Search results for: multi linear regression

Authors: Yiming Jin, Ping Dong

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The primary resonance of a pipeline close to a plane boundary is investigated in this paper. Based on classic Van der Pol equation and added a nonlinear item, a new wake oscillator model is proposed to predict the vortex-induced vibration (VIV) of a circular cylinder close to a plane boundary. Then, with the multi-scale method, the approximate solution for the case of the primary resonance is obtained. Besides, to study the characteristic of the primary resonance, the effects of the mass ration, frequency, damp ratio and gap ratio on the frequency-response curves of the pipeline are analysed. On the whole, the trend of the numerical results match up with that of the experimental data well and the mass ration, frequency, damp ratio and gap ratio play an important role in the vortex-induced vibration (VIV) of a circular cylinder close to a plane boundary, especially, the smaller is the mass ratio, the larger impact the gap ratio has on the frequency-response curves of the primary resonance.

Keywords: primary resonance, gap ratio, vortex-induced vibration, multi-scale method

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Authors: Srinivasacharya Darbhasayanam, Jagadeeshwar Pashikanti

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This paper analyzes the Soret and Dufour effects on mixed convection flow, heat and mass transfer from an exponentially stretching surface in a viscous fluid with Hall Effect. The governing partial differential equations are transformed into ordinary differential equations using similarity transformations. The nonlinear coupled ordinary differential equations are reduced to a system of linear differential equations using the successive linearization method and then solved the resulting linear system using the Chebyshev pseudo spectral method. The numerical results for the velocity components, temperature and concentration are presented graphically. The obtained results are compared with the previously published results, and are found to be in excellent agreement. It is observed from the present analysis that the primary and secondary velocities and concentration are found to be increasing, and temperature is decreasing with the increase in the values of the Soret parameter. An increase in the Dufour parameter increases both the primary and secondary velocities and temperature and decreases the concentration.

Keywords: Exponentially stretching sheet, Hall current, Heat and Mass transfer, Soret and Dufour Effects

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Authors: E. Feizi Ashtiani, S. Jamshidi, M.H Niksokhan, A. Feizi Ashtiani

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Waste load allocation (WLA) policies may use multi-objective optimization methods to find the most appropriate and sustainable solutions. These usually intend to simultaneously minimize two criteria, total abatement costs (TC) and environmental violations (EV). If other criteria, such as inequity, need for minimization as well, it requires introducing more binary optimizations through different scenarios. In order to reduce the calculation steps, this study presents value index as an innovative decision making approach. Since the value index contains both the environmental violation and treatment costs, it can be maximized simultaneously with the equity index. It implies that the definition of different scenarios for environmental violations is no longer required. Furthermore, the solution is not necessarily the point with minimized total costs or environmental violations. This idea is testified for Haraz River, in north of Iran. Here, the dissolved oxygen (DO) level of river is simulated by Streeter-Phelps equation in MATLAB software. The WLA is determined for fish farms using multi-objective particle swarm optimization (MOPSO) in two scenarios. At first, the trade-off curves of TC-EV and TC-Inequity are plotted separately as the conventional approach. In the second, the Value-Equity curve is derived. The comparative results show that the solutions are in a similar range of inequity with lower total costs. This is due to the freedom of environmental violation attained in value index. As a result, the conventional approach can well be replaced by the value index particularly for problems optimizing these objectives. This reduces the process to achieve the best solutions and may find better classification for scenario definition. It is also concluded that decision makers are better to focus on value index and weighting its contents to find the most sustainable alternatives based on their requirements.

Keywords: waste load allocation (WLA), value index, multi objective particle swarm optimization (MOPSO), Haraz River, equity

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Authors: Nixon Kuruvila, H. V. Ravindra

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Wire-cut Electro Discharge Machining (WEDM) is a special form of conventional EDM process in which electrode is a continuously moving conductive wire. The present study aims at determining parametric influence and optimum process parameters of Wire-EDM using Taguchi’s Technique and Genetic algorithm. The variation of the performance parameters with machining parameters was mathematically modeled by Regression analysis method. The objective functions are Dimensional Accuracy (DA) and Material Removal Rate (MRR). Experiments were designed as per Taguchi’s L16 Orthogonal Array (OA) where in Pulse-on duration, Pulse-off duration, Current, Bed-speed and Flushing rate have been considered as the important input parameters. The matrix experiments were conducted for the material Oil Hardened Non Shrinking Steel (OHNS) having the thickness of 40 mm. The results of the study reveals that among the machining parameters it is preferable to go in for lower pulse-off duration for achieving over all good performance. Regarding MRR, OHNS is to be eroded with medium pulse-off duration and higher flush rate. Finally, the validation exercise performed with the optimum levels of the process parameters. The results confirm the efficiency of the approach employed for optimization of process parameters in this study.

