Search results for: performance standard

Authors: C. N. Vanitha, M. Usha

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In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.

Keywords: neural networks, pattern learning, security, wireless sensor networks

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Authors: Madhusudana S. N., Reeta Mani, Ashwini S. Satishchandra P., Netravati, Udhani V., Fiaz A., Karande S.

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Rabies is an acute encephalitis which is considered 100% fatal despite occasional reports of survivors. However, in recent times more cases of human rabies survivors are being reported. In the last 5 years, there are six laboratories confirmed human rabies survivors in India alone. All cases were children below 15 years and all contracted the disease by dog bites. All of them also had received the full or partial course of rabies vaccination and 4 out of 6 had also received rabies immunoglobulin. All cases were treated in intensive care units in hospitals at Bangalore, Mumbai, Chandigarh, Lucknow and Goa. We report here the results of immunological and virological studies conducted at our laboratory on these patients. The clinical samples that were obtained from these patients were Serum, CSF, nuchal skin biopsy and saliva. Serum and CSF samples were subjected to standard RFFIT for estimation of rabies neutralizing antibodies. Skin biopsy, CSF and saliva were processed by TaqMan real-time PCR for detection of viral RNA. CSF, saliva and skin homogenates were also processed for virus isolation by inoculation of suckling mice. The PBMCs isolated from fresh blood was subjected to ELISPOT assay to determine the type of immune response (Th1/Th2). Both CSF and serum were also investigated for selected cytokines by Luminex assay. The level of antibodies to virus G protein and N protein were determined by ELISA. All survivors had very high titers of RVNA in serum and CSF 100 fold higher than non-survivors and vaccine controls. A five-fold rise in titer could be demonstrated in 4 out of 6 patients. All survivors had a significant increase in antibodies to G protein in both CSF and serum when compared to non-survivors. There was a profound and robust Th1 response in all survivors indicating that interferon gamma could play an important factor in virus clearance. We could isolate viral RNA in only one patient four years after he had developed symptoms. The partial N gene sequencing revealed 99% homology to species I strain prevalent in India. Levels of selected cytokines in CSF and serum did not reveal any difference between survivors and non-survivors. To conclude, survival from rabies is mediated by virus-specific immune responses of the host and clearance of rabies virus from CNS may involve the participation of both Th2 and Th1 immune responses.

Keywords: rabies, rabies treatment, rabies survivors, immune reponse in rabies encephalitis

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Authors: Parampal Kaur, Deepak Aggarwal

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In the computational Grid, fault tolerance is an imperative issue to be considered during job scheduling. Due to the widespread use of resources, systems are highly prone to errors and failures. Hence, fault tolerance plays a key role in the grid to avoid the problem of unreliability. Scheduling the task to the appropriate resource is a vital requirement in computational Grid. The fittest resource scheduling algorithm searches for the appropriate resource based on the job requirements, in contrary to the general scheduling algorithms where jobs are scheduled to the resources with best performance factor. The proposed method is to improve the fault tolerance of the fittest resource scheduling algorithm by scheduling the job in coordination with job replication when the resource has low reliability. Based on the reliability index of the resource, the resource is identified as critical. The tasks are scheduled based on the criticality of the resources. Results show that the execution time of the tasks is comparatively reduced with the proposed algorithm using real-time approach rather than a simulator.

Keywords: computational grid, fault tolerance, task replication, job scheduling

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Authors: Mesfer Al Duhayyim, Paul Newbury

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This paper proposes an effective adaptive e-learning system that uses a coloured concept map to show the learner's knowledge level for each concept in the chosen subject area. A Fuzzy logic system is used to evaluate the learner's knowledge level for each concept in the domain, and produce a ranked concept list of learning materials to address weaknesses in the learner’s understanding. This system obtains information on the learner's understanding of concepts by an initial pre-test before the system is used for learning and a post-test after using the learning system. A Fuzzy logic system is used to produce a weighted concept map during the learning process. The aim of this research is to prove that such a proposed novel adapted e-learning system will enhance learner's performance and understanding. In addition, this research aims to increase participants' overall understanding of their learning level by providing a coloured concept map of understanding followed by a ranked concepts list of learning materials.

Keywords: adaptive e-learning system, coloured concept map, fuzzy logic, ranked concept list

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Authors: Alan Wan

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In recent years, the body of literature on frequentist model averaging in statistics has grown significantly. Most of this work focuses on models with different mean structures but leaves out the variance consideration. In this paper, we consider a regression model with multiplicative heteroscedasticity and develop a model averaging method that combines maximum likelihood estimators of unknown parameters in both the mean and variance functions of the model. Our weight choice criterion is based on a minimisation of a plug-in estimator of the model average estimator's squared prediction risk. We prove that the new estimator possesses an asymptotic optimality property. Our investigation of finite-sample performance by simulations demonstrates that the new estimator frequently exhibits very favourable properties compared to some existing heteroscedasticity-robust model average estimators. The model averaging method hedges against the selection of very bad models and serves as a remedy to variance function misspecification, which often discourages practitioners from modeling heteroscedasticity altogether. The proposed model average estimator is applied to the analysis of two real data sets.

