Search results for: rational ball curve

Authors: Eun Mi Ryu, Ah Young An, Ji Yeon Kang, Yeong Soo Shin, Hee Sun Kim

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As the number of fire incidents has been increased, fire incidents significantly damage economy and human lives. Especially when high strength reinforced concrete is exposed to high temperature due to a fire, deterioration occurs such as loss in strength and elastic modulus, cracking, and spalling of the concrete. Therefore, it is important to understand risk of structural safety in building structures by studying structural behaviors and rehabilitation of fire damaged high strength concrete structures. This paper aims at investigating rehabilitation effect on fire damaged high strength concrete beams using experimental and analytical methods. In the experiments, flexural specimens with high strength concrete are exposed to high temperatures according to ISO 834 standard time temperature curve. After heated, the fire damaged reinforced concrete (RC) beams having different cover thicknesses and fire exposure time periods are rehabilitated by removing damaged part of cover thickness and filling polymeric mortar into the removed part. From four-point loading test, results show that maximum loads of the rehabilitated RC beams are 1.8~20.9% higher than those of the non-fire damaged RC beam. On the other hand, ductility ratios of the rehabilitated RC beams are decreased than that of the non-fire damaged RC beam. In addition, structural analyses are performed using ABAQUS 6.10-3 with same conditions as experiments to provide accurate predictions on structural and mechanical behaviors of rehabilitated RC beams. For the rehabilitated RC beam models, integrated temperature–structural analyses are performed in advance to obtain geometries of the fire damaged RC beams. After spalled and damaged parts are removed, rehabilitated part is added to the damaged model with material properties of polymeric mortar. Three dimensional continuum brick elements are used for both temperature and structural analyses. The same loading and boundary conditions as experiments are implemented to the rehabilitated beam models and nonlinear geometrical analyses are performed. Structural analytical results show good rehabilitation effects, when the result predicted from the rehabilitated models are compared to structural behaviors of the non-damaged RC beams. In this study, fire damaged high strength concrete beams are rehabilitated using polymeric mortar. From four point loading tests, it is found that such rehabilitation is able to make the structural performance of fire damaged beams similar to non-damaged RC beams. The predictions from the finite element models show good agreements with the experimental results and the modeling approaches can be used to investigate applicability of various rehabilitation methods for further study.

Keywords: fire, high strength concrete, rehabilitation, reinforced concrete beam

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Authors: Priyanka Chaudhary, M. Rizwan

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This paper presents a grid synchronization technique based on adaptive notch filter for SPV (Solar Photovoltaic) system along with MPPT (Maximum Power Point Tracking) techniques. An efficient grid synchronization technique offers proficient detection of various components of grid signal like phase and frequency. It also acts as a barrier for harmonics and other disturbances in grid signal. A reference phase signal synchronized with the grid voltage is provided by the grid synchronization technique to standardize the system with grid codes and power quality standards. Hence, grid synchronization unit plays important role for grid connected SPV systems. As the output of the PV array is fluctuating in nature with the meteorological parameters like irradiance, temperature, wind etc. In order to maintain a constant DC voltage at VSC (Voltage Source Converter) input, MPPT control is required to track the maximum power point from PV array. In this work, a variable step size P & O (Perturb and Observe) MPPT technique with DC/DC boost converter has been used at first stage of the system. This algorithm divides the dPpv/dVpv curve of PV panel into three separate zones i.e. zone 0, zone 1 and zone 2. A fine value of tracking step size is used in zone 0 while zone 1 and zone 2 requires a large value of step size in order to obtain a high tracking speed. Further, adaptive notch filter based control technique is proposed for VSC in PV generation system. Adaptive notch filter (ANF) approach is used to synchronize the interfaced PV system with grid to maintain the amplitude, phase and frequency parameters as well as power quality improvement. This technique offers the compensation of harmonics current and reactive power with both linear and nonlinear loads. To maintain constant DC link voltage a PI controller is also implemented and presented in this paper. The complete system has been designed, developed and simulated using SimPower System and Simulink toolbox of MATLAB. The performance analysis of three phase grid connected solar photovoltaic system has been carried out on the basis of various parameters like PV output power, PV voltage, PV current, DC link voltage, PCC (Point of Common Coupling) voltage, grid voltage, grid current, voltage source converter current, power supplied by the voltage source converter etc. The results obtained from the proposed system are found satisfactory.

Keywords: solar photovoltaic systems, MPPT, voltage source converter, grid synchronization technique

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Authors: Saif Alomari

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The paper aims at giving physical and mathematical descriptions of how the structural parameters of a resonant tunnelling diode (RTD) affect its output characteristics. Specifically, the value of the peak voltage, peak current, peak to valley current ratio (PVCR), and the difference between peak and valley voltages and currents ΔV and ΔI. A simulation-based approach using the Non-Equilibrium Green Function (NEGF) formalism based on the Silvaco ATLAS simulator is employed to conduct a series of designed experiments. These experiments show how the doping concentration in the emitter and collector layers, their thicknesses, and the width of the barriers and the quantum well influence the above-mentioned output characteristics. Each of these parameters was systematically changed while holding others fixed in each set of experiments. Factorial experiments are outside the scope of this work and will be investigated in future. The physics involved in the operation of the device is thoroughly explained and mathematical models based on curve fitting and underlaying physical principles are deduced. The models can be used to design devices with predictable output characteristics. These models were found absent in the literature that the author acanned. Results show that the doping concentration in each region has an effect on the value of the peak voltage. It is found that increasing the carrier concentration in the collector region shifts the peak to lower values, whereas increasing it in the emitter shifts the peak to higher values. In the collector’s case, the shift is either controlled by the built-in potential resulting from the concentration gradient or the conductivity enhancement in the collector. The shift to higher voltages is found to be also related to the location of the Fermi-level. The thicknesses of these layers play a role in the location of the peak as well. It was found that increasing the thickness of each region shifts the peak to higher values until a specific characteristic length, afterwards the peak becomes independent of the thickness. Finally, it is shown that the thickness of the barriers can be optimized for a particular well width to produce the highest PVCR or the highest ΔV and ΔI. The location of the peak voltage is important in optoelectronic applications of RTDs where the operating point of the device is usually the peak voltage point. Furthermore, the PVCR, ΔV, and ΔI are of great importance for building RTD-based oscillators as they affect the frequency response and output power of the oscillator.

Keywords: peak to valley ratio, peak voltage shift, resonant tunneling diodes, structural parameters

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Authors: Andrea N. Alarcon, Julian D. Castro, Gloria C. Rojas, Paola A. Vaca, Santiago Ortiz, Gustavo Martinez, Pablo D. Lemoine

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Recently, electoral results across many countries have shown to be inconsistent with rational decision theory, which states that individuals make decisions based on maximizing benefits and reducing risks. An alternative explanation has emerged: Fear and rage-driven vote have been proved to be highly effective for political persuasion and mobilization. This phenomenon has been evident in the 2016 elections in the United States, 2006 elections in Mexico, 1998 elections in Venezuela, and 2004 elections in Bolivia. In Colombia, it has occurred recently in the 2016 plebiscite for peace and 2018 presidential elections. The aim of this study is to explain this phenomenon using cultural cognition theory, referring to the psychological predisposition individuals have to believe that its own and its peer´s behavior is correct and, therefore, beneficial to the entire society. Cultural cognition refers to the tendency of individuals to fit perceived risks, and factual beliefs into group shared values; the Cultural Cognition Worldview Scales (CCWS) measures cultural perceptions through two different dimensions: Individualism-communitarianism and hierarchy-egalitarianism. The former refers to attitudes towards social dominance based on conspicuous and static characteristics (sex, ethnicity or social class), while the latter refers to attitudes towards a social ordering in which it is expected from individuals to guarantee their own wellbeing without society´s or government´s intervention. A probabilistic national sample was obtained from different polls from the consulting and public opinion company Centro Nacional de Consultoría. Sociodemographic data was obtained along with CCWS scores, a subjective measure of left-right ideological placement and vote intention for 2019 Mayor´s elections were also included in the questionnaires. Finally, the question “In your opinion, what is the greatest risk Colombia is facing right now?” was included to identify perceived risk in the population. Preliminary results show that Colombians are highly distributed among hierarchical communitarians and egalitarian individualists (30.9% and 31.7%, respectively), and to a less extent among hierarchical individualists and egalitarian communitarians (19% and 18.4%, respectively). Males tended to be more hierarchical (p < .000) and communitarian (p=.009) than females. ANOVA´s revealed statistically significant differences between groups (quadrants) for the level of schooling, left-right ideological orientation, and stratum (p < .000 for all), and proportion differences revealed statistically significant differences for groups of age (p < .001). Differences and distributions for vote intention and perceived risks are still being processed and results are yet to be analyzed. Results show that Colombians are differentially distributed among quadrants in regard to sociodemographic data and left-right ideological orientation. These preliminary results indicate that this study may shed some light on why Colombians vote the way they do, and future qualitative data will show the fears emerging from the identified values in the CCWS and the relation this has with vote intention.