Keywords: dimensional accuracy (DA), regression analysis (RA), Taguchi method (TM), volumetric material removal rate (VMRR)

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Authors: Saumya Srivastava, Rina Maiti

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In order to achieve a better performance of vehicle detection in a complex environment, we present an efficient approach for a multi-vehicle detection system using an adaptive sliding window technique. For a given frame, image segmentation is carried out to establish the region of interest. Gradient computation followed by thresholding, denoising, and morphological operations is performed to extract the binary search image. Near-region field and far-region field are defined to generate hypotheses using the adaptive sliding window technique on the resultant binary search image. For each vehicle candidate, features are extracted using a histogram of oriented gradients, and a pre-trained support vector machine is applied for hypothesis verification. Later, the Kalman filter is used for tracking the vanishing point. The experimental results show that the method is robust and effective on various roads and driving scenarios. The algorithm was tested on highways and urban roads in India.

Keywords: gradient, vehicle detection, histograms of oriented gradients, support vector machine

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Authors: Mohammad Parsaeimaram, Fang Congqi

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In this paper, one of the significant seismic design parameter as response modification factor in reinforced concrete (RC) buildings with base isolation system was evaluated. The seismic isolation system is a capable approach to absorbing seismic energy at the base and transfer to the substructure with lower response modification factor as compared to non-isolated structures. A response spectrum method and static nonlinear pushover analysis in according to Uniform Building Code (UBC-97), have been performed on building models involve 5, 8, 12 and 15 stories building with fixed and isolated bases consist of identical moment resisting configurations. The isolation system is composed of lead rubber bearing (LRB) was designed with help UBC-97 parameters. The force-deformation behavior of isolators was modeled as bi-linear hysteretic behavior which can be effectively used to create the isolation systems. The obtained analytical results highlight the response modification factor of considered base isolation system with higher values than recommended in the codes. The response modification factor is used in modern seismic codes to scale down the elastic response of structures.

Keywords: response modification factor, base isolation system, pushover analysis, lead rubber bearing, bi-linear hysteretic

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Authors: Shalini TV, Gangadharan GG, Sriranjini S Jaideep, ASN Seshasayee, Awadhesh Pandit

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Introduction: Prakriti (body-mind constitution of an individual) is a conventional, customized and unique understanding of which is essential for the personalized medicine described in Ayurveda, Indian System of Medicine. Based on the Doshas( functional, bio humoral unit in the body), individuals are categorized into three major Prakriti- Vata, Pitta, and Kapha. The human gut microbiome hosts plenty of highly diverse and metabolically active microorganisms, mainly dominated by the bacteria, which are known to influence the physiology of an individual. Few researches have shown the correlation between the Prakriti and the biochemical parameters. In this study, an attempt was made to explore any correlation between the Prakriti (phenotype of an individual) with the Genetic makeup of the gut microbiome in healthy individuals. Materials and methods: 270 multi-ethnic, healthy volunteers of both sex with the age group between 18 to 40 years, with no history of antibiotics in the last 6 months were recruited into three groups of Vata, Pitta, and Kapha. The Prakriti of the individual was determined using Ayusoft, a software designed by CDAC, Pune, India. The volunteers were subjected to initial screening for the assessment of their height, weight, Body Mass Index, Vital signs and Blood investigations to ensure they are healthy. The stool and saliva samples of the recruited volunteers were collected as per the standard operating procedure developed, and the bacterial DNA was isolated using Qiagen kits. The extracted DNA was subjected to 16s rRNA sequencing using the Illumina kits. The sequencing libraries are targeting the variable V3 and V4 regions of the 16s rRNA gene. Paired sequencing was done on the MiSeq system and data were analyzed using the CLC Genomics workbench 11. Results: The 16s rRNA sequencing of the V3 and V4 regions showed a diverse pattern in both the oral and stool microbial DNA. The study did not reveal any specific pattern of bacterial flora amongst the Prakriti. All the p-values were more than the effective alpha values for all OTUs in both the buccal cavity and stool samples. Therefore, there was no observed significant enrichment of an OTU in the patient samples from either the buccal cavity or stool samples. Conclusion: In healthy volunteers of multi-ethnicity, due to the influence of the various factors, the correlation between the Prakriti and the gut microbiome was not seen.

Keywords: gut microbiome, ayurveda Prakriti, sequencing, multi-ethnic urban population

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Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

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Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

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Authors: Gunasekaran Raja, Ramkumar Jayaraman, Rajakumar Arul, Kottilingam Kottursamy

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Broadband Wireless Network (BWN) is the promising technology nowadays due to the increased number of smartphones. Buffering scheme using network coding considers the reliability and proper degree distribution in Worldwide interoperability for Microwave Access (WiMAX) multi-hop network. Using network coding, a secure way of transmission is performed which helps in improving throughput and reduces the packet loss in the multicast network. At the outset, improved network coding is proposed in multicast wireless mesh network. Considering the problem of performance overhead, degree distribution makes a decision while performing buffer in the encoding / decoding process. Consequently, BuS (Buffer Scheme) based on network coding is proposed in the multi-hop network. Here the encoding process introduces buffer for temporary storage to transmit packets with proper degree distribution. The simulation results depend on the number of packets received in the encoding/decoding with proper degree distribution using buffering scheme.