Keywords: heteroscedasticity-robust, model averaging, multiplicative heteroscedasticity, plug-in, squared prediction risk

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Authors: Emine Laarouchi, Daniela Cancila, Laurent Soulier, Hakima Chaouchi

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Cyber-Physical Systems as drones proved their efficiency for supporting emergency applications. For these particular applications, travel time and autonomous navigation algorithms are of paramount importance, especially when missions are performed in urban environments with high obstacle density. In this context, however, safety properties are not properly addressed. Our ambition is to optimize the system safety level under autonomous navigation systems, by preserving performance of the CPS. At this aim, we introduce genetic algorithms in the autonomous navigation process of the drone to better infer its trajectory considering the possible obstacles. We first model the wished safety requirements through a cost function and then seek to optimize it though genetics algorithms (GA). The main advantage in the use of GA is to consider different parameters together, for example, the level of battery for navigation system selection. Our tests show that the GA introduction in the autonomous navigation systems minimize the risk of safety lossless. Finally, although our simulation has been tested for autonomous drones, our approach and results could be extended for other autonomous navigation systems such as autonomous cars, robots, etc.

Keywords: safety, unmanned aerial vehicles , CPS, ACPS, drones, path planning, genetic algorithms

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Authors: Andrés Lara Guillén, José M. Soriano Disla, Gemma Castejón Martínez, David Fernández-Gutiérrez

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The worldwide population is exponentially increasing, which causes a rising demand for food, energy and non-renewable resources. These demands must be attended to from a circular economy point of view. Under this approach, the obtention of strategic products from biowaste is crucial for the society to keep the current lifestyle reducing the environmental and social issues linked to the lineal economy. This is the main objective of the VALUEWASTE project. VALUEWASTE is about valorizing urban biowaste into proteins for food and feed and biofertilizers, closing the loop of this waste stream. In order to achieve this objective, the project validates three value chains, which begin with the anaerobic digestion of the biowaste. From the anaerobic digestion, three by-products are obtained: i) methane that is used by microorganisms, which will be transformed into microbial proteins; ii) digestate that is used by black soldier fly, producing insect proteins; and iii) a nutrient-rich effluent, which will be transformed into biofertilizers. VALUEWASTE is an innovative solution, which combines different technologies to valorize entirely the biowaste. However, it is also required to demonstrate that the solution is greener than other traditional technologies (baseline systems). On one hand, the proteins from microorganisms and insects will be compared with other reference protein production systems (gluten, whey and soybean). On the other hand, the biofertilizers will be compared to the production of mineral fertilizers (ammonium sulphate and synthetic struvite). Therefore, the aim of this study is to provide that biowaste valorization can reduce the environmental impacts linked to both traditional proteins manufacturing processes and mineral fertilizers, not only at a pilot-scale but also at an industrial one. In the present study, both baseline system and VALUEWASTE solution are evaluated through the Environmental Life Cycle Assessment (E-LCA). The E-LCA is based on the standards ISO 14040 and 14044. The Environmental Footprint methodology was the one used in this study to evaluate the environmental impacts. The results for the baseline cases show that the food proteins coming from whey have the highest environmental impact on ecosystems compared to the other proteins sources: 7.5 and 15.9 folds higher than soybean and gluten, respectively. Comparing feed soybean and gluten, soybean has an environmental impact on human health 195.1 folds higher. In the case of biofertilizers, synthetic struvite has higher impacts than ammonium sulfate: 15.3 (ecosystems) and 11.8 (human health) fold, respectively. The results shown in the present study will be used as a reference to demonstrate the better environmental performance of the bio-based products obtained through the VALUEWASTE solution. Other originalities that the E-LCA performed in the VALUEWASTE project provides are the diverse direct implications on investment and policies. On one hand, better environmental performance will serve to remove the barriers linked to these kinds of technologies, boosting the investment that is backed by the E-LCA. On the other hand, it will be a germ to design new policies fostering these types of solutions to achieve two of the key targets of the European Community: being self-sustainable and carbon neutral.

