bluuebunny/crossref_metadata_2025 huggingface dataset:
Damage Accumulation: A New Generalized Machine Health and Condition Indicator for Continuous Monitoring
Authors: Loverich Jacob , Frank Jeremy , Kraige David , Koopmann Gary , Belegundu Ashok , Grissom Michael
Published: March 2024

This paper presents a new Damage Accumulation machine health indicator that trades precision for ease of use and broad applicability, effectively addressing the challenges of practically applying continuous monitoring to large numbers of assets such as pumps, motors, gearboxes, and fans. Damage Accumulation offers comprehensive early warning indication for a broad range of faults that can in turn be used to trigger detailed analysis. Damage Accumulation is a time series vibration analysis technique that estimates the rate at which damage is accumulated at a given location. This indicator accounts for time-varying symptoms in machines which are often overlooked by traditional vibration diagnostic frequency analysis or time series analysis. It also considers the contribution of repeated load reversal cycles to component damage and the nonlinearity in the relationship between damage and vibration amplitude. This paper presents the fatigue analysis foundation of Damage Accumulation and demonstrates its efficacy in a bench test and in a high pressure pumping field example.
Sparse Coding-Based Failure Prediction for Prudent Operation of LED Manufacturing Equipment
Authors: Ren Jia-Min , Chueh Chuang-Hua , Kung H. T.
Published: March 2024

A sudden failure of a critical component in light-emitting diode (LED) manufacturing equipment would result in unscheduled downtime, leading to a possibly significant loss in productivity for the manufacturer. It is therefore important to be able to predict upcoming failures. A major obstacle to failure prediction is the limited amount of equipment lifecycle data available for training, as equipment failure is not expected to be frequent. This calls for machine learning techniques capable of making accurate failure predictions with limited training data. This paper describes such a method based on sparse coding. We demonstrate the prediction performance of the method on a real-world dataset from LED manufacturing equipment. We show that sparse coding can draw out salient features associated with failure cases, and can thus produce accurate failure predictions. We also analyze how sparse coding-based failure prediction can lead to significant efficiency improvements in equipment operation.
Parameters Optimization of Lebesgue Sampling-based Fault Diagnosis and Prognosis with Application to Li-ion Batteries
Authors: Yan Wuzhao , Zhang Bin , Orchard Marcos
Published: March 2024

Lebesgue sampling-based fault diagnosis and prognosis (LSFDP) is developed with the advantage of less computation requirement and smaller uncertainty accumulation. Same as other diagnostic and prognostic approaches, the accuracy and precision of LS-FDP are significantly influenced by the diagnostic and prognostic models. The predicted results will show great discrepancy with the real remaining useful life (RUL) in applications if the model is not accurate. In addition, the fixed model parameters cannot accommodate the varying stress factors that affect the fault dynamics. To address this problem, the parameters in the models are treated as time-varying ones and are adjusted online to accommodate changing dynamics. In this paper, a recursive least square (RLS) based method with a forgetting factor is employed to make the diagnostic and prognostic models online adaptive in LS-FDP. The design and implementation of LS-FDP are based on a particle filtering algorithm and are illustrated with experiments of Li-ion batteries. The experimental results show that the performance of LS-FDP with model adaptation is improved on both battery capacity estimation and RUL prediction.
Particle-Filtering-Based State-of-Health Estimation and End-of-Life Prognosis for Lithium-Ion Batteries at Operation Temperature
Authors: Pola Daniel , Guajardo Felipe , Jofré Esteban , Quintero Vanessa , Pérez Aramis , Acuña David , Orchard Marcos
Published: March 2024

We present the implementation of a particle-filtering-based framework that estimates the State-of-Health (SOH) and predicts the End-of-Life (EOL) of Lithium-Ion batteries, efficiently incorporating variations of ambient temperature in the analysis. The proposed approach uses an empirical statespace model, in which inputs are explicitly defined as the average temperature of operation and the output of an external module that detects self-recharge phenomena, on the other hand the output is a function that relates the current SOH and temperature with the Usable Capacity in that cycle. In addition, this approach allows to deal with data losses and outliers. In order to correct erroneous initial conditions in state estimates, an Outer Feedback Correction Loop is implemented. Finally, this framework is validated using degradation data from four sources: experimental degradation data from two Li-Ion 18650 cells, accelerated degradation data openly provided by NASA Ames Research Center, and artificially generated degradation data at different ambient temperatures.
Case Study in Improving the Health of a Remote Monitoring & Diagnostics Center
Authors: Heda Sanjeev
Published: March 2024