Keywords: communitarianism, cultural cognition, egalitarianism, hierarchy, individualism, perceived risks

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Authors: Kyung Jin Kim, Jiwon Kwak, Jae-Hoon Lee, Soo Suk Lee

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MicroRNAs (miRNA) are a class of endogenous, single-stranded, small, and non-protein coding RNA molecules typically 20-25 nucleotides long. They are thought to regulate the expression of other genes in a broad range by binding to 3’- untranslated regions (3’-UTRs) of specific mRNAs. The detection of miRNAs is very important for understanding of the function of these molecules and in the diagnosis of variety of human diseases. However, detection of miRNAs is very challenging because of their short length and high sequence similarities within miRNA families. So, a simple-to-use, low-cost, and highly sensitive method for the detection of miRNAs is desirable. In this study, we demonstrate a novel bi-directional extension (BDE) assay. In the first step, a specific linear RT primer is hybridized to 6-10 base pairs from the 3’-end of a target miRNA molecule and then reverse transcribed to generate a cDNA strand. After reverse transcription, the cDNA was hybridized to the 3’-end which is BDE sequence; it played role as the PCR template. The PCR template was amplified in an SYBR green-based quantitative real-time PCR. To prove the concept, we used human brain total RNA. It could be detected quantitatively in the range of seven orders of magnitude with excellent linearity and reproducibility. To evaluate the performance of BDE assay, we contrasted sensitivity and specificity of the BDE assay against a commercially available poly (A) tailing method using miRNAs for let-7e extracted from A549 human epithelial lung cancer cells. The BDE assay displayed good performance compared with a poly (A) tailing method in terms of specificity and sensitivity; the CT values differed by 2.5 and the melting curve showed a sharper than poly (A) tailing methods. We have demonstrated an innovative, cost-effective BDE assay that allows improved sensitivity and specificity in detection of miRNAs. Dynamic range of the SYBR green-based RT-qPCR for miR-145 could be represented quantitatively over a range of 7 orders of magnitude from 0.1 pg to 1.0 μg of human brain total RNA. Finally, the BDE assay for detection of miRNA species such as let-7e shows good performance compared with a poly (A) tailing method in terms of specificity and sensitivity. Thus BDE proves a simple, low cost, and highly sensitive assay for various miRNAs and should provide significant contributions in research on miRNA biology and application of disease diagnostics with miRNAs as targets.

Keywords: bi-directional extension (BDE), microRNA (miRNA), poly (A) tailing assay, reverse transcription, RT-qPCR

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Authors: Yuan-Hao Lee, Chia-Wei Lee, Ming-Fang Lin, Tzu-Huei Wu, Chih-Hsiang Ko, Wing P. Chan

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Computed tomography (CT) scan of the head is a good method for investigating cranial lesions. However, radiation-induced oxidative stress can be accumulated in the eyes and promote carcinogenesis and cataract. In this regard, we aimed to protect the eyes with barium sulfate shield(s) during CT scans and investigate the resultant image quality and radiation dose to the eye. Patients who underwent health examinations were selectively enrolled in this study in compliance with the protocol approved by the Ethics Committee of the Joint Institutional Review Board at Taipei Medical University. Participants’ brains were scanned with a water-based marker simultaneously by a multislice CT scanner (SOMATON Definition Flash) under a fixed tube current-time setting or automatic tube current modulation (TCM). The lens dose was measured by Gafchromic films, whose dose response curve was previously fitted using thermoluminescent dosimeters, with or without barium sulfate or bismuth-antimony shield laid above. For the assessment of image quality CT images at slice planes that exhibit the interested regions on the zygomatic, orbital and nasal bones of the head phantom as well as the water-based marker were used for calculating the signal-to-noise and contrast-to-noise ratios. The application of barium sulfate and bismuth-antimony shields decreased 24% and 47% of the lens dose on average, respectively. Under topogram-based TCM, the dose saving power of bismuth-antimony shield was mitigated whereas that of barium sulfate shield was enhanced. On the other hand, the signal-to-noise and contrast-to-noise ratios of DSCT images were decreased separately by barium sulfate and bismuth-antimony shield, resulting in an overall reduction of the CNR. In contrast, the integration of topogram-based TCM elevated signal difference between the ROIs on the zygomatic bones and eyeballs while preferentially decreasing the signal-to-noise ratios upon the use of barium sulfate shield. The results of this study indicate that the balance between eye exposure and image quality can be optimized by combining eye shields with topogram-based TCM on the multislice scanner. Eye shielding could change the photon attenuation characteristics of tissues that are close to the shield. The application of both shields on eye protection hence is not recommended for seeking intraorbital lesions.

Keywords: computed tomography, barium sulfate shield, dose saving, image quality

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Authors: Aleksandr I. Korobov, Natalia V. Shirgina, Aleksey I. Kokshaiskiy, Vyacheslav M. Prokhorov

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The paper presents the results of an experimental study of the effect of reverse mechanical load-unloading on the mechanical, linear, and nonlinear elastic properties of n-AMg6/C60 nanocomposite. Samples for experimental studies of n-AMg6/C60 nanocomposite were obtained by grinding AMg6 polycrystalline alloy in a planetary mill with 0.3 wt % of C60 fullerite in an argon atmosphere. The resulting product consisted of 200-500-micron agglomerates of nanoparticles. X-ray coherent scattering (CSL) method has shown that the average nanoparticle size is 40-60 nm. The resulting preform was extruded at high temperature. Modifications of C60 fullerite interferes the process of recrystallization at grain boundaries. In the samples of n-AMg6/C60 nanocomposite, the load curve is measured: the dependence of the mechanical stress σ on the strain of the sample ε under its multi-cycle load-unloading process till its destruction. The hysteresis dependence σ = σ(ε) was observed, and insignificant residual strain ε < 0.005 were recorded. At σ≈500 MPa and ε≈0.025, the sample was destroyed. The destruction of the sample was fragile. Microhardness was measured before and after destruction of the sample. It was found that the loading-unloading process led to an increase in its microhardness. The effect of the reversible mechanical stress on the linear and nonlinear elastic properties of the n-AMg6/C60 nanocomposite was studied experimentally by ultrasonic method on the automated complex Ritec RAM-5000 SNAP SYSTEM. In the n-AMg6/C60 nanocomposite, the velocities of the longitudinal and shear bulk waves were measured with the pulse method, and all the second-order elasticity coefficients and their dependence on the magnitude of the reversible mechanical stress applied to the sample were calculated. Studies of nonlinear elastic properties of the n-AMg6/C60 nanocomposite at reversible load-unloading of the sample were carried out with the spectral method. At arbitrary values of the strain of the sample (up to its breakage), the dependence of the amplitude of the second longitudinal acoustic harmonic at a frequency of 2f = 10MHz on the amplitude of the first harmonic at a frequency f = 5MHz of the acoustic wave is measured. Based on the results of these measurements, the values of the nonlinear acoustic parameter in the n-AMg6/C60 nanocomposite sample at different mechanical stress were determined. The obtained results can be used in solid-state physics, materials science, for development of new techniques for nondestructive testing of structural materials using methods of nonlinear acoustic diagnostics. This study was supported by the Russian Science Foundation (project №14-22-00042).

