bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of Engine-Out Emissions Using Deep Convolutional Neural Networks
Authors: Warey Alok , Gao Jian , Grover Ronald
Published: April 2021

Analysis-driven pre-calibration of a modern automotive engine is extremely valuable in significantly reducing hardware investments and accelerating engine designs compliant with stricter emission regulations. Advanced modelling tools, such as a Virtual Engine Model (VEM) using Computational Fluid Dynamics (CFD), are often used within the framework of a Design of Experiments for Powertrain Engineering (DEPE) with the goal of streamlining significant portions of the calibration process. The success of the methodology largely relies on the accuracy of analytical predictions, especially engine-out emissions. Results show excellent agreements in engine performance parameters (with R2 > 98%) and good agreements in NOx and combustion noise (with R2 > 87%), while the Carbon Monoxide (CO), Unburned Hydrocarbons (HC) and Smoke emissions predictions remain a challenge even with a large n-heptane mechanism consisting of 144 species and 900 reactions and refined mesh resolution. In this study, a Machine Learning (ML) approach is presented to correlate in-cylinder images of Equivalence Ratio, Temperature, Velocity field and Turbulent Kinetic Energy (TKE) at Exhaust Valve Opening (EVO) to engine-out emissions of CO, HC and Smoke measured experimentally. The images generated from CFD simulations and experimentally measured emissions data were used to train a deep Convolutional Neural Network (CNN) based on the VGG16 architecture. The prediction performance of the trained model was evaluated on held-out data. The deep learning model led to a significant improvement in prediction of all emissions species and captured the qualitative trends as well. This model could be used as an emissions prediction sub-model in the Virtual Engine Model framework leading to significantly lower computational costs by avoiding the use of expensive chemistry solvers. Gradient-weighted Regression Activation Mapping (Grad-RAMS) was used to draw insights into image features that had a strong influence on the emissions predictions.
Electrified Deceleration Cylinder Cutoff Engine Control Benefits and Strategies
Authors: Wilcutts Mark , Wolk Benjamin , Yang Xiaojian , Wang Robert
Published: April 2021

Cylinder deactivation is a fuel consumption and CO2 reduction technology for internal combustion engines that deactivates cylinders at light to moderate loads, allowing the remaining firing cylinders to operate near optimum efficiency. Dynamic Skip Fire (DSF) uses full-authority cylinder deactivation that allows any cylinder of the engine to be deactivated in sequence. In previous SAE papers, both DSF technology and the synergies between DSF and electrification (eDSF) have been described.

Recent engine technology includes deactivation mechanisms that do not effectively incorporate individual cylinder control. Nevertheless, it is still quite possible to improve the efficiency of engines equipped with these ganged-deactivation mechanisms. By grouping all cylinders into a deactivation mode, no air is pumped through the cylinders as it would be during the corresponding conventional operation that deactivates fuel alone. The engine equipped with deactivating cylinders can eliminate pumping work and provide more opportunity to recuperate electrical energy from the vehicle, as well as prevent oxygen saturation of the exhaust aftertreatment system.

This paper presents the substantial benefits of this approach, showing both simulated and experimental fuel economy improvements for a C-segment vehicle exceeding 5% in the WLTC class 3 drive cycle. It discusses control strategies for managing transitions in and out of deactivation to achieve customer-pleasing NVH and drivability. Finally, the value proposition is presented for this electrified deceleration cylinder cutoff (eDCCO) system, also known as Hybrid++.
Advancing Platooning with ADAS Control Integration and Assessment Test Results
Authors: Borhan Hoseinali , Lammert Michael , Kelly Kenneth , Zhang Chen , Brady Nathan , YU Chia-Siung , Liu Jingxuan
Published: April 2021

The application of cooperative adaptive cruise control (CACC) to heavy-duty trucks known as truck platooning has shown fuel economy improvements over test track ideal driving conditions. However, there are limited test data available to assess the performance of CACC under real-world driving conditions. As part of the Cummins-led U.S. Department of Energy Funding Opportunity Announcement award project, truck platooning with CACC has been tested under real-world driving conditions and the results are presented in this paper. First, real-world driving conditions are characterized with the National Renewable Energy Laboratory’s Fleet DNA database to define the test factors. The key test factors impacting long-haul truck fuel economy were identified as terrain and highway traffic with and without advanced driver-assistance systems (ADAS). Track and on-highway testing guided by SAE J1321 procedures were conducted to assess truck platooning operation under the characterized real-world driving conditions. On-highway testing is done on a route in Indiana representing operation of long-haul Class 8 trucks in the United States. The road includes low-, medium-, and high-grade segments. The highway test results of a two-truck platooning configuration indicate considerable fuel-saving reduction comparing to the test track data collected under ideal driving conditions. The test data indicate that platooning could lead to increases in fuel consumption during traffic or high-grade portions of the route, causing reduction of the overall fuel saving on the road comparing to test track results. However, integration of ADAS features on the lead truck during on-road tests leads to significant improvement of fuel saving for both trucks in CACC operation.
In-Vehicle Validation of Heavy-Duty Vehicle Fuel Savings via a Hierarchical Predictive Online Controller
Authors: Pelletier Evan , Bai Wushuang , Alvarez Tiburcio Miguel , Borek John , Boyle Stephen , Earnhardt Christian , Gao Liming , Geyer Stephen , Graham Christopher , Groelke Ben , Magee Mark , Palmeter Kyle , Rodriguez Manuel , Xu Chu , Fathy Hosam , Naghnaeian Mohammad , Stockar Stephanie , Vermillion Christopher , Brennan Sean
Published: April 2021