Keywords: anaerobic digestion, biofertilizers, circular economy, nutrients recovery

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Authors: Ramesh Rajagopalan, Adam Dahlstrom

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Noise removal techniques play a vital role in the performance of electrocardiographic (ECG) signal processing systems. ECG signals can be corrupted by various kinds of noise such as baseline wander noise, electromyographic interference, and power-line interference. One of the significant challenges in ECG signal processing is the degradation caused by additive 50 or 60 Hz power-line interference. This work investigates the removal of power line interference and suppression of transient response for filtering noise corrupted ECG signals. We demonstrate the effectiveness of Infinite Impulse Response (IIR) notch filter with time varying pole radius for improving the transient behavior. The temporary change in the pole radius of the filter diminishes the transient behavior. Simulation results show that the proposed IIR filter with time varying pole radius outperforms traditional IIR notch filters in terms of mean square error and transient suppression.

Keywords: notch filter, ECG, transient, pole radius

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Authors: Synthia Atas Sari

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The purpose of this paper is to examine the impact of reward system which determine by government over the work of Board of Commissioners to implement good corporate governance in Indonesian state-owned enterprises. To do so, this study analyzes the adequacy of the remuneration, the job attractiveness, and the board commitment and dedication with the remuneration system. Qualitative method used to examine the significant features and challenges to the government policy over the remuneration determination for the board of commissioners to their roles. Data gathered through semi-structure in-depth interview to the twenty-one participants over nine Indonesian stated-owned enterprises and written documents. Findings of this study indicate that government policies over the remuneration system is not effective to increase the performance of board of commissioners in implementing good corporate governance in Indonesian stated-owned enterprises due to unattractiveness of the remuneration amount, demotivate active members, and conflict interest over members of the remuneration committee.

Keywords: reward system, board of commissioners, stated-owned enterprises, government policy

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Authors: Chen-Fang Tsai, Shin-Li Lu

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Distribution-free control chart is an oncoming area from the statistical process control charts in recent years. Some researchers have developed various nonparametric control charts and investigated the detection capability of these charts. The major advantage of nonparametric control charts is that the underlying process is not specifically considered the assumption of normality or any parametric distribution. In this paper, two nonparametric exponentially weighted moving average (EWMA) control charts based on nonparametric tests, namely NE-S and NE-M control charts, are proposed for monitoring process variability. Generally, weighted moving average (GWMA) control charts are extended by utilizing design and adjustment parameters for monitoring the changes in the process variability, namely NG-S and NG-M control charts. Statistical performance is also investigated on NG-S and NG-M control charts with run rules. Moreover, sensitivity analysis is performed to show the effects of design parameters under the nonparametric NG-S and NG-M control charts.

Keywords: Distribution-free control chart, EWMA control charts, GWMA control charts

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Authors: Suneeta Kumari, Abanti Sahoo

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Industrial effluents treatment is a major problem in the world. All wastewater treatment methods have some problems in the environment. Due to this reason, today many natural biopolymers are being used in the waste water treatment because those are safe for our environment. In this study, synthesis and characterization of polyethersulfone/chitosan membranes (Thin film composite membrane) are carried out. Fish scales are used as raw materials. Different characterization techniques such as Fourier transform infrared spectroscopy (FTIR), X-ray powder diffraction (XRD), scanning electron microscope (SEM) and Thermal gravimetric analysis (TGA) are analysed for the synthesized membrane. The performance of membranes such as flux, rejection, and pore size are also checked. The synthesized membrane is used for the treatment of steel industry waste water where Biochemical oxygen demand (BOD), Chemical Oxygen Demand (COD), pH, colour, Total dissolved solids (TDS), Total suspended solids (TSS), Electrical conductivity (EC) and Turbidity aspects are analysed.

Keywords: fish scale, membrane synthesis, treatment of industrial effluents, chitosan

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Authors: Jing Zhao, Yongqing Bai, Qiaofang Shi, Huaihao Zhang

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Advances in software technology enable computational chemistry to be commonly applied in various research fields, especially in pedagogy. Thus, in order to expand and improve experimental instructions of computational chemistry for undergraduates, we designed an introductory experiment—research on acrylamide molecular structure and physicochemical properties. Initially, students construct molecular models of acrylamide and polyacrylamide in Gaussian and Materials Studio software respectively. Then, the infrared spectral data, atomic charge and molecular orbitals of acrylamide as well as solvation effect of polyacrylamide are calculated to predict their physicochemical performance. At last, rheological experiments are used to validate these predictions. Through the combination of molecular simulation (performed on Gaussian, Materials Studio) with experimental verification (rheology experiment), learners have deeply comprehended the chemical nature of acrylamide and polyacrylamide, achieving good learning outcomes.

