Search results for: multiple input multiple output

Authors: Zhenyu Zhang, Hsi-Hsien Wei

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Highway network systems play a vital role in disaster response for disaster-damaged areas. Damaged bridges in such network systems can impede disaster response by disrupting transportation of rescue teams or humanitarian supplies. Therefore, emergency inspection and repair of bridges to quickly collect damage information of bridges and recover the functionality of highway networks is of paramount importance to disaster response. A widely used measure of a network’s capability to recover from disasters is resilience. To enhance highway network resilience, plenty of studies have developed various repair scheduling methods for the prioritization of bridge-repair tasks. These methods assume that repair activities are performed after the damage to a highway network is fully understood via inspection, although inspecting all bridges in a regional highway network may take days, leading to the significant delay in repairing bridges. In reality, emergency repair activities can be commenced as soon as the damage data of some bridges that are crucial to emergency response are obtained. Given that emergency bridge inspection and repair (EBIR) activities are executed simultaneously in the response phase, the real-time interactions between these activities can occur – the blockage of highways due to repair activities can affect inspection routes which in turn have an impact on emergency repair scheduling by providing real-time information on bridge damages. However, the impact of such interactions on the optimal emergency inspection routes (EIR) and emergency repair schedules (ERS) has not been discussed in prior studies. To overcome the aforementioned deficiencies, this study develops a routing and scheduling model for EBIR while accounting for real-time inspection-repair interactions to maximize highway network resilience. A stochastic, time-dependent integer program is proposed for the complex and real-time interacting EBIR problem given multiple inspection and repair teams at locations as set post-disaster. A hybrid genetic algorithm that integrates a heuristic approach into a traditional genetic algorithm to accelerate the evolution process is developed. Computational tests are performed using data from the 2008 Wenchuan earthquake, based on a regional highway network in Sichuan, China, consisting of 168 highway bridges on 36 highways connecting 25 cities/towns. The results show that the simultaneous implementation of bridge inspection and repair activities can significantly improve the highway network resilience. Moreover, the deployment of inspection and repair teams should match each other, and the network resilience will not be improved once the unilateral increase in inspection teams or repair teams exceeds a certain level. This study contributes to both knowledge and practice. First, the developed mathematical model makes it possible for capturing the impact of real-time inspection-repair interactions on inspection routing and repair scheduling and efficiently deriving optimal EIR and ERS on a large and complex highway network. Moreover, this study contributes to the organizational dimension of highway network resilience by providing optimal strategies for highway bridge management. With the decision support tool, disaster managers are able to identify the most critical bridges for disaster management and make decisions on proper inspection and repair strategies to improve highway network resilience.

Keywords: disaster management, emergency bridge inspection and repair, highway network, resilience, uncertainty

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Authors: Jonas De Kerf, Rein De Cooman, Sara De Gieter

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For many decades, scholars have aimed to understand how work can become more meaningful by maximizing both potential and enhancing feelings of satisfaction. One of the largest contributions towards such positive psychology was made with the introduction of the concept of ‘flow,’ which refers to a condition in which people feel intense engagement and effortless action. Since then, valuable research on work-related flow has indicated that this state of mind is related to positive outcomes for both organizations (e.g., social, supportive climates) and workers (e.g., job satisfaction). Yet, scholars still do not fully comprehend how such deep involvement at work is obtained, given the notion that flow is considered a short-term, complex, and dynamic experience. Most research neglects that people who experience flow ought to be optimally challenged so that intense concentration is required. Because attention is at the core of this enjoyable state of mind, this study aims to comprehend how elements that affect workers’ cognitive functioning impact flow at work. Research on cognitive performance suggests that working on mentally demanding tasks (e.g., information processing tasks) requires workers to concentrate deeply, as a result leading to flow experiences. Based on social facilitation theory, working on such tasks in an isolated environment eases concentration. Prior research has indicated that working at home (instead of working at the office) or in a closed office (rather than in an open-plan office) impacts employees’ overall functioning in terms of concentration and productivity. Consequently, we advance such knowledge and propose an interaction by combining cognitive task characteristics and work environments among part-time teleworkers. Hence, we not only aim to shed light on the relation between cognitive tasks and flow but also provide empirical evidence that workers performing such tasks achieve the highest states of flow while working either at home or in closed offices. In July 2022, an experience-sampling study will be conducted that uses a semi-random signal schedule to understand how task and environment predictors together impact part-time teleworkers’ flow. More precisely, about 150 knowledge workers will fill in multiple surveys a day for two consecutive workweeks to report their flow experiences, cognitive tasks, and work environments. Preliminary results from a pilot study indicate that on a between level, tasks high in information processing go along with high self-reported fluent productivity (i.e., making progress). As expected, evidence was found for higher fluency in productivity for workers performing information processing tasks both at home and in a closed office, compared to those performing the same tasks at the office or in open-plan offices. This study expands the current knowledge on work-related flow by looking at a task and environmental predictors that enable workers to obtain such a peak state. While doing so, our findings suggest that practitioners should strive for ideal alignments between tasks and work locations to work with both deep involvement and gratification.

Keywords: cognitive work, office lay-out, work location, work-related flow

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Authors: Van-Thuan Tran, Minh-Khai Nguyen, Geum-Bae Cho

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Recently, multilevel inverters have become more attractive for researchers due to low total harmonic distortion (THD) in the output voltage and low electromagnetic interference (EMI). This paper proposes a single-phase cascaded H-bridge quasi switched boost inverter (CHB-qSBI) for renewable energy sources applications. The proposed inverter has the advantage over the cascaded H-bridge quasi-Z-source inverter (CHB-qZSI) in reducing two capacitors and two inductors. As a result, cost, weight, and size are reduced. Furthermore, the dc-link voltage of each module is controlled by individual shoot-through duty cycle to get the same values. Therefore, the proposed inverter solves the imbalance problem of dc-link voltage in traditional CHB inverter. This paper shows the operating principles and analysis of the single-phase cascaded H-bridge quasi switched boost inverter. Also, a control strategy for the proposed inverter is shown. Experimental and simulation results are shown to verify the operating principle of the proposed inverter.

