bluuebunny/crossref_metadata_2025 huggingface dataset:
Hydraulic Lash Adjuster Dynamic Behavior Prediction Using Multibody Dynamic Simulation and Co-Relation with Testing to Improve Valve Train Design Robustness
Authors: Chandiok Prateek , Poonia Sanjay , Kundu Soumen , Bharti Anil Kant
Published: December 2024

In recent years, world-wide automotive manufacturers have been continuously working to improve the fuel efficiency of IC engine and valve train friction contribute up to 30% of overall friction loss. Oil viscosity plays an important role in reducing overall engine friction, but it adversely affects the function of Valve train in terms of wear and reliability.

Now a days HLA/RFF type (Type-II) valve train is mostly used in Internal Combustion engine to reduce friction and automatic lash adjustment. HLA (hydraulic lash adjuster) plays a crucial role in the RFF/HLA type valvetrain in IC engine. Understanding the valve train dynamic behavior due to HLA is essential for engine designers to improve engine performance and durability.

The study aims to accurately predict the behavior of Hydraulic lash adjuster under various operating conditions using multibody dynamic simulation approach. Most significant concern in HLA operation is potential occurrence of “Valve pump up”, an undesired phenomenon characterized by excessive HLA plunger displacement that can lead to Engine valve seating issues, engine performance degradation and risk of significant damage to engine due to valve and piston hitting.

This paper presents an approach that combines multibody dynamic simulation with experimental correlation to predict and analyze the dynamic behavior of hydraulic lash adjuster, specially targeting the detection of valve pump up occurrences. Actual dynamic behavior of valve and HLA under worst case scenario checked in testing and Correlation is established between multibody dynamic simulation and actual testing.
Applying AI to Reduce Software Testing Defects’ Rejection
Authors: Trivedi Shubham , Hebbale Ramkumar Ramya
Published: December 2024

The efficiency and accuracy of defect control are critical components in software testing, as they determine the final product's quality and cost. Rejection of defects for various reasons, like non-reproducibility, erroneous classification or inadequate information, is one of the largest issues that testers face. This paper presents an AI-driven approach that reduces the number of defect rejections by using the past defect data to give testers real-time advises and warnings.

When a tester reports an issue, the model looks at the problem's description and title, making inferences and recommendations based on historical data to increase the fault's correctness. This feedback strategy reduces rejection rates and increases the overall efficiency of defect management by helping testers resolve potential issues before submitting a defect.

The recommended solution involves training an AI model on a large dataset of previous defects, which includes details on DefectTitle, Description, ResolutionCategory, ReasonForRejection (optionally with RejectionCategory) and ResolutionDescription to be more accurate in its recommendation. The model uses natural language processing (NLP) technique to transform textual data into meaningful numerical representations. BERT (Bidirectional Encoder Representation for Transformers) language model is deployed for understanding the context and semantics of defect titles and descriptions. Next, a binary classification technique- Random Forest Algorithm is used to predict if the issue could be accepted or denied. Regular retraining and continuous monitoring ensure that the model remains accurate and flexible over time.

This paper covers in depth the deployment process, model training, data preparation, and real-world implications of deploying this system in a real software testing environment. This strategy solves a common software testing problem in a scalable and efficient manner by utilizing artificial intelligence.
Vehicle Underbody Grommets Detection Using Computer Vision and Co-ordinate System
Authors: Dhumal Abhishek Trimbak , Mishra Jagdish , Tote Anuj , Nurukurthi Lakshmi , Kumar Prakash
Published: December 2024

Manual installation of vehicle underbody Grommets is tedious task which sometimes results in incomplete & inaccurate installation. Based on process quality guidelines, this comes under potential defect category at End of line inspection area for which few hours of manual efforts are required for identifying location of error & doing rework activity. Existing deep learning & image comparison method falls short in identifying error location. To overcome this challenge, deep learning algorithm with co-ordinate system developed which comprises of identifying class or category of entities present in test and reference image and indexing it as Grommet or Hole. This further comprises of determining 2D position in terms of X and Y co-ordinate of each indexed hole & Grommet between reference & test image. This approach results in precise comparison & identification of error in terms of missing or misplacing of Grommets which also offers significant saving in manual installation and rework efforts.
Investigation of Automotive IC Engine Water Pump Housing Failure and Resolution through Experimental Modal Analysis and Study of Vibration Characteristics of the System
Authors: R Mahesh Bharathi
Published: December 2024