Keywords: upper-division undergraduate, computer-based learning, laboratory instruction, molecular modeling

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Authors: Edward W. Staley, Corban G. Rivera, Ashley J. Llorens

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Advances in reinforcement learning (RL) have resulted in recent breakthroughs in the application of artificial intelligence (AI) across many different domains. An emerging landscape of development environments is making powerful RL techniques more accessible for a growing community of researchers. However, most existing frameworks do not directly address the problem of learning in complex operating environments, such as dense urban settings or defense-related scenarios, that incorporate distributed, heterogeneous teams of agents. To help enable AI research for this important class of applications, we introduce the AI Arena: a scalable framework with flexible abstractions for distributed multi-agent reinforcement learning. The AI Arena extends the OpenAI Gym interface to allow greater flexibility in learning control policies across multiple agents with heterogeneous learning strategies and localized views of the environment. To illustrate the utility of our framework, we present experimental results that demonstrate performance gains due to a distributed multi-agent learning approach over commonly-used RL techniques in several different learning environments.

Keywords: reinforcement learning, multi-agent, deep learning, artificial intelligence

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Authors: WooYoung Jung, Buntheng Chhorn, Min-Gi Kim

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Recently, damage to domestic facilities by strong winds and typhoons are growing. Therefore, this study focused on sign structure among various vulnerable facilities. The evaluation of the wind fragility was carried out considering the destruction of the anchor, which is one of the various failure modes of the sign structure. The performance evaluation of the anchor was carried out to derive the wind fragility. Two parameters were set and four anchor types were selected to perform the pull-out and shear tests. The resistance capacity was estimated based on the experimental results. Wind loads were estimated using Monte Carlo simulation method. Based on these results, we derived the wind fragility according to anchor type and wind exposure category. Finally, the evaluation of the wind fragility was performed according to the experimental parameters such as anchor length and anchor diameter. This study shows that the depth of anchor was more significant for the safety of structure compare to diameter of anchor.

Keywords: sign structure, wind fragility, set anchor, pull-out test, shear test, Monte Carlo simulation

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Authors: A. Augustyn, M. Florek, M. Herezniak

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Since the public authorities are constantly pressured by the public opinion to showcase the tangible and measurable results of their efforts, the evaluation of place brand-related activities becomes a necessity. Given the political and social character of place branding process, the legitimization of the branding efforts requires the compliance of the objectives set out in the city brand strategy with the actual needs, expectations, and aspirations of various internal stakeholders. To deliver on the diverse promises, city authorities and brand managers need to translate them into the measurable indicators against which the brand strategy effectiveness will be evaluated. In concert with these observations are the findings from branding and marketing literature with a widespread consensus that places should adopt a more systematic and holistic approach in order to ensure the performance of their brands. However, the measurement of the effectiveness of place branding remains insufficiently explored in theory, even though it is considered a significant step in the process of place brand management. Therefore, the aim of the research presented in the current paper was to collect insights on the nature of effectiveness measurement of city brand strategies and to juxtapose these findings with the theoretical assumptions formed on the basis of the state-of-the-art literature review. To this end, 15 international academic experts (out of 18 initially selected) with affiliation from ten countries (five continents), were individually interviewed. The standardized set of 19 open-ended questions was used for all the interviewees, who had been selected based on their expertise and reputation in the fields of place branding/marketing. Findings were categorized into four modules: (i) conceptualizations of city brand effectiveness, (ii) methodological issues of city brand effectiveness measurement, (iii) the nature of measurement process, (iv) articulation of key performance indicators (KPIs). Within each module, the interviewees offered diverse insights into the subject based on their academic expertise and professional activity as consultants. They proposed that there should be a twofold understanding of effectiveness. The narrow one when it is conceived as the aptitude to achieve specific goals, and the broad one in which city brand effectiveness is seen as an increase in social and economic reality of a place, which in turn poses diverse challenges for the measurement concepts and processes. Moreover, the respondents offered a variety of insights into the methodological issues, particularly about the need for customization and flexibility of the measurement systems, for the employment of interdisciplinary approach to measurement and implications resulting therefrom. Considerable emphasis was put on the inward approach to measurement, namely the necessity to monitor the resident’s evaluation of brand related activities instead of benchmarking cities against the competitive set. Other findings encompass the issues of developing appropriate KPIs for the city brand, managing the measurement process and the inclusion of diverse stakeholders to produce a sound measurement system. Furthermore, the interviewees enumerated the most frequently made mistakes in measurement mainly resulting from the misunderstanding of the nature of city brands. This research was financed by the National Science Centre, Poland, research project no. 2015/19/B/HS4/00380 Towards the categorization of place brand strategy effectiveness indicators – findings from strategic documents of Polish district cities – theoretical and empirical approach.