Keywords: renewable energy sources, cascaded h-bridge inverter, quasi switched boost inverter, quasi z-source inverter, multilevel inverter

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Authors: Bikshalu Kalagadda, Srikanth Rangu

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The important object of images segmentation is to extract objects with respect to some input features. One of the important methods for image segmentation is Level set method. Generally medical images and synthetic images with low contrast of pixel profile, for such images difficult to locate interested features in images. In conventional level set function, develops irregularity during its process of evaluation of contour of objects, this destroy the stability of evolution process. For this problem a remedy is proposed, a new hybrid algorithm is Clustering Level Set Evolution. Kernel fuzzy particles swarm optimization clustering with the Distance Regularized Level Set (DRLS) and Selective Binary, and Gaussian Filtering Regularized Level Set (SBGFRLS) methods are used. The ability of identifying different regions becomes easy with improved speed. Efficiency of the modified method can be evaluated by comparing with the previous method for similar specifications. Comparison can be carried out by considering medical and synthetic images.

Keywords: segmentation, clustering, level set function, re-initialization, Kernel fuzzy, swarm optimization

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Authors: Gabrielle Holt

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This paper is looking to prove the efficacy of the Schulte Holt IOL Toric Calculator Processor (Schulte Holt ITCP). It has been completed using manually inputted data into the Barrett Toric Calculator and comparing the number of minutes taken to complete the Toric calculations, the number of errors identified during completion, and distractions during completion. It will then compare that data to the number of minutes taken for the Schulte Holt ITCP to complete also, using the Barrett method, as well as the number of errors identified in the Schulte Holt ITCP. The data clearly demonstrate a momentous advantage to the Schulte Holt ITCP and notably reduces time spent doing Toric Calculations, as well as reducing the number of errors. With the ever-growing number of cataract surgeries taking place around the world and the waitlists increasing -the Schulte Holt IOL Toric Calculator Processor may well demonstrate a way forward to increase the availability of ophthalmologists and ophthalmic staff while maintaining patient safety.

Keywords: Toric, toric lenses, ophthalmology, cataract surgery, toric calculations, Barrett

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Authors: Adil Loya, Kamran Maqsood, Muhammad Duraid

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Aerodynamic stability coefficients are necessary to be known before any unmanned aircraft flight is performed. This requires expertise on aerodynamics and stability control of the aircraft. To enable efficacious performance of aircraft requires that a well-defined flight path and aerodynamics should be defined beforehand. This paper presents a study on the aerodynamics of an unmanned aero vehicle (UAV) during flight conditions. Current research holds comparative studies of different parameters for flight aerodynamic, measured using two different open source analytical software programs. These software packages are DATCOM and XLRF5, which help in depicting the flight aerodynamic variables. Computational fluid dynamics (CFD) was also used to perform aerodynamic analysis for which Star CCM+ was used. Output trends of the study demonstrate high accuracies between the two software programs with that of CFD. It can be seen that the Coefficient of Lift (CL) obtained from DATCOM and XFLR is similar to CL of CFD simulation. In the similar manner, other potential aerodynamic stability parameters obtained from analytical software are in good agreement with CFD.

Keywords: XFLR5, DATCOM, computational fluid dynamic, unmanned aero vehicle

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Authors: M. Goodarzi, M. Mohammadi, A. Gharib

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Solar energy is a renewable energy which has attracted special attention in many countries. Solar cathodic protectionsystems harness the sun’senergy to protect underground pipelinesand tanks from galvanic corrosion. The object of this study is to design and the economic analysis a cathodic protection system by impressed current supplied with solar energy panels applied to underground pipelines. In the present study, the technical and economic analysis of using solar energy for cathodic protection system in southwestern of Iran (Khuzestan province) is investigated. For this purpose, the ecological conditions such as the weather data, air clearness and sunshine hours are analyzed. The economic analyses were done using computer code to investigate the feasibility analysis from the using of various energy sources in order to cathodic protection system. The overall research methodology is divided into four components: Data collection, design of elements, techno economical evaluation, and output analysis. According to the results, solar renewable energy systems can supply adequate power for cathodic protection system purposes.

Keywords: renewable energy, solar energy, solar cathodic protection station, lifecycle cost method

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Authors: Haohua Zong, Marios Kotsonis

