bluuebunny/crossref_metadata_2025 huggingface dataset:
Flight Tests of a Remaining Flying Time Prediction System for Small Electric Aircraft in the Presence of Faults
Authors: Hogge Edward F. , Kulkarni Chetan S. , Vazquez Sixto L. , Smalling Kyle M. , Strom Thomas H. , Hill Boyd L. , Quach Cuong C.
Published: March 2024

This paper addresses the problem of building trust in the online prediction of a battery powered aircraft’s remaining flying time. A series of flight tests is described that make use of a small electric powered unmanned aerial vehicle (eUAV) to verify the performance of the remaining flying time prediction algorithm. The estimate of remaining flying time is used to activate an alarm when the predicted remaining time is two minutes. This notifies the pilot to transition to the landing phase of the flight. A second alarm is activated when the battery charge falls below a specified limit threshold. This threshold is the point at which the battery energy reserve would no longer safely support two repeated aborted landing attempts. During the test series, the motor system is operated with the same predefined timed airspeed profile for each test. To test the robustness of the prediction, half of the tests were performed with, and half were performed without, a simulated powertrain fault. The pilot remotely engages a resistor bank at a specified time during the test flight to simulate a partial powertrain fault. The flying time prediction system is agnostic of the pilot’s activation of the fault and must adapt to the vehicle’s state. The time at which the limit threshold on battery charge is reached is then used to measure the accuracy of the remaining flying time predictions. Accuracy requirements for the alarms are considered and the results discussed.
A Compressed Sensing Feature Extraction Approach for Diagnostics and Prognostics in Electromagnetic Solenoids
Authors: Knoebel Christian , Wenzl Hanna , Reuter Johannes , Guehmann Clemens
Published: March 2024

One major realm of Condition Based Maintenance is finding features that reflect the current health state of the asset or component under observation. Most of the existing approaches are accompanied with high computational costs during the different feature processing phases making them infeasible in a real-world scenario. In this paper a feature generation method is evaluated compensating for two problems: (1) storing and handling large amounts of data and (2) computational complexity. Both aforementioned problems are existent e.g. when electromagnetic solenoids are artificially aged and health indicators have to be extracted or when multiple identical solenoids have to be monitored. To overcome those problems, Compressed Sensing (CS), a new research field that keeps constantly emerging into new applications, is employed. CS is a data compression technique allowing original signal reconstruction with far fewer samples than Shannon- Nyquist dictates, when some criteria are met. By applying this method to measured solenoid coil current, raw data vectors can be reduced to a way smaller set of samples that yet contain enough information for proper reconstruction. The obtained CS vector is also assumed to contain enough relevant information about solenoid degradation and faults, allowing CS samples to be used as input to fault detection or remaining useful life estimation routines. The paper givessome results demonstrating compression and reconstruction of coil current measurements and outlines the application of CS samples as condition monitoring data by determining deterioration and fault related features. Nevertheless, some unresolved issues regarding information loss during the compression stage, the design of the compression method itself and its influence on diagnostic/prognostic methods exist.
Fleet Knowledge for Prognostics and Health Management – Identifying Fleet Dimensions and Characteristics for the Categorization of Fleets
Authors: Wagner Carolin , Hellingrath Bernd
Published: March 2024

Current prognostics and health management approaches are often not able to meet expectations due to their limited ability to accurately detect abnormal machine conditions, identify failures and estimate the remaining useful life. This is in many cases attributed to the lack of real data and knowledge about the component or machine under consideration. Instead, experimental data is often used for algorithm training, which is not able to reflect the complexity of real-world systems. To improve prognostics and health management approaches condition data from fleets of machines rather than single units can be taken into consideration. Therefor machine conditions are assessed against situations encountered by machines in the same fleet and knowledge is transferred to allow algorithms to intelligently learn and improve their capabilities.Several approaches have recently been presented in the literature, which make use of the fleet knowledge for condition-based maintenance. These approaches are designed for specific fleet compositions and characteristics. Therefore, in order to incorporate fleet knowledge into diagnostic and prognostic approaches the fleet under consideration and resulting requirements have to be analyzed. With this information, it is possible to determine whether fleet-based approaches are applicable in general to the specific case as well as facilitate the selection of a suitable fleet-based approach. Three types of fleets are distinguished in the literature, namely identical, homogeneous and heterogeneous fleets. This distinction makes reference to the structural dimension of fleets. For fleet-based approaches, however additional dimensions should be taken into account. These include among others the operating condition in the fleet (e.g. identical, different, or dynamically changing) and the type of available data (e.g. sensor reading, context data, textual description). This paper aims at identifying and analyzing different dimensions and respective characteristics of fleets to be considered in the context of prognostics and health management. The results are synthesized in a classification structure to support the categorization of fleets.
Anomaly Detection Using Dynamical Linear Models and Sequential Testing on a Marine Engine System
Authors: Vanem Erik , Storvik Geir Olve
Published: March 2024

