Search results for: dual task

Authors: Qin Yang, Fan Zhang, Juan Du, Chongkui Sun, Krishna Kota, Yun-Ling Zheng

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Telomeres are specialized structures at chromosome ends consisting of tandem repetitive DNA sequences [(TTAGGG)n in humans] and associated proteins, which are necessary for telomere function. Telomere lengths are tightly regulated within a narrow range in normal human somatic cells, the basis of cellular senescence and aging. Previous studies have extensively focused on how short telomeres are extended and have demonstrated that telomerase plays a central role in telomere maintenance through elongating the short telomeres. However, the molecular mechanisms of regulating excessively long telomeres are unknown. Here, we found that telomerase enzymatic component hTERT plays a dual role in the regulation of telomeres length. We analyzed single telomere alterations at each chromosomal end led to the discoveries that hTERT shortens excessively long telomeres and elongates short telomeres simultaneously, thus maintaining the optimal telomere length at each chromosomal end for an efficient protection. The hTERT-mediated telomere shortening removes large segments of telomere DNA rapidly without inducing telomere dysfunction foci or affecting cell proliferation, thus it is mechanistically distinct from rapid telomere deletion. We found that expression of hTERT generates telomeric circular DNA, suggesting that telomere homologous recombination may be involved in this telomere shortening process. Moreover, the hTERT-mediated telomere shortening is required its enzymatic activity, but telomerase RNA component hTR is not involved in it. Furthermore, shelterin protein TPP1 interacts with hTERT and recruits it on telomeres to mediate telomere shortening. In addition, telomere-associated proteins, DKC1 and TCAB1 also play roles in this process. This novel hTERT-mediated telomere shortening mechanism not only exists in cancer cells, but also in primary human cells. Thus, the hTERT-mediated telomere shortening is expected to shift the paradigm on current molecular models of telomere length maintenance, with wide-reaching consequences in cancer and aging fields.

Keywords: aging, hTERT, telomerase, telomeres, human cells

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Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski

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In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.

Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection

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Authors: Edward Schwalb, Matthias Dehmer, Michael Schlenkrich, Farzaneh Taslimi, Ketron Mitchell-Wynne, Horen Kuecuekyan

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We present a generalized feature extraction approach for supporting Machine Learning (ML) algorithms which perform tasks similar to Finite-Element Analysis (FEA). We report results for estimating the Head Injury Categorization (HIC) of vehicle engine compartments across various impact scenarios. Our experiments demonstrate that models learned using features derived with a simple discretization approach provide a reasonable approximation of a full simulation. We observe that Decision Trees could be as effective as Neural Networks for the HIC task. The simplicity and performance of the learned Decision Trees could offer a trade-off of a multiple order of magnitude increase in speed and cost improvement over full simulation for a reasonable approximation. When used as a complement to full simulation, the approach enables rapid approximate feedback to engineering teams before submission for full analysis. The approach produces mesh independent features and is further agnostic of the assembly structure.

Keywords: mechanical design validation, FEA, supervised decision tree, convolutional neural network.

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Authors: Jalil Boudjadar

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Dynamic Voltage and Frequency Scaling (DVFS) multicore platforms are promising execution platforms that enable high computational performance, less energy consumption and flexibility in scheduling the system processes. However, the resulting interleaving and memory interference together with per-core frequency tuning make real-time guarantees hard to be delivered. Besides, energy consumption represents a strong constraint for the deployment of such systems on energy-limited settings. Identifying the system configurations that would achieve a high performance and consume less energy while guaranteeing the system schedulability is a complex task in the design of modern embedded systems. This work studies the trade-off between energy consumption, cores utilization and memory bottleneck and their impact on the schedulability of DVFS multicore time-critical systems with a hierarchy of shared memories. We build a model-based framework using Parametrized Timed Automata of UPPAAL to analyze the mutual impact of performance, energy consumption and schedulability of DVFS multicore systems, and demonstrate the trade-off on an actual case study.

Keywords: time-critical systems, multicore systems, schedulability analysis, energy consumption, performance analysis

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Authors: Mohamed Boubekri, Jaewook Lee

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The assessment of the daylighting performance of a design solution is a complex task due to the changing nature of daylight. A few quantitative metrics are available to designers to assess such a performance, among them are the mean hourly illuminance (MHI), the daylight factor (DF), the daylight autonomy (DA) and the useful daylight illuminance (UDI). Each of these metrics has criteria and limitations that affect the outcome of the evaluation. When to use one metric instead of another depends largely on the design goals to be achieved. Using Design Iterate Validate Adapt (DIVA) daylighting simulation program we set out to examine the performance behavior of these four metrics with the changing dimensions of three shading devices: a horizontal overhang, a horizontal louver system, and a vertical louver system, and compare their performance behavior as the orientation of the window changes. The context is a classroom of a prototypical elementary school in South Korea. Our results indicate that not all four metrics behave similarly as we vary the size of each shading device and as orientations changes. The UDI is the metric that leads to outcome most different than the other three metrics. Our conclusion is that not all daylighting metrics lead to the same conclusions and that it is important to use the metric that corresponds to the specific goals and objectives of the daylighting solution.