This paper provides a case study where data analytics techniques are used to improve the health of a global remote monitoring & diagnostics (RM&D) center for power plants that is a key part of our industrial internet infrastructure. The “Industrial Internet” is being heralded as a transformative, disruptive technology that is part of the digitization of traditional industries all over the world. A key technical capability includes the ability to continuously monitor critical assets (like power generation equipment) with sensors and other measurements to get leading indicators of anomalous behavior and identify opportunities for optimizing the performance and life of this equipment. This allows our customers to maximize their asset’s availability, reliability, and performance and is usually achieved by connecting these assets to dedicated RM&D centers that aggregate and analyze the data streaming in from all over the world.An RM&D center is a complex system with hardware (data acquisition boxes, communication hardware, processing servers), software and analytics in place to ensure the generation of timely notifications and recommendations. There is a need to understand the health of this complex system and to quickly diagnose and mitigate issues before any disruption occurs that impacts the ability to monitor. What makes this case study unique is the combination of qualitative and quantitative input variables that need to be considered, which is different from traditional PHM applications which tend to be based on sensor derived numeric or binary data.Details in the paper include extraction of key system health features (server health, data integrity, analytic robustness, etc.) from computer logs. These parameters, once collected, can be analyzed using various statistical techniques (Multivariate Outliers, Process Control, Mixture Distributions) to develop system health indices using methods like Principal Component Analysis, Factor Analysis as well as kernel PCA methods. We explore the development of anomaly detection methods using regression (Multilevel regression, Logistic regression), Clustering (K-Means, Hierarchic, Normal Mixtures and Latent Class Analysis) and Classification (Decision Trees, Bootstrap Forest, Discriminant) techniques followed by their comparison in terms of computational cost and classification accuracy. We then go further to pinpoint the likely cause (Bayesian Diagnosis) of disruptions, and demonstrate how this approach can be generalized for a variety of industrial assets. All of results/analysis are based on representative data from a global Remote Monitoring and Diagnostic center that is used to monitor a significant portion of the world’s electric power generation fleet.
Hidden Markov Model-Based Detection and Classification of Foreign Objects in Heat-Exchanger Tubes
Authors: Banerjee Portia , Udpa Lalita , Udpa Satish
Published: March 2024

In recent years, there has been significant interest in prognosis and health management of heat exchanger tubes in steam generators (SG) using eddy current (EC) non-destructive evaluation (NDE) techniques. One of the recent challenges encountered in SG tube inspection is the presence of foreign objects lodged outside the tubes. Extreme vibrations cause these loose parts to rub against the tube wall and form wears on their outer surfaces which can be dangerous in the high pressure environment. Hence, there is a strong need for reliable automated signal analysis systems for early detection of foreign objects and prevention of harmful radioactive leaks at nuclear facilities. In this paper, a hidden Markov model (HMM) based classifier is proposed which can estimate the material of the foreign object from EC inspection signal. Unknown loose part material interferes with EC analysis results and lead to errors in signal processing parameters which in turn can degrade performance and reliability of automated detection systems. The proposed algorithm implements a continuous HMM classifier by using magnitude and phase based measurements obtained from the foreign object. Results of applying the algorithm on experimental data from SG tube inspection is presented, demonstrating its benefits in increasing the robustness and performance of automated signal analysis systems in detecting loose parts.
Evaluation of Features with Changing Effectiveness for Prognostics
Authors: Atamuradov Vepa , Camci Fatih
Published: March 2024

Feature evaluation is crucial to identify the best features and to achieve high accuracy in diagnostics and prognostics. Feature evaluation for prognostics is a developing research area with several publications in recent years. Most, if not all, of existing methods to evaluate features for prognostics base on the feature changes in the whole life of the system under observation. In other words, feature values collected throughout the failure degradation are analyzed to create a goodness value for the feature. In reality, the goodness of the features may change during the failure progression. A feature may be good representative of failure progression in the initial phase but not in the final phases, or vice versa. This paper presents dynamic nature of representation capabilities of features throughout the failure degradation and proposes a novel approach to evaluate the features considering their dynamic nature. Proposed approach involves feature segmentation based on their representation capabilities and feature fusion utilizing the segmented evaluations. The presented approach has been applied in simulated and real degradation datasets. Real degradation dataset were obtained from accelerated aging tests of Li-ion batteries in the lab environment. The results from both datasets show that dynamic feature evaluation improves SoH estimation accuracy.
Distributed Adaptive Fault-Tolerant Formation Control of Second-Order Multi-Agent Systems with Actuator Faults
Authors: Khalili Mohsen , Zhang Xiaodong , Cao Yongcan
Published: March 2024

This paper presents an adaptive fault-tolerant control (FTC) scheme for leader-follower formation of uncertain secondorder mobile agents with actuator faults. A local FTC component is designed for each agent in the distributed system by using local measurements and suitable information exchanged between neighboring agents. Each local FTC component consists of a fault detection module and a reconfigurable controller module comprised of a baseline controller and an adaptive fault-tolerant controller activated after fault detection. Under certain assumptions, the closed-loop system stability and leader-follower formation properties of the distributed system are rigorously established under different modes of behavior of the FTC system. A simulation example is used to illustrate the effectiveness of the FTC method.
Prognostic Reasoner based Adaptive Power Management System for a More Electric Aircraft
Authors: Sebastian Robin.K. , Peripinayagam Suresh , Jennions Ian.K. , Alghassi Alireza
Published: March 2024