This paper presents a study on the use of Dynamical Linear Models for anomaly detection and condition monitoring of a marine engine system. Various sensors are installed at different places within the engine system and records essential parameters such as power output from the engine, engine speed, bearing temperatures and various other temperatures, speeds and pressures for selected engine components. The idea is to utilize the information in these sensor signals in order to monitor the condition of the engine. Such a condition monitoring system should include means of fault detection, diagnosis and prognostics, where robust anomaly detection is a prerequisite for reliable management of the system. DynamicalLinear Models (DLM) constitute a flexible framework for modelling of sensor signals, where the sensor signals are modelled conditional on some latent states, and the model provides forecasts of the signals that can be compared to new sensor readings. Statistical sequential model testing will then be performed on the forecast errors and model breakdown can be an indication of deviation from normal conditions and possibleimpending failures of the engine system. This will then call for further diagnostics and prognostics tasks to interpret the nature of the deviations. The Dynamical Linear Model framework can accommodate a range of candidate models. However, very complicated models in high dimensions may be computationally expensive to estimate and apply, so various pre-processing techniques are investigated in this paper to improve model performance, including simple regression models, cluster analysis and principal component transformation.
Fault Detection and Prognosis of Time Series Data with Random Projection Filter Bank
Authors: Pourazarm Sepideh , Farahmand Amir-massoud , Nikovski Daniel
Published: March 2024

We introduce Random Projection Filter Bank (RPFB) as a general framework for feature extraction from time series data. RPFB is a set of randomly generated stable autoregressive filters that are convolved with the input time series. Filters in RPFB extract different aspects of the time series, and together they provide a reasonably good summary of the time series. These features can then be used by any conventional machine learning algorithm for solving tasks such as time series prediction, and fault detection and prognosis with time series data. RPFB is easy to implement, fast tocompute, and parallelizable. Through a series of experiments we show that RPFB alongside conventional machine learning algorithms can be effective in solving data-driven fault detection and prognosis problems.
Case Studies in using Consumer Analytics with PHM Strategy
Authors: Vittal Sameer , Sporer Mark
Published: March 2024

As part of the “Digital-Industrial Revolution”, the world is seeing the rapid transformation and digitization of the world’s energy value network – from generation, through transmission & distribution, to end user consumption. This new paradigm comprises of new business products and services built on data flows that accompany energy flows; where the insight gained from sensors and analytics drives better decision making and customer outcomes. This is what drives the digital strategies of Original Equipment Manufacturers of large industrial assets like power plants, oil & gas equipment, aviation fleets, etc.In this paper, we look at how analytical methods originally developed in the consumer industry can be applied to industrial data. This helps guide the development of Prognostics & Health Management strategies that are tuned to customer preferences and value models, in addition to engineering inputs. These methods complement, rather than replace, FMEA-driven strategies that are traditionally used in PHM systems design.
A reliability-based prognostics framework for railway track management
Authors: Chiachío Juan , Chiachío Manuel , Prescott Darren , Andrews John
Published: March 2024

Railway track geometry deterioration due to traffic loading is a complex problem with important implications in cost and safety. Without appropriate maintenance, track deterioration can lead to severe speed restrictions or disruptions, and in extreme cases, to train derailment. This paper proposes a physics-based reliability-based prognostics framework as a paradigm shift to approach the problem of railway track management. As key contribution, a geo-mechanical elastoplastic model for cyclic ballast settlement is adopted and embedded into a particle filtering algorithm for sequential state estimation and RUL prediction. The suitability of the proposed methodology is investigated and discussed through a case study using published data taken from a laboratory simulation of train loading and tamping on ballast carried out at the University of Nottingham (UK).
Feature Extraction for Bearing Prognostics using Correlation Coefficient Weight
Authors: Kim Seokgoo , Lim Chaeyoung , Choi Joo-Ho
Published: March 2024

Bearing is an essential mechanical component in rotary machineries. To prevent its unpredicted failures and undesired downtime cost, many researches have been made in the field of Prognostics and Health Management (PHM). Key issues in bearing PHM is to establish a proper health indicator (HI) reflecting its current health state properly at the early stage. However, conventional features have shown some limitations that make them less useful for early diagnostics and prognostics. This paper proposes a feature extraction method using traditional envelope analysis and weighted sum with correlation coefficient. The developed methods are demonstrated using IMS bearing data from NASA Ames Prognostics Data Repository. In the end, proposed feature is compared with traditional time-domain features.
Integration of prognostics at a system level: a Petri net approach
Authors: Chiachío Manuel , Chiachío Juan , Sankararam Shankar , Andrews John
Published: March 2024

This paper presents a mathematical framework for modeling prognostics at a system level, by combining the prognostics principles with the Plausible Petri nets (PPNs) formalism, first developed in M. Chiachío et al. [Proceedings of the Future Technologies Conference, San Francisco, (2016), pp. 165-172]. The main feature of the resulting framework resides in its efficiency to jointly consider the dynamics of discrete events, like maintenance actions, together with multiple sources of uncertain information about the system state like the probability distribution of end-of-life, information from sensors, and information coming from expert knowledge. In addition, the proposed methodology allows us to rigorously model the flow of information through logic operations, thus making it useful for nonlinear control, Bayesian updating, and decision making. A degradation process of an engineering sub-system is analyzed as an example of application using condition-based monitoring from sensors, predicted states from prognostics algorithms, along with information coming from expert knowledge. The numerical results reveal how theinformation from sensors and prognostics algorithms can be processed, transferred, stored, and integrated with discreteevent maintenance activities for nonlinear control operations at system level.
Improvement of a Hydrogenerator Prognostic Model by using Partial Discharge Measurement Analysis
Authors: Lévesque Mélanie , Amyot Normand , Hudon Claude , Bélec Mario , Blancke Olivier
Published: March 2024