Keywords: encoding and decoding, buffer, network coding, degree distribution, broadband wireless networks, multicast

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Authors: Debabrata Bhadra, B. K. Chaudhuri

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The operation of organic thin film transistors (OFETs) with low voltage is currently a prevailing issue. We have fabricated anthracene thin-film transistor (TFT) with an ultrathin layer (~450nm) of Poly-vinylidene fluoride (PVDF)/CuO nanocomposites as a gate insulator. We obtained a device with excellent electrical characteristics at low operating voltages (<1V). Different layers of the film were also prepared to achieve the best optimization of ideal gate insulator with various static dielectric constant (εr ). Capacitance density, leakage current at 1V gate voltage and electrical characteristics of OFETs with a single and multi layer films were investigated. This device was found to have highest field effect mobility of 2.27 cm2/Vs, a threshold voltage of 0.34V, an exceptionally low sub threshold slope of 380 mV/decade and an on/off ratio of 106. Such favorable combination of properties means that these OFETs can be utilized successfully as voltages below 1V. A very simple fabrication process has been used along with step wise poling process for enhancing the pyroelectric effects on the device performance. The output characteristic of OFET after poling were changed and exhibited linear current-voltage relationship showing the evidence of large polarization. The temperature dependent response of the device was also investigated. The stable performance of the OFET after poling operation makes it reliable in temperature sensor applications. Such High-ε CuO/PVDF gate dielectric appears to be highly promising candidates for organic non-volatile memory and sensor field-effect transistors (FETs).

Keywords: organic field effect transistors, thin film transistor, gate dielectric, organic semiconductor

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Authors: ZhenlinJu, Christopher P. Vellano, RehanAkbani, Yiling Lu, Gordon B. Mills

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The epithelial–mesenchymal transition (EMT) is a process by which epithelial cells acquire mesenchymal characteristics, such as profound disruption of cell-cell junctions, loss of apical-basolateral polarity, and extensive reorganization of the actin cytoskeleton to induce cell motility and invasion. A hallmark of EMT is its capacity to promote metastasis, which is due in part to activation of several transcription factors and subsequent downregulation of E-cadherin. Unfortunately, current approaches have yet to uncover robust protein marker sets that can classify tumors as possessing strong EMT signatures. In this study, we utilize reverse phase protein array (RPPA) data and consensus clustering methods to successfully classify a subset of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumors into an EMT protein signaling group (EMT group). The overall survival (OS) of patients in the EMT group is significantly worse than those in the other Hormone and PI3K/AKT signaling groups. In addition to a shrinkage and selection method for linear regression (LASSO), we applied training/test set and Monte Carlo resampling approaches to identify a set of protein markers that predicts the EMT status of CESC tumors. We fit a logistic model to these protein markers and developed a classifier, which was fixed in the training set and validated in the testing set. The classifier robustly predicted the EMT status of the testing set with an area under the curve (AUC) of 0.975 by Receiver Operating Characteristic (ROC) analysis. This method not only identifies a core set of proteins underlying an EMT signature in cervical cancer patients, but also provides a tool to examine protein predictors that drive molecular subtypes in other diseases.

Keywords: consensus clustering, TCGA CESC, Silhouette, Monte Carlo LASSO

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Authors: Likewin Thomas, M. V. Manoj Kumar, B. Annappa

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Traditional execution process follows the path of execution drawn by the process analyst without observing the behaviour of resource and other real-time constraints. Identifying process model, predicting the behaviour of resource and recommending the optimal path of execution for a real time process is challenging. The proposed AlfyMiner: αyM iner gives a new dimension in process execution with the novel techniques Process Model Analyser: PMAMiner and Resource behaviour Analyser: RBAMiner for recommending the probable path of execution. PMAMiner discovers next probable activity for currently executing activity in an online process using variant matching technique to identify the set of next probable activity, among which the next probable activity is discovered using decision tree model. RBAMiner identifies the resource suitable for performing the discovered next probable activity and observe the behaviour based on; load and performance using polynomial regression model, and waiting time using queueing theory. Based on the observed behaviour αyM iner recommend the probable path of execution with; next probable activity and the best suitable resource for performing it. Experiments were conducted on process logs of CoSeLoG Project1 and 72% of accuracy is obtained in identifying and recommending next probable activity and the efficiency of resource performance was optimised by 59% by decreasing their load.