This research work presents a novel approach that addresses the concept of an adaptive power management system design and development framed in the Prognostics and Health Monitoring(PHM) perspective of an Electrical power Generation and distribution system(EPGS).PHM algorithms were developed to detect the health status of EPGS components which can accurately predict the failures and also able to calculate the Remaining Useful Life(RUL), and in many cases reconfigure for the identified system and subsystem faults. By introducing these approach on Electrical power Management system controller, we are gaining a few minutes lead time to failures with an accurate prediction horizon on critical systems and subsystems components that may introduce catastrophic secondary damages including loss of aircraft. The warning time on critical components and related system reconfiguration must permits safe return to landing as the minimum criteria and would enhance safety. A distributed architecture has been developed for the dynamic power management for electrical distribution system by which all the electrically supplied loads can be effectively controlled.A hybrid mathematical model based on the Direct-Quadrature (d-q) axis transformation of the generator have been formulated for studying various structural and parametric faults. The different failure modes were generated by injecting faults into the electrical power system using a fault injection mechanism. The data captured during these studies have been recorded to form a “Failure Database” for electrical system. A hardware in loop experimental study were carried out to validate the power management algorithm with FPGA-DSP controller. In order to meet the reliability requirements a Tri-redundant electrical power management system based on DSP and FPGA has been developed.
Enhancing Turbine Performance Degradation Prediction with Atmospheric Factors
Authors: Jiang Xiaomo , Lin TsungPo , Mendoza Eduardo
Published: March 2024

Heavy duty gas turbine engines are not only ingesting the air, but also eating a myriad of aerosol particles, which may have various negative effects on the turbine operation efficiency as well as the component failure. This paper attempts to develop predictive degradation models for gas turbines by integrating satellite collected atmospheric factors, on-site monitoring data, and physics-based calculated performance results. Multiple variables are analyzed and employed for predictive modeling. The vital variables are identified by using data exploratory correlation analysis and stepwise regression analysis. The performance degradation calculation is obtained from physics based thermodynamic heat balance of gas turbine. It requires balancing mass and energy of gas turbine to match measurement data through thermodynamic cycle matching. The performance degradation prior to the offline water wash is used as the predictor. Artificial neural network modeling is employed to establish the predictive models. A procedure is presented to explain the proposed methodology, and results are discussed. This paper provides an effective methodology and procedure to apply big data for the performance degradation prediction of gas turbines.
Impedance-based Health Monitoring of Electromagnetic Coil Insulation Subjected to Corrosive Deterioration
Authors: Jameson N. Jordan , Azarian Michael H. , Pecht Michael
Published: March 2024

Electromagnetic coils are widely used components in a variety of industries and systems, and their failure can lead to catastrophic failure of the system in which they are placed. Past studies have revealed the electromagnetic coil insulation to be a weakness, and there are presently no available methods to detect degradation in the coil insulation in-situ. Prior work in the AC motor community on twisted pairs of magnet wire has shown that insulation capacitance measurements can reveal useful diagnostic information. Yet, no studies have tracked the coil impedance spectrum over an aging/degradation period, no methods are available to identify frequencies in the impedance spectrum to be used, nor is there discussion concerning methods to employ coil impedance to determine a degradation mechanism. This paper develops an approach of detection of the aging of insulation in low-voltage electromagnetic coils when subjected to corrosive environmental conditions by assessing changes in impedance responses. The complex impedance was resolved into its real and imaginary parts and the Spearman correlation coefficient was used to find regions of interest within the impedance spectrum. The results indicate that real and imaginary impedance provide information that can assist in condition-based maintenance procedures for electromagnetic coils. Furthermore, by incorporating frequency-correlation analysis, the individual frequencies which provide diagnostic and prognostic information can be identified.
Application of a Relative Humidity Sensor for Monitoring Water Vapor Concentration inside Enclosures
Authors: Hatchell Brian , Gonzales Eric , Sinkov Anton , Luzi Lorenzo , Cakerri Azem
Published: March 2024

The presence of water can have adverse effects on the performance of mechanical and electrical hardware. Standing or condensed water can cause shorting or grounding issues in the short term and can accelerate corrosion in the long term. High humidity can also accelerate corrosion as well as cause swelling of electrical components, seals, or composite materials. Water exposure data can be used to actively intervene prior to degradation(e.g. to fix a water leak) or to calculate the remaining life of a system that has been exposed to water or high humidity. The U.S. Army has recognized the importance of environmental monitoring and failure prediction in weapon systems to ensure readiness, to enhance safety, and toimprove weapon performance. Since 2000, Picatinny Arsenal has sponsored the development of specialized environmental monitoring sensors with on-board diagnostics in various new weapon/ammunition systems. In this article we present an effort at the Pacific Northwest National Laboratory to develop an asset health monitor to assess water concentration inside the storage container of an artillery shell. Desiccant is used in the container to maintain the water vapor concentration below 5000 parts per million; a higher water concentration would indicate container leakage or the need to replace the desiccant. Various types of water sensors, including standing water indicators, corrosion indicators, and humidity sensors, were identified and categorized according to type of water exposure, accuracy, and response time. A commercially available relative humidity sensor was identified as a cost effective approach to measuring water vapor concentration inside the container. Relative humidity can be converted to waterconcentration using the water vapor saturation pressure at ambient temperature. Using diurnal temperature simulations, it was determined that the accuracy of the relative humidity sensor would be unsatisfactory in high temperature/low humidity situations where a small change in relative humidity results is a large change in water vapor concentration. To overcome this obstacle, a strategic sampling technique was developed to increase the accuracy of the water concentration measurement that is based on relative humidity sensor data. The relative humidity sensorwas integrated into the Remote Readiness Asset Prognostic/Diagnostic System (RRAPDS), which is an asset health monitor developed by PNNL to measure environmental conditions in munition containers. RRAPDS will be secured to the container with a probe that extends into the container to monitor internal temperature and humidity. RRAPDS includes a microcontroller to process environmental data and execute diagnostic routines. Thispaper will describe the approach that was developed to estimate water concentration using relative humidity sensor data and describe the sampling technique used to maximize the accuracy of the water vapor concentration assessment.
Identification of Industrial Robot Arm Work Cell Use Cases and a Test Bed to Promote Monitoring, Diagnostic, and Prognostic Technologies
Authors: Weiss Brian A. , Klinger Alexander
Published: March 2024