Availability and performance of hydrogenerators are key features that have driven electrical utilities to implement monitoring and diagnostic methods in order to evolve to condition based maintenance (CBM). Ten years ago, Hydro-Quebec has implemented a home-built web-basedapplication, called MIDA, to cover most of its power plants. MIDA centralizes diagnostic data from several tools, aggregates all diagnostic results and calculates a health index for each hydrogenerators. Data from MIDA used in conjunction with PHM techniques can feed a prognostic model that will provide useful equipment information and lead to the implementation of predictive maintenance. The prognostic framework used for hydrogenerators is based on a failure mechanism and symptom analysis (FMSA) approach. For the stator, a major component of hydrogenerators, more than 100 failure mechanisms have been consigned in the form of causal trees or graphs. A large number of these failure mechanisms involve the presence of partial discharges (PD) before failure occurs. At Hydro-Quebec, PD measurements on hydrogenerators have been carried out over the past 30 years and a significant PD database is integrated in MIDA. The analysis of this huge amount of data is of paramount importance to understand the behavior and evolution of the discharge activity in order to build a robust prognostic approach using physics based as well as data driven models. To that end, this paper presents case studies that shed some light on key features related to the evolution of PD activity in hydrogenerators. The paper discusses how to use this data in the prognostic model to assess warning signs before failure occurs.
Preliminary Results on Condition Monitoring of Fiber Ropes using AutomaticWidth and Discrete Length Measurements
Authors: Falconer Shaun , Gromsrud Andreas , Oland Espen , Grasmo Geir
Published: March 2024

As the offshore sector moves to deeper waters, fiber ropes have the potential to replace more traditional solutions such as steel wire ropes for deep sea lifting and heave-compensated operations. While steel wire ropes must account for their own weight when determining the maximum depth that a payload can be deployed, fiber ropes such as high modulus polyethylene (HMPE), are more buoyant than their steel counterparts, enabling payloads to be deployed at deeper depths using smaller cranes. For this reason, companies are actively developing fiber rope cranes to be used in industry. The inherent issue with these designs is monitoring the condition of the fiber rope due the multitude of damage mechanisms and condition indicators that exist, therefore determining the time to rupture remains an unsolved problem. To this end, this paper considers the use of computer vision to monitor the width at discrete length sections and use that as a potential condition indicator. Furthermore, the paper describes in detail how OpenCV is applied to detect the contour of the rope to find the width, how the experiment has been performed, as well as other practical experiences from testing a 28mm Dyneema R fiber rope. The experimental results show an exponential relationship between the applied tension and the reduction in width (which was reduced by more than 10% before rupture), and it is believed that if the width can be monitored at discretesections along the rope over time, the width itself will prove to be a good condition indicator.
HPPN-based Prognosis for Hybrid Systems
Authors: Ribot Pauline , Chanthery Elodie , Gaudel Quentin
Published: March 2024

This paper presents a model-based prognosis method for hybrid systems i.e. that have both discrete and continuous behaviors. The current state of the hybrid system is estimated by a diagnosis process and the prognosis process uses this state estimation to predict the future states and to determine the end of life (EOL) or the remaining useful life (RUL) of the system. The Hybrid Particle Petri Nets (HPPN) formalism is used to model the hybrid system behavior and degradation. A HPPN-based diagnoser has already been defined to provide a current state estimation that takes uncertainty about the system model and observations into account. We propose to generate a prognoser from the HPPN model of the system.This prognoser is initialized and updated with the result of the HPPN-based diagnoser. It computes a distribution of beliefs over the future mode trajectories of the system and predicts the system RUL/EOL. The prognosis methodology is demonstrated on a three tanks example.
Small-ScaleWind Turbine Recurrence and Cost Modeling as a Function of Operational Covariates from Supervisory Control and Data Acquisition Systems
Authors: Czahor Michael S. , Meeker William Q.
Published: March 2024

Small-scale wind turbine (SWT) installations saw a dramatic increase between 2008 and 2012. Recently, the trend within industry has shifted towards installing larger wind turbines, leaving little attention for installed SWT reliability. Unfortunately, multiple downtime events raise concerns about thereliability and availability of the large number of installed SWTs. SWTs are repairable systems that can return to an operational state after a downtime or repair event. When a SWT experiences multiple events over time, these are known as recurrent events. The reliability of SWTs is examined in this paper using data from 21 individual 100 kW wind turbines. SWTs periodically record dynamic covariate data in the form of a vector time series using supervisory control and data acquisition (SCADA) systems. One type of event experienced by SWTs is known as a “service event,” which is a time when a SWT is put into service mode for a repair or false alarm. Due to the proprietary nature of the data used in this paper,different kinds of service events are combined, even though different failure modes and event types exist. We explore recurring service events and the associated cost of each “service event” and propose methodologies to link dynamic covariate data to downtime costs to assist in quantifying the variation of downtime across wind turbines. Data used in this work was provided from a power systems company in the United States. We outline a nonhomogenous Poisson process (NHPP) model with a Bayesian hierarchical power law structure for the count process and an autoregressive time series use rate model with a Bayesian framework to describe posterior parameter distributions. Using the posterior results, we develop a conditional and unconditional method to predict downtime mean cumulative functions (MCFs) for wind turbines.
Bearing Health Condition Prediction Using Deep Belief Network
Authors: Zhao Guangquan , Liu Xiaoyong , Zhang Bin , Zhang Guohui , Niu Guangxing , Hu Cong
Published: March 2024