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Plasma synthetic jet actuator (PSJA) is a new concept of zero net mass flow actuator which utilizes pulsed arc/spark discharge to rapidly pressurize gas in a small cavity under constant-volume conditions. The unique combination of high exit jet velocity (>400 m/s) and high actuation frequency (>5 kHz) provides a promising solution for high-speed high-Reynolds-number flow control. This paper focuses on the performance of PSJA in repetitive working mode which is more relevant to future flow control applications. A two-electrodes PSJA (cavity volume: 424 mm3, orifice diameter: 2 mm) together with a capacitive discharge circuit (discharge energy: 50 mJ-110 mJ) is designed to enable repetitive operation. Time-Resolved Particle Imaging Velocimetry (TR-PIV) system working at 10 kHz is exploited to investigate the influence of discharge frequency on performance of PSJA. In total, seven cases are tested, covering a wide range of discharge frequencies (20 Hz-560 Hz). The pertinent flow features (shock wave, vortex ring and jet) remain the same for single shot mode and repetitive working mode. Shock wave is issued prior to jet eruption. Two distinct vortex rings are formed in one cycle. The first one is produced by the starting jet whereas the second one is related with the shock wave reflection in cavity. A sudden pressure rise is induced at the throat inlet by the reflection of primary shock wave, promoting the shedding of second vortex ring. In one cycle, jet exit velocity first increases sharply, then decreases almost linearly. Afterwards, an alternate occurrence of multiple jet stages and refresh stages is observed. By monitoring the dynamic evolution of exit velocity in one cycle, some integral performance parameters of PSJA can be deduced. As frequency increases, the jet intensity in steady phase decreases monotonically. In the investigated frequency range, jet duration time drops from 250 µs to 210 µs and peak jet velocity decreases from 53 m/s to approximately 39 m/s. The jet impulse and the expelled gas mass (0.69 µN∙s and 0.027 mg at 20 Hz) decline by 48% and 40%, respectively. However, the electro-mechanical efficiency of PSJA defined by the ratio of jet mechanical energy to capacitor energy doesn’t show significant difference (o(0.01%)). Fourier transformation of the temporal exit velocity signal indicates two dominant frequencies. One corresponds to the discharge frequency, while the other accounts for the alternation frequency of jet stage and refresh stage in one cycle. The alternation period (300 µs approximately) is independent of discharge frequency, and possibly determined intrinsically by the actuator geometry. A simple analytical model is established to interpret the alternation of jet stage and refresh stage. Results show that the dynamic response of exit velocity to a small-scale disturbance (jump in cavity pressure) can be treated as a second-order under-damping system. Oscillation frequency of the exit velocity, namely alternation frequency, is positively proportional to exit area, but inversely proportional to cavity volume and throat length. Theoretical value of alternation period (305 µs) agrees well with the experimental value.

Keywords: plasma, synthetic jet, actuator, frequency effect

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Authors: Chika Catherine Ayogu

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Rehabilitation plays an important and integral part in the life of roadway rehabilitation management system. It is a systematic method for inspection and rating the roadway condition in a given area. The system performs a cost effective analysis of various maintenance and rehabilitation strategies. Finally the system prioritize and recommend roadway rehabilitation and maintenance to maximize results within a given budget amount. During execution of maintenance activity, the system also tracks labour, materials, equipment and cost for activities performed. The system implements physical assessment field inspection and rating of each street segment which is then entered into a database. The information is analyzed using a software, and provide recommendations and project future conditions. The roadway management system provides a deterioration curve for each segment based on input then assigns the most cost-effective maintenance strategy based on conditions, surface type and functional classification, and available budget. This paper investigates the roadway management system and its capabilities to assist in applying the right treatment to the right roadway at the right time so that expected service life of the roadway is extended as long as possible with acceptable cost.

Keywords: effectiveness, rehabilitation, roadway, software system

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Authors: Saif Khan Sunny, Md. Masud-ul-alam

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This study presents the association of photosynthetic pigment: chlorophyll-a (chl-a) and physical parameters: sea surface temperature (SST), dissolved oxygen (DO), sea surface salinity (SSS), and total dissolved solids (TDS) in the northeastern Bay of Bengal. At 15 sampling stations in the bay near the eastern coast of Teknaf, photosynthetic pigment and environmental variables were measured for surface water where acetone extraction was used for ch-a. Samples of seawater were taken in March 2021, where chlorophyll-a content varies from 0.554 to 9.696 mg/m3 in surface water over the sampling site. Higher concentrations may be attributable to the nutrient supply of hatcheries and the delivery of fluvial input. The observed SST, DO, SSS, and TDS in the north-eastern Bay of Bengal are 26.65 to 28.6 °C, 6.26 to 8.03 mg/l, 29.3 to 33.1 PSU, and 22.4 to 25.3 ppm, respectively. Temperature and chl-a had a positive association (0.18), according to an analysis of the cross-correlation matrix. Again, a negative correlation (0.34) between dissolved oxygen and temperature is significant at p < 0.05. Total dissolved solids and dissolved oxygen have a significant negative correlation (0.70) where p is < 0.001.

Keywords: photosynthetic pigment, nutrient supply, chlorophyll, physical parameters

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Authors: Byungmin Cho, Dongchul Lee, Taejin Kim, Minhee Lee

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The procedure for the seismic retrofit of existing buildings includes the seismic evaluation. In the evaluation step, it is assessed whether the buildings have satisfactory performance against seismic load. Based on the results of that, the buildings are upgraded. To evaluate seismic performance of the buildings, it usually goes through the model transformation from elastic analysis to inelastic analysis. However, when the data is not delivered through the interwork, engineers should manually input the data. In this process, since it leads to inaccuracy and loss of information, the results of the analysis become less accurate. Therefore, in this study, the process for the seismic evaluation of existing buildings using structural information modeling is suggested. This structural information modeling makes the work economic and accurate. To this end, it is determined which part of the process could be computerized through the investigation of the process for the seismic evaluation based on ASCE 41. The structural information modeling process is developed to apply to the seismic evaluation using Perform 3D program usually used for the nonlinear response history analysis. To validate this process, the seismic performance of an existing building is investigated.