The National Institute of Standards and Technology (NIST) is performing research to advance the state of the art in monitoring, diagnostic, and prognostic technologies (collectively known as prognostics and health management (PHM)) to enhance decision-making at the factory floor to promote smarter maintenance and control strategies. One specific thrust in this hierarchical research is focused at the work cell level. A robot system is the focus of this research level where the manufacturing community would benefit from measurement science (e.g., performance metrics, test methods, reference datasets, software tools) to design, deploy, verify, and validate PHMC technologies aimed at a robot system work cell. NIST’s identification of representative manufacturing robot work cell use cases will provide the foundation for which it will construct its own physical test bed. The test bed is designed to emulate the chosen robot system use case and afford sufficient flexibility to add, subtract, or upgrade components and capabilities to be commensurate with common industrial practices. This paper presents various use case options that NIST has considered and highlights the one that will be the foundation of the physical test bed. Additionally, the initial test bed design is introduced.
Prognosis of Connector Disconnection Using a Canary-Based Approach
Authors: Du Xinyu , Nagose Atul , Bloom Aaron , Julson Timothy
Published: March 2024

The electrical connector disconnection is a common problem in automotive systems. It can be caused by bad design, manufacturing issues, ageing in harsh environment, or low product quality. An imminent disconnection during driving may result in severe safety issues. A loose connection manifests itself as intermittent faults of various vehicle components, which is hard to diagnose and likely leads to unnecessary component replacement or dealership revisit. In order to predict the connector disconnection, a low-cost canary-based approach is proposed in this paper. A shortened male terminal is employed to foretell the loose factory terminals in the same connector housing. The dimension and placement of the shortened terminal are theoretically and experimentally investigated to achieve optimal performance. The proposed solution is tested and compared to other common diagnostic and prognostic approaches, including inductance–, capacitance–, resistance–based approaches, time domain reflectometry, and frequency domain transmissometry, using a connector bench test setup. The placement variation test and the accelerated vibration test are performed to simulate the long-term real driving scenario as well. It’s shown that the proposed solution is capable ofpredicting connector disconnection robustly before the vehicle functionality is affected.
Energy Management of Electric Bicycles Given a Traveling Elevation Profile
Authors: Seria Sebastián , Quintero Vanessa , Espinoza Pablo A. , Pérez Aramis , Jaramillo Francisco , Benavides Matías , Orchard Marcos
Published: March 2024

This research proposes a method for energy management in electric bicycles with Lithium-Ion batteries. This method optimizes the way energy is consumed to maximize the rider’s comfort, subject to constraints on the battery State-of-Charge once destination is reached. The algorithm considers the elevation profile of the route chosen by the rider, predicting the battery energy consumption based on physical parameters of the user and the bicycle. The route is partitioned into equispaced segments, and the optimization problem is then formulated to decide when to pedal or when to use the bicycle electric motor. Binary Particle Swarm Optimization (BPSO) is used to solve the optimization problem, while particle-filter-based estimators are used to determine the initial battery State-of-Charge. We surmise that management of the variability associated with the State-of-Charge swing range, in a systematic manner, will help to increase the battery life.
Trends in Research Techniques of Prognostics for Gas Turbines and Diesel Engines
Authors: Bernardo Joseph T. , Reichard Karl M.
Published: March 2024

Research techniques of prognostics for gas turbines and diesel engines have advanced in recent years. An analysis of trends in these techniques would benefit researchers assessing growth in the field and planning future research efforts. Prognostics research techniques were identified in 1,734 published papers dated 1997-2016 from both the Prognostics and Health Management (PHM) Society and papers identified by CiteSeerx that were published at venues other than the PHM Society. In order to categorize papers by research technique, a taxonomy of prognostics was created. Additionally, the papers were categorized into two topics: gas turbines and diesel engines. In a large proportion of papers, trends in research techniques of prognostics for gas turbines and diesel engines reflected improvements in the speed of multi-core computer processors, the development of optimized learning methods, and the availability of large training sets. The variety of prognostics research techniques that were identified in this review demonstrated the growth in prognostics research and increased use of this knowledge in the field. This systematic analysis of trends in research techniques of prognostics for gas turbines and diesel engines is useful to assess growth and utilization of knowledge in the larger field, and to provide a rationale (i.e., strategy, basis, structure) for planning the most effective use of limited research resources and funding.
An Observer-based On-line Electrolytic Capacitor Health Monitoring System
Authors: Foube Laurent , Mollov Stefan
Published: March 2024