Bearings play a critical role in maintaining safety and reliability of rotating machinery. Bearings health condition prediction aims to prevent unexpected failures and minimize overall maintenance costs since it provides decision making information for condition-based maintenance. This paper proposes a Deep Belief Network (DBN)-based data-driven health condition prediction method for bearings. In this prediction method, a DBN is used as the predictor, which includes stacked RBMs and regression output. Our main contributions include development of a deep leaning-based data-driven prognosis solution that does not rely on explicit model equations and prognostic expertise, and providing comprehensive prediction results on five representative run-to-failure bearings. The IEEE PHM 2012 challenge dataset is used to demonstrate the effectiveness of the proposed method, and the results are compared with two existing methods. The results show that the proposed method has promising performance in terms of short-term health condition prediction and remaining useful life prediction for bearings.
FACTORS INFLUENCING THE OUTCOME OF ACETABULAR FRACTURES TREATED OPERATIVELY BY SINGLE APPROACH.
Authors: Kunwar Abhijeet , Singh Ajit , Verma Ajeet Kumar , Kumar Sunil
Published: March 2024

Introduction: The treatment of acetabular fractures is a complex area of orthopaedics that is being continually rened.
There have been many changes in the recommended treatment modalities over time as the understanding of acetabular
anatomy and fracture patterns developed which acts as a rst step in decision making for the mode of treatment. Multiple factors inuence
clinical outcome following an acetabular fracture, including pre-existing conditions, injury-related factors, surgical considerations, and
postoperative complications. This is a prospective longitudinal cohort study, conducted on 30 pati Methodology: ents who were admitted in
Trauma Centre, BHU under Department of Orthopaedics, Institute of Medical Sciences, BHU from August 2018 to July 2020. It is a Clinical
study of patients aged between 18 to 65 years with transverse, T–type and both column acetabular fractures. Outcome parameters that were
evaluated were Matta radiographic scoring and Amount of blood loss and duration of surgery. In our Results: study mean age of patients was
35±12 years with the age ranging from 20 years to 60 years. We have included three types of acetabular fractures, namely both column fractures
(25 cases), transverse (3 cases) and T type (2 cases).All three types of fractures were operated by single approach. Timing of surgery following
trauma was 6 -14 days. Maximum number of cases was operated within 10 days with good outcome. Mean timing is 8.47 days. It has signicant
effect on clinical outcome. There is no signicant difference in the mean age of the patients for radiological group i.e. statistically not signicant.
Road trafc accident was the most common mode of injury (83%). Also we had 86.7% good to excellent results radiologically assessed by Matta
radiographic criteria. The incidence of Acetabular fractures is increasing, owing to the increased Conclusion: frequency of road trafc accidents
and high velocity injuries with younger males being the most affected.
“A DESCRIPTIVE STUDY TO ASSESS THE KNOWLEDGE REGARDING STANDARD PRECAUTION AMONG THE NURSING STUDENTS OF NEMCARE INSTITUTE OF NURSING SCIENCES, MIRZA, KAMRUP(R), ASSAM”
Authors: Ahmed Nazma , Kalita Dipanjali
Published: March 2024

Background Of The Study: Standard precautions are meant to reduce the risk of transmission of blood borne and other
pathogens from both recognized and unrecognized sources. They are the basic level of infection control precautions
which are to be used, as a minimum, in the care of all patients. Hand hygiene is a major component of standard precautions and one of the most
effective methods to prevent transmission of pathogens associated with health care. In addition to hand hygiene, the use of personal protective
equipment should be guided by risk assessment and the extent of contact anticipated with blood and body uids, or pathogens. The control of
spread of pathogens from the source is key to avoid transmission. Among source control measures, respiratory hygiene/cough etiquette,
developed during the severe acute respiratory syndrome (SARS) outbreak, is now considered as part of standard precautions. To assess the Aim:
level of knowledge and to nd out the association regarding standard precaution among the Nursing Students of a Nursing College. Method:
Quantitative Research Approach was used, Descriptive Research Design was adopted in the study, the samples were collected using nonprobability convenient sampling technique from 60 nursing students. Out of 60 Nursing students, Maj Results: ority 47(78%) of the respondents
were in the age group of 21-23 years. Majority 3(95%) of the respondents were female. Majority 41 (70%) of the respondents were from GNM
3rd year. Majority 35(58%) of the respondents were hospital exposure for 3-6 months during the entire course., majority i.e. 46(77%) of the
nursing students had Good knowledge, 10 (16%) had average knowledge and only 4(7%) of the nursing students had poor knowledge regarding
standard precaution. The study shows that the majority of the student have average knowledge regard Conclusion: ing Standard precautions
among nursing students and there is no signicant association with their demographic variables.
A COMPARATIVE STUDY OF DEXMEDETOMIDINE AND MIDAZOLAM TO INTRATHECAL BUPIVACAINE ON POSTOPERATIVE ANALGESIA.
Authors: Bhaskar Mudavath , Devi V. Sarada
Published: March 2024