Keywords: daylight factor, hourly daylight illuminance, daylight autonomy, useful daylight illuminance

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Authors: Shreya Pare, Parvin Akhter

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Remotely sensing image classification becomes a very challenging task owing to the high dimensionality of hyperspectral images. The pixel-wise classification methods fail to take the spatial structure information of an image. Therefore, to improve the performance of classification, spatial information can be integrated into the classification process. In this paper, the multilevel thresholding algorithm based on a modified fuzzy entropy function is used to perform the segmentation of hyperspectral images. The fuzzy parameters of the MFE function have been optimized by using a new meta-heuristic algorithm based on the Tree-Search algorithm. The segmented image is classified by a large distribution machine (LDM) classifier. Experimental results are shown on a hyperspectral image dataset. The experimental outputs indicate that the proposed technique (MFE-TSA-LDM) achieves much higher classification accuracy for hyperspectral images when compared to state-of-art classification techniques. The proposed algorithm provides accurate segmentation and classification maps, thus becoming more suitable for image classification with large spatial structures.

Keywords: classification, hyperspectral images, large distribution margin, modified fuzzy entropy function, multilevel thresholding, tree search algorithm, hyperspectral image classification using tree search algorithm

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Authors: Asim Quddus

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A miniaturized three dimensional co-planar waveguide (CPW) two-port MIMO antenna, exhibiting high isolation and WLAN band-notched characteristics is presented in this paper for ultrawideband (UWB) communication applications. The microstrip patch antenna operates as a single UWB antenna element. The proposed design is a cuboid-shaped structure having compact size of 35 x 27 x 45 mm³. Radiating as well as decoupling structure is placed around cuboidal polystyrene sheet. The radiators are 27 mm apart, placed Face-to-Face in vertical direction. Decoupling structure is placed on the side walls of polystyrene. The proposed antenna consists of an oval shaped radiating patch. A rectangular structure with fillet edges is placed on ground plan to enhance the bandwidth. The proposed antenna exhibits a good impedance match (S11 ≤ -10 dB) over frequency band of 2 GHz – 10.6 GHz. A circular slotted structure is employed as a decoupling structure on substrate, and it is placed on the side walls of polystyrene to enhance the isolation between antenna elements. Moreover, to achieve immunity from WLAN band distortion, a modified, inverted crescent shaped slotted structure is etched on radiating patches to achieve band-rejection characteristics at WLAN frequency band 4.8 GHz – 5.2 GHz. The suggested decoupling structure provides isolation better than 15 dB over the desired UWB spectrum. The envelope correlation coefficient (ECC) and gain for the MIMO antenna are analyzed as well. Finite Element Method (FEM) simulations are carried out in Ansys High Frequency Structural Simulator (HFSS) for the proposed design. The antenna is realized on a Rogers RT/duroid 5880 with thickness 1 mm, relative permittivity ɛr = 2.2. The proposed antenna achieves a stable omni-directional radiation patterns as well, while providing rejection at desired WLAN band. The S-parameters as well as MIMO parameters like ECC are analyzed and the results show conclusively that the design is suitable for portable MIMO-UWB applications.

Keywords: 3-D antenna, band-notch, MIMO, UWB

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Authors: Elizabeth Mahenge

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Unemployment is a problem that face many graduates all over the world. Every year universities in many parts of the world produce graduates who are looking for an employment. Swahili, a Bantu language originated in East African coast, can be used as an avenue for youth’s employment in South Africa. This paper helps youth to know about job opportunities available through teaching Swahili language. The objective of this paper is capacity building to youths to be teachers of Swahili and be ready to compete in the marketplace. The methodology was through two weeks online training on how to teach Swahili as a foreign language. The communicative approach and task-based approach were used.  Participants to this training were collected through a WhatsApp group advertisement about “short training for Swahili teachers for foreigners”. A total number of 30 participants registered but only 11 attended the training. Training was online via zoom. The contribution of this paper is that by being fluent in Swahili one would benefit with teaching job opportunities anywhere in the world. Hence the problem of unemployment among the youths would be reduced as they can employ themselves or being employed in academic institutions anywhere in the world. The paper calls for youths in South Africa to opt for Swahili language courses to be trained and become experts in the teaching Swahili as a foreign language.