This work presents a cost-motivated method for reliability health monitoring of an electrolytic capacitor. The monitoring system uses a combination of analogue and digital electronics to optimize the cost. The capacitor aging is detected by on-line and in-situ detection of the evolution of the ESR (Equivalent Series Resistance) which is the most reliable ageing indicator. The algorithm uses waveforms naturally present in the power electronic train and thus the condition monitoring can be performed continuously and without interruption of the industrial process. The monitoring method estimates the ESR by adjusting a capacitor model that emulates the monitored capacitor. The limitation in accuracy of the estimation has no significant influence on the lifetime determination. The principle of the method is explained, along with the design tradeoffs and the monitoring procedure is described in detail. The paper also presents the experimental results obtained from a prototype during accelerated in-situ/inverter ageing tests.
Unobtrusive Software and System Health Management with R2U2 on a parallel MIMD Coprocessor
Authors: Schumann Johann , Moosbrugger Patrick
Published: March 2024

Dynamic monitoring of software and system health of a complex cyber-physical system requires observers that continuously monitor variables of the embedded software in order to detect anomalies and reason about their root causes. There exists a variety of techniques for code instrumentation, but instrumentation might change runtime behavior and could require costly software re-certification. In this paper, we present R2U2/E, a novel realization of our real-time, Realizable, Responsive, and Unobtrusive Unit (R2U2). The R2U2/E observers are executed in parallel on a dedicated 16 or 64 core EPIPHANY co-processor, thereby avoiding additional computational overhead to the system under observation. A DMA-based shared memory access architecture allows R2U2/E to operate without any code instrumentation or program interference.
A Case for the Use of Data-driven Methods in Gas Turbine Prognostics
Authors: Baptista Marcia , Nascimento Jr. Cairo L. , Prendinger Helmut , Henriques Elsa
Published: March 2024

The goal of data-driven methods is to remove dependence on classical models of structured expert judgment and draw insights to causal relationships directly from the data. This paper investigates the potential of using data-driven methods, namely uni-variate multiple linear regression, k-nearest neighbors, feed-forward neural networks, random forests and linear support vector regression to predict the end of life(EOL) and remaining useful life (RUL) of engineering systems. The algorithms are demonstrated on a real-world largescale dataset consisting of a multidimensional time series of health monitoring indicators collected from a set of commercial aircraft gas turbine engines. A stratified version of10-fold cross-validation is used to compare the prognostics performance of the five prognostics models. An experiencebased Weibull model is chosen as the baseline method. Models are evaluated according to established metrics in the field including median absolute error, median absolute deviation and relative accuracy. The prediction results indicate that support vector regression and random forests are the most accuratemodels. Neural networks and k-nearest neighbors also show improved forecast skill compared to the baseline model while beating the more traditional technique of linear regression. In regards to error spread, results are not as expressive even though all the selected data-driven methods provide good results, outperforming the baseline.
Diagnostics of machine tool linear axes via separation of geometric error sources
Authors: Vogl Gregory W. , Sharp Michael E.
Published: March 2024

Manufacturers need automated, efficient, and robust methods to diagnose the condition of their machine tool linear axes with minimal disruptions to production. Recently, a method was developed to use data from an inertial measurement unit (IMU) to measure changes in geometric error motions. A linear axis testbed, established for verification and validation purposes, revealed that the IMU-based method was capable of measuring translational and angular deviations with acceptable test uncertainty ratios. In this study, a rail of the linear axis testbed was mechanically degraded to simulate spalling, a common degradation mechanism that can occur during machine tool operations. The rail was degraded in discrete steps from its nominal state (no degradation) to its final state (a failure state of the rail), and IMU and laser-based reference data was collected at each test stage. The contribution of geometric errors from the rail-based degradation were then separated with a technique that utilizes the various data for each run. Diagnostic metrics can then be defined for use with the IMU to facilitate industrial applications by informing the user of the magnitude and location of wear and any violations of performance tolerances.
CALDWELL LUC APPROACH IN ERA OF ENDOSCOPIC SINUS SURGERY
Authors: Lamkhade Saurabh Baban , Carvalho Cassandra , Velankar Haritosh , Dhumal Akshada
Published: March 2024

Endoscopic sinus surgery has been widely used for many years, but the Caldwell-Luc procedure remains valuable for
ENT surgeons, particularly for accessing the maxillary sinus. [1] The Caldwell-Luc technique has well-documented
indications, including severe midfacial injuries, oro-antral stulae, malignant exophthalmos, foreign body removal, and access to various areas
such as the pterygomaxillary space, pterygopalatine fossa, orbital oor, infraorbital nerve infratemporal fossa, and nasopharynx. [2] However,
the traditional Caldwell-Luc approach had drawbacks like numbness of the teeth, ap dehiscence, and recurrent sinusitis. Functional endoscopic
sinus surgery has gained popularity due to its minimal invasiveness and physiological approach. This study aims to evaluate the indications and
surgical procedure of Caldwell-Luc surgery, showcasing its versatility in an era dominated by endoscopic sinus surgery.
COVID SPIKES ON TOBACCO STICKS - TOBACCO HABIT CHANGES DURING COVID-19 PANDEMIC
Authors: A Keerthana , R Nivetha , Sucharitha Sree T. , Venugopal R.
Published: March 2024

