Search results for: corrosion prediction ductile fracture

Authors: Rajkumar Ghosh

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Out-of-sequence thrusts (OOST) are reported at different geographic locations with various local names along Siwalik Himalaya (SH), Lesser Himalaya (LH), Higher Himalaya (HH) from Bhutan, India, Nepal, and Pakistan Himalayan range. Most of OOSTs have been identified within the upper LH, and the lower HH based on geochronological age jump across. These thrusts activated from Late Miocene to recent. The Chaura Thrust (CT) was deciphered from age jump of Apatite Fission Track (AFT) and considered as blind thrust base on variable exhumation rates in Chaura region, Satluj river valley, Himachal Pradesh. CT is located north of Jhakri Thrust (JhT) and is also differently identified as Sarahan thrust (ST). Structural documentation from the rocks near the OOST in Chaura was not so far done. Detail structural study of the Jeori Group of rocks was carried out in this study to understand the manifestation of the Chaura thrust and associated structures in meso- to micro-scale. Box fold, scar fold, kink fold, crenulation cleavages, and boudins are developed in the Chaura region. These structures usually do not indicate shear sense. When studied under an optical microscope, the Chaura samples reveal that the mica fish are usually lenticular with aspect ratio (R) varying from 6–11 and inclination angle (α) from 15–40°. According to ‘R’ and ‘α’, elongated sigmoid shaped mica fish and parallelogram shaped mica fish were also documented. Asymmetric mica fish demonstrate top-to-S/SW ductile shear, which is similar as that of Chaura thrust. Grain boundary migration (GBM) structures in quartzo-feldspathic grains from Jeori Group of rocks indicate deformation temperature ranging from 400 to 650°C. This can indicate that the OOST at Chaura, i.e., the Chaura Thrust, underwent thrusting in the ductile regime.

Keywords: out-of-sequence thrust, chaura thrust, sarahan thrust, jakhri thrust, higher himalaya, s/c- fabric

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Authors: Xiaodong Li, Peng Gao, Chao-Jung Huang, Shiying Hao, Xuefeng B. Ling, Yongxia Han, Yaqi Zhang, Le Zheng, Chengyin Ye, Modi Liu, Minjie Xia, Changlin Fu, Bo Jin, Karl G. Sylvester, Eric Widen

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Predicting the risk of Pancreatic Adenocarcinoma (PA) in advance can benefit the quality of care and potentially reduce population mortality and morbidity. The aim of this study was to develop and prospectively validate a risk prediction model to identify patients at risk of new incident PA as early as 3 months before the onset of PA in a statewide, general population in Maine. The PA prediction model was developed using Deep Neural Networks, a deep learning algorithm, with a 2-year electronic-health-record (EHR) cohort. Prospective results showed that our model identified 54.35% of all inpatient episodes of PA, and 91.20% of all PA that required subsequent chemoradiotherapy, with a lead-time of up to 3 months and a true alert of 67.62%. The risk assessment tool has attained an improved discriminative ability. It can be immediately deployed to the health system to provide automatic early warnings to adults at risk of PA. It has potential to identify personalized risk factors to facilitate customized PA interventions.

Keywords: cancer prediction, deep learning, electronic health records, pancreatic adenocarcinoma

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Authors: Mehmet Budak, Mehmet Fatih Ustundag

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Objectives: In this research, we studied the extent of exposure to physical violence and committing violence in patients diagnosed with schizophrenia in comparison to a control group consisting of patients with psychiatric diseases other than psychotic and mood disorders. Method: Between August 2019 and October 2019, a total of 100 hospitalized patients diagnosed with schizophrenia (clinically in remission, Brief Psychiatric Rate Scale < 30) were sequentially studied while undergoing inpatient treatment at Erenkoy Mental Health Training and Research Hospital. From the outpatient clinic, 50 patients with psychiatric disorders other than psychotic disorders or mood disorders were consecutively included as a control group. All participants were evaluated by the sociodemographic data that also questions the history of violence, physical examination, bilateral comparative hand, and forearm anterior-posterior and lateral radiography. Results: While 59% of patients with schizophrenia and 28% of the control group stated that they were exposed to physical violence at least once in a lifetime (p < 0,001); a defensive wound or fracture was detected in 29% of patients with schizophrenia and 2% of the control group (p < 0.001). On the other hand, 61% of patients diagnosed with schizophrenia, and 32% of the control group expressed that they committed physical violence at least once in a lifetime (p: 0.001). A self-destructive wound or fracture was detected in 53% of the patients with schizophrenia and 24% of the control group (p: 0,001). In the schizophrenia group, the rate of committing physical violence is higher in those with substance use compared to those without substance use (p:0.049). Also, wounds and bone fractures (boxer’s fracture) resulting from self-injury are more common in schizophrenia patients with substance use (p:0,002). In the schizophrenia group, defensive wounds and parry fractures (which are located in the hand, forearm, and arm usually occur as a result of a trial to shield the face against an aggressive attack and are known to be the indicators of interpersonal violence) are higher in those with substance use compared to those who do not (p:0,007). Conclusion: This study shows that exposure to physical violence and the rate of violence is higher in patients with schizophrenia compared to the control group. It is observed that schizophrenia patients who are stigmatized as being aggressive are more exposed to violence. Substance use in schizophrenia patients increases both exposure to physical violence and the use of physical violence. Physical examination and anamnesis that question violence are important tools to reveal the exposure to violence in patients. Furthermore, some specific bone fractures and wounds could be used to detect victimization even after plenty of time passes.