Keywords: nanocomposite, generation of acoustic harmonics, nonlinear acoustic parameter, hysteresis

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Authors: Michael N. O'Sullivan, Con Sheahan

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Researchers have spent decades developing tools and techniques to aid teams in the new product development (NPD) process. Despite this, it is evident that there is a huge gap between their academic prevalence and their industry adoption. For the fuzzy front-end, in particular, there is a wide range of tools to choose from, including the Kano Model, the House of Quality, and many others. In fact, there are so many tools that it can often be difficult for teams to know which ones to use and how they interact with one another. Moreover, while the benefits of using these tools are obvious to industrialists, they are rarely used as they carry a learning curve that is too steep and they become too complex to manage over time. In essence, it is commonly believed that they are simply not worth the effort required to learn and use them. This research explores a streamlined process for the fuzzy front-end, assembling the most effective tools and making them accessible to everyone. The process was developed iteratively over the course of 3 years, following over 80 final year NPD teams from engineering, design, technology, and construction as they carried a product from concept through to production specification. Questionnaires, focus groups, and observations were used to understand the usability issues with the tools involved, and a human-centred design approach was adopted to produce a solution to these issues. The solution takes the form of physical toolkit, similar to a board game, which allows the team to play through an example of a new product development in order to understand the process and the tools, before using it for their own product development efforts. A complimentary website is used to enhance the physical toolkit, and it provides more examples of the tools being used, as well as deeper discussions on each of the topics, allowing teams to adapt the process to their skills, preferences and product type. Teams found the solution very useful and intuitive and experienced significantly less confusion and mistakes with the process than teams who did not use it. Those with a design background found it especially useful for the engineering principles like Quality Function Deployment, while those with an engineering or technology background found it especially useful for design and customer requirements acquisition principles, like Voice of the Customer. Products developed using the toolkit are added to the website as more examples of how it can be used, creating a loop which helps future teams understand how the toolkit can be adapted to their project, whether it be a small consumer product or a large B2B service. The toolkit unlocks the potential of these beneficial tools to those in industry, both for large, experienced teams and for inexperienced start-ups. It allows users to assess the market potential of their product concept faster and more effectively, arriving at the product design stage with technical requirements prioritized according to their customers’ needs and wants.

Keywords: new product development, fuzzy front-end, usability, Kano model, quality function deployment, voice of customer

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Authors: Shahram Naghizadeh Raeisi, Ali Alghooneh, Seyed Jalal Razavi Zahedkolaei

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Herein, a shelf stable (no refrigeration required) UHT processed, aseptically packaged whey protein drink was formulated by using a new strategy in microparticulate process. Applying thermal and two-dimensional mechanical treatments simultaneously, a modified protein (MWPC-80) was produced. Then the physical, thermal and thermodynamic properties of MWPC-80 were assessed using particle size analysis, dynamic temperature sweep (DTS), and differential scanning calorimetric (DSC) tests. Finally, using MWPC-80, a new RTD beverage was formulated, and shelf stability was assessed for three months at ambient temperature (25 °C). Non-isothermal dynamic temperature sweep was performed, and the results were analyzed by a combination of classic rate equation, Arrhenius equation, and time-temperature relationship. Generally, results showed that temperature dependency of the modified sample was significantly (Pvalue<0.05) less than the control one contained WPC-80. The changes in elastic modulus of the MWPC did not show any critical point at all the processed stages, whereas, the control sample showed two critical points during heating (82.5 °C) and cooling (71.10 °C) stages. Thermal properties of samples (WPC-80 & MWPC-80) were assessed using DSC with 4 °C /min heating speed at 20-90 °C heating range. Results did not show any thermal peak in MWPC DSC curve, which suggested high thermal resistance. On the other hands, WPC-80 sample showed a significant thermal peak with thermodynamic properties of ∆G:942.52 Kj/mol ∆H:857.04 Kj/mole and ∆S:-1.22Kj/mole°K. Dynamic light scattering was performed and results showed 0.7 µm and 15 nm average particle size for MWPC-80 and WPC-80 samples, respectively. Moreover, particle size distribution of MWPC-80 and WPC-80 were Gaussian-Lutresian and normal, respectively. After verification of microparticulation process by DTS, PSD and DSC analyses, a 10% why protein beverage (10% w/w/ MWPC-80, 0.6% w/w vanilla flavoring agent, 0.1% masking flavor, 0.05% stevia natural sweetener and 0.25% citrate buffer) was formulated and UHT treatment was performed at 137 °C and 4 s. Shelf life study did not show any jellification or precipitation of MWPC-80 contained beverage during three months storage at ambient temperature, whereas, WPC-80 contained beverage showed significant precipitation and jellification after thermal processing, even at 3% w/w concentration. Consumer knowledge on nutritional advantages of whey protein increased the request for using this protein in different food systems especially RTD beverages. These results could make a huge difference in this industry.

Keywords: high protein whey beverage, micropartiqulation, two-dimentional mechanical treatments, thermodynamic properties

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Authors: Ousmane Sarr

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The Senegalese coasts have high productivity of fishery resources due to the frequency of intense up-welling system that occurs along its coast, caused by the maritime trade winds making its waters nutrients rich. Fishing plays a primordial role in Senegal's socioeconomic plans and food security. However, a global diagnosis of the Senegalese maritime fishing sector has highlighted the challenges this sector encounters. Among these concerns, some significant stocks, a priority target for artisanal fishing, need further assessment. If no efforts are made in this direction, most stock will be overexploited or even in decline. It is in this context that this research was initiated. This investigation aimed to apply a multi-modal approach (LBB, Catch-only-based CMSY model and its most recent version (CMSY++); JABBA, and JABBA-Select) to assess the stock of Atlantic bonito, Sarda sarda (Bloch, 1793) in the Senegalese Exclusive Economic Zone (SEEZ). Available catch, effort, and size data from Atlantic bonito over 15 years (2004-2018) were used to calculate the nominal and standardized CPUE, size-frequency distribution, and length at retentions (50 % and 95 % selectivity) of the species. These relevant results were employed as input parameters for stock assessment models mentioned above to define the stock status of this species in this region of the Atlantic Ocean. The LBB model indicated an Atlantic bonito healthy stock status with B/BMSY values ranging from 1.3 to 1.6 and B/B0 values varying from 0.47 to 0.61 of the main scenarios performed (BON_AFG_CL, BON_GN_Length, and BON_PS_Length). The results estimated by LBB are consistent with those obtained by CMSY. The CMSY model results demonstrate that the SEEZ Atlantic bonito stock is in a sound condition in the final year of the main scenarios analyzed (BON, BON-bt, BON-GN-bt, and BON-PS-bt) with sustainable relative stock biomass (B2018/BMSY = 1.13 to 1.3) and fishing pressure levels (F2018/FMSY= 0.52 to 1.43). The B/BMSY and F/FMSY results for the JABBA model ranged between 2.01 to 2.14 and 0.47 to 0.33, respectively. In contrast, The estimated B/BMSY and F/FMSY for JABBA-Select ranged from 1.91 to 1.92 and 0.52 to 0.54. The Kobe plots results of the base case scenarios ranged from 75% to 89% probability in the green area, indicating sustainable fishing pressure and an Atlantic bonito healthy stock size capable of producing high yields close to the MSY. Based on the stock assessment results, this study highlighted scientific advice for temporary management measures. This study suggests an improvement of the selectivity parameters of longlines and purse seines and a temporary prohibition of the use of sleeping nets in the fishery for the Atlantic bonito stock in the SEEZ based on the results of the length-base models. Although these actions are temporary, they can be essential to reduce or avoid intense pressure on the Atlantic bonito stock in the SEEZ. However, it is necessary to establish harvest control rules to provide coherent and solid scientific information that leads to appropriate decision-making for rational and sustainable exploitation of Atlantic bonito in the SEEZ and the Eastern Atlantic Ocean.

Keywords: multi-model approach, stock assessment, atlantic bonito, SEEZ

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Authors: Jun Ren, Zhen-Hua Ming, He-Feng Huang, Jian-Zhong Sheng

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Adverse intrauterine stimulus during critical or sensitive periods in early life, may lead to health risk not only in later life span, but also further generations. Intrauterine hyperglycaemia, as a major feature of gestational diabetes mellitus (GDM), is a typical adverse environment for both F1 fetus and F1 gamete cells development. However, there is scare information of phenotypic difference of metabolic memory between somatic cells and germ cells exposed by intrauterine hyperglycaemia. The direct transmission effect of intrauterine hyperglycaemia per se has not been assessed either. In this study, we built a GDM mice model and selected male GDM offspring without pre-diabetic phenotype as our founders, to exclude postnatal diabetic influence on gametes, thereby investigate the direct transmission effect of intrauterine hyperglycaemia exposure on F2 offspring, and we further compared the metabolic difference of affected F1-GDM male offspring and F2 offspring. A GDM mouse model of intrauterine hyperglycemia was established by intraperitoneal injection of streptozotocin after pregnancy. Pups of GDM mother were fostered by normal control mothers. All the mice were fed with standard food. Male GDM offspring without metabolic dysfunction phenotype were crossed with normal female mice to obtain F2 offspring. Body weight, glucose tolerance test, insulin tolerance test and homeostasis model of insulin resistance (HOMA-IR) index were measured in both generations at 8 week of age. Some of F1-GDM male mice showed impaired glucose tolerance (p < 0.001), none of F1-GDM male mice showed impaired insulin sensitivity. Body weight of F1-GDM mice showed no significance with control mice. Some of F2-GDM offspring exhibited impaired glucose tolerance (p < 0.001), all the F2-GDM offspring exhibited higher HOMA-IR index (p < 0.01 of normal glucose tolerance individuals vs. control, p < 0.05 of glucose intolerance individuals vs. control). All the F2-GDM offspring exhibited higher ITT curve than control (p < 0.001 of normal glucose tolerance individuals, p < 0.05 of glucose intolerance individuals, vs. control). F2-GDM offspring had higher body weight than control mice (p < 0.001 of normal glucose tolerance individuals, p < 0.001 of glucose intolerance individuals, vs. control). While glucose intolerance is the only phenotype that F1-GDM male mice may exhibit, F2 male generation of healthy F1-GDM father showed insulin resistance, increased body weight and/or impaired glucose tolerance. These findings imply that intrauterine hyperglycaemia exposure affects germ cells and somatic cells differently, thus F1 and F2 offspring demonstrated distinct metabolic dysfunction phenotypes. And intrauterine hyperglycaemia exposure per se has a strong influence on F2 generation, independent of postnatal metabolic dysfunction exposure.