Keywords: cross-organization process mining, process behaviour, path of execution, polynomial regression model

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Authors: Keyao Li, Sai On Cheung

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Incorporation of alternative dispute resolution (ADR) mechanism has been the main feature of current trend of construction project dispute resolution (CPDR). ADR approaches have been identified as efficient mechanisms and are suitable alternatives to litigation and arbitration. Moreover, the use of ADR in this multi-tiered dispute resolution process often leads to repeated evaluations of a same dispute. Multi-tiered CPDR may become a breeding ground for cognitive biases. When completed knowledge is not available at the early tier of construction dispute resolution, disputing parties may form preconception of the dispute matter or the counterpart. This preconception would influence their information processing in the subsequent tier. Disputing parties tend to search and interpret further information in a self-defensive way to confirm their early positions. Their imbalanced information collection would boost their confidence in the held assessments. Their attitudes would be hardened and difficult to compromise. The occurrence of cognitive bias, therefore, impedes efficient dispute settlement. This study aims to explore ways to minimize bias in CPDR. Based on a comprehensive literature review, three types of bias minimizing approaches were collected: strategy-based, attitude-based and process-based. These approaches were further operationalized into bias minimizing measures. To verify the usefulness and practicability of these bias minimizing measures, semi-structured interviews were conducted with ten CPDR third party neutral professionals. All of the interviewees have at least twenty years of experience in facilitating settlement of construction dispute. The usefulness, as well as the implications of the bias minimizing measures, were validated and suggested by these experts. There are few studies on cognitive bias in construction management in general and in CPDR in particular. This study would be the first of its type to enhance the efficiency of construction dispute resolution by highlighting strategies to minimize the biases therein.

Keywords: bias, construction project dispute resolution, minimization, multi-tiered, semi-structured interview

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Authors: Hee Sun (Sunny) Choi, Gerhard Bruyns, Wang Zhang, Sky Cheng, Saijal Sharma

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This research addresses the need for a comprehensive framework that can guide the design and assessment of multi-level public spaces and public realms and their impact on the built environment. The study aims to understand and measure the neural mechanisms involved in this process. By doing so, it can lay the foundation for vertical and volumetric urbanism and ensure consistency and excellence in the field while also supporting scientific research methods for urban design with cognitive neuroscientists. To investigate these aspects, the paper focuses on the neighborhood scale in Hong Kong, specifically examining multi-level public spaces and quasi-public spaces within both commercial and residential complexes. The researchers use predictive Artificial Intelligence (AI) as a methodology to assess and comprehend the applicability of the urban design framework for vertical and volumetric urbanism. The findings aim to identify the factors that contribute to successful public spaces within a vertical living environment, thus introducing a new typology of public spaces.

Keywords: vertical urbanism, scientific research methods, spatial cognition, urban design guideline

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Authors: Rabiaal Adawiyah Shazali, Corina Joseph

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The disclosure of Intellectual Capital (IC) information is getting more vital in today’s era of a knowledge-based economy. Companies are advised by accounting bodies to enhance IC disclosure which complements the conventional financial disclosures. There are no accounting standards for Intellectual Capital Disclosure (ICD), therefore the disclosure is entirely voluntary. Hence, this study aims to investigate the extent of ICD and to examine the relationship between corporate governance and ICD in Malaysia. This study employed content analysis of 100 annual reports by the top 100 public listed companies in Malaysia during 2012. The uniqueness of this study lies on its underpinning theory used where it applies the institutional isomorphism theory to support the effect of the attributes of corporate governance towards ICD. In order to achieve the stated objective, multiple regression analysis were employed to conduct this study. From the descriptive statistics, it was concluded that public listed companies in Malaysia have increased their awareness towards the importance of ICD. Furthermore, results from the multiple regression analysis confirmed that corporate governance affects the company’s ICD where the frequency of audit committee meetings and the board size has positively influenced the level of ICD in companies. Findings from this study would provide an incentive for companies in Malaysia to enhance the disclosure of IC. In addition, this study would assist Bursa Malaysia and other regulatory bodies to come up with a proper guideline for the disclosure of IC.

Keywords: annual report, content analysis, corporate governance, intellectual capital disclosure

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Authors: R. Ahmadi, A. Khodadadi, M. Abdollahi

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The removal of copper from a dilute synthetic wastewater (10 mg/L) was studied by ion flotation at laboratory scale. Anionic sodium dodecyl sulfate (SDS) was used as a collector and ethanol as a frother. Different parameters such as pH, collector and frother concentrations, foam height and bubble size distribution (multi bubble ion flotation) were tested to determine the optimum flotation conditions in a Denver type flotation machine. To see into the effect of bubbles size distribution in this paper, a nano-micro bubble generator was designed. The nano and microbubbles that are generated in this way were combined with normal size bubbles generated mechanically. Under the optimum conditions (concentration of SDS: 192mg/l, ethanol: 0.5%v/v, pH value: 4 and froth height=12.5 cm) the best removal obtained for the system Cu/SDS with a dry foam (water recovery: 15.5%) was 85.6%. Coalescence of nano-microbubbles with bubbles of normal size belonging to mechanical flotation cell improved the removal of Cu to a maximum floatability of 92.8% and reduced the water recovery to a 13.1%.The flotation time decreased considerably at 37.5% when the multi bubble ion flotation was used.