Keywords: fracture, physical violence, schizophrenia, substance use

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Authors: T. S. Sidhu

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Amongst the existing coatings methods, the cold spray is new upcoming process to deposit coatings. As from the name itself, the cold spray coating takes place at very low temperature as compare to other thermal spray coatings. In all other thermal spray coating process the partial melting of the coating powder particles takes place before deposition, but cold spray process takes place in solid state. In cold spray process, the bonding of coating power with substrate is not metallurgical as in other thermal spray processes. Due to supersonic speed and less temperature of spray particles, solid state, dense, and oxide free coatings are produced. Due to these characteristics, the cold spray coatings have been used to protect the materials against hot corrosion. In the present study, the cold spray process, cold spray fundaments, its types, and its applications for high temperatures are discussed in the light of presently available literature. In addition, the assessment of cold spray with the competitive technologies has been conferred with available literature.

Keywords: cold spray coating, hot corrosion, thermal spray coating, high-temperature materials

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Authors: Marine Segui, Ruxandra Mihaela Botez

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OpenVSP is an aerodynamic solver developed by National Aeronautics and Space Administration (NASA) that allows building a reliable model of an aircraft. This software performs an aerodynamic simulation according to the angle of attack of the aircraft makes between the incoming airstream, and its speed. A reliable aerodynamic model of the Cessna Citation X was designed but it required a lot of computation time. As a consequence, a prediction method was established that allowed predicting lift and drag coefficients for all Mach numbers and for all angles of attack, exclusively for stall conditions, from a computation of three angles of attack and only one Mach number. Aerodynamic coefficients given by the prediction method for a Cessna Citation X model were finally compared with aerodynamics coefficients obtained using a complete OpenVSP study.

Keywords: aerodynamic, coefficient, cruise, improving, longitudinal, openVSP, solver, time

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Authors: Anny Zambrano, German Gonzalez, Yair Quintero

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The continuum damage concept is used to study the interaction between hydraulic fractures and natural fractures, the objective is representing the path and relation among this two fractures types and predict its complex behavior without the need to pre-define their direction as occurs in other finite element applications, providing results more consistent with the physical behavior of the phenomenon. The approach uses finite element simulations through Abaqus software to model damage fracturing, the fracturing process by damage propagation in a rock. The modeling the phenomenon develops in two dimensional (2D) so that the fracture will be represented by a line and the crack front by a point. It considers nonlinear constitutive behavior, finite strain, time-dependent deformation, complex boundary conditions, strain hardening and softening, and strain based damage evolution in compression and tension. The complete governing equations are provided and the method is described in detail to permit readers to replicate all results. The model is compared to models that are published and available. Comparisons are focused in five interactions between natural fractures (NF) and hydraulic fractures: Fractured arrested at NF, crossing NF with or without offset, branching at intersecting NFs, branching at end of NF and NF dilation due to shear slippage. The most significant new finding is, that is not necessary to use pre-defined addresses propagation and stress condition can be evaluated as a dominant factor in the process. This is important because it can model in a more real way the generated complex hydraulic fractures, and be a valuable tool to predict potential problems and different geometries of the fracture network in the process of fracturing due to fluid injection.