Keywords: inheritance, insulin resistance, intrauterine hyperglycaemia, offspring

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Authors: Chang Liang, Weizhi Gong, Yan Zhang

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Purpose: Hepatocellular carcinoma (HCC) is a malignant tumor with a high mortality rate and poor prognosis worldwide. Murine double minute 2 (MDM2) regulates the tumor suppressor p53, increasing cancer risk and accelerating tumor progression. Speckle-type POX virus and zinc finger protein (SPOP), a key of subunit of Cullin-Ring E3 ligase, inhibits tumor genesis and progression by the ubiquitination of its downstream substrates. This study aimed to clarify whether SPOP and MDM2 are mutually regulated in HCC and the correlation between SPOP and MDM2 and the prognosis of HCC patients. Methods: First, the expression of SPOP and MDM2 in HCC tissues were detected by TCGA database. Then, 53 paired samples of HCC tumor and adjacent tissues were collected to evaluate the expression of SPOP and MDM2 using immunohistochemistry. Chi-square test or Fisher’s exact test were used to analyze the relationship between clinicopathological features and the expression levels of SPOP and MDM2. In addition, Kaplan‒Meier curve analysis and log-rank test were used to investigate the effects of SPOP and MDM2 on the survival of HCC patients. Last, the Multivariate Cox proportional risk regression model analyzed whether the different expression levels of SPOP and MDM2 were independent risk factors for the prognosis of HCC patients. Results: Bioinformatics analysis revealed the low expression of SPOP and high expression of MDM2 were related to worse prognosis of HCC patients. The relationship between the expression of SPOP and MDM2 and tumor stem-like features showed an opposite trend. The immunohistochemistry showed the expression of SPOP protein was significantly downregulated while MDM2 protein significantly upregulated in HCC tissue compared to that in para-cancerous tissue. Tumors with low SPOP expression were related to worse T stage and Barcelona Clinic Liver Cancer (BCLC) stage, but tumors with high MDM2 expression were related to worse T stage, M stage, and BCLC stage. Kaplan–Meier curves showed HCC patients with high SPOP expression and low MDM2 expression had better survival than those with low SPOP expression and high MDM2 expression (P < 0.05). A multivariate Cox proportional risk regression model confirmed that a high MDM2 expression level was an independent risk factor for poor prognosis in HCC patients (P <0.05). Conclusion: The expression of SPOP protein was significantly downregulated, while the expression of MDM2 significantly upregulated in HCC. The low expression of SPOP and high expression. of MDM2 were associated with malignant progression and poor prognosis of HCC patients, indicating a potential therapeutic target for HCC patients.

Keywords: hepatocellular carcinoma, murine double minute 2, speckle-type POX virus and zinc finger protein, ubiquitination

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Authors: Antonella Falanga, Armando Cartenì

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Today, an unprecedented amount of data coming from several sources, including mobile devices, sensors, tracking systems, and online platforms, characterizes our lives. The term “big data” not only refers to the quantity of data but also to the variety and speed of data generation. These data hold valuable insights that, when extracted and analyzed, facilitate informed decision-making. The 4Vs of big data - velocity, volume, variety, and value - highlight essential aspects, showcasing the rapid generation, vast quantities, diverse sources, and potential value addition of these kinds of data. This surge of information has revolutionized many sectors, such as business for improving decision-making processes, healthcare for clinical record analysis and medical research, education for enhancing teaching methodologies, agriculture for optimizing crop management, finance for risk assessment and fraud detection, media and entertainment for personalized content recommendations, emergency for a real-time response during crisis/events, and also mobility for the urban planning and for the design/management of public and private transport services. Big data's pervasive impact enhances societal aspects, elevating the quality of life, service efficiency, and problem-solving capacities. However, during this transformative era, new challenges arise, including data quality, privacy, data security, cybersecurity, interoperability, the need for advanced infrastructures, and staff training. Within the transportation sector (the one investigated in this research), applications span planning, designing, and managing systems and mobility services. Among the most common big data applications within the transport sector are, for example, real-time traffic monitoring, bus/freight vehicle route optimization, vehicle maintenance, road safety and all the autonomous and connected vehicles applications. Benefits include a reduction in travel times, road accidents and pollutant emissions. Within these issues, the proper transport demand estimation is crucial for sustainable transportation planning. Evaluating the impact of sustainable mobility policies starts with a quantitative analysis of travel demand. Achieving transportation decarbonization goals hinges on precise estimations of demand for individual transport modes. Emerging technologies, offering substantial big data at lower costs than traditional methods, play a pivotal role in this context. Starting from these considerations, this study explores the usefulness impact of big data within transport demand estimation. This research focuses on leveraging (big) data collected during the COVID-19 pandemic to estimate the evolution of the mobility demand in Italy. Estimation results reveal in the post-COVID-19 era, more than 96 million national daily trips, about 2.6 trips per capita, with a mobile population of more than 37.6 million Italian travelers per day. Overall, this research allows us to conclude that big data better enhances rational decision-making for mobility demand estimation, which is imperative for adeptly planning and allocating investments in transportation infrastructures and services.

Keywords: big data, cloud computing, decision-making, mobility demand, transportation

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Authors: Stephanie Watson, Fabrice Stephenson, Conrad Pilditch, Carolyn Lundquist

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Spatial data detailing both the distribution and density of functionally important marine species are needed to inform management decisions. Species distribution models (SDMs) have proven helpful in this regard; however, models often focus only on species occurrences derived from spatially expansive datasets and lack the resolution and detail required to inform regional management decisions. Boosted regression trees (BRT) were used to produce high-resolution SDMs (250 m) at two spatial scales predicting probability of occurrence, abundance (count per sample unit), density (count per km2) and uncertainty for seven coastal seafloor taxa that vary in habitat usage and distribution to examine prediction differences and implications for coastal management. We investigated if small scale regionally focussed models (82,000 km2) can provide improved predictions compared to data-rich national scale models (4.2 million km2). We explored the variability in predictions across model type (occurrence vs abundance) and model scale to determine if specific taxa models or model types are more robust to geographical variability. National scale occurrence models correlated well with broad-scale environmental predictors, resulting in higher AUC (Area under the receiver operating curve) and deviance explained scores; however, they tended to overpredict in the coastal environment and lacked spatially differentiated detail for some taxa. Regional models had lower overall performance, but for some taxa, spatial predictions were more differentiated at a localised ecological scale. National density models were often spatially refined and highlighted areas of ecological relevance producing more useful outputs than regional-scale models. The utility of a two-scale approach aids the selection of the most optimal combination of models to create a spatially informative density model, as results contrasted for specific taxa between model type and scale. However, it is vital that robust predictions of occurrence and abundance are generated as inputs for the combined density model as areas that do not spatially align between models can be discarded. This study demonstrates the variability in SDM outputs created over different geographical scales and highlights implications and opportunities for managers utilising these tools for regional conservation, particularly in data-limited environments.

Keywords: Benthic ecology, spatial modelling, multi-scalar modelling, marine conservation.