Keywords: foreign language, linguistic market, Swahili, employment

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Authors: Fazl Ullah, Rahmat Ullah

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This paper shows a comprehensive learning focused on the optimization of gas production in shale gas reservoirs through hydraulic fracturing. Shale gas has emerged as an important unconventional vigor resource, necessitating innovative techniques to enhance its extraction. The key objective of this study is to examine the influence of fracture parameters on reservoir productivity and formulate strategies for production optimization. A sophisticated model integrating gas flow dynamics and real stress considerations is developed for hydraulic fracturing in multi-stage shale gas reservoirs. This model encompasses distinct zones: a single-porosity medium region, a dual-porosity average region, and a hydraulic fracture region. The apparent permeability of the matrix and fracture system is modeled using principles like effective stress mechanics, porous elastic medium theory, fractal dimension evolution, and fluid transport apparatuses. The developed model is then validated using field data from the Barnett and Marcellus formations, enhancing its reliability and accuracy. By solving the partial differential equation by means of COMSOL software, the research yields valuable insights into optimal fracture parameters. The findings reveal the influence of fracture length, diversion capacity, and width on gas production. For reservoirs with higher permeability, extending hydraulic fracture lengths proves beneficial, while complex fracture geometries offer potential for low-permeability reservoirs. Overall, this study contributes to a deeper understanding of hydraulic cracking dynamics in shale gas reservoirs and provides essential guidance for optimizing gas production. The research findings are instrumental for energy industry professionals, researchers, and policymakers alike, shaping the future of sustainable energy extraction from unconventional resources.

Keywords: fluid-solid coupling, apparent permeability, shale gas reservoir, fracture property, numerical simulation

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Authors: Rozeena Baig, R. Rehana Rehman, Rifat Ahmed

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Background: Osteoporosis, characterized by low bone mineral density, poses a global health concern. Diagnosis increases the likelihood of developing osteoporosis, a multifactorial disorder marked by low bone mass, elevating the risk of fractures in the lumbar spine, femoral neck, hip, vertebras, and distal forearm, particularly in postmenopausal women due to bone loss influenced by various pathophysiological factors. Objectives: The aim is to investigate the association of serum cytokine, bone turnover marker, bone mineral density and TLR4 gene polymorphism in pre and post-menopausal women and to find if any of these can be the potential predictor of osteoporosis in postmenopausal women. Material and methods: The study participants consisted of Group A (n=91) healthy pre-menopausal women and Group B (n=102) healthy postmenopausal women having ≥ 5 years’ history of menopause. ELISA was performed for cytokine (TNFα) and bone turnover markers (carboxytelopeptides), respectively. Bone Mineral Density (BMD)was measured through a dual X-ray absorptiometry (DEXA) scan. Toll-like Receptors 4 (TLR4) gene polymorphisms (A896G; Asp299Gly) and (C1196T; Thr399Ile) were investigated by PCR and Sanger sequencing. Results: Statistical analysis reveals a positive correlation of age and BMI with T scores in the premenopausal group, whereas in post-menopausal group found a significant negative correlation between age and T-score at hip (r = - 0.352**), spine (r = - .306**), and femoral neck (r = - 0.344**) and a significant negative correlation of BMI with TNF-α (- 0.316**). No association and significant differences were observed for TLR4 genotype and allele frequencies among studied groups However, both SNPs exhibited significant association with each other. Conclusions: This study concludes that BMI, BMD and TNF-α are the potential predictors of osteoporosis in post-menopausal women. However, CTX and TLR4 gene polymorphism did not appear as potential predictors of bone loss in this study and apparently cannot help in predicting bone loss in post-menopausal women.

Keywords: osteoporosis, post-menopausal, pre-menopausal woemn, genetics mutaiont, TLR4 genepolymorphsum

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Authors: Murat Colak, Berkay Cakir Saridogan

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Since the dawn of modern corporations, almost every single employee has been working in the same loop, which contains three basic steps: going to work, providing the needs for the work, and getting back home. Only a small amount of people were able to break that standard and live outside the box. As the 2019 pandemic hit the Earth and most companies shut down their physical offices, that loop had to change for everyone. This means that the old management styles had to be significantly re-arranged to the "work from home" type of business methods. The methods include online conferences and meetings, time and task tracking using algorithms, globalization of the work, and, most importantly, remote working. After the global epidemic started, even the tech giants were concerned. Now, it can be seen those technology companies have an incredible step-up in their shares compared to the other companies because they know how to manage such situations even better than every other industry. This study aims to take the old traditional management styles in big companies and compare them with the post-covid methods (2019-2022). As a result of this comparison made using the annual reports and shared statistics, this study aims to explain why the winners of this crisis are the technology companies.

Keywords: Covid-19, technology companies, business models, remote work

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Authors: Parisi L., Hamili D., Azlan N.