Cooling system for an IC engine, consisting of the Water pump (WP), Radiator and Fan, plays an important role in maintaining thermal efficiency of the engine and protects the engine from overheating. Based on the vehicle application requirement, Fan will be mounted directly either on Crankshaft or WP pulley. But wherever increase in Fan speed ratio are in demand, it is preferred to mount the Fan on WP pulley. So it important to understand the WP housing structural strength with respect to vibration loads contributed from Radiator Fan assembly. This paper presents investigation of Failure of WP Housing during engine validation at engine test bed with Electronic Viscous Fan, based on the different operating conditions of the engine and fan as per the validation cycle. While the accessories are loading and the corresponding stresses are high when the fan is engaged. But in the current case, the failure of WP housing happened only during Fan clutch disengaged condition. Experimental Frequency Response Function (FRF) were carried out to identify the mode shapes and resonant frequencies. Vibration on WP housing were compared with Fan engaged and disengaged condition to identify the critical frequency ranges that minimize vibrational impacts on the WP housing. The results indicate a significant correlation between Fan Blade Pass Frequency (FBPF) and vibrational amplitude on WP housing. By optimizing FBPF, it is successfully mitigated high vibration levels, thereby enhancing the structural integrity and operational reliability of the WP housing. In addition, the results of crack initiation points, strain gauge measurements, structural and modal analyses are examined to enhance the WP housing strength.
An Analytical Method to Study Control Strategy of Torque-Vectoring Transmissions for Electric Vehicles Using MATLAB Simulink
Authors: Ramakrishnan Gowtham Raj , Baheti Palash
Published: December 2024

Torque vectoring offers drive flexibility and continuous individual wheel torque regulation, which is unavailable in conventional transmission systems. Electric vehicles with multiple drivetrains and torque-vectoring system can significantly enhance vehicle response and handling, and thus the active safety, efficiency, and performance of the vehicle in all driving conditions. The current methodology of predicting performance characteristics is limited through slip rate calculations and yaw rate calculations. The vehicle dynamic performance evaluations with above said methodologies holds good for dynamic cornering. But in the scenarios where the vehicle moving in straight drive with different wheel traction requirements on either side (split-μ condition) and that requires torque vectoring. These above methods do not help to evaluate the performance of vehicle. Because these methodologies are based on predicting dynamic center-of-gravity values of vehicle. In the proposed methodology, torque-vectoring condition during straight drive scenarios is evaluated along with dynamic cornering using various control strategies. The traction available at each wheel due to split-μ condition is taken in consideration for evaluating the performance requirements of a vehicle. A MATLAB Simulink model of an electric vehicle with above said parameters is developed to perform simulation, which in a way overcomes the split traction requirements in both straight drive and dynamic cornering based on feedback. The study focuses on evaluating various parameters such as energy demand, torque distribution, steer angle, lateral acceleration, and longitudinal acceleration in different driving scenarios.
Active Hydro-Pneumatic Suspension Active Disturbance Rejection Sliding Mode Control for Improved Vehicle Stability
Authors: Niu Changsheng , Liu Xiaoang , Jia Xing , Gong Bo , Xu Bo
Published: December 2024

Hydro-pneumatic suspension is widely used because of its desirable nonlinear stiffness and damping characteristics. However, the presence of parameter uncertainties and high nonlinearities in the system, lead to unsatisfactory control performance of the traditional controller in practical applications. In response to this challenge, this paper proposes a novel stability control method for active hydro-pneumatic suspension (AHPS). Firstly, a nonlinear mathematical model of the hydro-pneumatic suspension, considering the seal friction, is established based on the hydraulic principle and the knowledge of Fluid dynamics. On the basis of the established hydro-pneumatic suspension nonlinear model, a vehicle dynamics model is established. Secondly, an active disturbance rejection sliding mode controller (ADRSMC) is designed for the vertical, roll, and pitch motions of the sprung mass. The lumped disturbance caused by the model nonlinearities and uncertainties is estimated by the extended state observer (ESO), which is then integrated into the sliding mode control law. This allows the control law to actively adapt to the working state of the suspension system, which can effectively address the impact of uncertainties and nonlinearities on the system. Finally, the simulations are carried out on bump and random roads, two typical working conditions. The results show that the proposed ADRSMC can reduce the amplitude of vehicle acceleration by more than 50% compared to traditional passive hydro-pneumatic suspension, and the optimization effect is better than active disturbance rejection control ADRC). It significantly improves the stability of the vehicle. This study provides a valuable reference for the design of active hydro-pneumatic suspension control strategies.
Customized Vehicle Feature Control through Driver Recognition Technology
Authors: Marimuthu Ranjithkumar
Published: December 2024

This paper presents a novel approach for customizing vehicle features through driver recognition technology. The system combines Cultural Adaptive Face Recognition (CAFR) using FaceNet and Contrastive Language-Image Pretraining (CLIP) models, along with OpenCV, to recognize drivers and customize vehicle feature control. To identify a driver, the system compares their features against a pre-existing database using FaceNet, which generates efficient face embeddings. The driver image and contextual information collected is processed by OpenAI’s CLIP to generate CLIP embeddings which leverages multimodal learning. FaceNet and CLIP embeddings’ fusion is done and are stored in the Qdrant search database for efficient retrieval and similarity searches. Once the driver is recognized, the system adjusts vehicle features such as temperature settings, music selections, and seat adjustments according to the driver's preferences. Additionally, the system implements optical character recognition (OCR) using OpenCV to extract information real time information from ID cards and other documents, further customizing features to the driver’s needs. The system's novelty lies in its ability to integrate multiple technologies to provide a seamless and personalized driving experience, enhancing driver assistance. The paper's findings demonstrate the system's effectiveness in recognizing and tracking driver behavior, as well as setting up customized features. This technology has the potential to improve road safety, reduce driver fatigue, and enhance the overall driving experience.
Simulation Methodology for Hydrogen Concentration and Dilution Strategy in a Hydrogen Fueled Internal Combustion Engine
Authors: Sahu Abhay Kumar , Nagawade Shubham , Veerbhadra Swati
Published: December 2024