Keywords: froth flotation, copper, water treatment, optimization, recycling

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Authors: Liyun Chang, Cheng-Hsiang Tsai

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To ensure the radiation precisely delivering to the target of cancer patients, the linear accelerator equipped with the pretreatment on-board imaging system is introduced and through it the patient setup is verified before the daily treatment. New generation radiotherapy using beam-intensity modulation, usually associated the treatment with steep dose gradients, claimed to have achieved both a higher degree of dose conformation in the targets and a further reduction of toxicity in normal tissues. However, this benefit is counterproductive if the beam is delivered imprecisely. To avoid shooting critical organs or normal tissues rather than the target, it is very important to carry out the quality assurance (QA) of this on-board imaging system. The QA of the On-Board Imager® (OBI) system of one Varian Clinac-iX linear accelerator was performed through our procedures modified from a relevant report and AAPM TG142. Two image modalities, 2D radiography and 3D cone-beam computed tomography (CBCT), of the OBI system were examined. The daily and monthly QA was executed for five months in the categories of safety, geometrical accuracy and image quality. A marker phantom and a blade calibration plate were used for the QA of geometrical accuracy, while the Leeds phantom and Catphan 504 phantom were used in the QA of radiographic and CBCT image quality, respectively. The reference images were generated through a GE LightSpeed CT simulator with an ADAC Pinnacle treatment planning system. Finally, the image quality was analyzed via an OsiriX medical imaging system. For the geometrical accuracy test, the average deviations of the OBI isocenter in each direction are less than 0.6 mm with uncertainties less than 0.2 mm, while all the other items have the displacements less than 1 mm. For radiographic image quality, the spatial resolution is 1.6 lp/cm with contrasts less than 2.2%. The spatial resolution, low contrast, and HU homogenous of CBCT are larger than 6 lp/cm, less than 1% and within 20 HU, respectively. All tests are within the criteria, except the HU value of Teflon measured with the full fan mode exceeding the suggested value that could be due to itself high HU value and needed to be rechecked. The OBI system in our facility was then demonstrated to be reliable with stable image quality. The QA of OBI system is really necessary to achieve the best treatment for a patient.

Keywords: CBCT, image quality, quality assurance, OBI

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Authors: Abdullah Al Farwan, Ya Zhang

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In today’s educational arena, it is critical to understand educational data and be able to evaluate important aspects, particularly data on student achievement. Educational Data Mining (EDM) is a research area that focusing on uncovering patterns and information in data from educational institutions. Teachers, if they are able to predict their students' class performance, can use this information to improve their teaching abilities. It has evolved into valuable knowledge that can be used for a wide range of objectives; for example, a strategic plan can be used to generate high-quality education. Based on previous data, this paper recommends employing data mining techniques to forecast students' final grades. In this study, five data mining methods, Decision Tree, JRip, Naive Bayes, Multi-layer Perceptron, and Random Forest with wrapper feature selection, were used on two datasets relating to Portuguese language and mathematics classes lessons. The results showed the effectiveness of using data mining learning methodologies in predicting student academic success. The classification accuracy achieved with selected algorithms lies in the range of 80-94%. Among all the selected classification algorithms, the lowest accuracy is achieved by the Multi-layer Perceptron algorithm, which is close to 70.45%, and the highest accuracy is achieved by the Random Forest algorithm, which is close to 94.10%. This proposed work can assist educational administrators to identify poor performing students at an early stage and perhaps implement motivational interventions to improve their academic success and prevent educational dropout.

Keywords: classification algorithms, decision tree, feature selection, multi-layer perceptron, Naïve Bayes, random forest, students’ academic performance

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Authors: Yun-Tsuen Chen, Shih-Ting Huang, Pi-Fen Cheng, Heng-Hua Wang, Hui-Zhu Chen

