sought to propose a theory which could explain the students’ career choice from a social environmental perspective. Primary data for this study were collected from 120 students selected from fourteen academic departments of the Noakhali Science and Technology University (NSTU) in Bangladesh based on snow-ball sampling. The study revealed that several factors involving students’ family preferences, teachers’ advice, job prestige, job security, remunerations, scope of promotion, scope of pension, scope of professional development, personal interests, academic majors, educational attainments and career development trainings have significant association with the students’ career decision-making. However, gender and social class did not have significant effects on the students’ career choice. The study proposed a career choice theory which indicates that the students’ career choice and career preferences are not determined by their personal interests alone; rather they are determined by the interplay of several social, cultural and economic forces. The study indicated that the majority of the respondents undertake self-study to pursue their preferred jobs. While career development trainings play an important role in developing the competencies of the students for jobs, a majority of the respondents do not have such trainings. As such, the study suggested that the students of the NSTU should undertake career development courses as a strategy for job preparedness. Finally, the study suggested that the NSTU should set up a career guidance and counseling cell to link their students with the current labor market.
Exploring the reflective typology of novice EFL teachers in a Thai university
Authors: Jumpakate Thidaporn , Wilang Jeffrey Dawala , Kong Corsica
Published: June 2021

This paper presents the reflective typology elicited from the three data sets of reflective writings written by novice teachers in Thailand's university context. As part of an action research study to improve a novice teacher’s professional development, two teachers volunteered to write reflections for a semester of their assigned courses. Three data sets of reflective writings were submitted at the end of a 16-week semester. Due to our desire to provide a more meaningful interpretation of the reflective writings, grounded theory was used, resulting in two emerging themes-descriptive code, which refers to the description of a specific event in the classroom, and reflective code, which include acts in the classroom where the teacher considered an option and reflected on it. In the typology derived from our study, reflections on teaching, students, and self are common among three data sets. Moreover, the instructional act is a common specific code in both descriptive and reflective codes. The typology can be used to investigate novice teachers’ reflective acts to further their professional development.
Students’ attitudes towards physics in Nine Years Basic Education in Rwanda
Authors: Mbonyiryivuze Agnes , Yadav Lakhan Lal , Amadalo Maurice Musasia
Published: June 2021

This study investigated students’ attitudes towards physics in Nine Year Basic Education (9YBE) in Rwanda. Data were collected from 380 students from Kayonza and Gasabo Districts using a physics attitudes test. Findings illustrated that more than a quarter of participants felt that learning physics is boring. About 39% think that the subject of physics does not relate to the real-world experience. A significant number of participants had negative attitudes towards physics in terms of the effort required for learning. The findings also showed that the overall level for participants in physics problem-solving skills was low. The item-by-item analysis showed that the differences between responses of students from rural schools and their counterparts from urban schools in categories of problem-solving and physics concepts connections and understanding are statistically significant. It was found that many students in rural schools need to know more about the interpretation of a new equation to be able to apply it to a new physics problem.
Prevalence of academic resilience of social science students in facing the industry 5.0 era
Authors: Rachmawati Indriyana , Multisari Widya , Triyono T. , Simon Irene Maya , Da Costa Augusto
Published: June 2021

Academic resilience is an individual's academic resilience in facing academic pressure. In fact, in the industry 5.0 era resilience is needed by individuals to face various challenges in the future. This study determined the prevalence of academic resilience of Social Science students in facing the industry 5.0 era. This research employed survey design. The sample was 116 students of Social Sciences who were selected using proportional stratified random sampling technique. The data collection used academic resilience questionnaire. It was tested for validity and reliability with a KMO and Bartlett's Test value of 0.741. The data was analyzed descriptively. Students' academic resilience was shown by having competence, self-confidence, character, commitment, interest, and self-control to overcome difficult situations at hand. Commitment is an important aspect for individuals to be tough in academic situations.
A comparative study of simulation results between PHERB, ADVISOR and AUTONOMIE models
Authors: Norbakyah J. S. , Salisa A. R.
Published: June 2021

In this research, a new model development and innovation of conventional boat named plug-in hybrid electric recreational boat (PHERB) was introduced to overcome the fuel economy nowadays. This paper focus on the comparison simulation results of PHERB with advanced vehicle simulator (ADVISOR) and an automotive simulation and analysis tool (AUTONOMIE) models in terms of electric machine, energy system storage, propeller and boat model. The model of the PHERB is consequent and applied mathematically in the MATLAB/SIMULINK environment to study its functioning performance. Besides that, fuel economy and emissions of PHERB, ADVISOR and AUTONOMIE model are equated and the pros and cons were discussed in this paper based on simulation result.
RT-lab based real-time simulation of flywheel energy storage system associated to a variable-speed wind generator
Authors: Amel Bensaid , Soraya Zebirate , Abdelkader Chaker
Published: June 2021

This paper presents a new simulator used to distribute and execute real-time simulations: the RT-LAB, developed by opal-RT technologies (Montreal, Canada). One of its essential characteristics is the perfect integration with MATLAB/Simulink. The RT-LAB allows the conversion of Simulink models in real time via real-time workshop (RTW) and their execution on one or more processors. In this context, the paper focuses on the RT-LAB real-time simulation as a complement to the MATLAB/Simulink environment, which has been used to perform the simulation of the Flywheel energy storage system (FESS -variable speed wind generation (VSWG) assembly. The purpose of employing a fairly new real-time platform (RT-LAB OP -5600) is to reduce the test and prototype time. This application will be executed on each element of our model that was previously developed under MATLAB/Simulink. The real-time simulation results are observed in the workstation.
A neuro-fuzzy approach for tracking maximum power point of photovoltaic solar system
Authors: Ibnelouad Aouatif , Elkari Abdeljalil , Ayad Hassan , Mjahed Mostafa
Published: June 2021

This work presents a hybrid soft-computing methodology approach for intelligent maximum power point tracking (MPPT) techniques of a photovoltaic (PV) system under any expected operating conditions using artificial neural network-fuzzy (neuro-fuzzy). The proposed technique predicts the calculation of the duty cycle ensuring optimal power transfer between the PV generator and the load. The neuro-fuzzy hybrid method combines artificial neural network (ANN) to direct the controller to the region where the MPP is located with its reference voltage estimator and its block of neural order. After that, the fuzzy logic controller (FLC) with rule inference begins to establish the photovoltaic solar system at the MPP. The obtained simulation results using MATLAB/simulink software for the proposed approach compared to ANN and the perturb and observe (P&O), proved that neuro-fuzzy approach fulfilled to extract the optimum power with pertinence, efficiency and precision