Keywords: continuum damage, hydraulic fractures, natural fractures, complex fracture network, stiffness

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Authors: U. N. Bhatty, A. Bhatia, A. George, F. Fiaz

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Post-operatively, patients with the neck of femur fractures are the high-risk of venous thromboembolic events (VTE). NICE have issued guidelines in this regard. We investigated whether these guidelines were being followed. 124 patients undergoing neck of femur fracture surgery were retrospectively analysed at a major orthopaedic centre in England. 9% of patients received adequate anticoagulation (16.5% mortality). An education campaign subsequently took place, circular emails were sent to junior doctors and posters advertised. A reaudit 4 months later showed only 12% of the 68 patients received adequate anticoagulation (11.8% mortality). The education campaign failed to improve prescribing behaviours. Furthermore, as morbidity was not measured, the consequence of poor prescription is underestimated. Perhaps, poor prescribing is because of the silent nature of effective thromboprophylaxis; reducing its perceived effectiveness. Simple interventions are insufficient to change these habits and more intense work is needed; such as compulsory proformas for all high-risk patients.

Keywords: fracture, hip, orthopaedics, thromboembolism

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Authors: E. A. Mlybari, M. S. Elbisy, A. H. Alshahri, O. M. Albarakati

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Sea level rise threatens to increase the impact of future storms and hurricanes on coastal communities. Accurate sea level change prediction and supplement is an important task in determining constructions and human activities in coastal and oceanic areas. In this study, support vector machines (SVM) is proposed to predict daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal parameter values of kernel function are determined using a genetic algorithm. The SVM results are compared with the field data and with back propagation (BP). Among the models, the SVM is superior to BPNN and has better generalization performance.

Keywords: tides, prediction, support vector machines, genetic algorithm, back-propagation neural network, risk, hazards

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Authors: Nisrine Benzbiria, Mohammed Azzi, Mustapha Zertoubi

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2XXX series of aluminum alloys are widely employed in several applications, such as beverages, automotive, and aerospace industries. However, they are particularly prone to localized corrosion, such as pitting, often induced by a difference in corrosion potential measured for intermetallic phases and pure metal. The galvanic cells comprising Al–Cu– Mn–Fe intermetallic phases control cathodically the dissolution rate as oxygen reduction reaction kinetics are privileged on Al–Cu–Mn–Fe particles. Hence, understanding the properties of cathode sites and the processes involved must be carried out. Our interest is to outline the cathodic behavior of AA2024-T3 in sodium sulfate solution using electrochemical techniques. Oxygen reduction reaction (ORR) was investigated in the mixed charge transfer and mass transport regime using the Koutecky-Levich approach. An environmentally benign inhibitor was considered to slow the ORR on the Cu-rich cathodic phases. The surface morphology of the electrodes was investigated with SEM/EDS and AFM. The obtained results were discussed accordingly.

Keywords: AA2024-T3, neutral medium, ORR kinetics, Koutecky-Levich, DFT

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Authors: Hasan G. Elmazoghi, Aisha I. Alzayani, Lubna S. Bentaher

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Rainfall is a highly non-linear phenomena, which requires application of powerful supervised data mining techniques for its accurate prediction. In this study the Artificial Neural Network (ANN) technique is used to predict the mean monthly historical rainfall data collected from BENINA station in Benghazi for 31 years, the period of “1977-2006” and the results are compared against the observed values. The specific objective to achieve this goal was to determine the best combination of weather variables to be used as inputs for the ANN model. Several statistical parameters were calculated and an uncertainty analysis for the results is also presented. The best ANN model is then applied to the data of one year (2007) as a case study in order to evaluate the performance of the model. Simulation results reveal that application of ANN technique is promising and can provide reliable estimates of rainfall.

Keywords: neural networks, rainfall, prediction, climatic variables

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Authors: Ali Reza Sattarzadeh, Ronny J. Kutadinata, Pubudu N. Pathirana, Van Thanh Huynh

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Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction.

Keywords: deep learning algorithms, intelligent transportation systems, spatiotemporal features, traffic flow prediction

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Authors: Langhit Kurar, Loren Charles