Keywords: existing building, nonlinear analysis, seismic performance, structural information modeling

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Authors: Sharma Aseem, Dhurat Rachita, Pawar Varsha, Khalse Manisha

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Introduction: Androgenetic alopecia (AGA) is a prevalent condition characterized by progressive hair thinning driven by genetic and androgen-related factors. The current FDA-approved treatments include oral finasteride and topical minoxidil, though many patients seek combination therapies to enhance results. This study aims to evaluate the effectiveness of a combination therapy involving Minoxidil, Finasteride, and Capixyl™ based on feedback from dermatologists. Methodology: A survey, validated by experts, was distributed to 29 leading dermatologists across India (in Tier 1 and 2 cities). The survey examined real-world clinical experiences, focusing on patient outcomes and the overall effectiveness of the mentioned formulation. Results: Among the surveyed dermatologists, 41.4% identified women aged 35-40 as the most frequently diagnosed with female pattern hair loss. The combination therapy with Minoxidil, Finasteride, and Capixyl™ was utilized by 34.5% of dermatologists for over 60 patients per month. The majority highlighted the benefits of this combination therapy, which acts via multiple mechanisms, such as vasodilation and dihydrotestosterone (DHT) receptor blockade, resulting in improved hair regrowth. Additionally, patients demonstrated better clinical outcomes, enhanced compliance, and fewer side effects. Demographically, younger patients, particularly those with AGA for less than 10 years, responded more positively to the treatment. Early intervention led to quicker and more significant results. Overall satisfaction among dermatologists was high, with 86.2% expressing positive feedback on the therapy. In terms of treatment outcomes, 51.7% of dermatologists observed visible results within 4-6 months, while 34.5% noticed a significant reduction in hair fall within 8-12 weeks. Improvements in scalp health were reported by 48.3%, and 51.7% saw an increased hair density within 3-4 months. Despite mild side effects such as scalp irritation, dryness, flaking, and occasional issues like folliculitis, headaches, itching, and redness, patient satisfaction remained high. Dermatologists reported that 93.1% of patients experienced faster and better hair regrowth with Capixyl™ compared to Minoxidil alone. Suggestions for improving the formulation included incorporating peptides like Saw Palmetto and enhancing product packaging to better meet patient needs. Discussion: The combination of Minoxidil, Finasteride, and Capixyl™ yielded positive clinical outcomes, especially in improving hair density, scalp health, and overall patient satisfaction. Dermatologists found that Capixyl™ peptides enhanced the therapeutic effect, promoting hair regrowth and improving compliance. While side effects were generally mild, there were suggestions to further improve the formulation by adding additional peptides like Saw Palmetto. Conclusion: The combination of Minoxidil and Finasteride fortified with Capixyl™ presents a promising therapeutic option for managing AGA. Dermatologists reported significant improvements in hair density, scalp health, and patient satisfaction. With its favorable efficacy and manageable side effects, this formulation proves to be a valuable addition to the treatment landscape for AGA.

Keywords: androgenetic alopecia, combination therapy, minoxidil, finasteride, capixyl

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Authors: Abdelaziz Beddiaf

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In this work, we present an analysis based on the study of mobility which is a very important electrical parameter of a piezoresistor and which is directly bound to the piezoresistivity effect in piezoresistive pressure sensors. We determine how the temperature affects mobility when the electric potential is applied. For this, a theoretical approach based on mobility in a p-type Silicon piezoresistor with that of a finite difference model for self-heating is developed. So, the evolution of mobility has been established versus time for different doping levels and with temperature rise provoked by self-heating using a numerical model combined with that of mobility. Furthermore, it has been calculated for some geometrical parameters of the sensor, such as membrane side length and thickness. Also, it is computed as a function of bias voltage. It was observed that mobility is strongly affected by the temperature rise induced by the applied potential when the sensor is actuated for a prolonged time as a consequence of drifting in the output response of the sensor. Finally, this work makes it possible to predict their temperature behavior due to self-heating and to improve this effect by optimizing the geometric properties of the device and by reducing the voltage source applied to the bridge.

Keywords: Sensors, Piezoresistivity, Mobility, Bias voltage

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Authors: Salma Parvin, M. A. Alim

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The convective and radiative heat transfer performance and entropy generation on forced convection through a direct absorption solar collector (DASC) is investigated numerically. Four different fluids, including Cu-water nanofluid, Al₂O₃-waternanofluid, TiO₂-waternanofluid, and pure water are used as the working fluid. Entropy production has been taken into account in addition to the collector efficiency and heat transfer enhancement. Penalty finite element method with Galerkin’s weighted residual technique is used to solve the governing non-linear partial differential equations. Numerical simulations are performed for the variation of mass flow rate. The outcomes are presented in the form of isotherms, average output temperature, the average Nusselt number, collector efficiency, average entropy generation, and Bejan number. The results present that the rate of heat transfer and collector efficiency enhance significantly for raising the values of m up to a certain range.

Keywords: DASC, forced convection, mass flow rate, nanofluid

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Authors: Sajjad H. Kasanagh, Perviz Ahmedzade, Alexander Fainleib, Taylan Gunay

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Modification of asphalt is a fundamental method around the world mainly on the purpose of providing more durable pavements which lead to diminish repairing cost during the lifetime of highways. Various polymers such as styrene-butadiene-styrene (SBS) and ethylene vinyl acetate (EVA) make up the greater parts of the all-over asphalt modifiers generally providing better physical properties of asphalt by decreasing temperature dependency which eventually diminishes permanent deformation on highways such as rutting. However, some waste and low-cost materials such as recycled plastics and ground rubber tire have been attempted to utilize in asphalt as modifier instead of manufactured polymer modifiers due to decreasing the eventual highway cost. On the other hand, the usage of recycled plastics has become a worldwide requirement and awareness in order to decrease the pollution made by waste plastics. Hence, finding an area in which recycling plastics could be utilized has been targeted by many research teams so as to reduce polymer manufacturing and plastic pollution. To this end, in this paper, thermoplastic dynamic vulcanizate (TDV) obtained from recycled post-consumer polyethylene and ground tire rubber (GTR) were used to provide an efficient modifier for asphalt which decreases the production cost as well and finally might provide an ecological solution by decreasing polymer disposal problems. TDV was synthesized by the chemists in the research group by means of the abovementioned components that are considered as compatible physical characteristic of asphalt materials. TDV modified asphalt samples having different rate of proportions of 3, 4, 5, 6, 7 wt.% TDV modifier were prepared. Conventional tests, such as penetration, softening point and roll thin film oven (RTFO) tests were performed to obtain fundamental physical and aging properties of the base and modified binders. The high temperature performance grade (PG) of binders was determined by Superpave tests conducted on original and aged binders. The multiple stress creep and recovery (MSCR) test which is relatively up-to-date method for classifying asphalts taking account of their elasticity abilities was carried out to evaluate PG plus grades of binders. The results obtained from performance grading, and MSCR tests were also evaluated together so as to make a comparison between the methods both aiming to determine rheological parameters of asphalt. The test results revealed that TDV modification leads to a decrease in penetration, an increase in softening point, which proves an increasing stiffness of asphalt. DSR results indicate an improvement in PG for modified binders compared to base asphalt. On the other hand, MSCR results that are compatible with DSR results also indicate an enhancement on rheological properties of asphalt. However, according to the results, the improvement is not as distinct as observed in DSR results since elastic properties are fundamental in MSCR. At the end of the testing program, it can be concluded that TDV can be used as modifier which provides better rheological properties for asphalt and might diminish plastic waste pollution since the material is 100% recycled.