Manufacturers of internal combustion engines are changing their focus to non-conventional fuels like hydrogen in response to the worrying global warming situation. When compared to conventional fuels like gasoline or diesel, the use of gaseous hydrogen fuel in an internal combustion engine powered by hydrogen can lessen the engine's negative environmental effects. But occasionally, hydrogen can leak from the high-pressure fuel injection system to the engine top cover and as blowby within the crankcase. Static zones may emerge because of these H2 leaks. Potential explosion or fire can result when the H2 concentration in these stagnation zones is more than 4% and triggers a minimum ignition energy of 0.02 mJ.

A CFD simulation methodology incorporating multi-species model, piston, and crank motion to estimate the H2 concentration within crankcase is developed. The simulation development phases has been presented in the paper. The blowby values are determined from the experimental measurement and used as the inputs. The dilution strategy by varying the number of vent ports, location, size for crankcase is examined using this simulation. The flow requirements at various speed also deduced. Further, for under hood and engine top cover, underhood analysis is carried out to ascertain the accumulation within the engine and its compartment. Fan ON and OFF scenario is studied, and results are discussed.

H2ICE blowby and H2 % concentration at breather tube out is correlated with test for a baseline case. A good correlation matching the experimental trend is observed in the simulation.

The study helped engine program to complete the optimization and design verification through simulation and get the best concept implemented.
SoC Estimation for LFP Battery Using Extended Kalman Filter and Particle Filter with Adaptive Battery Parameters
Authors: Ns Farhan Ahamed Hameed , Jha Kaushal , Shankar Ram C S
Published: December 2024

In recent years, Lithium Iron Phosphate (LFP) has become a popular choice for Li-ion battery (LIB) chemistry in Electric Vehicles (EVs) and energy storage systems (ESS) due to its safety, long lifecycle, absence of cobalt and nickel, and reliance on common raw materials, which mitigates supply chain challenges. State-of-charge (SoC) is a crucial parameter for optimal and safe battery operation. With advancements in battery technology, there is an increasing need to develop and refine existing estimation techniques for accurately determining critical battery parameters like SoC. LFP batteries' flat voltage characteristics over a wide SoC range challenge traditional SoC estimation algorithms, leading to less accurate estimations. To address these challenges, this study proposes EKF and PF-based SoC estimation algorithms for LFP batteries. A second-order RC Equivalent Circuit Model (ECM) was used as the dynamic battery model, with model parameters varying as a function of SoC and accounting for temperature variations. The Hybrid Pulse Power Characterization (HPPC) test was performed at 15°C, 25°C, 35°C, and 45°C, and model parameters were obtained using the Nelder-Mead simplex algorithm. Simulations were conducted on MATLAB Simulink and validated using the Worldwide Harmonized Light Vehicle Test Procedure (WLTP) and Modified Indian Drive Cycle (MIDC). The proposed methods were evaluated for Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and computation time. Results showed that PF outperformed EKF by 40% regarding RMSE for WLTP and MIDC profiles. However, EKF computations were 90% faster than PF. The study concludes that EKF and PF can effectively be utilized for SoC estimation of LFP batteries, providing valuable insights for future Battery Management Systems (BMSs).
Methodology for Designing Traction Motor for an Electric Vehicle with Tandem Application of Vehicle System 1D Simulation and Electromagnetic Simulation
Authors: Ghule Gopal Arjun , Neelakantan Subramoniyan
Published: December 2024

This paper presents a comprehensive methodology for sizing an electric motor for a given vehicle performance targets and analyzing the motor performance at different operating zones of the electric vehicle. Designing the powertrain of an electric vehicle starts with understanding the on-road performance requirements of the vehicle relevant to the application such as top speed, gradient and acceleration targets. It is critical to define the operating performance boundary of the vehicle based on end user preference. This paper illustrates a comprehensive approach of implementing 1D simulation tool namely GT-SUITE to simulate the vehicle model for different on-road performance targets so as to conclude the traction motor specifications [3]. These specifications along with the other boundaries of the vehicle such as battery limitations and MCU limitations are taken as input parameters for the electromagnetic simulation assisted by Ansys Motor-CAD to design optimized motor that can meet the application requirements. The methodology also deals with the coupled deployment of Motor-CAD and GT-SUITE to analyze the electric vehicle system in terms of different aspects such as efficiency and performance [2], thus providing a comprehensive framework for engineers and researchers engaged in developing electric propulsion systems for xEVs fostering innovation and sustainability in the automotive industry.
A Multivariable Function Based Optimised Charging Algorithm for Electric Vehicle Battery Management Systems
Authors: Rajawat Shiv Pratap , Moorthi Sathiya , Soni Lokesh , Jain Swati
Published: December 2024