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Introduction: In light of recent BOAST 12 (August 2016) published guidance on management of ankle fractures, the project aimed to highlight key discrepancies throughout the care trajectory from admission to point of discharge at a district general hospital. Wide breadth of data covering three key domains: accident and emergency, radiology, and orthopaedic surgery were subsequently stratified and recommendations on note documentation, and outpatient follow up were made. Methods: A retrospective twelve month audit was conducted reviewing results of ankle fracture management in 37 patients. Inclusion criterion involved all patients seen at Darent Valley Hospital (DVH) emergency department with radiographic evidence of an ankle fracture. Exclusion criterion involved all patients managed solely by nursing staff or having sustained purely ligamentous injury. Medical notes, including discharge summaries and the PACS online radiographic tool were used for data extraction. Results: Cross-examination of the A & E domain revealed limited awareness of the BOAST 12 recent publication including requirements to document skin integrity and neurovascular assessment. This had direct implications as this would have changed the surgical plan for acutely compromised patients. The majority of results obtained from the radiographic domain were satisfactory with appropriate X-rays taken in over 95% of cases. However, due to time pressures within A & E, patients were often left without a post manipulation XRAY in a backslab. Poorly reduced fractures were subsequently left for a long period resulting in swollen ankles and a time-dependent lag to surgical intervention. This had knocked on implications for prolonged inpatient stay resulting in hospital-acquired co-morbidity including pressure sores. Discussion: The audit has highlighted several areas of improvement throughout the disease trajectory from review in the emergency department to follow up as an outpatient. This has prompted the creation of an algorithm to ensure patients with significant fractures presenting to the emergency department are seen promptly and treatment expedited as per recent guidance. This includes timing for X-rays taken in A & E. Re-audit has shown significant improvement in both documentation at time of presentation and appropriate follow-up strategies. Within the orthopedic domain, we are in the process of creating an ankle fracture pathway to ensure imaging and weight bearing status are made clear to the consulting clinicians in an outpatient setting. Significance/Clinical Relevance: As a result of the ankle fracture algorithm we have adapted the BOAST 12 guidance to shape an intrinsic pathway to not only improve patient management within the emergency department but also create a standardised format for follow up.

Keywords: ankle, fracture, BOAST, radiology

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Authors: Joanna Maj, Awais Hussain, Lyndsey Vu, Catherine Roxas

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Background: Bone injuries in babies are common conditions that arise during delivery. Fractures of the clavicle, humerus, femur, and skull are the most common neonatal bone injuries sustained from labor and delivery. During operative deliveries, zealous tractions, ineffective delivery techniques, improper uterine incision, and inadequate relaxation of the uterus can lead to bone fractures in the newborn. Neonatal anatomy is unique. Just as children are not mini-adults, newborns are not mini children. A newborn’s anatomy and physiology are significantly different from a pediatric patient's. In this paper, we describe common orthopedic trauma in newborn babies. We provide a comprehensive overview of the different types of bone injuries in newborns. We hypothesize that the rate of bone fractures sustained at birth is higher in cases of operative deliveries. Methods: Relevant literature was selected by using the PubMed database. Search terms included orthopedic conditions in newborns, neonatal anatomy, and bone fractures in neonates during operative deliveries. Inclusion criteria included age, gender, race, type of bone injury and progression of bone injury. Exclusion criteria were limited in the medical history of cases reviewed and comorbidities. Results: This review finds that a clavicle fracture is the most common type of neonatal orthopedic injury sustained at birth in both operative and non-operative deliveries. We confirm the hypothesis that infants born via operative deliveries have a significantly higher rate of bone fractures than non-cesarean section deliveries. Conclusion: Newborn babies born via operative deliveries have a higher rate of bone fractures of the clavicle, humerus, and femur. A clavicle bone fracture in newborns is most common during emergency operative deliveries in new mothers. We conclude that infants born via an operative delivery sustained more bone injuries than infants born via non-cesarean section deliveries.

Keywords: clavicle fracture, humerus fracture, neonates, newborn orthopedics, orthopedic surgery, pediatrics, orthopedic trauma, orthopedic trauma during delivery, cesarean section, obstetrics, neonatal anatomy, neonatal fractures, operative deliveries, labor and delivery, bone injuries in neonates

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Authors: Hamza Nejib, Okba Taouali

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This paper presents two of the most knowing kernel adaptive filtering (KAF) approaches, the kernel least mean squares and the kernel recursive least squares, in order to predict a new output of nonlinear signal processing. Both of these methods implement a nonlinear transfer function using kernel methods in a particular space named reproducing kernel Hilbert space (RKHS) where the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. Then KAF is the developing filters in RKHS. We use two nonlinear signal processing problems, Mackey Glass chaotic time series prediction and nonlinear channel equalization to figure the performance of the approaches presented and finally to result which of them is the adapted one.