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The aim of this study was to design and simulate a particular type of Asynchronous State Machine (ASM), namely a ‘traffic light controller’ (TLC), operated at a frequency of 0.5 Hz. The design task involved two main stages: firstly, designing a 4-bit binary counter using J-K flip flops as the timing signal and subsequently, attaining the digital logic by deploying ASM design process. The TLC was designed such that it showed a sequence of three different colours, i.e. red, yellow and green, corresponding to set thresholds by deploying the least number of AND, OR and NOT gates possible. The software Multisim was deployed to design such circuit and simulate it for circuit troubleshooting in order for it to display the output sequence of the three different colours on the traffic light in the correct order. A clock signal, an asynchronous 4-bit binary counter that was designed through the use of J-K flip flops along with an ASM were used to complete this sequence, which was programmed to be repeated indefinitely. Eventually, the circuit was debugged and optimized, thus displaying the correct waveforms of the three outputs through the logic analyzer. However, hazards occurred when the frequency was increased to 10 MHz. This was attributed to delays in the feedback being too high.

Keywords: asynchronous state machine, traffic light controller, circuit design, digital electronics

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Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

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Developing speed model is a challenging task particularly under mixed traffic condition where the traffic composition plays a significant role in determining vehicular speed. The present research has been conducted to model individual vehicular speed in the context of mixed traffic on an urban arterial. Traffic speed and volume data have been collected from three midblock arterial road sections in New Delhi. Using the field data, a volume based speed prediction model has been developed adopting the methodology of Artificial Neural Network (ANN). The model developed in this work is capable of estimating speed for individual vehicle category. Validation results show a great deal of agreement between the observed speeds and the predicted values by the model developed. Also, it has been observed that the ANN based model performs better compared to other existing models in terms of accuracy. Finally, the sensitivity analysis has been performed utilizing the model in order to examine the effects of traffic volume and its composition on individual speeds.

Keywords: speed model, artificial neural network, arterial, mixed traffic

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Authors: Arubi Thompson Abari

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Globally, a writer is absolutely necessary to the society, for he mirrors and projects the society, grumbles and protects against the ills that hinders its development. A writer is committed to the language, social-cultural, political and economic factors that determine the sustenance of human dignity in the society. In this 21st century. The literary artist holds literature as a tool for the restoration and sustenance of human dignity. In Nigeria, literature is politically committed because colonialism gives birth to the modern Nigerian literature. Literature thus was regarded as one of the greatest weapons against colonialism in Nigeria. Nigerian literature is aimed at the restoration and sustenance of the dignity of Nigerians in the 21st century. A literary writer is a member of the society and his sensibility is conditioned by the socio-political situations around him. A writer cannot be excused from the task of regeneration and restoration of his past lost glorious days that must be done. This academic paper therefore showcases the efficacy of literature in bringing about the sustenance of human dignity in the 21st century. Consequently, the paper in its introduction clarifies some vital concepts. It discusses the forms of literature, portrays the ability and capability of literature as a tool for the sustenance of human dignity globally, and makes useful recommendations for the growth of knowledge in the 21st century and beyond.

Keywords: literature, sustenance, human dignity, 21st century

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Authors: Kaine Gulozer

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Listening comprehension in a foreign language context has been a constant challenge for Turkish speakers of English. Syntactic priming (SP) of relative clauses might affect the perception of subsequent sentences of identical structure and this could have an impact on the listening comprehension of second or foreign language learners. There has been little attempt to investigate the syntactic priming of English subject relative clauses and object relative clauses in relation to perception for the learners of English in Turkish context. This study investigates SP effects on low-proficiency EFL learners’ production of English relative clauses. Both qualitative and quantitative method along with a pre-test and post-test tasks were adopted, recruiting 62 EFL learners to receive a six-week listening instruction on relative clauses. Testing instruments for language production included the two tasks: (1) the visual- cued presentation and recall and (2) the auditory-cued presentation and recall. Students’ listening comprehension in task 1 and 2 were recorded and transcribed. Fifteen of the participants were also interviewed. The results of the dependent samples t-test analyses revealed that SP had a significant effect on the overall perception of relative clauses.

Keywords: listening comprehension, relative clauses, structural priming, syntactic persistance, syntactic priming

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Authors: S. Ibrahim, U. B. Abubakar, S. Danbirni, A. Usman, F. M. Ballah, C. A. Kudi, L. Lawson, G. H. Abdulrazak, I. A. Abdulkadir

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Performing culture and characterization of mycobacteria in low resource settings like Nigeria is a very difficult task to undertake because of the very few and limited laboratories carrying out such an experiment; this is a largely due to stringent and laborious nature of the tests. Hence, a rapid, simple and accurate test for characterization is needed. The “SD BIOLINE TB Ag MPT 64 Rapid ®” is a simple and rapid immunochromatographic test used in differentiating Mycobacteria into Mycobacterium tuberculosis (NTM). The 100 sputa were obtained from patients suspected to be infected with tuberculosis and presented themselves to hospitals for check-up and treatment were involved in the study. The samples were cultured in a class III Biosafety cabinet and level III biosafety practices were followed. Forty isolates were obtained from the cultured sputa, and there were identified as Acid-fast bacilli (AFB) using Zeihl-Neelsen acid-fast stain. All the isolates (AFB positive) were then subjected to the SD BIOLINE Analyses. A total of 31 (77.5%) were characterized as MTBC, while nine (22.5%) were NTM. The total turnaround time for the rapid assay was just 30 minutes as compared to a few days of phenotypic and genotypic method. It was simple, rapid and reliable test to differentiate MTBC from NTM.