This paper presents the evolution of a series of connected, automated vehicle technologies from simulation to in-vehicle validation for the purposes of minimizing the fuel usage of a class-8 heavy duty truck. The results reveal that an online, hierarchical model-predictive control scheme, implemented via the use of extended horizon driver advisories for velocity and gear, achieves fuel savings comparable to predictions from software-in-the-loop (SiL) simulations and engine-in-the-loop (EiL) studies that operated with a greater degree of powertrain and chassis automation. The work of this paper builds on prior work that presented in detail this predictive control scheme that successively optimizes vehicle routing, arrival and departure at signalized intersections, speed trajectory planning, platooning, predictive gear shifting, and engine demand torque shaping. This paper begins by outlining the controller development progression from a previously published engine-in-the-loop study to the in-vehicle driver-in-the-loop testing highlighted in this work. The purpose of this field testing is to quantify the level of agreement between field data and prior results, particularly noting the influence of noise, disturbances and unmodeled dynamics. Also detailed are the steps to effectively implement the predictive horizons in experimentation with human rather than automated driver inputs by replacing the conventional speedometer with an augmented driver display. The driver display seeks to maintain driver awareness and route preview simultaneously. Additional features of the instrumented truck testing protocol include detailed route maps on speed limits, intersections, traffic and road grade; highly accurate fuel flowrate measurements calibrated via gravimetric methods; and closed-loop control between the predictive controller, driver, and vehicle controller area network (CAN). These combined capabilities allow for model validation, investigation of the effects of human-in-the-loop on controller performance, and identification of unmodeled dynamics and disturbances. The results show that the driver advisory implementation matches fuel consumption predictions from software-in-the-loop simulations and engine-in-the-loop studies to within 2-9%. Following the study of in-vehicle performance and variability, these results also validate, using real road tests, the up to 21% fuel savings achieved by using the hierarchical control scheme with a combination of multiple chassis and powertrain optimization technologies.
Downsized Boosted Dilute Combustion, Exhaust Compounded (DBDC+EC) Experimental Engine Design, Thermodynamic Model Comparison, and Performance Potential Predictions
Authors: Andruskiewicz Peter , Durrett Russell , Najt Paul
Published: April 2021

An experimental piston compounded engine was designed with guidance from thermodynamic modeling, then was built and tested to compare the model predictions to measured results. The piston-compounded concept has shown great potential for improvements in efficiency over current state-of-the-art light-duty engines through the use of an efficient second expansion process to more fully recover energy still present in the exhaust gasses, and was further developed into the Downsized Boosted Dilute Combustion, Exhaust Compounded (DBDC+EC) engine presented here. This paper documents some of the more unique design elements of this engine as well as a performance comparison between test data and modeling expectations. Ultimately, an experimental stoichiometric spark-ignited piston compounded engine was designed, five blocks were built, and collectively they were run for thousands of hours. The experimental engines achieved a minimum of 222 g/kW-hr BSFC with a wide region of operation under 250 g/kW-hr, and the performance matched predictions from a thermodynamic model that reflected the as-built hardware. Predictions for a 2nd generation DBDC+EC engine result in a 10% fuel economy improvement over a similar downsized boosted stoichiometric SI engine, with an additional 9% improvement for lean operation at low load. These result in minimum BSFCs of 212 and 206 g/kW-hr for the stoichiometric and lean DBD+EC Gen 2 engines respectively.
Comparison of Excess Air (Lean) vs EGR Diluted Operation in a Pre-Chamber Air/Fuel Scavenged Dual Mode, Turbulent Jet Ignition Engine at High Dilution Rate (~40%)
Authors: Atis Cyrus , Schock Harold
Published: April 2021