Background: Although midazolam and Dexmedetomidine regulate spinal analgesia through distinct mechanisms, no
human studies assess their effects on postoperative analgesia following neuraxial administration. We Aim: looked at the
clinical safety prole and length of time that effective analgesia lasted when intrathecal bupivacaine was combined with either
Dexmedetomidine or Midazolam. It is a prospective study carried out in the Department of Anaesthesio Materials & Methods: logy at Malla
Reddy Medical College for Women, Hyderabad. A total of 60 patients were taken and were divided into 3 groups, Control (n=20),
Dexmedetomidine (n=20), and Midazolam (n=20) groups, respectively. Ethics approval was taken before the initiation of the study. The Results:
Dexmedetomidine group experienced a considerably longer duration of effective analgesia (time to rst analgesic request) (286 ± 65 minutes). At
the end of the rst 15 minutes following intrathecal injection, patients from the Dexmedetomidine group had a mean rank sedation score of 35.47
for Dexmedetomidine, 25.01 for Midazolam, and 30.80 for control, indicating that they were more sedated than patients from the Midazolam and
control groups. There were no discernible variations in the side effects during the trial period. The duration of the two-segment sensory regression
and the time to initially request analgesia were not prolonged by Midazolam. The duration of effect Conclusion: ive analgesia is greatly extended
when Dexmedetomidine (5 mcg) is added to 3 mL of intrathecal hyperbaric Bupivacaine (0.5%), as compared to 1 mg of Midazolam or a placebo
(0.9% normal saline), with similar occurrences of adverse effects.
PATTERN OF CHILDHOOD BLINDNESS IN EASTERN MAHARASHTRA
Authors: George Dabre Zeenal , Sahasrabudhe V. M.
Published: March 2024

Aim: Method: To determine the causes of blindness among children in the eastern region of Maharashtra. This is an
observational cross – sectional study where 634 children less than 16 years of age with visual acuity of less than 6/60 in
better eye (NPCB) were included. All patients coming to ophthalmology OPD and children referred from school heath camps from august 2019-
july2022 were included in this study. The WHO/PBL eye examination record for children with blindness and low vision was referred to categorize
the causes of blindness and to record the ndings, using the denitions in the coding instructions. Results: 634 children were included. Based on
the anatomical site involved, whole globe anomalies contributed to about 41.8% followed by lenticular (24.45%) and corneal causes (19.6%).
Vitamin A deciency was the most common cause of preventable blindness accounting for 12.6% and Cataract was the most common cause of
treatable blindness (23.34). Almost half (46.68%) of the study population had either preventable or treatable causes of blindness A Conclusion:
paradigm shift in causes of blindness has been observed over the years, however there's still a long way to go to reduce overall burden of childhood
blindness especially in rural areas.
RARE COHABITATION OF PAPILLARY CARCINOMA IN THYROGLOSSAL DUCT CYST : A CASE REPORT WITH REVIEW OF LITERATURE
Authors: A. Mohite Anjana. , Gehlot Dr. Vinit , Patil Balasaheb , S. Mane Rajashri.
Published: March 2024

Introduction: Papillary Carcinoma originating from thyroglossal duct cyst (TGDC) is very rare, occurring in less than
one percent of cases. The diagnosis is often made in these situations following the removal of benign TGDC.We provide a
case report that details a case of a thirty-years man who had Sistrunk's surgery for TGDC and was subsequently diagnosed to have Papillary
Cancer.It is debatable how to handle these cases; the options include either a periodic monitoring following Sistrunk's Procedure or Complete
thyroid gland excision +/- radio-active iodine (RAI) ablation and suppresive hormonal therapy. A th Case Presentation: irty-years man came to
our OPD with anterior midline neck swelling which moved upward with deglutition and tongue protrusion.On radiology,distinct cystic lesion was
seen. Sistrunk's Procedure was performed and tissue was sent for histo-pathological reporting. A papillary carcinoma foci was identied in the
TGDC by histo-pathology. As a follow up and completion treatment, our patient was subjected to complete thyroidectomy. The most Discussion:
frequent anomalies are TGDC which are encountered in thyroid development. Within TGDC remains, malignancy is present in one to two percent
of cases with most being Papillary Carcinoma. TGDC are most common in females with ratio being 3:2.
DEXA SCREENING IN CKD-MBD: A KEY TO PREVENTING OSTEOPOROSIS
Authors: Dod Kritika , Vandna Vandna , Thakur Rashika , Shandil Rakesh
Published: March 2024