Keywords: culture, mycobacteria, non tuberculous mycobacterium, SD Bioline

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Authors: Dovile Petkeviciute-Barysiene

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Legal technologies not only create new ways for accessing and providing legal services but also transform the role of legal practitioners. Both lawyers and users of legal services expect automated solutions to outperform people with objectivity and impartiality. Although fairness of the automated decisions is crucial, research on assessing various characteristics of automated processes related to the perceived fairness has only begun. One of the major obstacles to this research is the lack of comprehensive understanding of what legal actions are automated and could be meaningfully automated, and to what extent. Neither public nor legal practitioners oftentimes cannot envision technological input due to the lack of general without illustrative examples. The aim of this study is to map decision making stages and automation levels which are and/or could be achieved in legal actions related to pre-trial and trial processes. Major legal decisions and judgments are identified during the consultations with legal practitioners. The dual-process model of information processing is used to describe cognitive processes taking place while making legal decisions and judgments during pre-trial and trial action. Some of the existing legal technologies are incorporated into the analysis as well. Several published automation level taxonomies are considered because none of them fit well into the legal context, as they were all created for avionics, teleoperation, unmanned aerial vehicles, etc. From the information processing perspective, analysis of the legal decisions and judgments expose situations that are most sensitive to cognitive bias, among others, also help to identify areas that would benefit from the automation the most. Automation level analysis, in turn, provides a systematic approach to interaction and cooperation between humans and algorithms. Moreover, an integrated map of legal decisions and judgments, information processing characteristics, and automation levels all together provide some groundwork for the research of legal technology perceived fairness and acceptance. Acknowledgment: This project has received funding from European Social Fund (project No 09.3.3-LMT-K-712-19-0116) under grant agreement with the Research Council of Lithuania (LMTLT).

Keywords: automation levels, information processing, legal judgment and decision making, legal technology

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Authors: Ajit Rai, Hamza Deroui, Blandine Vacher, Khwansiri Ninpan, Arthur Aumont, Francesco Vitillo, Robert Plana

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Large-scale critical industrial scheduling problems are based on Resource-Constrained Project Scheduling Problems (RCPSP), that necessitate integration with other optimization problems (e.g., vehicle routing, supply chain, or unique industrial ones), thus requiring practical solutions (i.e., modular, computationally efficient with feasible solutions). To the best of our knowledge, the current industrial state of the art is not addressing this holistic problem. We propose an original modular solution that answers the issues exhibited by the delivery of complex projects. With three interlinked entities (project, task, resources) having their constraints, it uses a greedy heuristic with a dynamic cost function for each task with a situational assessment at each time step. It handles large-scale data and can be easily integrated with other optimization problems, already existing industrial tools and unique constraints as required by the use case. The solution has been tested and validated by domain experts on three use cases: outage management in Nuclear Power Plants (NPPs), planning of future NPP maintenance operation, and application in the defense industry on supply chain and factory relocation. In the first use case, the solution, in addition to the resources’ availability and tasks’ logical relationships, also integrates several project-specific constraints for outage management, like, handling of resource incompatibility, updating of tasks priorities, pausing tasks in a specific circumstance, and adjusting dynamic unit of resources. With more than 20,000 tasks and multiple constraints, the solution provides a feasible schedule within 10-15 minutes on a standard computer device. This time-effective simulation corresponds with the nature of the problem and requirements of several scenarios (30-40 simulations) before finalizing the schedules. The second use case is a factory relocation project where production lines must be moved to a new site while ensuring the continuity of their production. This generates the challenge of merging job shop scheduling and the RCPSP with location constraints. Our solution allows the automation of the production tasks while considering the rate expectation. The simulation algorithm manages the use and movement of resources and products to respect a given relocation scenario. The last use case establishes a future maintenance operation in an NPP. The project contains complex and hard constraints, like on Finish-Start precedence relationship (i.e., successor tasks have to start immediately after predecessors while respecting all constraints), shareable coactivity for managing workspaces, and requirements of a specific state of "cyclic" resources (they can have multiple states possible with only one at a time) to perform tasks (can require unique combinations of several cyclic resources). Our solution satisfies the requirement of minimization of the state changes of cyclic resources coupled with the makespan minimization. It offers a solution of 80 cyclic resources with 50 incompatibilities between levels in less than a minute. Conclusively, we propose a fast and feasible modular approach to various industrial scheduling problems that were validated by domain experts and compatible with existing industrial tools. This approach can be further enhanced by the use of machine learning techniques on historically repeated tasks to gain further insights for delay risk mitigation measures.