Keywords: city branding, effectiveness, experts’ insights, measurement

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Authors: Nithiya C., Komathi B. J., Praveena N. G., Samuda Prathima

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At present, the ultimate aim in digital system designs, especially at the gate level and lower levels of design abstraction, is power optimization. Adders are a nearly universal component of today's integrated circuits. Most of the research was on the design of high-speed adders to execute addition based on various adder structures. This paper discusses the ideal path for selecting an arithmetic unit for IoT applications. Based on the analysis of eight types of 16-bit adders, we found out Carry Look-ahead (CLA) produces low power. Additionally, multiplier and accumulator (MAC) unit is implemented with the Booth multiplier by using the low power adders in the order of preference. The design is synthesized and verified using Synopsys Design Compiler and VCS. Then it is implemented by using Cadence Encounter. The total power consumed by the CLA based booth multiplier is 0.03527mW, the total area occupied is 11260 um², and the speed is 2034 ps.

Keywords: carry look-ahead, carry select adder, CSA, internet of things, ripple carry adder, design rule check, power delay product, multiplier and accumulator

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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: Chen-Yin Kuo, Yung-Hsin Lee

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Previous studies applying residual income valuation (RIV) model generally use panel data and single-equation model to forecast stock prices. Unlike these, this paper uses Taiwan longitudinal data to estimate multi-equation time-series models such as Vector Autoregressive (VAR), Vector Error Correction Model (VECM), and conduct out-of-sample forecasting. Further, this work assesses their forecasting performance by two instruments. In favor of extant research, the major finding shows that VECM outperforms other three models in forecasting for three stock sectors over entire horizons. It implies that an error correction term containing long-run information contributes to improve forecasting accuracy. Moreover, the pattern of composite shows that at longer horizon, VECM produces the greater reduction in errors, and performs substantially better than VAR.

Keywords: residual income valuation model, vector error correction model, out of sample forecasting, forecasting accuracy

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Authors: Elias K. Maragos, Petros E. Maravelakis

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In Dynamic Data Envelopment Analysis (DDEA), which is a subfield of Data Envelopment Analysis (DEA), the productivity of Decision Making Units (DMUs) is considered in relation to time. In this case, as it is accepted by the most of the researchers, there are outputs, which are produced by a DMU to be used as inputs in a future time. Those outputs are known as intermediates. The common models, in DDEA, do not take into account the shape of the distribution of those inputs, outputs or intermediates data, assuming that the distribution of the virtual value of them does not deviate from linearity. This weakness causes the limitation of the accuracy of the analytical power of the traditional DDEA models. In this paper, the authors, using the concept of piecewise linear inputs and outputs, propose an extended DDEA model. The proposed model increases the flexibility of the traditional DDEA models and improves the measurement of the dynamic performance of DMUs.

Keywords: Dynamic Data Envelopment Analysis, DDEA, piecewise linear inputs, piecewise linear outputs

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Authors: M. Y. Maila, H. P. Makhubele

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Roasting is one of the oldest preservation method used in foods such as nuts and seeds. It is a process by which heat is applied to dry foodstuffs without the use of oil or water as a carrier. Groundnut seeds, also known as peanuts when sun dried or roasted, are among the oldest oil crops that are mostly consumed as a snack, after roasting in many parts of South Africa. However, roasting can denature proteins, destroy amino acids, decrease nutritive value and induce undesirable chemical changes in the final product. The aim of this study, therefore, was to evaluate the effect of various roasting times on the food value of the runner groundnut seeds. A constant temperature of 160 °C and various time-intervals (20, 30, 40, 50 and 60 min) were used for roasting groundnut seeds in an oven. Roasted groundnut seeds were then cooled and milled to flour. The milled sundried, raw groundnuts served as reference. The proximate analysis (moisture, energy and crude fats) was performed and the results were determined using standard methods. The antioxidant content was determined using HPLC. Mineral (cobalt, chromium, silicon and iron) contents were determined by first digesting the ash of sundried and roasted seed samples in 3M Hydrochloric acid and then determined by Atomic Absorption Spectrometry. All results were subjected to ANOVA through SAS software. Relative to the reference, roasting time significantly (p ≤ 0.05) reduced moisture (71%–88%), energy (74%) and crude fat (5%–64%) of the runner groundnut seeds, whereas the antioxidant content was significantly (p ≤ 0.05) increased (35%–72%) with increasing roasting time. Similarly, the tested mineral contents of the roasted runner groundnut seeds were also significantly (p ≤ 0.05) reduced at all roasting times: cobalt (21%–83%), chromium (48%–106%) and silicon (58%–77%). However, the iron content was significantly (p ≤ 0.05) unaffected. Generally, the tested runner groundnut seeds had higher food value in the raw state than in the roasted state, except for the antioxidant content. Moisture is a critical factor affecting the shelf life, texture and flavor of the final product. Loss of moisture ensures prolonged shelf life, which contribute to the stability of the roasted peanuts. Also, increased antioxidant content in roasted groundnuts is essential in other health-promoting compounds. In conclusion, the overall reduction in the proximate and mineral contents of the runner groundnuts seeds due to roasting is sufficient to suggest influences of roasting time on the food value of the final product and shelf life.