Conventional Constant Current- Constant voltage (CC-CV) based charging techniques initially consist of Constant Current (CC) phase for quick charging of the battery till it reaches the safety voltage limit wherein the Constant Voltage (CV) phase starts. Then the CV phase of the charging ensures safe charging of the battery till it is fully charged but it takes comparatively a long duration of time to the amount of charge pumped into the battery. Adoption of efficient charging algorithms are crucial for optimising the charging time, reducing the range anxiety and improving the long-term health of electric vehicle (EV) batteries. This paper proposes an innovative charging algorithm that optimises the transition from Constant Current (CC) to Constant Voltage (CV) charging stages utilising a multivariable function based on the real-time data of State-of-Charge (SoC), temperature, State-of-Health (SoH) and battery impedance parameters. By dynamically adjusting the charging parameters based on these key battery metrics, the algorithm aims to minimise charging time while preserving battery health. Through extensive simulations over the MATLAB/Simulink and experimental validation, the effectiveness of the proposed algorithm is demonstrated, resulting in significant improvements over traditional CC-CV charging methods. This State-of-Charge (SoC), temperature, State-of-Health (SoH) and battery impedance parameters based optimised charging algorithm offers a promising channel for enhancing the performance and longevity of EV batteries, ultimately contributing to the advancement of sustainable transportation solutions.
Evaluation of Mechanical Properties of Reduced Stress Localized Aluminium Composites for Automobile Applications
Authors: Valsan Ashray
Published: December 2024

The modern-day development in the field of mobility demands the development of advanced engineering materials for various engineering applications. Composite materials play a pivotal role in the advancement of mobility by achieving overall weight reduction and thereby contributing to the sustainability of the environment. Metal matrix composites has played a crucial role over the last few decades in the automotive industry replacing the conventional metal in achieving a better strength to weight ratio. Metal matrix composites can be a combination of a metal and a ceramic combined at a macroscopic level to achieve better mechanical and tribological properties at a reduced weight to strength ratio. Aluminium being one of the largest metals widely used in automobiles, are gradually being replaced with Aluminium metal matrix composites. Aluminium – silicon carbide composite is a key interest among the researchers due to the attractive mechanical and tribological properties that enhance the performance of automobiles. The most common applications of aluminium silicon carbide composites in automobiles are pistons, cardan shaft, connecting rods etc. This study is focused on the chemical interfacial reaction between silicon carbide and aluminium matrix which occurs during the elevated temperature of casting process and thereby creating stress localization due to the formation of aluminium carbide in the matrix. The chemical reaction also results in the degradation of silicon carbide which can lead to poor performance of the composite. Hence it is very important to keep this chemical reaction to a minimum level. This research is centered on finding out an effective solution for hindering this chemical reaction by adding a material which is cheap, easily available and existing as an industrial waste material. Utilizing it for the composites can contribute to the sustainability of nature as well. Various mechanical properties are evaluated for this hybrid composites to evaluate the performances.
Method of Exhaust Temperature Estimation and Monitoring of Diesel Engine
Authors: Jagtap Virendra Shashikant , Ganguly Gourav , Mitra Partha
Published: December 2024

Looking at the current scenario in transportation industry, in majority of the conventional powertrains, internal combustion (IC) engines fueled by diesel serve as the powerhouse. In all locomotives driven by IC engine, it is essential to monitor critical engine parameters to ensure good engine health and performance. Exhaust temperature of engine is a very critical parameter which gives the information about in-cylinder combustion. In traditional diesel engine layouts, exhaust temperature measurement relies on physical temperature sensor.

The proposed methodology is focused on applying the data driven models for providing an estimated value of the exhaust temperature. Based on the estimated value of exhaust temperature, this technique can be used to monitor the IC engine. This methodology uses an advanced Artificial Intelligence technique for providing an accurate estimate of exhaust gas temperature. Real world vehicle data was used for training, validating, and testing the data driven model. The data driven model is python based and incorporates the use of Keras. Keras is an application programming interface (API) used for building neural networks.