Introduction: Many individuals aficted with Chronic kidney disease (CKD), particularly those in stages 3 to 5, exhibit
a substantial decline in bone mineral density, leading to an increased vulnerability to fractures and a notable surge in
related morbidity and mortality. Moreover, Chronic kidney disease–mineral and bone disorder (CKD-MBD) remains inadequately investigated
within this population. Thus, this study aimed to elucidate the metabolic bone prole of CKD patients utilizing DEXA (dual x-ray
absorptiometry). In this cross-sectional study, patients diagnosed with stages 3, 4, and 5 CKD were Material and Methods: assessed. The serum
concentrations of calcium, phosphorus, intact parathyroid hormone (iPTH), 25-hydroxy vitamin D, and total alkaline phosphatase were
determined for all participants. Additionally, bone mineral density (BMD) was evaluated at the neck of the femur (NOF), lumbar spine (LS), and
distal end of radius using dual-energy X-ray absorptiometry (DEXA) The prevalence rates of various b Results: iochemical abnormalities were
as follows: hypocalcemia (85%), hyperphosphatemia (74%), elevated alkaline phosphatase (89%), secondary hyperparathyroidism (68%),
vitamin D deciency (60%), and vitamin D insufciency (26%). Osteoporosis, as determined by the femoral neck T-Score, was identied in 35
patients (35%), while 47 patients (47%) displayed osteoporosis at the lumbar spine (LS). Additionally, osteoporosis was detected in 40 patients at
the radius. This data indicates a trend wherein as CKD advances to later stages, there is a notable increase in the prevalence of osteoporosis and
osteopenia, with a particular emphasis on the lumbar spine. Hence, CKD represents a substantial ris Conclusion: k factor for osteoporosis,
independent of conventional risk factors. We emphasize the importance of early screening, identication, and management of osteoporosis to
alleviate the related morbidity and mortality within this specic patient cohort.
A COMPARATIVE STUDY ON BLOOD PRESSURE AND PULSE RATE USING CONVENTIONAL AND DIGITAL METHODS IN PHASE I MBBS STUDENTS
Authors: George Shiny , Elizabeth Joy Anu , A L Anagha , Binu Jeffy
Published: March 2024

Mercury sphygmomanometer has been used as the golden standard for measuring blood pressure and three nger method
for pulse rate. As digital devices are widely used these days for the estimation of the Blood pressure and pulse rate, this
study was carried out to nd out whether any signicant difference in the readings for the measurement of blood pressure and pulse rate using
digital method and conventional method. Study included 96 Phase I MBBS students. In conventional method blood pressure was recorded by
using mercury sphygmomanometer and radial pulse by three nger method. Blood pressure and Pulse rate were recorded simultaneously using
fully automatic /digital blood pressure monitor. The difference in systolic blood pressures by both methods was found to be signicant (p value
0.000). The difference in diastolic blood pressures by both methods was also found to be signicant (p value 0.001). The mean pulse rate by
conventional method and digital method was found to be signicant (p value 0.000). As there was signicant difference in systolic and diastolic
blood pressure between the mercuric sphygmomanometer and digital device , digital devices should be restricted to home monitoring or where
there is deciency of the trained personal to measure blood pressure. If it has to be used in the clinical setting for diagnostic or monitoring
purposes, it should be used with utmost caution.
POSTEROMEDIAL APPROACH FOR ONE MONTH OLD ISOLATED POSTEROMEDIAL TIBIAL COLUMN FRACTURE IN A 44 YEAR OLD MALE
Authors: Sangam Sagar , Khade Srikant , Kulkarni Abhishek
Published: March 2024

Stabilizing posterior column tibial plateau fractures is widely
acknowledged as crucial for maintaining a well-aligned joint and
reducing the risk of surgical failure. Insufcient reduction or
stabilization has been linked to increased surgical risks. The
signicance of the posterior column, particularly in cases of posterior
shear injuries, is increasingly acknowledged. Restoring the
mechanical axis of the knee is identied as the foremost prognostic
factor in treating tibial plateau fractures. While discussions often focus
on coronal alignment, restoring sagittal plane mechanical alignment is
equally vital. When dealing with anterior translation of the distal
fragment along with posterior sag of the femur and a posterior plateau
fragment, optimal treatment involves posterior reduction and buttress
plate xation. Hence, approaches facilitating access to these fragments
are essential for achieving proper joint alignment and stability.
INCREDIBLE EFFECT OF RASAYANA IN IMMUNITY
Authors: Priyanka Priyanka , Sharma Sachin
Published: March 2024

Ayurveda is a holistic science that emphasizes the maintenance and promotion of health through the prevention and
curing aspects. According to Sushruta Samhita, term swasthya is the equilibrium state of Dosha, Agni, Dhatu, and Mala.
Three doshas (vata, pitta, kapha), Agni (dhatvagni, bhutagni and jatharagni), Sapta Dhatu (Ras, rakta, mansa, meda, asthi, majja, shukra) and
Trimala are responsible for maintaining good health. Rasayan is one of the eight clinical specialties of Ayurveda. The Rasayan therapy improves
the qualities of Rasa and enriches it with nutrients so one can attain longevity, memory, intelligence, freedom from disorder, youthfulness,
excellence of luster, complexion, and voice. The word “Rasayana” means the way to attain excellent Rasadi Dhatus. In Ayurveda, one of the major
methods of presentation of positive health has been described i.e. Rasayana. This resistance power of the body, which prevents the development of
diseases, is called Immunity or Vyadhikshamatva. The ultimate aim of Rasayana therapy is to correct Dosha imbalance and improve Agni and
Dhatu function which overall improves strength and immunity. The application of Rasayan therapy comes in perspective of premature ageing and
death. Rasayanas are used for preventive, curative, and health-promotive purposes.
DENGUE FEVER PREVALENCE IN RURAL AREA OF MATHURA DISTRICT OF UTTAR PRADESH.
Authors: Singh Prachee , Rani Anju
Published: March 2024