Keywords: dry roasting, legume, oil source, peanuts

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Authors: Feyza Eda Akyurek, Bayram Sahin, Kadir Gelis, Eyuphan Manay, Murat Ceylan

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Turbulent forced convection heat transfer and pressure drop characteristics of Al₂O₃–water nanofluid flowing through a concentric tube heat exchanger with and without coiled wire turbulators were studied experimentally. The experiments were conducted in the Reynolds number ranging from 4000 to 20000, particle volume concentrations of 0.8 vol.% and 1.6 vol.%. Two turbulators with the pitches of 25 mm and 39 mm were used. The results of nanofluids indicated that average Nusselt number increased much more with increasing Reynolds number compared to that of pure water. Thermal conductivity enhancement by the nanofluids resulted in heat transfer enhancement. Once the pressure drop of the alumina/water nanofluid was analyzed, it was nearly equal to that of pure water at the same Reynolds number range. It was concluded that nanofluids with the volume fractions of 0.8 and 1.6 did not have a significant effect on pressure drop change. However, the use of wire coils in heat exchanger enhanced heat transfer as well as the pressure drop.

Keywords: turbulators, heat exchanger, nanofluids, heat transfer enhancement

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Authors: Pitsanu Tongkhow, Pichet Jiraprasertwong

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This research investigates risk factors for defective products in autoparts factories. Under a Bayesian framework, a generalized linear mixed model (GLMM) in which the dependent variable, the number of defective products, has a Poisson distribution is adopted. Its performance is compared with the Poisson GLM under a Bayesian framework. The factors considered are production process, machines, and workers. The products coded RT50 are observed. The study found that the Poisson GLMM is more appropriate than the Poisson GLM. For the production Process factor, the highest risk of producing defective products is Process 1, for the Machine factor, the highest risk is Machine 5, and for the Worker factor, the highest risk is Worker 6.

Keywords: defective autoparts products, Bayesian framework, generalized linear mixed model (GLMM), risk factors

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Authors: Aadhya Kaul, Gautam Manocha, Preeti Nagrath

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Our environment comprises of a wide variety of plants that are similar to each other and sometimes the similarity between the plants makes the identification process tedious thus increasing the workload of the botanist all over the world. Now all the botanists cannot be accessible all the time for such laborious plant identification; therefore, there is an urge for a quick classification model. Also, along with the identification of the plants, it is also necessary to classify the plant as healthy or not as for a good lifestyle, humans require good food and this food comes from healthy plants. A large number of techniques have been applied to classify the plants as healthy or diseased in order to provide the solution. This paper proposes one such method known as anomaly detection using autoencoders using a set of collections of leaves. In this method, an autoencoder model is built using Keras and then the reconstruction of the original images of the leaves is done and the threshold loss is found in order to classify the plant leaves as healthy or diseased. A dataset of plant leaves is considered to judge the reconstructed performance by convolutional autoencoders and the average accuracy obtained is 71.55% for the purpose.

Keywords: convolutional autoencoder, anomaly detection, web application, FLASK

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Authors: Abdullateef Abdulkarim Jimoh, Muhammad Lawal A., Usman Muhammad, Hamisu Abdullahi, Nuhu Abdullahi Jega

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Smart city projects can be aided by urban development policies in public facilities, but economic resources to finance urban system reorganization is an issue to various governments. This is further compounded with the impact of the slowing down of national economies. The aim of this paper is to emphasize the need to prioritize the economic benefits of smart city projects and, specifically, in towns transforming into cities like Birnin kebbi. The smart-city projects can aim at developing a new form of ‘‘modernity and civilization’’ of the productive economy. This study adopts the descriptive statistical approach to identify the key performance indicators (KPI) for tracking the progress of cities and its developmental objectives. It has been established that numerous aspects of the modernization policies can enhance the competitiveness of territories, particular in aspects of social cohesion, the diffusion of knowledge, creativity, accessibility, etc.