Keywords: asphalt, ground tire rubber, recycled polymer, thermoplastic dynamic vulcanizate

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Authors: Natàlia Garcia Soler, Timothy Moss

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Despite the multiple benefits of sustainable urban drainage, as demonstrated in numerous case studies across the world, urban rainwater harvesting techniques are generally restricted to isolated model projects. The leap from niche to mainstream has, in most cities, proved an elusive goal. Why policies promoting rainwater harvesting are limited in their widespread implementation has seldom been subjected to systematic analysis. Much of the literature on the policy, planning and institutional contexts of these techniques focus either on their potential benefits or on project design, but very rarely on a critical-constructive analysis of past experiences of implementation. Moreover, the vast majority of these contributions are restricted to single-case studies. There is a dearth of knowledge with respect to, firstly, policy implementation processes and, secondly, multi-case analysis. Insights from both, the authors argue, are essential to inform more effective rainwater harvesting in cities in the future. This paper presents preliminary findings from a research project on rainwater harvesting in cities from a social science perspective that is funded by the Swedish Research Foundation (Formas). This project – UrbanRain – is examining the challenges and opportunities of mainstreaming rainwater harvesting in three European cities. The paper addresses two research questions: firstly, what lessons can be learned on suitable policy incentives and planning instruments for rainwater harvesting from a meta-analysis of the relevant international literature and, secondly, how far these lessons are reflected in a study of past and ongoing rainwater harvesting projects in a European forerunner city. This two-tier approach frames the structure of the paper. We present, first, the results of the literature analysis on policy and planning issues of urban rainwater harvesting. Here, we analyze quantitatively and qualitatively the literature of the past 15 years on this topic in terms of thematic focus, issues addressed and key findings and draw conclusions on research gaps, highlighting the need for more studies on implementation factors, actor interests, institutional adaptation and multi-level governance. In a second step we focus in on the experiences of rainwater harvesting in Berlin and present the results of a mapping exercise on a wide variety of projects implemented there over the last 30 years. Here, we develop a typology to characterize the rainwater harvesting projects in terms of policy issues (what problems and goals are targeted), project design (which kind of solutions are envisaged), project implementation (how and when they were implemented), location (whether they are in new or existing urban developments) and actors (which stakeholders are involved and how), paying particular attention to the shifting institutional framework in Berlin. Mapping and categorizing these projects is based on a combination of document analysis and expert interviews. The paper concludes by synthesizing the findings, identifying how far the goals, governance structures and instruments applied in the Berlin projects studied reflect the findings emerging from the meta-analysis of the international literature on policy and planning issues of rainwater harvesting and what implications these findings have for mainstreaming such techniques in future practice.

Keywords: institutional framework, planning, policy, project implementation, urban rainwater management

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Authors: Sheng-Fen Fan, Sung-Hui Tseng

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Attention deficit hyperactivity deficit (ADHD) child display diverse sensory processing difficulty behaviors. There is less evidence to figure out how the association between executive function and sensory deficit. To determine whether sensory deficit influence the executive functions, we examined sensory processing by SPM and try to indicate hot/cold executive function (EF) by BRIEF2, respectively. We found that the hot executive function deficit might associate with auditory processing in a variety of settings, and vestibular input to maintain balance and upright posture; the cold EF deficit might opposite to the hot EF deficit, the vestibular sensory modulation difficulty association with emotion shifting and emotional regulation. These results suggest that sensory processing might be another consideration factor to influence the higher cognitive control or emotional regulation of EF. Overall, this study indicates the distinction between hot and cold EF impairments with different sensory modulation problem. Moreover, for clinician, it needs more cautious consideration to conduct intervention with ADHD.

Keywords: hot executive function, cold executive function, sensory processing, ADHD

Procedia PDF Downloads 286

Authors: Medya Fathi

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When public transportation projects were delivered through design-bid-build and later design-build, governments found a serious issue: inadequate funding. With population growth, governments began to develop new arrangements in which the private sectors were involved to cut the financial burden. This arrangement, known as Public-Private Partnership (PPP), has its own risks; however, performance outputs can motivate or discourage its use. On top of such output's list are time and budget, which can be affected by the type of project delivery methods. Project completion within or ahead of schedule as well as within or under budget is among any owner’s objectives. With a higher application of PPP in the highway industry in the US and insufficient PPP research, the current study addresses the schedule and cost performance of PPP highway projects and determines which one outperforms the other. To meet this objective, after collecting performance data of all PPP projects, schedule growth and cost growth are calculated, and finally, statistical analysis is conducted to evaluate the PPP performance. The results and conclusions will be provided. This study can assist practitioners in applying PPP for transportation projects by showing its ability to save time and/or cost.