Introduction: Tobacco is the foremost preventable cause of death and disease in the world today, killing half of the
people who use it. The use of tobacco is a risk factor for many respiratory infections and increases the severity of
respiratory diseases. Experts have conrmed that tobacco users are more likely to develop severe symptoms or die from COVID-19, as it
primarily attacks the lungs. Hence the present study assessed the tobacco habit changes during COVID-19 pandemic and to determine the factors
associated with tobacco habits during pandemic. A mixed method study was conducted from December 20 Methodology: 21 – January 2022
among 200 tobacco users attending outpatient services in a tertiary care hospital, Tamil Nadu, South India with informed consent using an
interview administered structured questionnaire by convenient sampling method. Result: Out of 200 study participants, around 59% participants
have experienced decrease in tobacco consumption during COVID-19 pandemic because of lockdown (24%), lack of accessibility (19%) and
5.5% individuals was afraid of acquiring COVID-19 infection and similar percentage of individuals due to unavailability of products. But around
41% participants increased their tobacco usage in COVID -19 pandemic. Tobacco habits during COVID19 pandemic shows signicant
association with socio-economic status of consumer, current tobacco habits and plan to quit tobacco use. Conclusion: The COVID-19 pandemic
offered a singular chance to minimize and discontinue tobacco use among tobacco users, and lockdown-experiences might strengthen control
behaviors among users on a national and international level. During the COVID-19 pandemic, there may have been a decline in tobacco use due
to several factors, including fear of contracting the virus and product scarcity as a result of lockdown.
NATURAL CYCLE FROZEN EMBRYO TRANSFER IN A CASE OF PERSISTENT FLUID IN THE ENDOMETRIAL CAVITY
Authors: Thota Suma Varsha , Sindhu Kanumuri , Swathi Pandirla , Veneela Nallani
Published: March 2024

Fluid in the endometrium is not uncommon entity at the start of preparation of frozen embryo transfer, sometimes uid can
persist up to and including the day of frozen embryo transfer. Here we are reporting a case of 29 year old woman presented
with secondary infertility with bilateral tubal block due to bilateral tubal ligation. Basic Pelvic ultrasound revealed uid in the endometrium. Rest
of the USG was normal. After explaining all the treatment options the couple opted for invitro fertilization with self oocytes and donor sperm as
there was genetic abnormality in previous children. Controlled ovarian hyperstimulation was started, Oocyte retrieval was done. Patient was
advised for day 2 review to start frozen embryo transfer protocol medicine. Inspite of treatment with antibiotics and uid aspiration from
endometrial cavity, 3 cycles of Frozen embryo transfer were cancelled due to persistence of uid. 4th cycle was tried as natural cycle Frozen
embryo transfer, when patient came on day 14, endometrial thickness was 9mm triple line with no uid in the endometrium. Day 5 Embryo
transfer was done and patient conceived.
ASSOCIATION BETWEEN BODY MASS INDEX (BMI), WAIST CIRCUMFERENCE (WC) AND RESTING BLOOD PRESSURE (RBP) WITH ANKLE BRACHIAL INDEX (ABI) IN 20-40 YEAR OLD INDIVIDUALS – A PILOT STUDY.
Authors: Chawan Prachi Rajesh , Pol Titiksha , Karajgi Asmita
Published: March 2024

individually in 20-40 year old individuals. Methods: 40 participants in the age group of 20-40 years were included where
their BMI with the aid of therapist reported height and weight, waist circumference, resting blood pressure and ankle brachial index were assessed
after an adequate rest pause. Results: Of the 40 participants, majority (67%) were females with a mean age of 29.48± 6.37 years. The association
analysis was performed using Pearson's correlational test and Chi-Square test. The p-value of 0.37 and 0.97 and r value of -0.014 and -0.004 for
correlation between BMI and ABI (R) and ABI (L) was obtained. A p-value of 0.12 and 0.41 and r value of -0.244 and -0.13 for correlation
between WC and ABI (R) and ABI (L) was obtained. A p-value of 0.89 and an r-value of 0.004 and a p value of 0.89 and an r-value of 0.0037 for the
correlation between RBP and ABI and correlation between MAP and ABI was obtained. Conclusion: A negative weak and statistically nonsignicant correlation was established between BMI and ABI, WC and ABI, was obtained whereas, a positive weak ,and statistically nonsignicant correlation was established between RBP and ABI, MAP and ABI.
EXPLORATIVE STUDY OF VITAMIN D DEFICIENCY IN ELDERLY PATIENTS AND ITS ASSOCIATION WITH HIP FRACTURE
Authors: Kumar Sunil , Verma Ajeet Kumar , Srivastava Yashi , Verma Pragya
Published: March 2024