Keywords: economy, economic policy, public facilities, smart city, urbanization

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Authors: Jessica To

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Feedback is one of the powerful levers for enhancing students’ performance. However, some students are under-engaged with feedback because they lack responsibility for feedback uptake. To resolve this conundrum, recent literature proposes feedback partnerships in which students and teachers share the power and responsibilities to co-construct feedback. During feedback co-construction, students express feedback needs to teachers, and teachers respond to individuals’ needs in return. Though this approach can increase students’ feedback ownership, its application is lagging as the field lacks conceptual clarity and implementation guide. This presentation aims to discuss the conceptual framework of feedback partnerships and feedback co-construction strategies. It identifies the components of feedback partnerships and strategies which could facilitate feedback co-construction. A systematic literature review was conducted to answer the questions. The literature search was performed using ERIC, PsycINFO, and Google Scholar with the keywords “assessment partnership”, “student as partner,” and “feedback engagement”. No time limit was set for the search. The inclusion criteria encompassed (i) student-teacher partnerships in feedback, (ii) feedback engagement in higher education, (iii) peer-reviewed publications, and (iv) English as the language of publication. Those without addressing conceptual understanding and implementation strategies were excluded. Finally, 65 publications were identified and analysed using thematic analysis. For the procedure, the texts relating to the questions were first extracted. Then, codes were assigned to summarise the ideas of the texts. Upon subsuming similar codes into themes, four themes emerged: students’ responsibilities, teachers’ responsibilities, conditions for partnerships development, and strategies. Their interrelationships were examined iteratively for framework development. Establishing feedback partnerships required different responsibilities of students and teachers during feedback co-construction. Students needed to self-evaluate performance against task criteria, identify inadequacies and communicate their needs to teachers. During feedback exchanges, they interpreted teachers’ comments, generated self-feedback through reflection, and co-developed improvement plans with teachers. Teachers had to increase students’ understanding of criteria and evaluation skills and create opportunities for students’ expression of feedback needs. In feedback dialogue, teachers responded to students’ needs and advised on the improvement plans. Feedback partnerships would be best grounded in an environment with trust and psychological safety. Four strategies could facilitate feedback co-construction. First, students’ understanding of task criteria could be increased by rubrics explanation and exemplar analysis. Second, students could sharpen evaluation skills if they participated in peer review and received teacher feedback on the quality of peer feedback. Third, provision of self-evaluation checklists and prompts and teacher modeling of self-assessment process could aid students in articulating feedback needs. Fourth, the trust could be fostered when teachers explained the benefits of feedback co-construction, showed empathy, and provided personalised comments in dialogue. Some strategies were applied in interactive cover sheets in which students performed self-evaluation and made feedback requests on a cover sheet during assignment submission, followed by teachers’ response to individuals’ requests. The significance of this presentation lies in unpacking the conceptual framework of feedback partnerships and outlining feedback co-construction strategies. With a solid foundation in theory and practice, researchers and teachers could better enhance students’ engagement with feedback.

Keywords: conceptual framework, feedback co-construction, feedback partnerships, implementation strategies

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Authors: Andrey A. Chernousov, Ben Y. B. Chan

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The thermal behavior of a large-scale, phase change material (PCM) enhanced building envelope system was studied in regard to the need for pre-fabricated construction in subtropical regions. The proposed large-scale envelope consists of a reinforced aluminum skin, insulation core, phase change material and reinforced gypsum board. The PCM impact on an energy efficiency of an enveloped room was resolved by validation of the Energy Plus numerical scheme and optimization of a smart material location in the core. The PCM location was optimized by a minimization method of a cooling energy demand. It has been shown that there is good agreement between the test and simulation results. The optimal location of the PCM layer in Hong Kong summer conditions has been then recomputed for core thicknesses of 40, 60 and 80 mm. A non-dimensional value of the optimal PCM location was obtained to be same for all the studied cases and the considered external and internal conditions.

Keywords: thermal performance, phase change material, energy efficiency, PCM optimization

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Authors: Radosław Pastusiak, Anna Kaźmierska, Magdalena Jasiniak

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The impact of Special Economic Zones (SEZs) has been analyzed for many years by researchers. There are lot of theoretical studies proving the SEZs importance for regional development, however, there is lack of empirical studies (and they are mainly focused on China market) that are based on available data. The theoretical studies indicate the various impacts of enterprises operating within SEZs on the economy. The article proves that, in case of Poland, locating SEZs in municipalities is an important part of increasing municipalities’ income. Therefore SEZs have a positive impact on regional development. Municipality income is understood as taxes paid by taxpayers who depend on SEZ companies’ performance. The analysis includes the Corporate Income Tax (CIT), Personal Income Tax (PIT) and real estate tax. The effects of SEZs on regional development were narrowed to a few variables that are most significant for the financial system. The analysis indicates the significant impact of SEZs on the amount of taxes influencing the municipality budget.