Owing to complex multilayer architecture of the neural network and deep learning capabilities, this methodology provides accurate estimation of exhaust temperature in different ambient temperatures and different operating conditions of engine. Time series analysis and statistical analysis between actual exhaust temperature and estimated exhaust temperature is done for understanding the estimation capability of the model. Statistical analysis is in terms of mean absolute error (MAE) and R-squared score. Using this estimation, continuous monitoring of exhaust temperature is done. By providing accurate exhaust temperature estimation, proactive maintenance is facilitated by this methodology thereby enhancing reliability and cost-efficiency of diesel engines.
Implementation of a Hybrid Hardware-in-the-Loop (HIL) Test System for Automotive Software Validation and Verification
Authors: Yadav Vikaskumar , Bhade Nilesh
Published: December 2024

The automotive industry relies heavily on software to enhance safety, performance, and user experience. The increasing complexity of automotive software demands rigorous testing methodologies. Ensuring the quality and reliability of this software is critical. In this paper, an innovative approach to software validation and verification using a Hybrid Hardware-In-the-Loop (HIL) test system has been proposed. This methodology integrates diverse hardware and software tools to establish a flexible and efficient testing environment. HIL environment can evaluate Device Under Test (DUT) with minimal alterations. This comprehensive solution includes the development of test strategies, plant model simulation, and compliance assurance, all in accordance with automotive standards such as ASPICE, ISO26262. Introduction of a Personality module for Automotive ECU (DUT), enables testing of multiple products using the same HIL setup. This is achieved by loading a DUT-specific signal mapping configuration and adjusting the plant model simulation using a reconfigurable Input/Output of HIL device. This approach ensures a flexible and efficient testing process. The consistent test infrastructure, automation framework, and adaptable software repository facilitate smooth transitions as the DUT evolves. Furthermore, this approach has been extended to testing of control algorithms in power electronics domain. In this process power electronics high voltage components are converted to their equivalent transfer function that serves as a hardware-software interface. The equivalent circuits are then simulated using Simulink (an equivalent mathematical equation model) or circuit simulation model, facilitated by high-speed FPGA devices and a real-time operating system. This methodology complies with industry standards and safety requirements, providing a robust solution for automotive software testing.
Rear Twist Beam Optimization by Cross Beam and Bush Orientation Technique to Meet Performance Targets
Authors: Prabhakara Rao Vageesh , Rasal Shraddhesh , Asthana Shivam , M Sudhan , Vellandi Vikraman
Published: December 2024

Born Electric SUVs generally feature a high centre of gravity (CG) with greater rear axle weight. They use sophisticated multi-link suspension, which offers large scope for ride comfort and handling optimisation. The development costs and time associated with the process will position the vehicle in a premium segment. Usually, the twist beam (RTB) is used in lower rear axle weight category vehicles due to its simple design and cost effectiveness. This study showcases exploration for implementation of RTB on higher weight category vehicles, which usually uses multi-link suspension. The limitation of the twist beam is to exhibit lateral force compliance oversteer during cornering, due to large moment arm from wheel centre to twist beam bush. Lack of lateral stiffness when compared to multi-link suspension causes delayed rear axle yaw response. It also has reduced scope in ride-handling optimization due to its simple structure and kinematics. So, in order to maximize the performance envelope, system level key performance parameters were identified such as lateral force shear centre, vertical shear centre position, side view swingarm position and length. The parameters such has cross beam position and profile were explored along with bush orientation and stiffness characteristics. Sensitivity studies on the RTB design attributes were performed on multi-body dynamics tool to meet targeted performance metrics.

The results showcase that the combination of unique cross beam profile along its position provides significant potential for enhancement of rear axle performance. There by providing us key parameters to overcome the limitations of twist beam architecture to meet the platform targets.
Influence of Discrete Load Cases on Optimization of Chain Guide Design of Crawler Dozers under Various Ground Conditions
Authors: Masane Nishant , Bhosale Dhanaji , Sarma Neelam K
Published: December 2024

The undercarriage is a critical component in machines such as crawlers, excavators, and compact track loaders. It includes vital elements such as the track frame, chain guides, rollers, track chains, idlers, carrier rollers, final drive, and sprockets. Among all these machines, crawler dozers encounter harsh environments with various ground conditions. During operations, the chains are subjected to traverse and side loads, which cause the chains to tend to slip out of the bottom rollers. The chain guide plays a crucial role in assisting and maintaining the chain in the correct position. The forces acting on chain guides are influenced by factors such as track chain tension, roller wear, chain link wear, and counter-rotation (where one track moves forward while the other moves in reverse). Among all the load cases, there are two critical load cases which are vital to be studied in order to determine the required number of chain guides along with other attributes like profile or section modulus. The forces exerted on the tracks are determined by developing the empirical formulae on a banked road surface. It was found that the forces acting on the inner tracks are greater than those on the outer tracks. It was found that the inner undercarriage experiences highest force at 350 slope of the surface. Apart from the machine at banked surfaces, during the counter – rotation load case, the reaction forces generated on the rotation side of the track frame are greater in the inner side than on the opposite side. These forces acting on the rotation sidetracks are used to assess the shapes and numbers of chain guides.
Design and Development of Steering based Adaptive Headlight System
Authors: T Karthi , G Manikandan , P C Murugan , S Sakthivel , N Vinu , P Dineshkumar
Published: December 2024