Keywords: cost, delivery method, highway, public-private partnership, schedule, transportation

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Authors: Patrick Cormier, Cesar Meylan, Matt Jensen, Dana Agar-Newman, Chloe Werle, Ming-Chang Tsai, Marc Klimstra

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Sprint-derived horizontal force-velocity-power (FVP) profiles can be developed with adequate validity and reliability with satellite (GPS/GNSS) systems. However, FVP metrics are sensitive to small nuances in data processing procedures such that minor differences in defining the onset and end of the sprint could result in different FVP metric outcomes. Furthermore, in team-sports, there is a requirement for rapid analysis and feedback of results from multiple athletes, therefore developing standardized and automated methods to improve the speed, efficiency and reliability of this process are warranted. Thus, the purpose of this study was to compare different methods of sprint end detection on the development of FVP profiles from 10Hz GPS/GNSS data through goodness-of-fit and intertrial reliability statistics. Seventeen national team female soccer players participated in the FVP protocol which consisted of 2x40m maximal sprints performed towards the end of a soccer specific warm-up in a training session (1020 hPa, wind = 0, temperature = 30°C) on an open grass field. Each player wore a 10Hz Catapult system unit (Vector S7, Catapult Innovations) inserted in a vest in a pouch between the scapulae. All data were analyzed following common procedures. Variables computed and assessed were the model parameters, estimated maximal sprint speed (MSS) and the acceleration constant τ, in addition to horizontal relative force (F₀), velocity at zero (V₀), and relative mechanical power (Pmax). The onset of the sprints was standardized with an acceleration threshold of 0.1 m/s². The sprint end detection methods were: 1. Time when peak velocity (MSS) was achieved (zero acceleration), 2. Time after peak velocity drops by -0.4 m/s, 3. Time after peak velocity drops by -0.6 m/s, and 4. When the integrated distance from the GPS/GNSS signal achieves 40-m. Goodness-of-fit of each sprint end detection method was determined using the residual sum of squares (RSS) to demonstrate the error of the FVP modeling with the sprint data from the GPS/GNSS system. Inter-trial reliability (from 2 trials) was assessed utilizing intraclass correlation coefficients (ICC). For goodness-of-fit results, the end detection technique that used the time when peak velocity was achieved (zero acceleration) had the lowest RSS values, followed by -0.4 and -0.6 velocity decay, and 40-m end had the highest RSS values. For intertrial reliability, the end of sprint detection techniques that were defined as the time at (method 1) or shortly after (method 2 and 3) when MSS was achieved had very large to near perfect ICC and the time at the 40 m integrated distance (method 4) had large to very large ICCs. Peak velocity was reached at 29.52 ± 4.02-m. Therefore, sport scientists should implement end of sprint detection either when peak velocity is determined or shortly after to improve goodness of fit to achieve reliable between trial FVP profile metrics. Although, more robust processing and modeling procedures should be developed in future research to improve sprint model fitting. This protocol was seamlessly integrated into the usual training which shows promise for sprint monitoring in the field with this technology.

Keywords: automated, biomechanics, team-sports, sprint

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Authors: Dib Djalel, Mordjaoui Mourad

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The current trend towards the exploitation of various renewable energy resources has become indispensable, so it is important to improve the efficiency and reliability of the GPV photovoltaic systems. Maximum Power Point Tracking (MPPT) plays an important role in photovoltaic power systems because it maximize the power output from a PV system for a given set of conditions. This paper presents a new fuzzy logic control based MPPT algorithm for solar panel. The solar panel is modeled and analyzed in Matlab/Simulink. The Solar panel can produce maximum power at a particular operating point called Maximum Power Point(MPP). To produce maximum power and to get maximum efficiency, the entire photovoltaic panel must operate at this particular point. Maximum power point of PV panel keeps on changing with changing environmental conditions such as solar irradiance and cell temperature. Thus, to extract maximum available power from a PV module, MPPT algorithms are implemented and Perturb and Observe (P&O) MPPT and fuzzy logic control FLC, MPPT are developed and compared. Simulation results show the effectiveness of the fuzzy control technique to produce a more stable power.

Keywords: MPPT, photovoltaic panel, fuzzy logic control, modeling, solar power

Procedia PDF Downloads 483

Authors: Amenah Dhannoon, Ciaran Martin Hurley, Laura Wrafter, Podraic J. Regan

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Introduction: The COVID-19 pandemic continues to present unprecedented challenges for healthcare systems. This has resulted in the pragmatic shift in the practice of plastic surgery units worldwide. During this period, many units reported a significant fall in urgent melanoma referrals, leading to patients presenting with advanced disease requiring more extensive surgery and inferior outcomes. Our objective was to evaluate our unit's experience with both non-invasive and invasive melanoma during the COVID-19 pandemic and characterize our experience and contrast it to that experienced by our neighbors in the UK, mainland Europe and North America. Methods: a retrospective chart review was performed on all patients diagnosed with invasive and non-invasive cutaneous melanoma between March to December of 2019 (control) compared to 2020 (COVID-19 pandemic) in a single plastic surgery unit in Ireland. Patient demographics, referral source, surgical procedures, tumour characteristics, radiological findings, oncological therapies and follow-up were recorded. All data were anonymized and stored in Microsoft Excel. Results: A total of 589 patients were included in the study. Of these, 314 (53%) with invasive melanoma, compared to 275 (47%) with the non-invasive disease. Overall, more patients were diagnosed with both invasive and non-invasive melanoma in 2020 than in 2019 (p<0.05). However, significantly longer waiting times in 2020 (64 days) compared to 2019 (28 days) (p<0.05), with the majority of the referral being from GP in 2019 (83%) compared to 61% in 2020. Positive sentinel lymph node were higher in 2019 at 56% (n=28) compared to 24% (n=22) in 2020. There was no statistically significant difference in the tutor characteristics or metastasis status. Discussion: While other countries have noticed a fall in the melanoma diagnosis. Our units experienced a higher number of disease diagnoses. This can be due to multiple reasons. In Ireland, the government reached an early agreement with the private sector to continue elective surgery on an urgent basis in private hospitals. This allowed access to local anesthetic procedures and local skin cancer cases were triaged to non-COVID-19 provider centers. Our unit also adapted a fast, effective and minimal patient contact strategy for triaging skin cancer based on telemedicine. Thirdly, a skin cancer nurse specialist maintained patient follow-ups and triaging a dedicated email service. Finally, our plastic surgery service continued to maintain a virtual complex skin cancer multidisciplinary team meeting during the pandemic, ensuring local clinical governance has adhered to each clinical case. Conclusion: Our study highlights that with the prompt efficient restructuring of services, we could reserve successful management of skin cancer even in the most devastating times. It is important to reflect on the success during the pandemic and emphasize the importance of preparation for a potentially difficult future