Keywords: special economic zone, local finance, municipal finance, government

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Authors: Yori Gidron, Maartje Mol, Norbert Foudraine, Frits Van Osch, Joop Van Den Bergh, Moshe Farchi, Maud Straus

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Background: The worldwide pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-COV2), which began in 2019, also known as Covid-19, has infected over 136 million people and tragically took the lives of over 2.9 million people worldwide. Many of the complications and deaths are predicted by the inflammatory “cytokine storm.” One way to progress in the prevention of death is by finding a predictive and protective factor that inhibits inflammation, on the one hand, and which also increases anti-viral immunity on the other hand. The vagal nerve does precisely both actions. This study examined whether vagal nerve activity, indexed by heart-rate variability (HRV), predicts survival in patients with Covid-19. Method: We performed a pseudo-prospective study, where we retroactively obtained ECGs of 271 Covid-19 patients arriving at a large regional hospital in The Netherlands. HRV was indexed by the standard deviation of the intervals between normal heartbeats (SDNN). We examined patients’ survival at 3 weeks and took into account multiple confounders and known prognostic factors (e.g., age, heart disease, diabetes, hypertension). Results: Patients’ mean age was 68 (range: 25-95) and nearly 22% of the patients had died by 3 weeks. Their mean SDNN (17.47msec) was far below the norm (50msec). Importantly, relatively higher HRV significantly predicted a higher chance of survival, after statistically controlling for patients’ age, cardiac diseases, hypertension and diabetes (relative risk, H.R, and 95% confidence interval (95%CI): H.R = 0.49, 95%CI: 0.26 – 0.95, p < 0.05). However, since HRV declines rapidly with age and since age is a profound predictor in Covid-19, we split the sample by median age (40). Subsequently, we found that higher HRV significantly predicted greater survival in patients older than 70 (H.R = 0.35, 95%CI: 0.16 – 0.78, p = 0.01), but HRV did not predict survival in patients below age 70 years (H.R = 1.11, 95%CI: 0.37 – 3.28, p > 0.05). Conclusions: To the best of our knowledge, this is the first study showing that higher vagal nerve activity, as indexed by HRV, is an independent predictor of higher chances for survival in Covid-19. The results are in line with the protective role of the vagal nerve in diseases and extend this to a severe infectious illness. Studies should replicate these findings and then test in controlled trials whether activating the vagus nerve may prevent mortality in Covid-19.

Keywords: Covid-19, heart-rate Variability, prognosis, survival, vagal nerve

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Authors: Kamran Rashid, Tashfeen M. Azhar, Asad-ur-Rahman Wahla

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Current research aims to understand the role of successful buyer-supplier relationship in achieving supply chain competitiveness in a developing country perspective. Five hypotheses are developed to test structural model. Survey data is collected from the manufacturing sector of Pakistan. Analysis is conducted using Partial Least Squares (PLS) Structural Equation Modeling (SEM) through Smart PLS version 2.0 M3. Results demonstrate positive impact of effective supplier selection, buyer-supplier engagement, and information sharing capability on success of buyer supplier relationship. This successful buyer supplier relationship drives the supply chain firm financial and market performance. Additional analyses with large sample sizes are required in other developing countries to cross validate the results. Current study provides empirical evidence of the role of successful buyer supplier relationship in achieving supply chain competitiveness.

Keywords: supply chain management, successful buyer-supplier relationship, supply chain competitiveness, developing country

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Authors: Sanjay Rathee, Arti Kashyap

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Extraction of useful information from large datasets is one of the most important research problems. Association rule mining is one of the best methods for this purpose. Finding possible associations between items in large transaction based datasets (finding frequent patterns) is most important part of the association rule mining. There exist many algorithms to find frequent patterns but Apriori algorithm always remains a preferred choice due to its ease of implementation and natural tendency to be parallelized. Many single-machine based Apriori variants exist but massive amount of data available these days is above capacity of a single machine. Therefore, to meet the demands of this ever-growing huge data, there is a need of multiple machines based Apriori algorithm. For these types of distributed applications, MapReduce is a popular fault-tolerant framework. Hadoop is one of the best open-source software frameworks with MapReduce approach for distributed storage and distributed processing of huge datasets using clusters built from commodity hardware. However, heavy disk I/O operation at each iteration of a highly iterative algorithm like Apriori makes Hadoop inefficient. A number of MapReduce-based platforms are being developed for parallel computing in recent years. Among them, two platforms, namely, Spark and Flink have attracted a lot of attention because of their inbuilt support to distributed computations. Earlier we proposed a reduced- Apriori algorithm on Spark platform which outperforms parallel Apriori, one because of use of Spark and secondly because of the improvement we proposed in standard Apriori. Therefore, this work is a natural sequel of our work and targets on implementing, testing and benchmarking Apriori and Reduced-Apriori and our new algorithm ReducedAll-Apriori on Apache Flink and compares it with Spark implementation. Flink, a streaming dataflow engine, overcomes disk I/O bottlenecks in MapReduce, providing an ideal platform for distributed Apriori. Flink's pipelining based structure allows starting a next iteration as soon as partial results of earlier iteration are available. Therefore, there is no need to wait for all reducers result to start a next iteration. We conduct in-depth experiments to gain insight into the effectiveness, efficiency and scalability of the Apriori and RA-Apriori algorithm on Flink.

Keywords: apriori, apache flink, Mapreduce, spark, Hadoop, R-Apriori, frequent itemset mining

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