Charge dilution is widely considered as one of the leading strategies to realize further improvement in thermal efficiency from current generation spark ignition engines. While dilution with excess air (lean burn operation) provides substantial thermal efficiency benefits, drastically diminished NOx conversion efficiency of the widely used three-way-catalyst (TWC) during off-stoichiometric/lean burn operation makes the lean combustion rather impractical, especially for automotive applications. A more viable alternative to lean operation is the dilution with EGR. The problem with EGR dilution has been the substantially lower dilution tolerance limit with EGR and a consequent drop in thermal efficiency compared to excess air/lean operation. This is particularly applicable to the pre-chamber jet ignition technologies with considerably higher lean burn capabilities but much lower EGR tolerance due to the presence of a high fraction of residuals inside the pre-chamber. Dual Mode, Turbulent Jet Ignition (DM-TJI) technology with its unique ability to work with high external EGR dilution (up to 40% wet/mass basis) due to its additional air delivery to the prechamber offers a viable alternative to the lean burn strategy. DM-TJI could be the technology pathway to realize high EGR diluted combustion with comparable dilution limits to those of the lean burn strategy while still enabling effective use of TWC technology. Present study compares the excess air versus EGR dilution strategy under identical level of dilution (up to 40 %) in a DM-TJI equipped single cylinder engine operating on a high (13.3: 1) compression ratio. The results show that compared to the lean burn operation, EGR dilution provides marginally lower but still comparable thermal efficiency benefits with a marked improvement in NOx reduction, especially in a high compression, knock limited situation. This study showcases that high EGR dilution rates comparable to lean burn operation can be maintained with the DM-TJI system to achieve high thermal efficiency while still operating at stoichiometric air-fuel ratio.
The Impact of Advanced Fuels and Lubricants on Thermal Efficiency in a Highly Dilute Engine
Authors: Bunce Michael , Peters Nathan , Pothuraju Subramanyam Sai Krishna , Blaxill Hugh , Gao Jason , Choi Eugine
Published: April 2021

In spark ignited engines, thermal efficiency is strongly influenced by the quality of the combustion process as initiated by the ignition system. Jet Ignition is a combustion concept that utilizes a small pre-chamber to produce reactive jets which distribute ignition energy throughout the main combustion chamber. This distributed ignition energy can be leveraged to induce ignition in traditionally difficult-to-ignite regimes, such as in highly dilute mixtures. Highly dilute jet ignition combustion has been proven to produce thermal efficiencies significantly higher than those of conventional spark ignition combustion.

To fully exploit the efficiency potential of active jet ignition, multiple aspects of the engine architecture and peripheral systems must be adjusted. Efficiency sensitivities to compression ratio, boost system, and intake port design have been explored extensively. Less well understood are the sensitivities of thermal efficiency and other jet ignition combustion parameters to fuel and lubricant properties. With increasing interest in investigating fuel-engine system interactions, it is imperative to understand how future lubricants and fuels could synergistically enhance this highly efficient combustion mode.

Experimental data is obtained using a 1.5L 3-cylinder jet ignition engine. The engine is tested with a range of lubricant and fuel formulations, commercial and proprietary, at multiple compression ratios. Results are analyzed for the different fuel/lubricant/hardware combinations. High load engine operation is limited by knock, peak cylinder pressure, and peak rate of cylinder pressure rise; the governing limitation changes according to the fuel/lubricant/hardware combination being utilized, with implications for both peak load and thermal efficiency. Engine results demonstrate a peak brake thermal efficiency of 43.6% with the optimal fuel and lubricant evaluated, representing a 1.5 percentage point increase over the baseline jet ignition configuration, and a 9% improvement in cycle average fuel economy.
Increasing the Effective AKI of Fuels Using Port Water Injection (Part I)
Authors: Gopujkar Siddharth , Worm Jeremy , Barros Sam , Christensen Cord
Published: April 2021

Anti-knock index (AKI) is a metric that can be used to quantify the anti-knock performance of a fuel and is the metric used in the United States. AKI is the average of Research Octane Number (RON) and Motor Octane Number (MON), which are calculated for every fuel on a Cooperative Fuel Research (CFR) engine under controlled conditions according to ASTM test procedures. Fuels with higher AKI have better knock mitigating properties and can be run with a combustion phasing closer to MBT in the knock limited operating region of a gasoline engine. However, fuels with higher AKI tend to be costlier and less environmentally friendly to produce. As an alternative, the anti-knock characteristics of lower AKI fuels can be improved with water injection. In this sense, the water injection increases the ‘effective AKI’ of the fuel. The injected water absorbs heat, which lowers the in-cylinder temperatures and mitigates knock, which permits advancing the knock limited CA50 (KLCA50) and reducing fuel consumption for a given speed-load condition. This project focused on quantifying the amount of water required, both as a ratio with fuel, and absolute, to achieve an increase in ‘effective AKI’. This was accomplished by comparing the anti-knock performance of a high AKI reference fuel to lower AKI fuels supplemented with water injection. Tests were conducted at varying speeds and loads to investigate trends in the relationship between effective AKI per unit water injected. The results showed that engine speed does not have a profound effect on the required water-fuel ratio, but as the load is increased, less water is required to increase the effective AKI by a specific amount. The effect of water injection on COV of IMEP was also observed.
On Optical Semi-Quantitative Spectral Study of Low-Speed Pre-Ignition Sources in Spark Ignition Engines
Authors: Kalaskar Vickey , Moore Thomas , Swarts Andre
Published: April 2021

Low-Speed pre-ignition (LSPI) in modern-day, heavily downsized, boosted, and direct-injection spark ignition (SI) engines is a well-known problem. Several mechanisms contribute towards stochastic pre-ignition (SPI), the most prominent being crevice material droplet induced and deposit induced pre-ignition mechanisms. The droplet mechanism is typically dominated by the detergent additives present in the lubricant formulation; more specifically calcium and sodium-based detergent additives correlate strongly with the increased LSPI rates. Deposits flaking off the combustion chamber surfaces can also induce LSPI under certain conditions.