Introduction: Vitamin D is the most important vitamin for bone health; Hip Fracture is the most severe fragility Fracture
and is associated with a remarkable increase in mortality and morbidity. to assess the effect of vit Objective: amin d
deciency among elderly patients with hip fracture and without hip fractures. This study was conduct Material and Methods: ed during period
of May 2017- May 2018 at our tertiary care centre. Male patient sixty year or more and postmenopausal women fty year and above with or
without fracture were selected as per following inclusion and exclusion criteria It is a prospective, analytical, case control study to measure
assessments of vitamin D deciency. We studied serum vit D level, BMD score and FRAX score in these patients. In Observation and Result:
our study, among elderly patients who presented to us with hip fracture, 72% had vitamin D deciency and 2% patients had vitamin D
insufciency while 26% of patients with hip fracture had sufcient levels of vitamin D. The values of vitamin D in respective group are: In control
group, mean value of vitamin D level was 35.549 (SD ± 13.285) and the case group had mean value of 22.380 (SD±12.059).The data was
statistically signicant (p value 0.001). The mean value of BMD in control group: was 1.8278 (SD± 0.77778) and in case group was 2.7480(SD ±
0.72766). In our study we also included FRAX score and the mean value of FRAX score in control group is 5.6260 (SD±3.16362). In case group
the, mean value was 7.8560 (SD ± 4.09146 ). Elderly patient who presented to us with hip fracture ha Conclusions: d a low concentration of
vitamin D, BMD and high FRAX score as compared to the control group
PARASITIC INFECTIONS, AND THEIR ASSOCIATION WITH CHRONIC URTICARIA
Authors: Chillana Suchira , Mohit Chilana Kapil Alias
Published: March 2024

Introduction: Parasites have been proposed to be an underlying cause of chronic spontaneous urticaria, but a clear causal
relationship between them has not been established. This study was aimed to nd out the Aims And Objectives:
prevalence of intestinal parasitic infections in patients with underlying chronic urticaria and correlate it with associated gastrointestinal
symptoms , presence of urticaria, gender , age group and area(rural or urban). The stool samples of Material And Methods: suspected patients
were send to parasitology section from dermatology OPD from August 2018-august 2019 and subjected to routine stool investigations.
Macroscopic examination was carried out for the presence of adult worms or their body segments and stool samples were processed using saline
and iodine mounts and examined microscopically for ova , cysts and eggs of parasites. Total of 3850 Results: patients were referred from
dermatology opd with underlying urticaria . Out of them, 68 (17.7%) had parasitic infection . Maximum numbers of patients were in the age
group of 0-10yrs 28(41.2%). Total 84 parasites were isolated from 68 positive patients. Parasite most commonly isolated was Giardia lamblia
29(34.5%), followed by Entamaeba histolytica 22(26.2%), Ascaris lumbricoides 20(23.8 %), Hymenolepis nana 6(7.1%), followed by Taenia
species 4(4.7%) and Enterobius vermicularis was the least common isolate 2(2.4%) . Mixed infection with a combination of Entamoeba
histolytica and Giardia lamblia were most commonly isolated (9 out of 16 mixed infections). Only 60% positive patient had a history of urticaria
and 39 % patients have underlying gastrointestinal symptoms. 58.8% were male patients and 59% were from urban area. Intestinal Conclusion:
parasites can be attributed as one of the underlying cause of skin problems like chronic urticaria . Therefore routine stool examination should be
done in suspected cases.
A COMPARATIVE STUDY OF ONLAY AND PRE-PERITONEAL OPEN MESH REPAIR IN THE MANAGEMENT OF UMBILICAL HERNIA IN ADULTS.
Authors: Amruthavalli B.V. , Rajanidevi Gosala
Published: March 2024

Aims And Objectives Of The Study The main aims and objectives of this study is to: 1. To evaluate the outcome of onlay
and pre-peritoneal open mesh repair in umbilical hernias in adults regarding operative time, ease of procedure, hospital
stay, complications and recurrence if any Patients diagnosed with umbilical hernia in surgery depart Methods ment at Government General
Hospital (RIMS) hospital Ongole from May 2021 to April 2023 with a follow up for 10 Months. After a Methodology: dmission, patients
fullling the inclusion & exclusion criteria were taken into study after obtaining written informed consent. 60 cases were divided into two groups
for onlay and pre-peritoneal mesh repair. Intra operative study was done regarding operative time (operated by the same surgeon through the
course of study), ease of procedure and operative complications. Follow up was done to note the complications. The mean operative time Results
was 38.6 minutes in onlay group and 62.3 minutes in preperitoneal group showing statistical signicance. The onlay group had 23.3% and 20% of
seroma formation and wound infection respectively, while the pre-peritoneal group had 3.3% seroma formation and 3.3% wound infection
showing statistical signicance. The study found that onlay repair had more complication rates like Conclusion seroma and wound infection
compared to pre-peritoneal mesh repair. Even though time taken for onlay repair was less, increased complications limit its use.
A CASE REPORT OF ALOPECIA AREATA TREATED WITH CALCAREA CARBONICA BY USING POLARITY ANALYSIS METHOD
Authors: Nayak Amit , Nayak Pooja , Pandey Swati
Published: March 2024