Keywords: deterministic scheduling, optimization coupling, modular scheduling, RCPSP

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Authors: Xueyang Li

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This thesis investigates the impact of self-viewing on team creativity in virtual teamwork and examines the role of objective self-awareness and psychological safety in this context. The study uses a quantitative research approach and collects data from 304 participants working in virtual teams. We hypothesized that observing oneself in online meetings would lead to a heightened sense of objective self and thus lower team creativity and that psychological safety would moderate their relationship. We tested these hypotheses in a laboratory experiment manipulating whether participants were able to observe themselves during the completion of an online team creativity task and manipulating whether participants were subjected to a psychological safety intervention. The results indicate that self-observation has a negative effect on team creativity in virtual teamwork, while objective self-awareness mediates this relationship, and psychological safety plays a moderating role. We discuss several aspects of the theoretical explanation of the findings. This study contributes to the existing literature by highlighting the importance of self-observation in virtual teamwork and provides practical implications for managers and team leaders to promote creativity in virtual teams.

Keywords: objective self-awareness, psychological safety, self-viewing, team creativity, virtual teamwork

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Authors: Ruilian Zhao, Shukai Zhang, Yan Wang, Weiwei Wang

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Repairing broken test cases is an expensive and challenging task in evolving software systems. Although an automated repair technique with intent preservation has been proposed, but it does not take into account the association between test repairs and assertions, leading to a large number of irrelevant candidates and decreasing the repair capability. This paper proposes an assertion-driven test repair approach. Furthermore, an intent-oriented priority criterion is raised to guide the repair candidate generation, making the repairs closer to the intent of the test. In more detail, repair targets are determined through post-dominance relations between assertions and the methods that directly cause compilation errors. Then, test repairs are generated from the target in a bottom-up way, guided by the intent-oriented priority criteria. Finally, the generated repair candidates are prioritized to match the original test intent. The approach is implemented and evaluated on the benchmark of 4 open-source programs and 91 broken test cases. The result shows that the approach can fix 89% (81/91) of broken test cases, which is more effective than the existing intentpreserved test repair approach, and our intent-oriented priority criteria work well.

Keywords: test repair, test intent, software test, test case evolution

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Authors: Ahmad Umar, Ahmed A. Ibrahim, Mohsen A. Alhamami

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This study introduces a facile synthesis method for synthesizing reduced graphene oxide (rGO) nanosheets with surface decoration of polyaniline (PANI). The resultant rGO@PANI nanocomposite (NC) exhibit substantial potential as advanced electrode materials for high-performance supercapacitors. The strategic integration of PANI onto the rGO surface serves dual purposes, effectively mitigating the agglomeration of rGO films and augmenting their utility in supercapacitor applications. The PANI coating manifests a highly porous and nanosized morphology, fostering increased surface area and optimized mass transport by reducing diffusion kinetics. The nanosized structure of PANI contributes to the maximization of active sites, thereby bolstering the efficacy of the nanocomposites for diverse applications. The inherent conductive nature of the rGO surface significantly expedites electron transport, thereby amplifying the overall electrochemical performance of the nanocomposites. To systematically evaluate the influence of PANI concentration on the electrode performance, varying concentrations of PANI were incorporated. Notably, an elevated PANI concentration was found to enhance the response owing to the unique morphology of PANI. Remarkably, the 5% rGO@PANI NC emerged as the most promising candidate, demonstrating exceptional response characteristics with a specific capacitance of 314.2 F/g at a current density of 1 A/g. Furthermore, this catalyst exhibits outstanding long-term stability, retaining approximately 92% of its capacitance even after enduring 4000 cycles. This research underscores the significance of the synergistic integration of rGO and PANI in the design of high-performance supercapacitors. The elucidation of the underlying mechanisms governing the improved electrochemical properties contributes to the fundamental understanding of nanocomposite behavior, thereby paving the way for the rational design of next-generation energy storage materials.

Keywords: reduced graphene oxide, polyaniline, nanocomposites, supercapacitors, energy storage

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Authors: Jiajun Wang, Xiaoge Li

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The purpose of the aspect-level sentiment analysis task is to identify the sentiment polarity of aspects in a sentence. Currently, most methods mainly focus on using neural networks and attention mechanisms to model the relationship between aspects and context, but they ignore the dependence of words in different ranges in the sentence, resulting in deviation when assigning relationship weight to other words other than aspect words. To solve these problems, we propose a new aspect-level sentiment analysis model that combines a multi-channel convolutional network and graph convolutional network (GCN). Firstly, the context and the degree of association between words are characterized by Long Short-Term Memory (LSTM) and self-attention mechanism. Besides, a multi-channel convolutional network is used to extract the features of words in different ranges. Finally, a convolutional graph network is used to associate the node information of the dependency tree structure. We conduct experiments on four benchmark datasets. The experimental results are compared with those of other models, which shows that our model is better and more effective.