This study aimed to develop an optical method designed to investigate the nature of pre-ignition precursors. Southwest Research Institute (SwRI) utilized an optically accessible GM 2.0 L LHU engine to study the pre-ignition phenomenon and studied the nature of pre-ignition precursors using spectral information from one of the cylinders in this engine. A custom, simplified borescope was employed to gather visible light from the combustion chamber to detect the glowing pre-ignition precursors. Further, this light signal was sent to an array of four photomultiplier tube (PMT) detectors which were tuned to visible light emission wavelength bands of interest. Three of the four wavelengths (555, 623, 645 nm) were selected to be representative of calcium and CaOH while the fourth wavelength of 715 nm (red emission) was selected to be representative of glowing soot-like aggregates. A high calcium first-generation Dexos™ 1 compliant oil with moderate LSPI activity was used in this study. The analysis revealed that the precursors contained both the signals for calcium as well as the deposit wavelengths in different proportions. More specifically, it was observed that in a series of LSPI events, the first event was usually more biased towards calcium wavelengths suggesting a pre-ignition initiated by the droplet ejecting from the crevice. A relative semi-quantitative metric was developed to compare the light signals for different frequencies to further delve into the pre-ignition mechanism. Two additional fuels were tested with different particulate matter index (PMI) values to assess the impact of PMI on pre-ignition precursors. A bulk comparison of around 30,000 combustion cycles of the two additional test fuels revealed that the high PMI fuel showed more bias towards a deposit induced mechanism while the lower PMI fuel exhibited signs of both droplet and deposit induced pre-ignition precursors.
Fuel Effects on the Propensity to Establish Propagating Flames at SPI-Relevant Engine Conditions
Authors: Costanzo Vincent , Yu Xin , Chapman Elana , Davis Richard
Published: April 2021

In order to further understand the sequence of events leading to stochastic preignition in a spark-ignition engine, a methodology previously developed by the authors was used to evaluate the propensity of a wide range of fuels to establishing propagating flames under conditions representative of those at which stochastic preignition (SPI) occurs. The fuel matrix included single component hydrocarbons, binary mixtures, and real fuel blends.

The propensity of each fuel to establish a flame was correlated to multiple fuel properties and shown to exhibit consistent blending behaviors. No single parameter strongly predicted a fuel’s propensity to establish a flame, while multiple reactivity-based parameters exhibited moderate correlation. A two-stage model of the flame establishment process was developed to interpret and explain these results. In short, the fuel-air mixture must locally reach a chemical run-away ignition temperature and then the fuel-air mixture outside of this zone must sustain a propagating flame. This model explains why various fuel properties affect the overall flame establishment propensity of a given fuel.

The data of this study, and the understanding it has generated, helps elucidate the role of fuels on the flame establishment process under elevated pressure and temperature conditions, and ultimately aids in the understanding of the stochastic preignition problem.
Effect of Intake Temperature and Engine Speed on the Auto-Ignition Reactivity of the Fuels for HCCI Fuel Rating
Authors: Waqas Muhammad Umer , Hoth Alexander , Gainey Brian , Johansson Bengt , Kolodziej Christopher P.
Published: April 2021

The current research octane number (RON) and motor octane number (MON) gasoline tests are inadequate for describing the auto-ignition reactivity of fuels in homogeneous charge compression ignition (HCCI) combustion. Intake temperature and engine speed are two important parameters when trying to understand the fuel auto-ignition reactivity in HCCI combustion. The objective of this study was to understand the effect of high intake temperature (between 100 and 200 °C) and engine speed (600 and 900 rpm) on the auto-ignition HCCI reactivity ratings of fuels using an instrumented Cooperative Fuel Research (CFR) engine. The fuels used for this study included blends of iso-octane/n-heptane, toluene/n-heptane, ethanol/n-heptane, and gasolines with varying chemical compositions and octane levels. The CFR engine was operated at 600 and 900 rpm with an intake pressure of 1.0 bar and an excess air ratio (lambda) of 3. It was found that the relative HCCI reactivity ranking of the gasolines with a RON of 98 was constant at both engine speeds and high intake temperatures (150 °C and 200 °C). This implied that for HCCI fuel ratings, there exists a threshold intake temperature beyond which further increases in temperature do not change the relative rankings of the fuel’s HCCI reactivity. At these high intake air temperatures, changes in engine speed between 600 and 900 rpm did not affect the HCCI ratings of the RON98 gasolines either. However, the effects of intake temperature (between 150 and 200 °C) and engine speed (600 and 900 rpm) did become apparent when additional gasoline octane levels and chemical compositions were investigated.
New Equivalent Model and Modal Analysis of Permanent Magnet Motor Stator System
Authors: Wang Yongchao , Gao Hui , Chen Daliang
Published: April 2021