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Authors: Rideci Farias, Haroldo Paranhos

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The present work deals with the use of drainage geo-composite as a deep drainage and geogrid element to reinforce the base of the body of the landfill destined to the road pavement on geological faults in the stretch of the TO-342 Highway, between the cities of Miracema and Miranorte, in the State of Tocantins / TO, Brazil, which for many years was the main link between TO-010 and BR-153, after the city of Palmas, also in the state of Tocantins / TO, Brazil. For this application, geotechnical and geological studies were carried out by means of SPT percussion drilling, drilling and rotary drilling, to understand the problem, identifying the type of faults, filling material and the definition of the water table. According to the geological and geotechnical studies carried out, the area where the route was defined, passes through a zone of longitudinal fault to the runway, with strong breaking / fracturing, with presence of voids, intense alteration and with advanced argilization of the rock and with the filling up parts of the faults by organic and compressible soils leachate from other horizons. This geology presents as a geotechnical aggravating agent a medium of high hydraulic load and very low resistance to penetration. For more than 20 years, the region presented constant excessive deformations in the upper layers of the pavement, which after routine services of regularization, reconformation, re-compaction of the layers and application of the asphalt coating. The faults were quickly propagated to the surface of the asphalt pavement, generating a longitudinal shear, forming steps (unevenness), close to 40 cm, causing numerous accidents and discomfort to the drivers, since the geometric positioning was in a horizontal curve. Several projects were presented to the region's highway department to solve the problem. Due to the need for partial closure of the runway, the short time for execution, the use of geosynthetics was proposed and the most adequate solution for the problem was taken into account the movement of existing geological faults and the position of the water level in relation to several Layers of pavement and failure. In order to avoid any flow of water in the body of the landfill and in the filling material of the faults, a drainage curtain solution was used, carried out at 4.0 meters depth, with drainage geo-composite and as reinforcement element and inhibitor of the possible A geogrid of 200 kN / m of resistance was inserted at the base of the reconstituted landfill. Recent evaluations, after 13 years of application of the solution, show the efficiency of the technique used, supported by the geotechnical studies carried out in the area.

Keywords: geosynthetics, geocomposite, geogrid, road, recovery, geological failure

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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Authors: Petros M. Chronopoulos, Evangelos Z. Astreinidis

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A major issue of the structural assessment and rehabilitation of existing RC structures is the inadequate lap-splicing of the longitudinal reinforcement. Although prohibited by modern Design Codes, the practice of arranging lap-splices inside the critical regions of RC elements was commonly applied in the past. Today this practice is still the rule, at least for conventional new buildings. Therefore, a lot of relevant research is ongoing in many earthquake prone countries. The rehabilitation of deficient lap-splices of RC elements by means of external confinement is widely accepted as the most efficient technique. If correctly applied, this versatile technique offers a limited increase of flexural capacity and a considerable increase of local ductility and of axial and shear capacities. Moreover, this intervention does not affect the stiffness of the elements and does not affect the dynamic characteristics of the structure. This technique has been extensively discussed and researched contributing to vast accumulation of technical and scientific knowledge that has been reported in relevant books, reports and papers, and included in recent Design Codes and Guides. These references are mostly dealing with modeling and redesign, covering both the enhanced (axial and) shear capacity (due to the additional external closed hoops or jackets) and the increased ductility (due to the confining action, preventing the unzipping of lap-splices and the buckling of continuous reinforcement). An analytical and experimental program devoted to RC members with lap-splices is completed in the Lab. of RC/NTU of Athens/GR. This program aims at the proposal of a rational and safe theoretical model and the calibration of the relevant Design Codes’ provisions. Tests, on forty two (42) full scale specimens, covering mostly beams and columns (not walls), strengthened or not, with adequate or inadequate lap-splices, have been already performed and evaluated. In this paper, the results of twelve (12) specimens under fully reversed cyclic actions are presented and discussed. In eight (8) specimens the lap-splices were inadequate (splicing length of 20 or 30 bar diameters) and they were retrofitted before testing by means of additional external confinement. The two (2) most commonly applied confining materials were used in this study, namely steel and FRPs. More specifically, jackets made of CFRP wraps or light cages made of mild steel were applied. The main parameters of these tests were (i) the degree of confinement (internal and external), and (ii) the length of lap-splices, equal to 20, 30 or 45 bar diameters. These tests were thoroughly instrumented and monitored, by means of conventional (LVDTs, strain gages, etc.) and innovative (optic fibre-Bragg-grating) sensors. This allowed for a thorough investigation of the most influencing design parameter, namely the hoop-stress developed in the confining material. Based on these test results and on comparisons with the provisions of modern Design Codes, it could be argued that shorter (than the normative) lap-splices, commonly found in old structures, could still be effective and safe (at least for lengths more than an absolute minimum), depending on the required ductility, if a properly arranged and adequately detailed external confinement is applied.

Keywords: concrete, fibre-Bragg-grating sensors, lap-splices, retrofitting / rehabilitation

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Authors: Zoi Konsoula

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The increasing prevalence of Alzheimer’s disease (AD) and diabetes mellitus (DM) constitutes them major global health problems. Recently, the inhibition of key enzyme activity is considered a potential treatment of both diseases. Specifically, inhibition of acetylcholinesterase (AChE), the key enzyme involved in the breakdown of the neurotransmitter acetylcholine, is a promising approach for the treatment of AD, while inhibition of α-amylase retards the hydrolysis of carbohydrates and, thus, reduces hyperglycemia. Unfortunately, commercially available AChE and α-amylase inhibitors are reported to possess side effects. Consequently, there is a need to develop safe and effective treatments for both diseases. In the present study, pomegranate peel (PP) was extracted using various solvents of increasing polarity, while two extraction methods were employed, the conventional maceration and the ultrasound assisted extraction (UAE). The concentration of bioactive phytoconstituents, such as total phenolics (TPC) and total flavonoids (TFC) in the prepared extracts was evaluated by the Folin-Ciocalteu and the aluminum-flavonoid complex method, respectively. Furthermore, the anti-neurodegenerative and anti-hyperglycemic activity of all extracts was determined using AChE and α-amylase inhibitory activity assays, respectively. The inhibitory activity of the extracts against AChE and α-amylase was characterized by estimating their IC₅₀ value using a dose-response curve, while galanthamine and acarbose were used as positive controls, respectively. Finally, the kinetics of AChE and α-amylase in the presence of the most inhibitory potent extracts was determined by the Lineweaver-Burk plot. The methanolic extract prepared using the UAE contained the highest amount of phytoconstituents, followed by the respective ethanolic extract. All extracts inhibited acetylcholinesterase in a dose-dependent manner, while the increased anticholinesterase activity of the methanolic (IC₅₀ = 32 μg/mL) and ethanolic (IC₅₀ = 42 μg/mL) extract was positively correlated with their TPC content. Furthermore, the activity of the aforementioned extracts was comparable to galanthamine. Similar results were obtained in the case of α-amylase, however, all extracts showed lower inhibitory effect on the carbohydrate hydrolyzing enzyme than on AChE, since the IC₅₀ value ranged from 84 to 100 μg/mL. Also, the α-amylase inhibitory effect of the extracts was lower than acarbose. Finally, the methanolic and ethanolic extracts prepared by UAE inhibited both enzymes in a mixed (competitive/noncompetitive) manner since the Kₘ value of both enzymes increased in the presence of extracts, while the Vmax value decreased. The results of the present study indicate that PP may be a useful source of active compounds for the management of AD and DM. Moreover, taking into consideration that PP is an agro-industrial waste product, its valorization could not only result in economic efficiency but also reduce the environmental pollution.

Keywords: acetylcholinesterase, Alzheimer’s disease, α-amylase, diabetes mellitus, pomegranate

Procedia PDF Downloads 122

Authors: Antonella Falanga, Armando Cartenì

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"Big data" refers to extremely vast and complex sets of data, encompassing extraordinarily large and intricate datasets that require specific tools for meaningful analysis and processing. These datasets can stem from diverse origins like sensors, mobile devices, online transactions, social media platforms, and more. The utilization of big data is pivotal, offering the chance to leverage vast information for substantial advantages across diverse fields, thereby enhancing comprehension, decision-making, efficiency, and fostering innovation in various domains. Big data, distinguished by its remarkable attributes of enormous volume, high velocity, diverse variety, and significant value, represent a transformative force reshaping the industry worldwide. Their pervasive impact continues to unlock new possibilities, driving innovation and advancements in technology, decision-making processes, and societal progress in an increasingly data-centric world. The use of these technologies is becoming more widespread, facilitating and accelerating operations that were once much more complicated. In particular, big data impacts across multiple sectors such as business and commerce, healthcare and science, finance, education, geography, agriculture, media and entertainment and also mobility and logistics. Within the transportation sector, which is the focus of this study, big data applications encompass a wide variety, spanning across optimization in vehicle routing, real-time traffic management and monitoring, logistics efficiency, reduction of travel times and congestion, enhancement of the overall transportation systems, but also mitigation of pollutant emissions contributing to environmental sustainability. Meanwhile, in public administration and the development of smart cities, big data aids in improving public services, urban planning, and decision-making processes, leading to more efficient and sustainable urban environments. Access to vast data reservoirs enables deeper insights, revealing hidden patterns and facilitating more precise and timely decision-making. Additionally, advancements in cloud computing and artificial intelligence (AI) have further amplified the potential of big data, enabling more sophisticated and comprehensive analyses. Certainly, utilizing big data presents various advantages but also entails several challenges regarding data privacy and security, ensuring data quality, managing and storing large volumes of data effectively, integrating data from diverse sources, the need for specialized skills to interpret analysis results, ethical considerations in data use, and evaluating costs against benefits. Addressing these difficulties requires well-structured strategies and policies to balance the benefits of big data with privacy, security, and efficient data management concerns. Building upon these premises, the current research investigates the efficacy and influence of big data by conducting an overview of the primary and recent implementations of big data in transportation systems. Overall, this research allows us to conclude that big data better provide to enhance rational decision-making for mobility choices and is imperative for adeptly planning and allocating investments in transportation infrastructures and services.