Keywords: malignant melanoma, skin cancer, COVID-19, triage

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Authors: Reggie Chandra

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Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

Procedia PDF Downloads 169

Authors: Najib K. Dankadai, Ahmad 'Athif Mohd Faudzi, Khairuddin Osman, Muhammad Rusydi Muhammad Razif, IIi Najaa Aimi Mohd Nordin

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Researches in soft actuators are now growing rapidly because of their adequacy to be applied in sectors like medical, agriculture, biological and welfare. This paper presents system identification (SI) and control of the force generated by a two chambers pneumatic soft actuator (PSA). A force mathematical model for the actuator was identified experimentally using data acquisition card and MATLAB SI toolbox. Two control techniques; a predictive functional control (PFC) and conventional proportional integral and derivative (PID) schemes are proposed and compared based on the identified model for the soft actuator flexible mechanism. Results of this study showed that both of the proposed controllers ensure accurate tracking when the closed loop system was tested with the step, sinusoidal and multi step reference input through MATLAB simulation although the PFC provides a better response than the PID.

Keywords: predictive functional control (PFC), proportional integral and derivative (PID), soft actuator, system identification

Procedia PDF Downloads 325

Authors: Ishraq M. Anjum

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Distributed Bragg reflectors are used in vertical-cavity surface-emitting lasers (VCSELs) in order to achieve very high reflectivity. Use of metamaterial in place of distributed Bragg reflector can reduce the device size significantly. A silicon-based metamaterial near perfect reflector is designed to be used in place of distributed Bragg reflectors in VCSELs. Mie resonance in dielectric microparticles is exploited in order to design the metamaterial. A reflectivity of 98.31% is achieved using finite-difference time-domain method. An 808nm double intra-cavity contacted VCSEL structure with 1.5 λ cavity is proposed using this metamaterial near perfect reflector. The active region is designed to be composed of seven GaAs/AlGaAs quantum wells. Upon numerical investigation of the designed VCSEL structure, the threshold current is found to be 2.96 mA at an aperture of 40 square micrometers and the maximum output power is found to be 71 mW at a current of 141 mA. Miniaturization of conventional VCSELs is possible using this design.

Keywords: GaAs, LASER, metamaterial, VCSEL, vertical cavity surface emitting laser

Procedia PDF Downloads 182

Authors: Anudeep Appe, Bhanu Poluparthi, Lakshmi Kasivajjula, Udai Mv, Sobha Bagadi, Punya Modi, Aditya Singh, Hemanth Gunupudi, Spenser Troiano, Jeff Paul, Justin Stovall, Justin Yamamoto

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The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data is considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since its data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for ex. quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP (SHapley Additive exPlanations), a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: competition, DAGs, facility, healthcare, machine learning, market share, random forest, SHAP

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Authors: Marie Boost

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Despite activation policies, forced personal initiative to step out of unemployment and a general prosper economic situation, poverty and financial hardship constitute a crucial role in the daily lives of many families in Germany. In 2015, ~16 million persons (20.2%) of the German population are at risk of poverty or social exclusion. This is illustrated by an unemployment rate of 13.3% in the research area, located in East Germany. Despite this high amount of persons living in vulnerable households, we know little about how they manage to stabilize their lives or even overcome poverty – apart from solely relying on welfare state benefits or entering in a stable, well-paid job. Most of them are struggling in precarious living circumstances, switching from one or several short-term, low-paid jobs into self-employment or unemployment, sometimes accompanied by welfare state benefits. Hence, insecurity and uncertain future expectation form a crucial part of their lives. Within the EU-funded project “RESCuE”, resilient practices of vulnerable households were investigated in nine European countries. Approximately, 15 expert interviews with policy makers, representatives from welfare state agencies, NGOs and charity organizations and 25 household interviews have been conducted within each country. It aims to find out more about the chances and conditions of social resilience. The research is based on the triangulation of biographical narrative interviews, followed by participatory photo interviews, asking the household members to portray their typical everyday life. The presentation is focusing on the explanatory strength of this mixed-methods approach in order to show the potential of household practices to overcome financial hardship. The methodological combination allows an in-depth analysis of the families and households everyday living circumstances, including their poverty and employment situation, whether formal and informal. Active household budgeting practices, such as saving and consumption practices are based on subsistence or Do-It-Yourself work. Especially due to the photo-interviews, the importance of inherent cultural and tacit knowledge becomes obvious as it pictures their typical practices, like cultivation and gathering fruits and vegetables or going fishing. One of the central findings is the multiple purposes of these practices. They contribute to ease financial burden through consumption reduction and strengthen social ties, as they are mostly conducted with close friends or family members. In general, non-commodified practices are found to be re-commodified and to contribute to ease financial hardship, e.g. by the use of commons, barter trade or simple mutual exchange (gift exchange). These practices can substitute external purchases and reduce expenses or even generate a small income. Mixing different income sources are found to be the most likely way out of poverty within the context of a precarious labor market. But these resilient household practices take its toll as they are highly preconditioned, and many persons put themselves into risk of overstressing themselves. Thus, the potentials and risks of resilient household practices are reflected in the presentation.