This paper proposes a new type of permanent magnet motor stator system equivalent model considering insulation materials and modal analysis is performed. First, the analytic method is used to estimate the modal natural frequency of the stator core and the stator winding system respectively, and the influence of the winding on the system modal is analyzed. Then, a new type of winding equivalent model was established using the finite element method (FEM), two traditional models were established at the same time. Compared with the traditional model, the new winding equivalent model considers the influence of insulating materials, so the calculation results are more accurate. At the same time, due to the innovative modeling of the new winding equivalent model, the Young’s modulus of the winding equivalent material is increased, which effectively reduces the appearance of useless local modes and improves the calculation efficiency. Due to the reduction of useless local modes, the new winding equivalent model can quickly calculate and accurately identify the natural frequency of the 0-order mode of the stator system. Finally, modal tests are used to verify the correctness of the analytical method and the accuracy of the calculation results of the new equivalent model. This paper provides a new finite element modeling method for the modal analysis of the motor stator system, which improves the calculation accuracy and efficiency.
The Control Strategy for 4WD Hybrid Vehicle Based on Wavelet Transform
Authors: Qiao Yiran , Chu Liang , Chen Xinbo , Guo Chong , Xu Xiang
Published: April 2021

In this paper, in order to avoid the frequent switching of engine operating points and improve the fuel economy during driving, this paper proposes a control strategy for the 4-wheel drive (4WD) hybrid vehicle based on wavelet transform. First of all, the system configuration and the original control strategy of the 4WD hybrid vehicle were introduced and analyzed, which summarized the shortcomings of this control strategy. Then, based on the analyze of the original control strategy, the wavelet transform was used to overcome its weaknesses. By taking advantage over the superiority of the wavelet transform method in multi signal disposition, the demand power of vehicle was decomposed into the stable drive power and the instantaneous response power, which were distributed to engine and electric motor respectively. This process was carried out under different driving modes. The proposed control strategy not only ensured the dynamic property, but avoided the frequent switching of engine operating points by making the most of the fast-transient response of electric motor. Last but not least, in MATLAB/Simulink environment, the vehicle model and the control strategy model were built. Compared with the original control strategy, the simulation results showed that the proposed control strategy could reduce the range of engine operating points and make them more concentrated, which could effectively improve the working condition of engine. As a result, the proposed strategy improved the fuel economy, which verified the effectiveness of the control strategy based on wavelet transform.
Suitability Assessment of an Uncalibrated Body Force Based Fan Modeling Approach to Predict Automotive Underhood Airflows
Authors: Saini Palak , Defoe Jeffrey , Farbar Erin
Published: April 2021

The automotive fan is a critical component of the cooling module, providing the majority of the cooling airflow over the heat exchangers and to underbody components at low speed, idle, and key-off conditions. Accurately predicting the performance of the automotive cooling fan is critical for sizing heat exchangers and ensuring that underhood and underbody components remain below target temperatures. This is normally done with computational fluid dynamics, but in a full-vehicle simulation it is impractical to model the rotation of the fan blades using a sliding mesh approach. Thus, simplified models which capture the fan behavior are employed. In this paper, a body force-type fan modeling approach is adopted and assessed. Many industrial fan models are calibrated based on experiments or higher-fidelity simulations. This can slow the design process. The approach employed eliminates this step, requiring only fan geometry information and no a-priori performance data. An existing body force modeling approach is used. It has been shown to be suitable for fans of the type typically used in automotive cooling systems. The model is analyzed and validated against computations including the blades. The model is then applied to simulations of the flow around and through an entire vehicle at a variety of speeds. The model predicts the flow rate through the radiator to within 8% of the experimentally-measured value at idle. At high vehicle speed, the accuracy improves to 1%. The accuracy of the uncalibrated model in predicting the radiator flow rate is comparable to the current best-practice calibrated fan modeling techniques used in the industry. The impact of the findings is a reduction in the overall effort involved in simulating under-hood and underbody flows.
Effect of Driveshaft Angle on Turning Noise in Electric Vehicles and Solution Development Approach
Authors: Hazra Sandip , K Janardhan Reddy
Published: April 2021

Electric vehicles (EV’s) are very much noise, vibration and harshness (NVH) sensitive due to the absence of engine noise. The outline of this paper is based on vehicle level turning noise evaluation. The impact of the driveshaft angle in the frequency range of 1000-2000Hz. The level of noise while turning at driver and co-driver side is evaluated first. Then the possible countermeasure to address such noise issues are also discussed. The impact on the angular adjusted roller (AAR) joint and driveshaft angle is studied along with the impact on other parameters like powertrain mount stiffness, ground clearance and vehicle architecture.
Forest Product Industry and Engineered Wood Products: The Nigerian Experience
Authors: Ekundayo O.O. , Arum C. , Owoyemi J.M.
Published: April 2021

The forest product industry in Nigeria is barely surviving rather than thriving. Faced with a lot of challenges, the industry has witnessed the closure of many wood processing industries while a few are operating at low capacity. This paper examines engineered wood products and the outlook on wood and wood products in Nigeria. It discusses some of the challenges that have inhibited the growth of the industry and provides recommendations for reinventing the industry through development of modern value-added wood products for sustainable building construction.