Keywords: online prediction, KAF, signal processing, RKHS, Kernel methods, KRLS, KLMS

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Authors: Surapol Raadnui

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This particular work describes an experimental investigation on gear wear in which wear and pitting were intentionally allowed to occur, namely, moisture corrosion pitting, acid-induced corrosion pitting, hard contaminant-related pitting and mechanical induced wear. A back to back spur gear test rig and a grease lubricated worm gear rig were used. The tests samples of wear debris were collected and assessed through the utilization of an optical microscope in order to correlate and compare the debris morphology to pitting and wear degradation of the worn gears. In addition, weight loss from all test gear pairs were assessed with utilization of statistical design of experiment. It can be deduced that wear debris characteristics from both cases exhibited a direct relationship with different pitting and wear modes. Thus, it should be possible to detect and diagnose gear pitting and wear utilization of worn surfaces, generated wear debris and quantitative measurement such as weight loss.

Keywords: predictive maintenance, worm gear, spur gear, wear debris analysis, problem diagnostic

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Authors: Masahiro Ohmura, Koh Kakusho, Takeshi Okadome

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This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models.

Keywords: stock market prediction, social moods, regression model, DJIA

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Authors: William Elnemer, Nauman Hussain, Samir Al-Ali, Henry Shu, Diane Ghanem, Babar Shafiq

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Introduction: The incidence of infection in open tibial shaft injuries varies depending on the severity of the injury, with rates ranging from 1.8% for Gustilo-Anderson type I to 42.9% for type IIIB fractures. The timely administration of antibiotics upon presentation to the emergency department (ED) is an essential component of fracture management, and evidence indicates that prompt delivery of antibiotics is associated with improved outcomes. The objective of this study is to identify factors that contribute to the expedient administration of antibiotics. Methods: This is a retrospective study of open tibial shaft fractures at an academic Level I trauma center. Current Procedural Terminology (CPT) codes identified all patients treated for open tibial shaft fractures between 2015 and 2021. Open fractures were identified by reviewing ED and provider notes, and with ballistic fractures were considered open. Chart reviews were performed to extract demographics, fracture characteristics, postoperative outcomes, time to operative room, time to antibiotic order, and delivery. Univariate statistical analysis compared patients who received early antibiotics (EA), which were delivered within one hour of ED presentation, and those who received late antibiotics (LA), which were delivered outside of one hour of ED presentation. A multivariate analysis was performed to investigate patient, fracture, and transport/ED characteristics contributing to faster delivery of antibiotics. The multivariate analysis included the dependent variables: ballistic fracture, activation of Delta Trauma, Gustilo-Andersen (Type III vs. Type I and II), AO-OTA Classification (Type C vs. Type A and B), arrival between 7 am and 11 pm, and arrival via Emergency Medical Services (EMS) or walk-in. Results: Seventy ED patients with open tibial shaft fractures were identified. Of these, 39 patients (55.7%) received EA, while 31 patients (44.3%) received LA. Univariate analysis shows that the arrival via EMS as opposed to walk-in (97.4% vs. 74.2%, respectively, p = 0.01) and activation of Delta Trauma (89.7% vs. 51.6%, respectively, p < 0.001) was significantly higher in the EA group vs. the LA group. Additionally, EA cases had significantly shorter intervals between the antibiotic order and delivery when compared to LA cases (0.02 hours vs. 0.35 hours, p = 0.007). No other significant differences were found in terms of postoperative outcomes or fracture characteristics. Multivariate analysis shows that a Delta Trauma Response, arrival via EMS, and presentation between 7 am and 11 pm were independent predictors of a shorter time to antibiotic administration (Odds Ratio = 11.9, 30.7, and 5.4, p = 0.001, 0.016, and 0.013, respectively). Discussion: Earlier antibiotic delivery is associated with arrival to the ED between 7 am and 11 pm, arrival via EMS, and a coordinated Delta Trauma activation. Our findings indicate that in cases where administering antibiotics is critical to achieving positive outcomes, it is advisable to employ a coordinated Delta Trauma response. Hospital personnel should be attentive to the rapid administration of antibiotics to patients with open fractures who arrive via walk-in or during late-night hours.