Introduction-Among the 10 highest priority health issues presented, dengue was identied as one of the four main
infections threatening global health by World Health Organization in 2019. In India, Dengue control comes under the
purview of the NVBDCP, has been impeded by a lack of nancial and human resources, poor availability of point-of-care diagnostics, and
ineffective mosquito control methods. The dengue virus (DENV) is an RNA virus belonging to the genus avivirus, and there are four
serologically and genetically distinct serotypes, DENV 1–4. Infection can occur with any of the four serotypes, and multiple sequential
infections can also occur. This study was a hospital-based cross-sectional study conducted in the Mic Material &Methods- robiology
Laboratory of Tertiary Care Hospital . The detection of positive cases was done by estimation of nonstructural protein 1 (NS1) antigen and
immunoglobulin (Ig) M or IgG antibody. 31.8% samples were found positive for dengue fever. Female p Results- atients have registered more
(35.29%) positive share than that of male (29.20%) patients. Highest number of positive cases (45.07%) were registered in 19-30years age
bracket followed by 37.5% in age bracket of more than 60 years age . The lowest (16.32%) positive cases were found in children up to the age of
18 years . The platelets count below 25000 µl were recorded only in 2.63% of positive cases. 38.15% of positive cases have more than 200000 µl
platelets. IPD patients had more (38.32%) positive cases than OPD patients (16.67%). In the present Conclusion- study, only the primary
infection of Dengue fever was detected . There was no case found suffering with secondary infection . Dengue hemorrhagic fever (DHF) has not
been noticed in any case. The lowest positive cases were recorded in children. The positive cases have slightly more share in female patients than
male patients . The study revealed more positive cases in IPD patients than OPD patients.
KNOWLEDGE, ATTITUDE, AND SKILLS OF NURSES POSTED TO THE EMERGENCY DEPARTMENT AT A TERTIARY CARE TEACHING HOSPITAL IN UTTARAKHAND, INDIA: A CROSS-SECTIONAL STUDY
Authors: Joshi Dinesh Chandra , Singh Saini Ravinder , Kshiti Kshiti
Published: March 2024

Introduction: The Emergency Department (ED) is the point of rst contact for any critically ill patient, needing
immediate medical attention. The ability of the nurses to handle emergencies with care and condence can make a big
difference in a patient's comfort level. Aim: To study the knowledge, attitude, and skills of nurses posted to the Emergency Department towards
patient care at a tertiary care teaching hospital. Material And Methods: This cross-sectional study was conducted for one month at the
Emergency Department of Himalayan Hospital, Dehradun. All 53 nurses, posted to the emergency department were requested to participate in
this study. Data was collected with the help of a pre-tested structured questionnaire. The skill set of nurses was assessed with the help of an
observational checklist. Statistical analysis was performed using the data analysis tool in Microsoft Excel and Statistical Package for Social
Sciences version 23.0. Pearson coefcient of correlation (r-value) and p-value were calculated. The level of statistical signicance was set at 5%
(P < 0.05). Results: 33 (68.75%) nurses were females as compared to 15 (31.25%) in the male category. A maximum number of nurses 25 (52%)
were in the experience range of < 5 years. Age and experience showed a high positive r-value of 0.94 and a statistically signicant P-value of (<
0.001). Correlation between years of experience and knowledge revealed a positive correlation, with an r-value of 0.35 and a statistically
signicant p-value = 0.02 (< 0.05). It was observed that 66.5% of participants displayed a positive attitude, 10.8% were neutral and 22.7%
displayed a negative attitude. Overall 70.3% of participants had good nursing skills, and 60% of nurses displayed good behavioural and
communication skills. Conclusion: The study provides valuable insight into the Knowledge, attitude, and skills of nursing staff posted to the
emergency department. The study concluded that the knowledge, attitude, and skills of nursing staff posted in the emergency department were
satisfactory
STRATEGI FUNDRAISING DANA ZAKAT LEMBAGA AMIL ZAKAT
Authors: Irawan Feri
Published: March 2024