Keywords: Collaboration; Engineered wood products; Forestry product industry; Glulam; Sustainability.
An enhanced algorithm for scheduling dependent tasks in cloud computing environment
Authors: Hamza H. , Kana A.F.D , Tanko M.Y. , Aliyu S.
Published: April 2021

Cloud computing is a model that aims to deliver a reliable, customizable and scalable computing environment for end-users. Cloud computing is one of the most widely used technologies embraced by sectors and academia, offering a versatile and effective way to store and retrieve documents. The performance and efficiency of cloud computing services always depend upon the performance of the execution of user tasks submitted to the cloud system. Scheduling of user tasks plays a significant role in improving the performance of cloud services. Accordingly, many dependent task scheduling algorithms have been proposed to improve the performance of cloud services and resource utilization; however, most of the techniques for determining which task should be scheduled next are inefficient. This research provided an enhanced algorithm for scheduling dependent tasks in cloud that aims at improving the overall performance of the system. The Dependent tasks were represented as a directed acyclic graph (DAG) and the number of dependent tasks and their total running time were used as a heuristic for determining which path should be explored first. Best first search approach based on the defined heuristic was used to traverse the graph to determine which task should be scheduled next. The results of the simulation using WorkflowSim toolkit showed an average improvement of 18% and 19% on waiting time and turnaround time were achieved respectively.
A fuzzy logic risk control and self-assessment metrics for e-banking operational risk analysis
Authors: Ako R.E. , Oghorodi D. , Okpako A.E.
Published: April 2021

Operational risk is the risk of losses arising from the failure or inadequate internal processes, human resources, systems, and external events that affect the bank's operations as defined by Basel Committee on Banking Supervision. Defining a suitable set of risk measurement metrics is considered one of the most important issues for any risk analysis. It enables the quantitative evaluation of the risk exposure level and the effectiveness of internal control system. Risk measurement is needed to provide an effective means to quantify the risk of existing or planned systems to enable understanding of the overall security level and to guide decision making. Given the number of successful attacks against financial Institutions and the sophistication of the tactics used by attackers, existing classical measurement approaches are no longer enough. This study focuses on fuzzy logic-based metric identification to measurement of the risk exposure level, to enable financial institutions to see the overall risk level and security state of their E-banking systems and to assist with decision making. This will provide a newer dimension to risk management by shifting from risk measurement based on probability and classical set theory to Fuzzy Logic (FL) measurement. In this paper fuzzy logic-based metrics is presented and expressed as a function of six factors (triggering events, avoidance, recovery, Undesirable Operational State (UOS), cost of Undesirable Operational State (UOS) occurrence and severity of risk occurrence) as proposed by [1].
Detection and prediction of pluvial flood using machine learning techniques
Authors: Oladapo K.A. , Ayankoya F.Y. , Adekunle F.A. , Idowu S.A.
Published: April 2021

The periodical occurrence of emergency situations represents an important issue for mankind. Over the years, the world at large has experienced multiple misadventures both natural and man-made. A recent report showed that flood have affected more individuals than any other category of disaster in the 21st century with the highest percentage of 43% of all disaster events in 2019 and Africa been the second vulnerable continent after Asia. Handling flood risk with the intention of safety and comfort of the citizens as well as saving their environment is one of the major responsibilities of the leadership in each country especially in flood prone areas. Machine learning predictive analytic applications can improve the risk management. So, it is highly important to devise a scientific method for flood risk reduction since it cannot be eradicated. The paper proposes a pluvial flood detection and prediction system based on machine learning techniques. The proposed model will employ a fuzzy rule-based classification to appraise the performance of the machine learning algorithm on pluvial flood conditioning variables.
Nigeria at sixty: An analysis of user participation in an online national competition amidst the Covid-19 pandemic
Authors: Umar Idris Abubakar , Ibrahim Yazid Ado , Abubakar Bashir Salisu , Musa Zainab Magaji , Ahmad Muhammad Mahmoud
Published: April 2021

Activities across the world were halted due to the COVID-19 pandemic in the year 2020. Events such as sports, national celebrations, international exhibitions and conferences were cancelled or postponed due to the restrictions posed by governments to mitigate the pandemic. The Information Communication Technology (ICT) sector being loosely constrained by the restrictions, was mandated to facilitate some of these events. In this research, we made computational analysis, by leveraging an Information Technology (IT) platform (Website) to conduct pre-anniversary competitions in celebration of Nigeria’s 60th Anniversary. The aim of using the website was to gather Information regarding the pre-anniversary competitions to determine user participation in online events across Nigeria. Initially, information about the site was propagated via various media outlet to call for participation. Activities performed by participants across all states of the nation (Nigeria) were recorded, as well as the site activities for a designated period of time. An extensive analysis was conducted using the Google analytics tool. Performance show that much needed to be done to improve and engage citizens in participating in national events, which employ IT platforms as an alternative to the main event during the COVID-19 pandemic.
Causes of loan defaults within Microfinance institutions: Learning from micro and small business owners in Rwanda: A case of MSEs in Kigali
Authors: Twesige Daniel , Uwamahoro Alexis , Ndikubwimana Philippe , Gasheja Faustin , Misago Isaie  Kadhafi , Hategikimana Uzziel
Published: April 2021