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This paper discusses the experience of caring for a patient diagnosed with breast cancer and later received total mastectomy during a 2nd trimester pregnancy. She was hospitalized from January 31 to February 4, 2018. Using 'Gordon’s 11 Functional Health Patterns' through physical exams and interviews, the researcher assessed the patient’s physical and mental health and determined the patient to have anxiety, acute pain, and body image disturbance. After establishing a strong relationship with the patient, the researcher helped the patient express her anxiety and personal feelings. A multi-specialist team was formed to evaluate both the patient and her unborn child, before, during, and after surgery. This individualized care allowed the patient and her child to optimize the post-operative results. Aside from medication, the patient also received non-medicinal treatment, including improvement of sleep quality with body positioning, diaphragmatic breathing exercises for pain and stress relief after surgery. Throughout hospitalization, the patient’s physical and emotional needs were addressed daily with listening sessions and empathy. The patient’s husband was also incorporated in the patient’s recovery by teaching both he and the patient how to change the sterile wound dressing, which may have the added benefit of improving marital relationships through shared activities of nurturing. The patient was also given advice about how to improve self-confidence through clothing. Lastly, the patient was encouraged to join a support group for breast cancer patients. Through the sharing of experience in groups and within the family, the patient was helped to adapt to the change of her appearance and re-establish her self-confidence. This level of care expedited the patient’s return to her family life and role of being a mother.

Keywords: anxiety, body image disturbance, breast cancer during pregnancy, multi-specialist team

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Authors: Grzegorz Sierpiński, Ireneusz Celiński, Marcin Staniek

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The article discusses multi modal mobility in contemporary societies as a main planning and organization issue in the functioning of administrative bodies, a problem which really exists in the space of contemporary cities in terms of shaping modern transport systems. The article presents classification of available resources and initiatives undertaken for developing multi modal mobility. Solutions can be divided into three groups of measures–physical measures in the form of changes of the transport network infrastructure, organizational ones (including transport policy) and information measures. The latter ones include in particular direct support for people travelling in the transport network by providing information about ways of using available means of transport. A special measure contributing to this end is a trip planner. The article compares several selected planners. It includes a short description of the Green Travelling Project, which aims at developing a planner supporting environmentally friendly solutions in terms of transport network operation. The article summarizes preliminary findings of the project.

Keywords: mobility, modal split, multimodal trip, multimodal platforms, sustainable transport

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Authors: Farah Naja, Nahla Hwalla, Leila Itani, Shirine Baalbaki, Abla Sibai, Lara Nasreddine

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Purpose: To propose an index for assessing adherence to a Middle-Eastern version of the Mediterranean diet as represented by the traditional Lebanese Mediterranean diet (LMD), to evaluate the association between the LMD and selected European Mediterranean diets (EMD); to examine socio-demographic and lifestyle correlates of adherence to Mediterranean diet (MD) among Lebanese adults. Methods: Using nationally representative dietary intake data of Lebanese adults, an index to measure adherence to the LMD was derived. The choice of food groups used for calculating the LMD score was based on results of previous factor analyses conducted on the same dataset. These food groups included fruits, vegetables, legumes, olive oil, burghol, dairy products, starchy vegetables, dried fruits, and eggs. Using Pearson’s correlation and scores tertiles distributions agreement, the derived LMD index was compared to previously published EMD indexes from Greece, Spain, Italy, France, and EPIC. Results: Fruits, vegetables and olive oil were common denominators to all MD scores. Food groups, specific to the LMD, included burghol and dried fruits. The LMD score significantly correlated with the EMD scores, while being closest to the Italian (r=0.57) and farthest from the French (r=0.21). Percent agreement between scores’ tertile distributions and Kappa statistics confirmed these findings. Multivariate linear regression showed that older age, higher educational, female gender, and healthy lifestyle characteristics were associated with increased adherence to all MD studied. Conclusion: A novel LMD index was proposed to characterize Mediterranean diet in Lebanon, complementing international efforts to characterize the MD and its association with disease risk.

Keywords: mediterranean diet, adherence, Middle-East, Lebanon, Europe

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Authors: Kiana Zeighami, Morteza Ozlati Moghadam

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Designing a high accuracy and high precision motion controller is one of the important issues in today’s industry. There are effective solutions available in the industry but the real-time performance, smoothness and accuracy of the movement can be further improved. This paper discusses a complete solution to carry out the movement of three stepper motors in three dimensions. The objective is to provide a method to design a fully integrated System-on-Chip (SOC)-based motion controller to reduce the cost and complexity of production by incorporating Field Programmable Gate Array (FPGA) into the design. In the proposed method the FPGA receives its commands from a host computer via wireless internet communication and calculates the motion trajectory for three axes. A profile generator module is designed to realize the interpolation algorithm by translating the position data to the real-time pulses. This paper discusses an approach to implement the linear interpolation algorithm, since it is one of the fundamentals of robots’ movements and it is highly applicable in motion control industries. Along with full profile trajectory, the triangular drive is implemented to eliminate the existence of error at small distances. To integrate the parallelism and real-time performance of FPGA with the power of Central Processing Unit (CPU) in executing complex and sequential algorithms, the NIOS II soft-core processor was added into the design. This paper presents different operating modes such as absolute, relative positioning, reset and velocity modes to fulfill the user requirements. The proposed approach was evaluated by designing a custom-made FPGA board along with a mechanical structure. As a result, a precise and smooth movement of stepper motors was observed which proved the effectiveness of this approach.