Keywords: big data, public transport, sustainable mobility, transport demand, transportation planning

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Authors: Angela U. Makolo

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Protein-coding and Non-coding regions determine the biology of a sequenced transcriptome. Research advances have shown that Non-coding regions are important in disease progression and clinical diagnosis. Existing bioinformatics tools have been targeted towards Protein-coding regions alone. Therefore, there are challenges associated with gaining biological insights from transcriptome sequence data. These tools are also limited to computationally intensive sequence alignment, which is inadequate and less accurate to identify both Protein-coding and Non-coding regions. Alignment-free techniques can overcome the limitation of identifying both regions. Therefore, this study was designed to develop an efficient sequence alignment-free model for identifying both Protein-coding and Non-coding regions in sequenced transcriptomes. Feature grouping and randomization procedures were applied to the input transcriptomes (37,503 data points). Successive iterations were carried out to compute the gradient vector that converged the developed Protein-coding and Non-coding Region Identifier (PNRI) model to the approximate coefficient vector. The logistic regression algorithm was used with a sigmoid activation function. A parameter vector was estimated for every sample in 37,503 data points in a bid to reduce the generalization error and cost. Maximum Likelihood Estimation (MLE) was used for parameter estimation by taking the log-likelihood of six features and combining them into a summation function. Dynamic thresholding was used to classify the Protein-coding and Non-coding regions, and the Receiver Operating Characteristic (ROC) curve was determined. The generalization performance of PNRI was determined in terms of F1 score, accuracy, sensitivity, and specificity. The average generalization performance of PNRI was determined using a benchmark of multi-species organisms. The generalization error for identifying Protein-coding and Non-coding regions decreased from 0.514 to 0.508 and to 0.378, respectively, after three iterations. The cost (difference between the predicted and the actual outcome) also decreased from 1.446 to 0.842 and to 0.718, respectively, for the first, second and third iterations. The iterations terminated at the 390th epoch, having an error of 0.036 and a cost of 0.316. The computed elements of the parameter vector that maximized the objective function were 0.043, 0.519, 0.715, 0.878, 1.157, and 2.575. The PNRI gave an ROC of 0.97, indicating an improved predictive ability. The PNRI identified both Protein-coding and Non-coding regions with an F1 score of 0.970, accuracy (0.969), sensitivity (0.966), and specificity of 0.973. Using 13 non-human multi-species model organisms, the average generalization performance of the traditional method was 74.4%, while that of the developed model was 85.2%, thereby making the developed model better in the identification of Protein-coding and Non-coding regions in transcriptomes. The developed Protein-coding and Non-coding region identifier model efficiently identified the Protein-coding and Non-coding transcriptomic regions. It could be used in genome annotation and in the analysis of transcriptomes.

Keywords: sequence alignment-free model, dynamic thresholding classification, input randomization, genome annotation

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Paula K. Novelli, Margarida M. Barros, Luciana F. Flueri

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Utilization of agricultural by-products, wheat ban and soybean bran, as substrate for solid state fermentation (SSF) was studied, aiming the achievement of different enzymes from Aspergillus sp. with distinct biological characteristics and its application and improvement on animal nutrition. Aspergillus niger and Aspergillus oryzea were studied as they showed very high yield of phytase and protease production, respectively. Phytase activity was measure using p-nitrophenilphosphate as substrate and a standard curve of p-nitrophenol, as the enzymatic activity unit was the quantity of enzyme necessary to release one μmol of p-nitrophenol. Protease activity was measure using azocasein as substrate. Activity for phytase and protease substantially increased when the different biochemical characteristics were considered in the study. Optimum pH and stability of the phytase produced by A. niger with wheat bran as substrate was between 4.0 - 5.0 and optimum temperature of activity was 37oC. Phytase fermented in soybean bran showed constant values at all pHs studied, for optimal and stability, but low production. Phytase with both substrates showed stable activity for temperatures higher than 80oC. Protease from A. niger showed very distinct behavior of optimum pH, acid for wheat bran and basic for soybean bran, respectively and optimal values of temperature and stability at 50oC. Phytase produced by A. oryzae in wheat bran had optimum pH and temperature of 9 and 37oC, respectively, but it was very unstable. On the other hand, proteases were stable at high temperatures, all pH’s studied and showed very high yield when fermented in wheat bran, however when it was fermented in soybean bran the production was very low. Subsequently the upscale production of phytase from A. niger and proteases from A. oryzae were applied as an enzyme additive in fish fed for digestibility studies. Phytases and proteases were produced with stable enzyme activity of 7,000 U.g-1 and 2,500 U.g-1, respectively. When those enzymes were applied in a plant protein based fish diet for digestibility studies, they increased protein, mineral, energy and lipids availability, showing that these new enzymes can improve animal production and performance. In conclusion, the substrate, as well as, the microorganism species can affect the biochemical character of the enzyme produced. Moreover, the production of these enzymes by SSF can be up to 90% cheaper than commercial ones produced with the same fungi species but submerged fermentation. Add to that these cheap enzymes can be easily applied as animal diet additives to improve production and performance.

Keywords: agricultural by-products, animal nutrition, enzymes production, solid state fermentation

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Authors: Maria Arechavaleta, Mark Halpin

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In the United States, costs of solar energy systems have declined to the point that they are viable options for most consumers. However, there are no consistent procedures for specifying sufficient systems. The factors that must be considered are energy consumption, potential solar energy production, and cost. The traditional method of specifying solar energy systems is based on assumed daily levels of available solar energy and average amounts of daily energy consumption. The mismatches between energy production and consumption are usually mitigated using battery energy storage systems, and energy use is curtailed when necessary. The main consumer decision question that drives the total system cost is how much unserved (or curtailed) energy is acceptable? Of course additional solar conversion equipment can be installed to provide greater peak energy production and extra energy storage capability can be added to mitigate longer lasting low solar energy production periods. Each option increases total cost and provides a benefit which is difficult to quantify accurately. An approach to quantify the cost-benefit of adding additional resources, either production or storage or both, based on the statistical concepts of loss-of-energy probability and expected unserved energy, is presented in this paper. Relatively simple calculations, based on site-specific energy availability and consumption data, can be used to show the value of each additional increment of production or storage. With this incremental benefit-cost information, consumers can select the best overall performance combination for their application at a cost they are comfortable paying. The approach is based on a statistical analysis of energy consumption and production characteristics over time. The characteristics are in the forms of curves with each point on the curve representing an energy consumption or production value over a period of time; a one-minute period is used for the work in this paper. These curves are measured at the consumer location under the conditions that exist at the site and the duration of the measurements is a minimum of one week. While greater accuracy could be obtained with longer recording periods, the examples in this paper are based on a single week for demonstration purposes. The weekly consumption and production curves are overlaid on each other and the mismatches are used to size the battery energy storage system. Loss-of-energy probability and expected unserved energy indices are calculated in addition to the total system cost. These indices allow the consumer to recognize and quantify the benefit (probably a reduction in energy consumption curtailment) available for a given increase in cost. Consumers can then make informed decisions that are accurate for their location and conditions and which are consistent with their available funds.