The study analyse the factors that causes loan defaults within Microfinance institutions learning from the perception of entrepreneurs in Rwanda. Explanatory research design was used. Data was collected from primary and secondary sources using questionnaire and documentation. The study population included microfinance institutions within Kigali. The target population included MSEs that are classified within a portfolio of nonperforming loans. Structural Equation Modeling (SEM) was used to analyse the correlation between the study variables. The findings from the survey showed that loan delay, loan shortage, loan deviation, interest rate, improper management, business environment have a significant impact on nonperformance. The researcher recommended that entrepreneurs should be trained on financial discipline and how to manage the loan finance
Compliance with regional commitments on sexual and gender based violence in Uganda: Promoting external visibility to achieve internal viability
Authors: Mugabe Aggée Shyaka
Published: April 2021

This paper examines Uganda‘s compliance with commitments of the International Conference on the Great Lakes Region (ICGLR) on Sexual and Gender Based Violence (SGBV). The findings show that Uganda has partially complied with these commitments mainly by incorporating them into national laws and policies. Compliance is primarily motivated by domestic factors. Firstly, it aims at addressing a real need given that SGBV is a pervasive phenomenon in this country. Secondly, and in accordance with international relations perspective, this compliance, though partial, advances additional agendas, namely (1) promoting the country‘s positive image while diverting international and regional attention from internal governance deficits including controversial presidential elections of 2011 and 2016, (2) responding to advocacy pressure from women-led organisations‘ and (3) achieving political correctness in the eyes of national and international partners.
Short-term liquid storage of ram semen in various extenders
Authors: Maksimović N. , Milovanović A. , Barna T. , Caro Petrović V. , Pantelić V. , Lazarević M. , Stojanov I.
Published: April 2021

The aim of this study was to investigate the effects of three extenders on ram sperm quality after short-term liquid storage (24 hours’ holding time). The study included 20 crossbred rams (Pirot Pramenka x Wurttemberg x Ile de France), 12 months old. Animals were housed at the experimental sheep farm of the Institute for Animal Husbandry in Belgrade, Serbia. Semen was collected through electro ejaculation. The ejaculates were obtained from single services and routine field analysis of the semen was performed immediately after the collection. The semen was split and diluted with three extenders, namely Optidyl®, Andromed® and ultrahigh temperature processed (UHT) milk, in ratios of 1 : 50 or 1 : 100. The ejaculates were examined for sperm motility variables (sperm cell motility percentage, the progressive motility percentage, curvilinear velocity (VCL), straight line velocity (VSL), average path velocity (VAP), sperm linearity (LIN), straightness (STR), amplitude of lateral sperm head displacement (ALH), beat cross frequency (BCF) and circular tracks), and sperm morphology (live sperm percentage, percentage of normal sperm forms with intact acrosome, percentage of abnormal sperm forms and total damaged acrosome) by computer-assisted sperm analysis (CASA) and classic sperm cytology after supravital eosin/nigrosine/trypan blue staining, respectively. It was observed that the type of extender used in diluting ram semen is an important factor in the successful short-term liquid preservation (at 4 °C) of ram spermatozoa. In conclusion, this study showed that egg yolk (Optidyl) and soybean (Andromed)-based extenders gave better results of both sperm morphology and sperm motility parameters compared with UHT milk.Keywords: Diluents, morphology, motility, sperm
Effects of three slow-release urea inclusions in rice straw-based diets on yearling Bali bull performances
Authors: Kardaya D. , Wiryawan K.G. , Parakkasi A. , Winugroho H.M.
Published: April 2021

The effects of slow-release zinc-urea complexes (ZnU), urea-impregnated zeolite (UZ) and zinc-ureaimpregnated zeolite (ZnUZ) on the performance of yearling Bali bulls were assessed using 20 Bali bulls (145.3 ± 2.5 kg bodyweight (BW)), which were allocated to five treatments and four replications in a completely randomized design. The treatments were: Diets supplemented with no urea (NU) and with urea (U), ZnU, UZ and ZnUZ. The results of the in vivo study revealed that both ZnU and UZ might replace urea effectively by increasing feed intake. Moreover, substituting urea with ZnU, UZ or ZnUZ increased crude protein total tract apparent digestibility whereas ZnU or UZ replacing urea, improved fibre total tract apparent digestibility. Furthermore, inclusion of UZ in the diet improved live weight gain and feed efficiency in Bali bulls above that of the U and NU diets. Thus, the inclusion of ZnUZ in rice straw-based diets showed slow-release urea had positive impacts on feed intake and nutrient digestibility, and increased the efficiency of feed utilization in Bali bulls.Keywords: Apparent digestibility, feed intake, urea-impregnated zeolite, zinc
Production and slaughter performance of ostriches fed full-fat canola seed
Authors: Niemann G.J. , Brand T.S. , Hoffman L.C.
Published: April 2021