Driving at night presents a myriad of challenges, with one of the most significant being visibility, especially on curved roads. Despite the fact that only a quarter of driving occurs at night, research indicates that over half of driving accidents happen during this period. This alarming statistic underscores the urgent need for improved illumination solutions, particularly on curved roads, to enhance driver visibility and consequently, safety. Conventional headlamp systems, while effective in many scenarios, often fall short in adequately illuminating curved roads, thereby exacerbating the risk of accidents during nighttime driving. In response to this critical issue, considerable efforts have been directed towards the development of alternative technologies, chief among them being Adaptive Front Lighting Systems (AFS). The primary objective of this endeavor is to design and construct a prototype AFS that can seamlessly integrate into existing fixed headlamp systems. Throughout the conceptualization phase, key considerations revolve around prioritizing accuracy, reliability, and component availability to ensure the feasibility and effectiveness of the proposed solution. AFS represents a groundbreaking innovation in automotive lighting technology, aimed at addressing the shortcomings of conventional headlamp systems, particularly in illuminating curved roads. Unlike static headlamps, which emit a fixed beam pattern regardless of driving conditions, AFS adjusts headlamp intensity based on various factors such as vehicle speed, steering angle, and road curvature. By leveraging sensors and sophisticated control algorithms, AFS is able to anticipate the direction of travel and adjust the direction and intensity of the headlamp beam accordingly. This adaptive functionality not only enhances driver visibility on curved roads but also reduces the likelihood of accidents caused by limited visibility in challenging driving conditions. The conceptualization and design phase of the AFS prototype involve a meticulous process aimed at ensuring the feasibility and effectiveness of the proposed solution. Key considerations include the selection of suitable components, the development of robust control algorithms, and the integration of advanced technologies such as Arduino microcontrollers. One of the primary challenges in designing an AFS prototype lies in balancing performance with cost-effectiveness.
Cabin Crew Stations
Authors:   S-9B Cabin Interiors and Furnishings Committee
Published: December 2024

This SAE Aerospace Recommended Practice (ARP) provides guidance for the design and location of cabin crew stations, including emergency equipment installations at or near such stations, to enable the cabin crew to perform effectively in emergency situations, including emergency evacuations.

Recommendations regarding design of cabin crew stations apply to all such stations; recommendations regarding location apply to those stations located near or adjacent to floor level exits.
Application Layer - Diagnostics
Authors:   Truck and Bus Control and Communications Network Committee
Published: December 2024

SAE J1939-73 defines the SAE J1939 messages to accomplish diagnostic services and identifies the diagnostic connector to be used for the vehicle service tool interface. Diagnostic messages (DMs) provide the utility needed when the vehicle is being repaired. Diagnostic messages are also used during vehicle operation by the networked electronic control modules to allow them to report diagnostic information and self-compensate as appropriate, based on information received. Diagnostic messages include services such as periodically broadcasting active diagnostic trouble codes, identifying operator diagnostic lamp status, reading or clearing diagnostic trouble codes, reading or writing control module memory, providing a security function, stopping/starting message broadcasts, reporting diagnostic readiness, monitoring engine parametric data, etc. California-, EPA-, or EU-regulated OBD requirements are satisfied with a subset of the specified connector and the defined messages.
Valeur qualitative du digestat issu d’un digesteur continu des effluents de manioc
Authors: Ossepe Yapi Jean Louis Fernandez , Koko Anauma Casimir , Kpata-Konan Nazo Edith , Yao N’zué Benjamin
Published: December 2024

La méthanisation des effluents issus de la fabrique d’attiéké produit en plus du biogaz, du digestat qui pourrait être utilisé en agriculture.  Cette étude avait pour objectif de déterminer la qualité fertilisante de ce digestat en vue de son utilisation dans la culture du  manioc. Ainsi, des digestats issus de trois productions ont été caractérisés selon des méthodes conventionnelles. Les données recueillies  ont été soumises à des traitements statistiques. Les résultats ont révélé que les digestats avaient un pH moyen de 8,42 ± 0,18, une DCO  de 207,68 ± 60,64 mg O2/L, une DBO de 58,83 ± 29,02 mg O2/L et un COT de 35,81±22,25 mg/L. Ils renfermaient également des matières  fertilisantes dont l’azote (1,5 ± 0,07 g/L), le phosphore (0,738 ± 0,415 g/L) et le potassium (1,65 ± 0,60 g/L). Des éléments traces  métalliques ont été quantifiés avec des valeurs moyennes conformes à la réglementation. Par ailleurs, les analyses microbiologiques ont  montré la présence de coliformes totaux, Staphylococcus aureus, Bacillus cereus d’une part et l’absence des E. coli, Salmonella sp,  Clostridium perfringens et des ASR d’autre part. Dans l’ensemble, les résultats de l’étude ont montré que les digestats caractérisés présentaient des potentialités pour un épandage dans les cultures.
 