Keywords: battery energy storage systems, loss of load probability, residential renewable energy, solar energy systems

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Authors: Suruchi Saini, Hitender Kumar Tyagi

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With the rapid progress of the nanotechnology industry, it is becoming increasingly important to have compact semiconductor devices to function and offer the best results at various technology nodes. While performing the scaling of the device, several short-channel effects occur. To minimize these scaling limitations, some device architectures have been developed in the semiconductor industry. FinFET is one of the most promising structures. Also, the double-gate 2D Fin field effect transistor has the benefit of suppressing short channel effects (SCE) and functioning well for less than 14 nm technology nodes. In the present research, the MuGFET simulation tool is used to analyze and explain the electrical behaviour of a double-gate 2D Fin field effect transistor. The drift-diffusion and Poisson equations are solved self-consistently. Various models, such as Fermi-Dirac distribution, bandgap narrowing, carrier scattering, and concentration-dependent mobility models, are used for device simulation. The transfer and output characteristics of the double-gate 2D Fin field effect transistor are determined at 10 nm technology node. The performance parameters are extracted in terms of threshold voltage, trans-conductance, leakage current and current on-off ratio. In this paper, the device performance is analyzed at different structure parameters. The utilization of the Id-Vg curve is a robust technique that holds significant importance in the modeling of transistors, circuit design, optimization of performance, and quality control in electronic devices and integrated circuits for comprehending field-effect transistors. The FinFET structure is optimized to increase the current on-off ratio and transconductance. Through this analysis, the impact of different channel widths, source and drain lengths on the Id-Vg and transconductance is examined. Device performance was affected by the difficulty of maintaining effective gate control over the channel at decreasing feature sizes. For every set of simulations, the device's features are simulated at two different drain voltages, 50 mV and 0.7 V. In low-power and precision applications, the off-state current is a significant factor to consider. Therefore, it is crucial to minimize the off-state current to maximize circuit performance and efficiency. The findings demonstrate that the performance of the current on-off ratio is maximum with the channel width of 3 nm for a gate length of 10 nm, but there is no significant effect of source and drain length on the current on-off ratio. The transconductance value plays a pivotal role in various electronic applications and should be considered carefully. In this research, it is also concluded that the transconductance value of 340 S/m is achieved with the fin width of 3 nm at a gate length of 10 nm and 2380 S/m for the source and drain extension length of 5 nm, respectively.

Keywords: current on-off ratio, FinFET, short-channel effects, transconductance

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Authors: William C. Alexander

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The oscillating water column (OSC) wave energy converter (WEC) with deep water reactance (DWR) consists of a large hollow sphere filled with seawater at the base, referred to as the ‘stabilizer’, a hollow cylinder at the top of the device, with a said cylinder having a bottom open to the sea and a sealed top save for an orifice which leads to an air turbine, and a long, narrow rod connecting said stabilizer with said cylinder. A small amount of ballast at the bottom of the stabilizer and a small amount of floatation in the cylinder keeps the device upright in the sea. The floatation is set such that the mean water level is nominally halfway up the cylinder. The entire device is loosely moored to the seabed to keep it from drifting away. In the presence of ocean waves, seawater will move up and down within the cylinder, producing the ‘oscillating water column’. This gives rise to air pressure within the cylinder alternating between positive and negative gauge pressure, which in turn causes air to alternately leave and enter the cylinder through said top-cover situated orifice. An air turbine situated within or immediately adjacent to said orifice converts the oscillating airflow into electric power for transport to shore or elsewhere by electric power cable. Said oscillating air pressure produces large up and down forces on the cylinder. Said large forces are opposed through the rod to the large mass of water retained within the stabilizer, which is located deep enough to be mostly free of any wave influence and which provides the deepwater reactance. The cylinder and stabilizer form a spring-mass system which has a vertical (heave) resonant frequency. The diameter of the cylinder largely determines the power rating of the device, while the size (and water mass within) of the stabilizer determines said resonant frequency. Said frequency is chosen to be on the lower end of the wave frequency spectrum to maximize the average power output of the device over a large span of time (such as a year). The upper portion of the device (the cylinder) moves laterally (surge) with the waves. This motion is accommodated with minimal loading on the said rod by having the stabilizer shaped like a sphere, allowing the entire device to rotate about the center of the stabilizer without rotating the seawater within the stabilizer. A full-scale device of this type may have the following dimensions. The cylinder may be 16 meters in diameter and 30 meters high, the stabilizer 25 meters in diameter, and the rod 55 meters long. Simulations predict that this will produce 1,400 kW in waves of 3.5-meter height and 12 second period, with a relatively flat power curve between 5 and 16 second wave periods, as will be suitable for an open-ocean location. This is nominally 10 times higher power than similar-sized WEC spar buoys as reported in the literature, and the device is projected to have only 5% of the mass per unit power of other OWC converters.

Keywords: oscillating water column, wave energy converter, spar bouy, stabilizer

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Authors: Nicola G. G. Cecca

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In Italian IPSSEOAs, high schools that give a vocational education to students that will work in the field of Enogastronomy and Hotel Management, the course of Food Science allows the students to start and see food as a mixture of substances that they will transform during their profession. These substances are characterized not only by a chemical composition but also by a molecular structure that makes them nutritionally active. But the increasing number of new products proposed by Food Industry, the modern techniques of production and transformation, the innovative preparations required by customers have made many information reported in the most wide spread Food Science textbooks not up-to-date or too poor for the people who will work in catering sector. Often Authors offer information aged to Bohr’s Atomic Model and to the ‘Octet Rule’ proposed by G.N. Lewis to describe the Chemical Bond, without giving any reference to new as Orbital Atomic Model and Molecular Orbital Theory that, in the meantime, start to be old themselves. Furthermore, this antiquated information precludes an easy understanding of a wide range of properties of nutritive substances and many reactions in which the food constituents are involved. In this paper, our attention is pointed out to use GEOMAG™ to represent the dynamics with which the chemical bond is formed during the synthesis of the molecules. GEOMAG™ is a toy, produced by the Swiss Company Geomagword S.A., pointed to stimulate in children, aged between 6-10 years, their fantasy and their handling ability and constituted by metallic spheres and metallic magnetic bars coated by coloured plastic materials. The simulation carried out with GEOMAG™ is based on the similitude existing between the Coulomb’s force and the magnetic attraction’s force and in particular between the formulae with which they are calculated. The electrostatic force (F in Newton) that allows the formation of the chemical bond can be calculated by mean Fc = kc q1 q2/d2 where: q1 e q2 are the charge of particles [in Coulomb], d is the distance between the particles [in meters] and kc is the Coulomb’s constant. It is surprising to observe that the attraction’s force (Fm) acting between the magnetic extremities of GEOMAG™ used to simulate the chemical bond can be calculated in the same way by using the formula Fm = km m1 m2/d2 where: m1 e m2 represent the strength of the poles [A•m], d is the distance between the particles [m], km = μ/4π in which μ is the magnetic permeability of medium [N•A-2]. The magnetic attraction can be tested by students by trying to keep the magnetic elements of GEOMAG™ separate by hands or trying to measure by mean an appropriate dynamometric system. Furthermore, by using a dynamometric system to measure the magnetic attraction between the GEOMAG™ elements is possible draw a graphic F=f(d) to verify that the curve obtained during the simulation is very similar to that one hypnotized, around the 1920’s by Linus Pauling to describe the formation of H2+ in according with Molecular Orbital Theory.

Keywords: chemical bond, molecular orbital theory, magnetic attraction force, GEOMAG™

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Authors: Satish Kumar, Nisha Goyal

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In the stock market, individual investors exhibit different kinds of behaviour. Traditional finance is built on the notion of 'homo economics', which states that humans always make perfectly rational choices to maximize their wealth and minimize risk. That is, traditional finance has concern for how investors should behave rather than how actual investors are behaving. Behavioural finance provides the explanation for this phenomenon. Although finance has been studied for thousands of years, behavioural finance is an emerging field that combines the behavioural or psychological aspects with conventional economic and financial theories to provide explanations on how emotions and cognitive factors influence investors’ behaviours. These emotions and cognitive factors are known as behavioural biases. Because of these biases, investors make irrational investment decisions. Besides, the emotional and cognitive factors, the social influence of media as well as friends, relatives and colleagues also affect investment decisions. Psychological factors influence individual investors’ investment decision making, but few studies have used qualitative methods to understand these factors. The aim of this study is to explore the behavioural factors or biases that affect individuals’ investment decision making. For the purpose of this exploratory study, an in-depth interview method was used because it provides much more exhaustive information and a relaxed atmosphere in which people feel more comfortable to provide information. Twenty investment advisors having a minimum 5 years’ experience in securities firms were interviewed. In this study, thematic content analysis was used to analyse interview transcripts. Thematic content analysis process involves analysis of transcripts, coding and identification of themes from data. Based on the analysis we categorized the statements of advisors into various themes. Past market returns and volatility; preference for safe returns; tendency to believe they are better than others; tendency to divide their money into different accounts/assets; tendency to hold on to loss-making assets; preference to invest in familiar securities; tendency to believe that past events were predictable; tendency to rely on the reference point; tendency to rely on other sources of information; tendency to have regret for making past decisions; tendency to have more sensitivity towards losses than gains; tendency to rely on own skills; tendency to buy rising stocks with the expectation that this rise will continue etc. are some of the major concerns showed by experts about investors. The findings of the study revealed 13 biases such as overconfidence bias, disposition effect, familiarity bias, framing effect, anchoring bias, availability bias, self-attribution bias, representativeness, mental accounting, hindsight bias, regret aversion, loss aversion and herding bias/media biases present in Indian investors. These biases have a negative connotation because they produce a distortion in the calculation of an outcome. These biases are classified under three categories such as cognitive errors, emotional biases and social interaction. The findings of this study may assist both financial service providers and researchers to understand the various psychological biases of individual investors in investment decision making. Additionally, individual investors will also be aware of the behavioural biases that will aid them to make sensible and efficient investment decisions.