Indonesia merupakan salah satu negara yang memiliki jumlah penduduk paling banyak di dunia. Mayoritas penduduk Indonesia memeluk agama Islam. Hal tersebut dapat mendorong potensi seluruh kegiatan Islam dalam hal ibadah terutama zakat. Namun hal tersebut masih jauh dari total potensi zakat di Indonesia meski mengalami kenaikan setiap tahunnya. Penelitian ini bertujuan untuk mengetahui strategi menghimpun dana zakat yang dilakukan Lembaga Amil Zakat Al Manar Nusa Tenggara Timur (NTT). Jenis penelitian ini adalah kualitatif yaitu peneliti melihat sudut kualitas dari obyek penelitian ini. Adapun pendekatan dalam penelitian ini adalah pendekatan menghimpun dana. Pengumpulan data penelitian, peneliti telah melaksanakan wawancara, observasi maupun pengumpulan dokumentasi sebagai pelengkap dalam penelitian ini. Isi pokok pembahasan penelitian ini adalah strategi fundraising penghimpun dana zakat pada Lembaga Amil Zakat al Manar NTT dengan analisis SWOT (Strengths, Weaknesses, Opportunities, Threats). Hasil penelitian ini menunjukan bahwa strategi yang digunakan Lembaga Amil Zakat Al- Manar NTT dalam melakukan fundraising adalah melakukan edukasi zakat, membuat proposal, menyediakan konter zakat, dan melakukan digital fundraising. Berdasarkan analisis SWOT faktor kekuatan yang dimiliki yaitu Legalitas sebagai lembaga amil zakat provinsi, memiliki cabang yang cukup banyak, kompetensi amil dalam menghimpun dana, transparansi program dan laporan keuangan, dan memiliki platform donasi online. Sedangkan sisi kelemahannya yaitu program ciri khas lembaga, pengembangan SDM yang berkelanjutan, kestabilan capaian, sarana dan prasarana, monitoring kualitas kinerja di kantor layanan dan manajemen database muzakki. Berdasarkan analisis metrik SWOT strategi penghimpunan dana zakat di Lembaga Amil Zakat AL-Manar NTT berada pada titik koordinat (0,58 ; 0,60) pada kuadran I.
A Review of the Impact and Challenges of Hikmah Pedagogy in Teaching and Learning Environment
Authors: Aiyetoro Abdulkareem Idris , Ali Aliamat Omar , Petra Nur-Ashikin
Published: March 2024

Implementing an effective pedagogy that can enhance students' critical thinking, reasoning abilities, decision-making skills, and problem-solving capabilities is a key educational objective in the 21st century. This necessitates a shift from rote learning to interactive and meaningful learning experiences. Hikmah pedagogy, derived from Philosophy for Children (P4C), is an innovative approach that equips students with the essential skills for thinking, creativity, collaboration, communication, and caring, all while incorporating Islamic values through the Community of Inquiry (CoI) framework. Hikmah pedagogy adopts an infusion approach, fostering curiosity and promoting qualities such as critical thinking, open-mindedness, tolerance, and respect for others when exploring diverse perspectives. Research has demonstrated that the implementation of Hikmah pedagogy positively impacts teaching and learning across various subjects and educational levels. Hence, this review aims to assess the effects of Hikmah pedagogy on teaching and learning, as well as the challenges associated with its implementation. A total of 18 peer-reviewed articles were purposefully selected and analyzed using systematic content analysis (SLR). The findings indicate that Hikmah pedagogy enhances students' higher-order thinking skills (HOTs), critical thinking and reasoning abilities, open-mindedness, and communication skills. Additionally, the review highlights the importance of interactive stimuli, inquiry-based classroom management, and student engagement within the community of inquiry. Based on this evidence, educators are encouraged to incorporate Hikmah pedagogy as an effective teaching tool to cultivate students' thinking skills and moral development.
Using Remap-STAD Learning Assisted by The GitMind Application to Improve Students' Problem-Solving Skills in Biology Education
Authors: Agustina Rena Silvia , Zubaidah Siti , Susanto Hendra
Published: March 2024

Several studies have highlighted the essential nature of students' problem-solving skills. However, these studies have also revealed that students' problem-solving skills in Indonesia are currently lacking. Consequently, there is a need to empower and improve these skills. This research aims to investigate the impact of implementing the Remap-STAD learning model, with the assistance of the GitMind application, in order to enhance students' problem-solving abilities in the field of biology. The research design employed in this study is a pretest-posttest non-equivalent control group design. The research sample comprises 70 students from class X MIPA at SMAN 9 Malang, East Java. The experimental group was taught using the Remap-STAD learning model, supplemented with the GitMind application, while the control group was taught using the STAD learning model alone. Problem-solving skills were assessed through a description test comprising eight questions. Greenstein's (2012) scoring rubric, which encompasses eight indicators including problem identification, application of problem-solving steps, identification and evaluation of solutions, defense of solutions, real-world applications, inductive reasoning, and deductive reasoning, was employed for scoring. ANCOVA was utilized to analyze the data on problem-solving skills, with a significance level set at 5%. The Kolmogorov-Smirnov test was conducted to assess the normality of the data, while Levene's Test of Equality of Variances was used to evaluate homogeneity. The data analysis findings indicate significant treatment effects for the learning model (p < 0.05), thereby accepting the research hypothesis. The results demonstrate that the Remap-STAD learning model, combined with the GitMind application, can notably enhance students' problem-solving skills in the context of biology education. Consequently, the implementation of Remap-STAD, with assistance from the GitMind application, necessitates the development of instructional tools tailored to the chosen learning model and the specific skills being measured. This approach will promote and enhance learning activities, particularly in improving students' problem-solving capabilities.