Keywords: aspect-level sentiment analysis, attention, multi-channel convolution network, graph convolution network, dependency tree

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Authors: Ahemd Sameer, Braj Bhushan

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The objective of our study was to compare wayfinding strategies to remember route while navigation in an unfamiliar homogenous environment. Two videos developed using free ware Trimble Sketchup© each having nine identical turns (3 right, 3 left, 3 straight) with no distinguishing feature at any turn. Thirt-two male post-graduate students of IIT Kanpur participated in the study. The experiment was conducted in three phases. In the first phase participant generated a list of personally known items to be used as landmarks. In the second phase participant saw the first video and was required to remember the sequence of turns. In the second video participant was required to imagine a landmark from the list generated in the first phase at each turn and associate the turn with it. In both the task the participant was asked to recall the sequence of turns as it appeared in the video. In the third phase, which was 20 minutes after the second phase, participants again recalled the sequence of turns. Results showed that performance in the first condition i.e. without use of landmarks was better than imaginary landmark condition. The difference, however, became significant when the participant were tested again about 30 minutes later though performance was still better in no-landmark condition. The finding is surprising given the past research in memory and is explained in terms of cognitive factors such as mental workload.

Keywords: Wayfinding, Landmark, Homogenous Environment, Memory

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Authors: Dechrit Maneetham

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Pick and place task is one among the most important tasks in industrial field handled by 'Selective Compliance Assembly Robot Arm' (SCARA). Repeatability with high-speed movement in a horizontal plane is a remarkable feature of this type of manipulator. The challenge of design SCARA is the difficulty of achieving stability of high-speed movement with the long length of links. Shorter links arm can move more stable. This condition made the links should be considered restrict then followed by restriction of operation area (workspace). In this research, authors demonstrated on expanding SCARA robot’s workspace in horizontal area via linear sliding actuator that embedded to base link of the robot arm. With one additional prismatic joint, the previous robot manipulator with 3 degree of freedom (3-DOF), 2 revolute joints and 1 prismatic joint becomes 4-DOF PRRP manipulator. This designation increased workspace of robot from 0.5698m² performed by the previous arm (without linear actuator) to 1.1281m² by the proposed arm (with linear actuator). The increasing rate was about 97.97% of workspace with the same links' lengths. The result of experimentation also indicated that the operation time spent to reach object position was also reduced.

Keywords: kinematics, linear sliding actuator, manipulator, control system

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Authors: Zheng Zhihao

Abstract:

Recently, Knowledge Graph Framework Network (KGCN) has developed powerful capabilities in knowledge representation and reasoning tasks. However, traditional KGCN often uses a fixed weight mechanism when aggregating information, failing to make full use of rich structural information, resulting in a certain expression ability of node representation, and easily causing over-smoothing problems. In order to solve these challenges, the paper proposes an new graph neural network model called KGCN-STAR (Knowledge Graph Convolutional Network with Structural Adaptive Receptive Fields). This model dynamically adjusts the perception of each node by introducing a structural adaptive receptive field. wild range, and a subgraph aggregator is designed to capture local structural information more effectively. Experimental results show that KGCN-STAR shows significant performance improvement on multiple knowledge graph data sets, especially showing considerable capabilities in the task of representation learning of complex structures.

Keywords: knowledge graph, graph neural networks, structural adaptive receptive fields, information aggregation

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Authors: Luis F. Garcia, Julian E. Jaramillo, Jorge L. Chacón

Abstract:

Colombia is a country where the benefits of wind power industry are barely used because of the geography in some areas does not allow the implementation of onshore horizontal axis wind turbines. Furthermore, exist rural areas without access to the electrical grid. Therefore, there is currently a deficit of energy supply in some towns. This research took place in one of those areas (i.e. Chicamocha Canyon-Santander) where the answer to the energy supply problems could be the use of vertical axis wind turbines, which can be used for turbulent flows. Hence, one task of this research is the analysis of the wind resources in the Chicamocha Canyon in order to implement the wind energy. The wind turbines must be designed in such a way that the blades take good advantage of the wind resources in the area of interest. Consequently, in the current research the analysis of two different airfoils (i.e. NACA0018 and DU 06-W-200) through a 2D CFD simulation is carried out by means of a free-software (OpenFOAM). Predicted results using the “Spalart-Allmaras” turbulence model are similar to the wind tunnel data published in the literature. Moreover, global parameters such as dimensionless lift and drag coefficients were calculated. Finally, this research encourages VAWT studies under wind turbulent flows in order to achieve the best use of natural resources in Colombia.