Alopecia areata(AA) is a common form of non-scarring hair loss of scalp and/or body. Most common form of AA is of
patchy form. Major role for the etiopathogenesis of AA are Genetic predisposition, autoimmunity, and environment
factors. As far as Conventional method of treatment is concern Corticosteroid is the only way to treat this condition which is having lot of side
effects. This case report introduces Polarity Analysis (PA), the method of remedy selection , which allows more precise matches between patient
symptoms and homoeopathic remedy, and leads in turn to better result.
KEADILAN PENDAPATAN DENGAN PENGUPAHAN SISTEM BAGI HASIL
Authors: Patoni L.M Ikbal
Published: March 2024

Penelitian ini membahas tentang pengupahan dengan sistem bagi hasil dalam mewujudkan keadilan pendapatan pada Jasa cukur di Kota Lombok. Penelitian ini bertujuan untuk, 1. Mengetahui pengupahan dengan sistem bagi hasil pada Jasa cukur di kota Lombok, 2. Mengetahui keadilan pendapatan pada karyawan Jasa cukur di Kota Lombok. Jenis penelitian yang digunakan adalah penelitian study lapangan (field research) yang bersifat deskriptif kualitatif, berlokasi di Kota Lombok, lebih tepatnya di Top Jasa cukur dan Ganteng Jasa cukur. Pendekatan yang digunakan adalah pendekatan kualitatif. Teknik pengumpulan data pada penelitian ini berupa observasi, wawancara langsung dengan pihak terkait, dan dokumentasi, diolah menggunakan analisis reduksi data, penyajian data, serta penarikan kesimpulan. Hasil penelitian ini menunjukkan bahwa pengupahan dengan sistem bagi hasil pada Jasa cukur di Kota Lombok yaitu dengan menggunakan upah sistem bagi hasil profit sharing dengan persentase 50:50 dan revenue sharing dengan persentase 60:40 berdasarkan akad Mudharabah. Sedangkan keadilan pendapatan terhadap karyawan yakni berdasarkan persentase bagi hasil dan produktifitas karyawan, keadilan pendapatan terhadap karyawan pekerja berdasarkan proporsi pekerjaannya, keadilan pendapatan berdasarkan kelayakan atau kecukupan memenuhi kebutuhan, dan keadilan pendapatan bersifat transparansi.
Inventive Ways of Teaching Mechanical Systems and Control to Grade 9 Learners with Special Learning Needs
Authors: Kekana Elizabeth Katane , Mtshali Thokozani Isaac , Ramaligela Manto Sylvia
Published: March 2024

Mechanical systems and control are part of the Technology subject strand, which involves gaining knowledge and skills in using various machinery and mechanisms such as gears, belts, chains, elevators, and escalators. Technology teachers need to be familiar with innovative teaching methods for instructing learners with special needs. This study aimed to explore creative teaching strategies for teaching Mechanical Systems and Control to grade 9 learners with special needs in the Capricorn district of Limpopo. A qualitative exploratory case study design and homogenous purposive sampling were employed to select two Technology teachers from two special schools in the Capricorn district. The findings of this study revealed that technology teachers encountered significant challenges in identifying and implementing appropriate creative teaching strategies to deliver the content of Mechanical Systems and Control effectively to learners with special learning needs. The study also indicated that although Technology teachers utilized PowerPoint presentations, narrative discourses, textbooks, and artifacts during instruction, these strategies were not always effective for learners with special learning needs. Consequently, the study recommends that teachers receive comprehensive training on teaching learners with special learning needs, focusing on different pedagogies and methods such as the guided exploration method, technology integration, visualization, and the use of technological artifacts to enhance instruction in Mechanical Systems and Control. These learner-centered methods enable teachers to cater to learners' individual learning needs.
Development of E-Modules Based on Problem-Based Learning to Improve Problem-Solving Skills and Student Self-Efficacy
Authors: Hadira Hadira , Sari Murni Sapta , Sulisetijono Sulisetijono
Published: March 2024

The purpose of this research was to develop and assess the validity, practicality, and effectiveness of an e-module on plant development using problem-based learning. The study was conducted at Universitas Negeri Malang, involving 63 third-semester biology students as research participants. The research employed the research and development approach, following the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). The validity test and practitioner evaluation were conducted by three expert faculty members, including a content expert, a media expert, and a practitioner expert. The effectiveness of the e-module was assessed through pretests and posttests, analyzing students' problem-solving skills and self-efficacy using ANCOVA. The media validity test revealed a score of 89.33% in the valid category, while the material experts scored 100% in the very valid category. The practitioner evaluation resulted in a score of 96.19% in the very practical category. The effectiveness test indicated significant values of 0.002 and 0.023 for problem-solving skills and self-efficacy, respectively, both of which were below the 0.05 significance level. This implies that there is a significant difference in the mean scores between the classes using problem-based e-modules and those employing problem-based learning without e-modules. Based on these findings, it can be concluded that the developed e-module is highly suitable as a learning resource for students.