Keywords: antibiotics, emergency department, fracture management, open tibial shaft fractures, orthopaedic surgery, time to or, trauma fractures

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Authors: Petr Homola, Roman Růžek

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Laser beam micro-drilling (LBMD) is one of the most important non-contact machining processes of materials that are difficult to machine by means oeqf conventional machining methods used in various industries. The paper is focused on LBMD knock-down effect on Ti-6Al-4V (Grade 5) titanium alloy sheets properties. Two various process configurations were verified with a focus on laser damages in back-structure parts affected by the process. The effects of the LBMD on the material properties were assessed by means of tensile and fatigue tests and fracture surface analyses. Fatigue limit of LBMD configurations reached a significantly lower value between 15% and 30% of the static strength as compared to the reference raw material with 58% value. The farther back-structure configuration gives a two-fold fatigue life as compared to the closer LBMD configuration at a given stress applied.

Keywords: fatigue, fracture surface, laser beam micro-drilling, titanium alloy

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Authors: Isaac Mugume, Charles Basalirwa, Daniel Waiswa, Mary Nsabagwa, Triphonia Jacob Ngailo, Joachim Reuder, Sch¨attler Ulrich, Musa Semujju

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The Numerical weather prediction (NWP) models are considered powerful tools for guiding quantitative rainfall prediction. A couple of NWP models exist and are used at many operational weather prediction centers. This study considers two models namely the Consortium for Small–scale Modeling (COSMO) model and the Weather Research and Forecasting (WRF) model. It compares the models’ ability to predict rainfall over Uganda for the period 21st April 2013 to 10th May 2013 using the root mean square (RMSE) and the mean error (ME). In comparing the performance of the models, this study assesses their ability to predict light rainfall events and extreme rainfall events. All the experiments used the default parameterization configurations and with same horizontal resolution (7 Km). The results show that COSMO model had a tendency of largely predicting no rain which explained its under–prediction. The COSMO model (RMSE: 14.16; ME: -5.91) presented a significantly (p = 0.014) higher magnitude of error compared to the WRF model (RMSE: 11.86; ME: -1.09). However the COSMO model (RMSE: 3.85; ME: 1.39) performed significantly (p = 0.003) better than the WRF model (RMSE: 8.14; ME: 5.30) in simulating light rainfall events. All the models under–predicted extreme rainfall events with the COSMO model (RMSE: 43.63; ME: -39.58) presenting significantly higher error magnitudes than the WRF model (RMSE: 35.14; ME: -26.95). This study recommends additional diagnosis of the models’ treatment of deep convection over the tropics.

Keywords: comparative performance, the COSMO model, the WRF model, light rainfall events, extreme rainfall events

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Authors: Tahir Khaleeq, Patrick Lancaster, Keji Fakoya, Pedro Ferreira, Usman Ahmed

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Introduction: Virtual fracture clinics (VFC) have been shown to be a safe and cost-effective way of managing outpatient referrals to the orthopaedic department. During the coronavirus pandemic there has been a push to reduce unnecessary patient contact whilst maintaining patient safety. Materials and Methods: A protocol was developed by the clinical team in collaboration with Advanced Physiotherapy Practitioners (APP) on how to manage common musculoskeletal presentations to A&E prior to COVID as part of routine service development. Patients broadly triaged into 4 categories; discharge with advice, referral to VFC, referral to face to face clinic or discussion with on call team. The first 9 months of data were analysed to assess types of injury seen and outcomes. Results: In total 2489 patients were referred to VFC from internal and external sources. 734 patients were discharged without follow-up and 182 patients were discharged for physiotherapy review. Only 3 patients required admission. Regarding follow-ups, 431 patients had a virtual follow-up while 1036 of patients required further face to face follow up. 87 patients were triaged into subspecialty clinics. 37 patients were felt to have been referred inappropriately. Discussion: BOA guidelines suggest all patients need to be reviewed within 72 hours of their orthopaedic injury. Implementation of a VFC allows this target to be achieved and at the same time reduce patient contact. Almost half the patients were discharged following VFC review, the remaining patients were appropriately followed up. This is especially relevant in the current pandemic where reducing unnecessary trips to hospital will benefit the patient as well as make the most of the resources available.

Keywords: virtual fracture clinic, lockdown, trauma and orthopaedics, Covid- 19

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Authors: Jung-Ho Moon, Tae Kwon Ha

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M2 steels, the typical Co-free high speed steel (HSS) possessing hardness level of 63~65 HRc, are most widely used for cutting tools. On the other hand, Co-containing HSS’s, such as M35 and M42, show a higher hardness level of 65~67 HRc and used for high quality cutting tools. In the fabrication of HSS’s, it is very important to control cleanliness and eutectic carbide structure of the ingot and it is required to increase productivity at the same time. Production of HSS ingots includes a variety of processes such as casting, electro-slag remelting (ESR), forging, blooming, and wire rod rolling processes. In the present study, electro-slag rapid remelting (ESRR) process, an advanced ESR process combined by continuous casting, was successfully employed to fabricate HSS billets of M2, M35, and M42 steels. Distribution and structure of eutectic carbides of the billets were analysed and cleanliness, hardness, and composition profile of the billets were also evaluated.