Keywords: consumption practices, labor market, qualitative research, resilience

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Authors: Phumlani Msomi, Patrick Nonjola, Patrick Ndungu, James Ramontja

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A series of quaternized poly (2.6 dimethyl – 1.4 phenylene oxide)/ polysulfone (QPPO/PSF) blend anion exchange membrane (AEM) were successfully fabricated and characterized for methanol alkaline fuel cell application. Zinc Oxide (ZnO) nanoparticles were introduced in the polymer matrix to enhance the intrinsic properties of the AEM. To confirm successful fabrication, FT-IR spectroscopy and nuclear magnetic resonance (¹H NMR and HMBC ¹⁵N NMR) were used. The membrane properties were enhanced by the addition of ZnO nanoparticles. The addition of ZnO nanoparticles resulted to a higher ion exchange capacity (IEC) of 3.72 mmol.g⁻¹and a 30-fold ion conductivity (IC) increase of the nanocomposite due to no (zero (0)) methanol permeability at 30 °C and increased water uptake. The QPPO/PSF/2% ZnO composite retained over 80 % of its initial IC when evaluated for alkaline stability at room temperature. The maximum power output reached for the membrane electrode assembly (MEA) constructed with QPPO/PSF/2%ZnO is 69 mW.cm⁻², which is about three times more than the parent QPPO membrane. The above results indicate that QPPO/PSF-ZnO is a good candidate as an anion exchange membrane for fuel cell application.

Keywords: anion exchange membrane, fuel cell, zinc oxide, nanocomposite

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Authors: Alexander Hudson, Shaogang Gong

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Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate (≥80%) predictions of protein class and architecture from structures determined at low (>3A) resolution, using a deep convolutional neural network trained on high-resolution (≤3A) structures represented as 2D matrices. Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function. We investigate the impact of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical foundation for boosting with single-image super-resolution.

Keywords: transfer learning, protein distance maps, protein structure classification, neural networks

Procedia PDF Downloads 136

Authors: Faheemah N. Mustafaa, Tamarie Macon, Tabbye Chavous

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A major concern of university leaders worldwide is how to create environments where students from diverse racial/ethnic, national, and cultural backgrounds can thrive. Over the past decade or so in the United States, African American women have done exceedingly well in terms of college enrollment, academic performance, and completion. However, the relative academic successes of African American women in higher education has in some ways overshadowed social challenges many Black women continue to encounter on college campuses in the United States. Within predominantly White institutions (PWIs) in particular, there is consistent evidence that many Black students experience racially hostile climates. However, research studies on racial climates within PWIs have mostly focused on cross-sectional comparisons of minority and majority group experiences, and few studies have examined campus racial climate in relation to short- and longer-term well-being. One longitudinal study reported that African American women’s psychological well-being was positively related to their comfort in cross-racial interactions (a concept closely related to campus climate). Thus, our primary research question was: Do African American women’s perceptions of campus climate (tension and positive association) during their freshman year predict their reports of psychological distress and well-being (self-acceptance) during their sophomore year? Participants were part of a longitudinal survey examining African American college students’ academic identity development, particularly in Science, Technology, Engineering, and Mathematics (STEM) fields. The final subsample included 134 self-identified African American/Black women enrolled in PWIs. Accounting for background characteristics (mother’s education, family income, interracial contact, and prior levels of outcomes), we employed hierarchical regression to examine relationships between campus racial climate during freshman year and psychological adjustment one year later. Both regression models significantly predicted African American women’s psychological outcomes (for distress, F(7,91)= 4.34, p < .001; and for self-acceptance, F(7,90)= 4.92, p < .001). Although none of the controls were significant predictors, perceptions of racial tension on campus were associated with both distress and self-acceptance. More perceptions of tension were related to African American women’s greater psychological distress the following year (B= 0.22, p= .01). Additionally, racial tension predicted later self-acceptance in the expected direction: Higher first-year reports of racial tension were related to less positive attitudes toward the self during the sophomore year (B= -0.16, p= .04). However, perceptions that it was normative for Black and White students to socialize on campus (or positive association scores) were unrelated to psychological distress or self-acceptance. Findings highlight the relevance of examining multiple facets of campus racial climate in relation to psychological adjustment, with possible emphasis on the import of racial tension on African American women’s psychological adjustment. Results suggest that negative dimensions of campus racial climate may have lingering effects on psychological well-being, over and above more positive aspects of climate. Thus, programs targeted toward improving student relations on campus should consider addressing cross-racial tensions.

Keywords: higher education, psychological adjustment, university climate, university students

Procedia PDF Downloads 385

Authors: Arun Sanjel, Greg Speegle

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Every second, massive amounts of data are generated, and Data Intensive Scalable Computing (DISC) frameworks have evolved into effective tools for analyzing such massive amounts of data. Since the underlying architecture of these distributed computing platforms is often new to users, building a DISC application can often be time-consuming and prone to errors. The automated conversion of a sequential program to a DISC program will consequently significantly improve productivity. However, synthesizing a user’s intended program from an input specification is complex, with several important applications, such as distributed program synthesizing and code refactoring. Existing works such as Tyro and Casper rely entirely on deductive synthesis techniques or similar program synthesis approaches. Our approach is to develop a data-driven synthesis technique to identify sequential components and translate them to equivalent distributed operations. We emphasize using reinforcement learning and unit testing as feedback mechanisms to achieve our objectives.

Keywords: program synthesis, distributed computing, reinforcement learning, unit testing, DISC

Procedia PDF Downloads 106