Qualitative value of digestate from a continuous cassava effluent digester
Methanization of effluent from attiéké factory produces, in addition to biogas, digestate that could be used in agriculture. The aim of this  study was to determine the fertilizing quality of this digestate, with a view to its use in cassava cultivation. Digestates from three  productions were characterized using conventional methods. The data collected was subjected to statistical analyses. The results showed  that the digestates had an average pH of 8.42 ± 0.18, a COD of 207.68 ± 60.64 mg O2/L, a BOD of 58.83 ± 29.02 mg O2/L and a TOC of  35.81 ± 22.25 mg/L. They also contained fertilizing elements including nitrogen (1.5 ± 0.07 g/L), phosphorus (0.738 ± 0.415 g/L) and  potassium (1.65 ± 0.60 g/L). Trace metallic elements were quantified with average values in accordance with regulations. Microbiological  analyses showed the presence of total coliforms, Staphylococcus aureus and Bacillus cereus on the one hand, and the absence of E. coli,  Salmonella sp, Clostridium perfringens and RSA on the other. Overall, the results of the study showed that the digestates characterized  had the potential to be spread on crops.
Assessment of leaf blight on Colocasia esculenta and in vitro effect of three medicinal plants extracts on leaf blight disease pathogen in Oshie-Njikwa, North West Region-Cameroon
Authors: Asongwe Eugene Njeh , Nouck Alphonse Erve , Tonjock Rosemary Kinge
Published: December 2024

Taro (Colocasia esculenta) is a major staple food in Cameroon. This work assessed leaf blight disease on Colocasia esculenta and in vitro  effect of three medicinal plants extracts on the pathogen in Oshie. Questionnaires were administered to 200 randomly selected farmers  to assess the impact of the disease. Eight farms in Oshie were surveyed for leaf blight disease during a period of six weeks for disease  incidence and severity. Twenty diseased leaf samples were collected, inoculated on Potato Dextrose Agar and after 7 days of incubation,  pure cultures were identified. Crude extracts of hexane and methanol from leaves of Clematis hirsuta, Clematopsis scabiosifolia and  Telferia occidentalis were evaluated for in vitro antifungal activities on Phytophthora colocasiae. Leaf blight disease of taro had a huge  impact on farmers as it reduced yield of the tubers. Disease incidence ranged between 15% and 99%. Disease severity ranged from  46.24% to 61.82%. All three plants extract at 0.5 g/ml concentration had inhibitory activities on the test fungus. The three plants extracts obtained using methanol inhibited the growth of Phytopthora colocasiae more than the same three plants extracts obtained using  hexane. The three medicinal plants can be used to control taro leaf blight in vitro.
Effect of water salinity and hydrotimetric titer of water sources used for consumption in the commune of Ouinhi in Benin (West Africa)
Authors: Bokossa Hervé Kouessivi Janvier , Boni Gratien , Zinsou Lisette Carine , Djossou Parfait , Johnson Roch Christian
Published: December 2024

Despite the availability of Improved Water Sources (IWS), the population of Ouinhi in Benin still consumes surface water. Which is a major  public health problem. The present study aims to identify the physicochemical and microbiological parameters, the cause of the  population's rejection of the consumption of water from boreholes and other water supply works. To do this, a simple random selection  of twenty water sources was made for microbiological and physico-chemical analysis. The results showed that the salinity (1 mg/L) of IWS  was found to be 2.5 times higher than that of Unimproved Water Sources (UWS). Similarly, Total Dissolved Solids (173.4 and 218.4  ppm), calcium content (2.753 and 2.007 mg/L) and magnesium hardness (2.4 and 1.253 mg/L) were on average 1.6 times higher in  improved than in UWS. Analysis also show that all water sources were contaminated with total coliforms, thermotolerant coliforms, fecal  enterococci, E. coli and S. aureus in all samples from the two districts of Dasso and Tohouè. This situation contributes to the frequency of  hydrofecal diseases, and is a factor to be considered in awareness campaigns.
Technological Determinants of Human Resource Information System Effectiveness in Tanzania
Authors: Mugisha Kamala , Matimbwa Hadija
Published: December 2024

Organizations are increasingly moving from traditional paper files to digital filing systems to gain a competitive edge and improve access to human resources and HR-related information. This digitalization process not only makes data more accessible but also enhances its utilization for HR decision-making. As a result, HR-related information becomes more comprehensive, accurate, and up-to-date. This study specifically investigates the impact of technological factors on the effectiveness of human resource information systems (HRIS) within local government authorities (LGAs) in Tanzania. The research utilized a combination of descriptive and inferential statistics, including ordered logistic regression analysis, to examine the impact of technological characteristics on the effectiveness of Human Resource Information Systems (HRIS). The findings revealed that factors such as compatibility, complexity, usefulness, and reliability play a crucial role in determining the timeliness, comprehensiveness, and accuracy of data retrieval within HRIS. The study offers valuable insights for improving human resource management practices in LGAs) through the adoption of computerized HRIS.The discussed approach enhances decision-making and improves access to information, contributing to the achievement of organizational goals. It also emphasizes the importance of regularly updating systems to keep pace with changing technological landscapes.
Bacteriological status and antibiotics susceptibility pattern of isolates from ‘Suya’ (roasted Beef) sold in Calabar, Nigeria
Authors: Bassey Emmanuel Effiong , Mbah Maurice , Agbiji Nneka Ndifon
Published: December 2024