Keywords: high speed tool steel, eutectic carbide, microstructure, hardness, fracture toughness

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Authors: H. Bhuyan, S. Mändl, M. Favre, M. Cisternas, A. Henriquez, E. Wyndham, M. Walczak, D. Manova

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The 470 Li-24 Cr and 460Li-21 Cr are two alloys belonging to the next generation of super-ferritic nickel free stainless steel grades, containing titanium (Ti), niobium (Nb) and small percentage of carbon (C) and nitrogen (N). The addition of Ti and Nb improves in general the corrosion resistance while the low interstitial content of C and N assures finer precipitates and greater ductility compared to conventional ferritic grades. These grades are considered an economic alternative to AISI 316L and 304 due to comparable or superior corrosion. However, since 316L and 304 can be nitrided to improve the mechanical surface properties like hardness and wear; it is hypothesize that the tribological properties of these super-ferritic stainless steels grades can also be improved by plasma nitriding. Thus two sets of plasma immersion ion implantation experiments have been carried out, one with a high pressure capacitively coupled radio frequency plasma at PUC Chile and the other using a low pressure microwave plasma at IOM Leipzig, in order to explore further improvements in the mechanical properties of 470 Li-24 Cr and 460Li-21 Cr steel. Nitrided and unnitrided substrates have been subsequently investigated using different surface characterization techniques including secondary ion mass spectroscopy, scanning electron microscopy, energy dispersive x-ray analysis, Vickers hardness, wear resistance, as well as corrosion test. In most of the characterizations no major differences have been observed for nitrided 470 Li-24 Cr and 460Li-21 Cr. Due to the ion bombardment, an increase in the surface roughness is observed for higher treatment temperature, independent of the steel types. The formation of chromium nitride compound takes place only at a treatment temperature around 4000C-4500C, or above. However, corrosion properties deteriorate after treatment at higher temperatures. The physical characterization results show up to 25 at.% of nitrogen for a diffusion zone of 4-6 m, and a 4-5 times increase in hardness for different experimental conditions. The samples implanted with temperature higher than 400 °C presented a wear resistance around two orders of magnitude higher than the untreated substrates. The hardness is apparently affected by the different roughness of the samples and their different profile of nitrogen.

Keywords: ion implantation, plasma, RF and microwave plasma, stainless steel

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Authors: Ravinu Garg, Naresh V. Datla

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We study the fracture mechanics of peeling of thin films perfectly bonded to a rigid substrate of any random surface topology using an analytical formulation. A generalized theoretical model has been developed to determine the peel strength of thin elastic films. It is demonstrated that an improvement in the peel strength can be achieved by modifying the surface characteristics of the rigid substrate. Characterization study has been performed to analyze the effect of different parameters on effective peel force from the rigid surface. Different surface profiles such as circular and sinusoidal has been considered to demonstrate the bonding characteristics of film-substrate interface. Condition for the instability in the debonding of the film is analyzed, where the localized self-debonding arises depending upon the film and surface characteristics. This study is towards improved adhesion strength of thin films to rigid substrate using different textured surfaces.

Keywords: debonding, fracture mechanics, peel test, thin film adhesion

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Authors: Aakansha Gupta, Neha Vadnere, Tapasvi Soni, M. Anbarsi

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Protein secondary structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry; it is highly important in medicine and biotechnology. Some aspects of protein functions and genome analysis can be predicted by secondary structure prediction. This is used to help annotate sequences, classify proteins, identify domains, and recognize functional motifs. In this paper, we represent protein secondary structure as a mathematical model. To extract and predict the protein secondary structure from the primary structure, we require a set of parameters. Any constants appearing in the model are specified by these parameters, which also provide a mechanism for efficient and accurate use of data. To estimate these model parameters there are many algorithms out of which the most popular one is the EM algorithm or called the Expectation Maximization Algorithm. These model parameters are estimated with the use of protein datasets like RS126 by using the Bayesian Probabilistic method (data set being categorical). This paper can then be extended into comparing the efficiency of EM algorithm to the other algorithms for estimating the model parameters, which will in turn lead to an efficient component for the Protein Secondary Structure Prediction. Further this paper provides a scope to use these parameters for predicting secondary structure of proteins using machine learning techniques like neural networks and fuzzy logic. The ultimate objective will be to obtain greater accuracy better than the previously achieved.