Keywords: 3-axis linear interpolation, FPGA, motion controller, micro-stepping

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Authors: Sireesha Tamada, Debraj Bhattacharjee, Pranab K. Dan, Prabha Bhola

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The most important component in an automotive transmission system is the gearbox which controls the speed of the vehicle. In an automatic transmission, the right positioning of actuators ensures efficient transmission mechanism embodiment, wherein the challenge lies in formulating the number of actuators associated with modelling a gearbox. Data with respect to actuation and gear shifting sequence has been retrieved from the available literature, including patent documents, and has been used in this proposed heuristics based methodology for modelling actuation sequence in a gear box. This paper presents a methodological approach in designing a gearbox for the purpose of obtaining an optimal shifting sequence. The computational model considers factors namely, the number of stages and gear teeth as input parameters since these two are the determinants of the gear ratios in an epicyclic gear train. The proposed transmission schematic or stick diagram aids in developing the gearbox layout design. The number of iterations and development time required to design a gearbox layout is reduced by using this approach.

Keywords: automatic transmission, gear-shifting, multi-stage planetary gearbox, rank ordered clustering

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Authors: Hasan Serdar Öge, Esra Çiftçi, Kazım Karaboğa

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The main purpose of the research is to address the role of psychological harassment behaviors (mobbing) to which employees are exposed and personality characteristics over work alienation. Research population was composed of the employees of Provincial Special Administration. A survey with four sections was created to measure variables and reach out the basic goals of the research. Correlation and step-wise regression analyses were performed to investigate the separate and overall effects of sub-dimensions of psychological harassment behaviors and personality characteristic on work alienation of employees. Correlation analysis revealed significant but weak relationships between work alienation and psychological harassment and personality characteristics. Step-wise regression analysis revealed also significant relationships between work alienation variable and assault to personality, direct negative behaviors (sub dimensions of mobbing) and openness (sub-dimension of personality characteristics). Each variable was introduced into the model step by step to investigate the effects of significant variables in explaining the variations in work alienation. While the explanation ratio of the first model was 13%, the last model including three variables had an explanation ratio of 24%.

Keywords: alienation, five-factor personality characteristics, mobbing, psychological harassment, work alienation

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Jui Chen Wu, Ming Yi Hsu

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Background and Purpose: Shift work of nursing staff is inevitable in hospital to provide continuing medical care. However, shift work is considered as a health hazard that may cause physical and psychological problems. Serious workplace fatigue of nursing shift work might impact on family, social and work life, moreover, causes serious reduction of quality of medical care, or even malpractice. This study aims to explore relationships among nursing staff’s shift, workplace fatigue and quality of working life. Method: Structured questionnaires were used in this study to explore relationships among shift work, workplace fatigue and quality of working life in nursing staffs. We recruited 590 nursing staffs in different Community Teaching hospitals in Taiwan. Data analysed by descriptive statistics, single sample t-test, single factor analysis, Pearson correlation coefficient and hierarchical regression, etc. Results: The overall workplace fatigue score is 50.59 points. In further analysis, the score of personal burnout, work-related burnout, over-commitment and client-related burnout are 57.86, 53.83, 45.95 and 44.71. The basic attributes of nursing staff are significantly different from those of workplace fatigue with different ages, licenses, sleeping quality, self-conscious health status, number of care patients of chronic diseases and number of care people in the obstetric ward. The shift variables revealed no significant influence on workplace fatigue during the hierarchical regression analysis. About the analysis on nursing staff’s basic attributes and shift on the quality of working life, descriptive results show that the overall quality of working life of nursing staff is 3.23 points. Comparing the average score of the six aspects, the ranked average score are 3.47 (SD= .43) in interrelationship, 3.40 (SD= .46) in self-actualisation, 3.30 (SD= .40) in self-efficacy, 3.15 (SD= .38) in vocational concept, 3.07 (SD= .37) in work aspects, and 3.02 (SD= .56) in organization aspects. The basic attributes of nursing staff are significantly different from quality of working life in different marriage situations, education level, years of nursing work, occupation area, sleep quality, self-conscious health status and number of care in medical ward. There are significant differences between shift mode and shift rate with the quality of working life. The results of the hierarchical regression analysis reveal that one of the shifts variables 'shift mode' which does affect staff’s quality of working life. The workplace fatigue is negatively correlated with the quality of working life, and the over-commitment in the workplace fatigue is positively related to the vocational concept of the quality of working life. According to the regression analysis of nursing staff’s basic attributes, shift mode, workplace fatigue and quality of working life related shift, the results show that the workplace fatigue has a significant impact on nursing staff’s quality of working life. Conclusion: According to our study, shift work is correlated with workplace fatigue in nursing staffs. This results work as important reference for human resources management in hospitals to establishing a more positive and healthy work arrangement policy.