Full-fat canola seed (FFCS) is a locally produced alternative protein source that has potential for inclusion in the diet of ostriches. Chicks aged 84 days and weighing 24.7 ± 0.36 kg were fed five iso-nutritional diets with varying levels of FFCS. Birds were fed until slaughter at 309 days old (93.2 ± 1.82 kg). In each feeding phase, FFCS replaced soybean oilcake meal incrementally (0%, 25%, 50%, 75% and 100% of protein source). Dry matter intake (DMI) during the grower phase was lowest (1.52 kg/bird/day) for the 100%FFCS diet. Birds on the 100%FFCS also showed a 34% slower growth rate (average daily gain of 152.0 g/bird/day) compared to the other four diets (average growth rate of 230 g/bird/day) during the grower phase. The 0%FFCS (236.2 g/bird/day) and 50%FFCS (267.8 g/bird/day) diets resulted in a higher ADG. End weights during the grower phase for 0%FFCS, 25%FFCS, 50%FFCS and 75%FFCS (74.8, 72.2, 76.8, and 72.5 kg, respectively) did not differ significantly. The 100%FFCS resulted in a lower end weight (67.4 kg) for the growth phase, when compared to the 0%FFCS and 50%FFCS diets. For the overall trial period, the only differences were in ADG, with the 0%, 50% and 75%FFCS replacement diets showing the fastest growth. Fat pad weight was the only slaughter trait that revealed differences between diets, with 50%FFCS resulting in the heaviest fat pad weight. A maximum of 20.6% inclusion of FFCS should be used in diets in the grower phase, although in the other phases, FFCS could be included up to the maximum level evaluated (100% replacement of soybean oilcake meal) without detrimental effects on production parameters.Keywords: Average daily gain, Brassica napus, dressing percentage, dry matter intake, feed conversion ratio, glucosinolates, Struthio camelus
Effect of lactation stage and milking frequency on milk yield from udder quarters of cows
Authors: Bogucki M.
Published: April 2021

The aim of the study was to analyse daily milk yield from udder quarters of automatically milked primiparous and multiparous cows during lactation and with changing milking frequency. The daily milk yield of udder quarters was higher in multiparous than in primiparous cows. At the same time, differences were observed, especially in front and rear quarters, and also in left and right quarters. The front quarters (LF and RF) of primiparous cows produced an average of 6.75 and 6.71 kg milk, and those of multiparous cows by 0.78 and 0.51 kg milk more, respectively. Higher daily milk yield was characteristic of rear quarters: 8.22 kg (left) and 7.71 kg (right) in primiparous cows, and 10.06 and 10.47 kg in multiparous cows, respectively. With advancing lactation (from ≤100 to >300 days), the milk yield of all quarters was found to decrease. The lowest decrease was in left front quarters (by 3.40 kg) and the highest in right rear quarters (by 4.35 kg). This relationship was most evident in cows entering lactation (in the first lactation period of ≤100 days). As milking frequency increased, the milk yield of individual quarters increased markedly. In front quarters, left and right quarters showed similar milk yields for milking frequency of less than 2.50 times/day and more than 2.80 times/day. A similar relationship was observed for rear quarters. The contribution of front and rear quarters to daily production was 45.8–54.2% in primiparous cows and 41.8–58.2% in multiparous cows.Keywords: Milk, multiparous, primiparous, quarter milking
Adding wheat to a pre-partum diet of Holstein cows favours post-partum protein utilization and benefits calcium status
Authors: Alamouti H.M. , Amanlou H. , Rezayazdi K. , Vazirigohar M. , Ihsan M.Z. , Tahir M.N.
Published: April 2021

This study evaluated the effects of replacing corn with wheat on the lactation performance, ruminal fermentation, plasma metabolites and hormonal profiles of multiparous Holstein cows. Thirty-four multiparous cows were divided into two equal groups, based on parity, body condition score, and bodyweight. The basal diet was supplemented with 186 g/kg corn or wheat, and was offered from day -28 (±7) relative to the date of expected calving. After calving, all cows received the same diet for four weeks (day +28). The wheat diets increased (P <0.05) the body condition score in the peri-parturient period and caused greater changes in dry matter intake and bodyweight post-partum, with no differences in energy balance compared with the corn diets. Further, the wheat increased (P <0.05) the concentration of propionate in the rumen, thus reducing the ratio of acetate to propionate, and produced (P <0.05) more milk and milk protein, but had reduced levels of urea nitrogen in the milk and serum. Similarly, the wheat diets increased (P <0.05) the plasma concentrations of albumin and glucose, and reduced (P <0.05) triglycerides with no effect (P >0.10) post partum on insulin, cortisol, β-hydroxybutyric acid and aspartate aminotransferase. The replacement of corn with wheat increased (P <0.05) plasma calcium and reduced non-esterified fatty acids around calving (day 0±1) and post-partum (days 0 to +28), and reduced urinary pH during the last week relative to the date of expected calving.Keywords: calving, energy balance, grain source, peri-parturient period