Keywords: airfoil, wind turbine, turbulence modelling, Chicamocha, CFD

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Authors: Néstor Álvarez-Díaz, Pino Caballero-Gil, Héctor Reboso-Morales, Francisco Martín-Fernández

Abstract:

The recent tendency of "Internet of Things" (IoT) has developed in the last years, causing the emergence of innovative communication methods among multiple devices. The appearance of Bluetooth Low Energy (BLE) has allowed a push to IoT in relation to smartphones. In this moment, a set of new applications related to several topics like entertainment and advertisement has begun to be developed but not much has been done till now to take advantage of the potential that these technologies can offer on many business areas and in everyday tasks. In the present work, the application of BLE technology and smartphones is proposed on some business areas related to the optimization of resource allocation in huge facilities like airports. An indoor location system has been developed through triangulation methods with the use of BLE beacons. The described system can be used to locate all employees inside the building in such a way that any task can be automatically assigned to a group of employees. It should be noted that this system cannot only be used to link needs with employees according to distances, but it also takes into account other factors like occupation level or category. In addition, it has been endowed with a security system to manage business and personnel sensitive data. The efficiency of communications is another essential characteristic that has been taken into account in this work.

Keywords: bluetooth low energy, indoor location, resource assignment, smartphones

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Authors: Yin Yuanling

Abstract:

A Deep Feedback Recurrent Neural Network (DFRNN) and Bidirectional Long Short-Term Memory (BiLSTM) are designed to address the problem of low accuracy of traditional relationship extraction models. This method combines a deep feedback-based recurrent neural network (DFRNN) with a bi-directional long short-term memory (BiLSTM) approach. The method combines DFRNN, which extracts local features of text based on deep feedback recurrent mechanism, BiLSTM, which better extracts global features of text, and Self-Attention, which extracts semantic information. Experiments show that the method achieves an F1 value of 76.69% on the CEC dataset, which is 0.0652 better than the BiLSTM+Self-ATT model, thus optimizing the performance of the deep learning method in the event relationship extraction task.

Keywords: event relations, deep learning, DFRNN models, bi-directional long and short-term memory networks

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Authors: Ritwik Saha, Anindita Chaudhuri

Abstract:

The psychological dimension of work-based mobility of the contemporary time in the context of the ever-changing socio-economic process mounting the interest to address the consequential issues of quality of life and well-being of the migrant section of society. The negotiation with the fluidity of the job market and the changing psychosocial dimensions within and between psychosocial relations may disentangle the resilience as well as the mechanism of diligence toward migrant (marginal) life. The work-based mobility and its associated phenomena have highly impacted the migrant’s quality of life especially the marginalized (socioeconomically weak) ones along with their family members staying away from them. The subjective experiences of the journey of their migrant life and reconstruction of the psychosocial being in terms of existence and well-being at the host place are the minimal addressed issues in migrant literature. Hence this gap instigates to bring forth the issue with the present study exploring the phenomenal aspects of lived experiences, resilience, and sense-making of the well-being of migrant living by the marginalized migrant people engaging in unorganized space. In doing so qualitative research method was followed, and semi-structured interviews were used for data collection from the four selected migrant groups (Fuchkawala, Bhunjawala, Bhari - drinking water supplier, Construction worker) as they migrated to Kolkata and its metropolis area from different states of India, Five participants from each group (20 participants in total) age range between 20 to 45 were interviewed physically and participants’ observatory notes were taken to capture their lived experiences, audio recordings were transcribed and analyzed systematically following Charmaz’s three-layer coding of grounded theory. Being truthful to daily industry, the strong desire to build children’s future, the mastering mechanism to dual existence, use of traditional social network these four themes emerges after analysis of the data. However, incorporating fate as their usual way of life and making sense of well-being through their assertion is another evolving aspect of migrant life.

Keywords: lived experiences, marginal living, resilience, sense-making process, well-being

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Authors: Niloofar Fathizaviyehkord

Abstract:

Just as two hands cannot make a good boxer, knowing two or more languages cannot make a skillful interpreter. Interpreting, either consecutive or simultaneous, is a cognitively demanding task requiring not only linguistic and discourse knowledge but also strategic competence. Moreover, experience level can play a very crucial role in the strategies interpreters may employ since translation and interpretation quality is a matter of experience, besides other factors, as well. With regard to the significance of strategic competence, this study investigated what strategies are mainly employed by interpreters, what strategies are employed more frequently, and whether experience level can affect the choice of strategies by interpreters or not. To collect the necessary data, the first retrospective interviews were held with 20 interpreters experienced more or less in simultaneous and consecutive interpretation to see what strategies other than those classified in the literature are employed by interpreters. Then, several classifications of strategies in literature were merged with those emerging from the retrospective interviews to come up with a comprehensive questionnaire on interpreting strategies. After seeking five experts’ opinions regarding the wording/content of the questionnaire, it was given to 60 interpreters. The statistical analysis of the questionnaire data and experience level through ANOVA showed experience level could affect the choice of strategies. This study closes with the theoretical/practical implications of the findings for interpreter training.

Keywords: experience level, consecutive and simultaneous, interpretation strategies, translation

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