Keywords: nursing staff, shift, workplace fatigue, quality of working life

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Authors: Waleed Basuliman

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Anthropometric dimensions are considered one of the important factors when designing human-machine systems. In this study, the estimation of anthropometric dimensions has been improved by using Artificial Neural Network (ANN) model that is able to predict the anthropometric measurements of Saudi males in Riyadh City. A total of 1427 Saudi males aged 6 to 60 years participated in measuring 20 anthropometric dimensions. These anthropometric measurements are considered important for designing the work and life applications in Saudi Arabia. The data were collected during eight months from different locations in Riyadh City. Five of these dimensions were used as predictors variables (inputs) of the model, and the remaining 15 dimensions were set to be the measured variables (Model’s outcomes). The hidden layers varied during the structuring stage, and the best performance was achieved with the network structure 6-25-15. The results showed that the developed Neural Network model was able to estimate the body dimensions of Saudi male population in Riyadh City. The network's mean absolute percentage error (MAPE) and the root mean squared error (RMSE) were found to be 0.0348 and 3.225, respectively. These results were found less, and then better, than the errors found in the literature. Finally, the accuracy of the developed neural network was evaluated by comparing the predicted outcomes with regression model. The ANN model showed higher coefficient of determination (R2) between the predicted and actual dimensions than the regression model.

Keywords: artificial neural network, anthropometric measurements, back-propagation

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Authors: Hafez Abdo, Rob Ackrill

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Renewable energy (RE) promotion in the UK relies on multiple policy instruments, which are required to overcome the path dependency pressures favouring fossil fuels. These instruments include targeted funding schemes and economy-wide instruments embedded in the tax code. The resulting complexity of incentives raises important questions around the coherence and effectiveness of these instruments for RE generation. This complexity is exacerbated by UK RE policy being nested within EU policy in a multi-level governance (MLG) setting. To gain analytical traction on such complexity, this study will analyse policies promoting the on-farm generation of energy for heat and power, from farm and food waste, via anaerobic digestion. Utilising both primary and secondary data, it seeks to address a particular lacuna in the academic literature. Via a localised, in-depth investigation into the complexity of policy instruments promoting RE, this study will help our theoretical understanding of the challenges that MLG and path dependency pressures present to policymakers of multi-dimensional policies.

Keywords: anaerobic digestion, energy, green, policy, renewable, tax, UK

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Authors: Khalid Kaddoura, Ahmed Assad, Tarek Zayed

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Sewer pipelines are prone to deterioration over-time. In fact, their deterioration does not follow a fixed downward pattern. This is in fact due to the defects that propagate through their service life. Sewer pipeline defects are categorized into distinct groups. However, the main two groups are the structural and operational defects. By definition, the structural defects influence the structural integrity of the sewer pipelines such as deformation, cracks, fractures, holes, etc. However, the operational defects are the ones that affect the flow of the sewer medium in the pipelines such as: roots, debris, attached deposits, infiltration, etc. Yet, the process for each defect to emerge follows a cause and effect relationship. Deformation, which is the change of the sewer pipeline geometry, is one type of an influencing defect that could be found in many sewer pipelines due to many surrounding factors. This defect could lead to collapse if the percentage exceeds 15%. Therefore, it is essential to predict the deformation percentage before confronting such a situation. Accordingly, this study will predict the percentage of the deformation defect in sewer pipelines adopting the multiple regression analysis. Several factors will be considered in establishing the model, which are expected to influence the defamation defect severity. Besides, this study will construct a time-based curve to understand how the defect would evolve overtime. Thus, this study is expected to be an asset for decision-makers as it will provide informative conclusions about the deformation defect severity. As a result, inspections will be minimized and so the budgets.

Keywords: deformation, prediction, regression analysis, sewer pipelines

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Authors: Nisa Özge Önal, Kamil Karaçuha, Göksu Hazar Erdinç, Banu Bahar Karaçuha, Ertuğrul Karaçuha

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The study aims to use a mathematical approach with the fractional calculus which is developed to have the ability to continuously analyze the factors related to the children’s height development. Until now, tracking the development of the child is getting more important and meaningful. Knowing and determining the factors related to the physical development of the child any desired time would provide better, reliable and accurate results for childcare. In this frame, 7 groups for height percentile curve (3th, 10th, 25th, 50th, 75th, 90th, and 97th) of Turkey are used. By using discrete height data of 0-18 years old children and the least squares method, a continuous curve is developed valid for any time interval. By doing so, in any desired instant, it is possible to find the percentage and location of the child in Percentage Chart. Here, with the help of the fractional calculus theory, a mathematical model is developed. The outcomes of the proposed approach are quite promising compared to the linear and the polynomial method. The approach also yields to predict the expected values of children in the sense of height.

Keywords: children growth percentile, children physical development, fractional calculus, linear and polynomial model

Procedia PDF Downloads 138