Ready-to-eat Suya meats were randomly sampled and assessed for bacterial status and antibacterial susceptibility profiles of the isolated bacterial pathogens. Bacteriology of Suya samples and antibacterial susceptibility tests were determined using standard microbiological and disc diffusion methods respectively. The mean aerobic bacterial count (ABC) from point A, B, C, and D ranged from 5.9 × 10⁷ to 2.04 × 10⁸ CFU/g, 6.4 × 10⁶ to 2.44 × 10⁸ CFU/g, 5.8 × 10⁶ to 2.60 × 10⁸ CFU/g and 2.9 × 10⁶ to 1.76 × 10⁷ CFU/g respectively. A total of six (6) bacterial genera were identified including twenty-six (26) Gram-positive and sixteen (16) Gram-negative bacteria. The most frequently isolated bacteria were Bacillus sp (35.7%), Staphylococcus 'sp' (26.2%), Klebsiella sp (19%), Escherichia coli (11.9%), Enterobacter sp (4.8%) and Proteus sp (2.4%) respectively. Antibacterial susceptibility test results indicated that Ciprofloxacin, Gentamycin, and Sparfloxacin demonstrated high-level sensitivity and broad-spectrum activity on test isolates whereas Amoxicillin, Augmentin, and Septrin showed little or no effectiveness on the test organisms. The high incidence of bacterial contamination in Suya suggests that they are potential reservoirs for possible outbreak of food-borne diseases. Furthermore, proper antibiotic therapy and patient management in cases of consumption of contaminated Suya is hereby seriously advocated.
Association between tuberculosis and CD4 T-cells among HIV infected patients attending specialist hospital Sokoto.
Authors: Usman Abubakar Babangida , Yau Yassar Muhammad , Tukur Maryam , Folashade Salaudeen Zainab , Garba Musa , Ibrahim Muhammad Adamu , Eniola Gana Samuel , Hamid Kabir Magaji
Published: December 2024

Patients with Human Immunodeficiency Virus (HIV) infection are predisposed to numerous opportunistic infections due to decreased cell-mediated immunity. Tuberculosis is the most common opportunistic infection seen in HIV patients that contribute significantly to HIV-related morbidity and mortality throughout the world, especially in developing countries like Nigeria. People living with HIV accounted for 10% of the new TB cases reported worldwide.This cross-sectional study investigated the relationship between tuberculosis (TB) and CD4 T-cell counts among newly diagnosed HIV patients in Sokoto, Nigeria. A total of 154 HIV-positive individuals were enrolled, with 35.7% presenting with CD4 counts below 200 cells/μL, indicating severe immunosuppression. Despite the low CD4 levels, only 5.4% of these patients tested positive for TB, suggesting that factors such as prophylactic treatments or healthcare quality might be mitigating TB incidence. The study also revealed that TB was more prevalent among married individuals and those aged 31–60, highlighting specific demographic vulnerabilities. Socioeconomic factors, including limited formal education and occupation types, were significantly associated with low CD4 counts and TB co-infection, especially among the Hausa/Fulani ethnic group. These findings underscore the need for early HIV detection, integrated healthcare services, and targeted public health interventions that address both medical and social determinants of health to reduce TB burden among HIV-positive populations in Sokoto and similar settings.
Effect of aqueous extract of Allium cepa (Onion) on ethanol-induced dyslipidemia and inflammation in Albino rats
Authors: Ibama Onengiyeofori , Elekwachi Godselect Chibuihe , Nwogu Arit Okechukwu , Akpannanah Ubong Friday
Published: December 2024

Alcohol consumption has increased over the years due to social activities. However, chronic alcohol consumption is a known risk factor for several diseases, including dyslipidemia. This study evaluated the effect of Allium cepa (onion) aqueous extract on ethanol-induced dyslipidemia and inflammation in Albino Rats. Twenty-five rats, weighing 130-180g, were randomly divided into five groups of five. The rats were acclimatized for 14 days with free access to standard feed and water. Group I served as the negative control, while Group II was the positive control, receiving 50% ethanol at a dose of 0.5ml/100g. Groups III to V received 50% ethanol followed by oral administration of 200mg/kg, 400mg/kg, and 600mg/kg of Allium cepa extract, respectively, once daily for 30 days. The rats were then anesthetized with chloroform, and blood samples were collected to analyze C-reactive protein (CRP) and lipid profiles, including triglycerides (TG), total cholesterol (T. Chol), and HDL, with LDL calculated using Friedewald's equation. Data analysis was performed using SPSS version 24, with p<0.05 considered statistically significant. There was a significant decrease in CRP (p = 0.000035), T. Chol (P = 0.010), and LDL (P = 0.038) in the treatment groups compared to the positive control group, but there were no significant differences in HDL (P = 0.179) and TG (P = 0.899) across all groups. These findings suggest that Allium cepa extract may ameliorate ethanol-induced dyslipidemia and inflammation. Further studies are recommended.