Keywords: financial advisors, individual investors, investment decisions, psychological biases, qualitative thematic content analysis

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Authors: Rama Umesh Pandey, Jyoti Yadav

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Flooding is one of the waterborne disasters that causes extensive destruction in urban areas. Developing countries are at a higher risk of such damage and more than half of the global flooding events take place in Asian countries including India. Urban flooding is more of a human-induced disaster rather than natural. This is highly influenced by the anthropogenic factors, besides metrological and hydrological causes. Unplanned urbanization and poor management of cities enhance the impact manifold and cause huge loss of life and property in urban areas. It is an irony that urban areas have been facing water scarcity in summers and flooding during monsoon. This paper is an attempt to highlight the factors responsible for flooding in a city especially from an urban planning perspective and to suggest mitigating measures through spatial planning interventions. Analysis has been done in two stages; first is to assess the impacts of previous flooding events and second to analyze the factors responsible for flooding at macro and micro level in cities. Bhopal, a city in Central India having nearly two million population, has been selected for the study. The city has been experiencing flooding during heavy rains in monsoon. The factors responsible for urban flooding were identified through literature review as well as various case studies from different cities across the world and India. The factors thus identified were analyzed for both macro and micro level influences. For macro level, the previous flooding events that have caused huge destructions were analyzed and the most affected areas in Bhopal city were identified. Since the identified area was falling within the catchment of a drain so the catchment area was delineated for the study. The factors analyzed were: rainfall pattern to calculate the return period using Weibull’s formula; imperviousness through mapping in ArcGIS; runoff discharge by using Rational method. The catchment was divided into micro watersheds and the micro watershed having maximum impervious surfaces was selected to analyze the coverage and effect of physical infrastructure such as: storm water management; sewerage system; solid waste management practices. The area was further analyzed to assess the extent of violation of ‘building byelaws’ and ‘development control regulations’ and encroachment over the natural water streams. Through analysis, the study has revealed that the main issues have been: lack of sewerage system; inadequate storm water drains; inefficient solid waste management in the study area; violation of building byelaws through extending building structures ether on to the drain or on the road; encroachments by slum dwellers along or on to the drain reducing the width and capacity of the drain. Other factors include faulty culvert’s design resulting in back water effect. Roads are at higher level than the plinth of houses which creates submersion of their ground floors. The study recommends spatial planning interventions for mitigating urban flooding and strategies for management of excess rain water during monsoon season. Recommendations have also been made for efficient land use management to mitigate water logging in areas vulnerable to flooding.

Keywords: mitigating strategies, spatial planning interventions, urban flooding, violation of development control regulations

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Authors: Abdolamir Allameh, Shahnaz Esmaeili, Mina Allameh, Safoura Khajeniazi

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Stem cells with therapeutic applications can be isolated from human placenta/umblical cord blood (UCB) as well as the cord tissue (UC). Stem cells in culture are vulnerable to oxidative stress, particularly when subjected to differentiation process. The aim of this study was to examine the chnages in the rate of oxidation that occurs to cellular macromolecules during hepatic differentiation of mononuclear cells (MSCs). In addition, the impact of the hepatic differentiation process of MSC on cellular and biological activity of the cells will be undertaken. For this purpose, first mononuclear cells (MNCs) were isolated from human UCB which was obtained from a healthy full-term infant. The cells were cultured at a density of 3×10⁵ cells/cm² in DMEM- low-glucose culture media supplemented with 20% FBS, 2 mM L-glutamine, 100 μg/ml streptomycin and 100 U/ml penicillin. Cell cultures were then incubated at 37°C in a humidified 5% CO₂ incubator. After removing non-adherent cells by replacing culture medium, fibroblast-like adherent cells were resuspended in 0.25% trypsin-EDTA and plated in 25 cm² flasks (1×10⁴/ml). Characterization of the MSCs was routinely done by observing their morphology and growth curve. MSCs were subjected to a 2-step hepatocyte differentiation protocol in presence of hepatocyte growth factor (HGF), dexamethazone (DEX) and oncostatin M (OSM). The hepatocyte-like cells derived from MSCs were checked every week for 3 weeks for changes in lipid peroxidation, protein carbonyl formation and DNA oxidation i.e., 8-hydroxy-2'-deoxyguanosine (8-OH-dG) assay. During the 3-week differentiation process of MSCs to hepatocyte-like cells we found that expression liver-specific markers such as albumin, was associated with increased levels of lipid peroxidation and protein carbonyl formation. Whereas, undifferentiated MSCs has relatively low levels of lipid peroxidation products. There was a significant increase ( p < 0.05) in lipid peroxidation products in hepatocytes on days 7, 14, and 21 of differentiation. Likewise, the level of protein carbonyls in the cells was elevated during the differentiation. The level of protein carbonyls measured in hepatocyte-like cells obtained 3 weeks after differentiation induction was estimated to be ~6 fold higher compared to cells recovered on day 7 of differentiation. On the contrary, there was a small but significant decrease in DNA damage marker (8-OH-dG) in hepatocytes recovered 3 weeks after differentiation onset. The level of 8-OHdG which was in consistent with formation of reactive oxygen species (ROS). In conclusion, this data suggest that despite the elevation in oxidation of lipid and protein molecules during hepatocyte development, the cells were normal in terms of DNA integrity, morphology, and biologically activity.

Keywords: adult stem cells, DNA integrity, free radicals, hepatic differentiation

Procedia PDF Downloads 150

Authors: M. Bahari Mehrabani, Hua-Peng Chen

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Management and maintenance of coastal defence structures during the expected life cycle have become a real challenge for decision makers and engineers. Accurate evaluation of the current condition and future performance of flood defence structures is essential for effective practical maintenance strategies on the basis of available field inspection data. Moreover, as coastal defence structures age, it becomes more challenging to implement maintenance and management plans to avoid structural failure. Therefore, condition inspection data are essential for assessing damage and forecasting deterioration of ageing flood defence structures in order to keep the structures in an acceptable condition. The inspection data for flood defence structures are often collected using discrete visual condition rating schemes. In order to evaluate future condition of the structure, a probabilistic deterioration model needs to be utilised. However, existing deterioration models may not provide a reliable prediction of performance deterioration for a long period due to uncertainties. To tackle the limitation, a time-dependent condition-based model associated with a transition probability needs to be developed on the basis of condition grade scheme for flood defences. This paper presents a probabilistic method for predicting future performance deterioration of coastal flood defence structures based on condition grading inspection data and deterioration curves estimated by expert judgement. In condition-based deterioration modelling, the main task is to estimate transition probability matrices. The deterioration process of the structure related to the transition states is modelled according to Markov chain process, and a reliability-based approach is used to estimate the probability of structural failure. Visual inspection data according to the United Kingdom Condition Assessment Manual are used to obtain the initial condition grade curve of the coastal flood defences. The initial curves then modified in order to develop transition probabilities through non-linear regression based optimisation algorithms. The Monte Carlo simulations are then used to evaluate the future performance of the structure on the basis of the estimated transition probabilities. Finally, a case study is given to demonstrate the applicability of the proposed method under no-maintenance and medium-maintenance scenarios. Results show that the proposed method can provide an effective predictive model for various situations in terms of available condition grading data. The proposed model also provides useful information on time-dependent probability of failure in coastal flood defences.

Keywords: condition grading, flood defense, performance assessment, stochastic deterioration modelling

Procedia PDF Downloads 233