Keywords: model parameters, expectation maximization algorithm, protein secondary structure prediction, bioinformatics

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Authors: Japinder Wadhawan, Pradeep Rajan, Alok K. Saran, Navdeep S. Sidhu, Daanvir K. Dhir

Abstract:

Currently, research focus in the development of agricultural equipment and tractor parts in India is innovation and use of alternate materials like austempered ductile iron (ADI). Three-point linkage mechanism of the tractor is susceptible to unpredictable load conditions in the field, and one of the critical components vulnerable to failure is lift arm. Conventionally, lift arm is manufactured either by forging or casting (SG Iron) and main objective of the present work is to reduce the failure occurrences in the lift arm, which is achieved by changing the manufacturing material, i.e ADI, without changing existing design. Effect of four pertinent variables of manufacturing ADI, viz. austenitizing temperature, austenitizing time, austempering temperature, austempering time, was investigated using Taguchi method for design of experiments. To analyze the effect of parameters on the mechanical properties, mean average and signal-to-noise (S/N) ratio was calculated based on the design of experiments with L9 orthogonal array and the linear graph. The best combination for achieving the desired mechanical properties of lift arm is austenitization at 860°C for 90 minutes and austempering at 350°C for 60 minutes. Results showed that the developed component is having 925 MPA tensile strength, 7.8 per cent elongation and 120 joules toughness making it more suitable material for lift arm manufacturing. The confirmatory experiment has been performed and found a good agreement between predicted and experimental value. Also, the CAD model of the existing design was developed in computer aided design software, and structural loading calculations were performed by a commercial finite element analysis package. An optimized shape of the lift arm has also been proposed resulting in light weight and cheaper product than the existing design, which can withstand the same loading conditions effectively.

Keywords: austempered ductile iron, design of experiment, finite element analysis, lift arm

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Authors: S. H. Arnaud Kanga, O. Hili, S. Dabo-Niang

Abstract:

Spatial prediction is an issue appealing and attracting several fields such as agriculture, environmental sciences, ecology, econometrics, and many others. Although multiple non-parametric prediction methods exist for spatial data, those are based on the conditional expectation. This paper took a different approach by examining a non-parametric spatial predictor of the conditional quantile. The study especially observes the stationary multidimensional spatial process over a rectangular domain. Indeed, the proposed quantile is obtained by inverting the conditional distribution function. Furthermore, the proposed estimator of the conditional distribution function depends on three kernels, where one of them controls the distance between spatial locations, while the other two control the distance between observations. In addition, the almost complete convergence and the convergence in mean order q of the kernel predictor are obtained when the sample considered is alpha-mixing. Such approach of the prediction method gives the advantage of accuracy as it overcomes sensitivity to extreme and outliers values.

Keywords: conditional quantile, kernel, nonparametric, stationary

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Authors: Suzan Arslanturk, Mohammad-Reza Siadat, Theophilus Ogunyemi, Ananias Diokno

Abstract:

Missing data is a common challenge in statistical analyses of most clinical survey datasets. A variety of methods have been developed to enable analysis of survey data to deal with missing values. Imputation is the most commonly used among the above methods. However, in order to minimize the bias introduced due to imputation, one must choose the right imputation technique and apply it to the correct type of missing data. In this paper, we have identified different types of missing values: missing data due to skip pattern (SPMD), undetermined missing data (UMD), and genuine missing data (GMD) and applied rough set imputation on only the GMD portion of the missing data. We have used rough set imputation to evaluate the effect of such imputation on prediction by generating several simulation datasets based on an existing epidemiological dataset (MESA). To measure how well each dataset lends itself to the prediction model (logistic regression), we have used p-values from the Wald test. To evaluate the accuracy of the prediction, we have considered the width of 95% confidence interval for the probability of incontinence. Both imputed and non-imputed simulation datasets were fit to the prediction model, and they both turned out to be significant (p-value < 0.05). However, the Wald score shows a better fit for the imputed compared to non-imputed datasets (28.7 vs. 23.4). The average confidence interval width was decreased by 10.4% when the imputed dataset was used, meaning higher precision. The results show that using the rough set method for missing data imputation on GMD data improve the predictive capability of the logistic regression. Further studies are required to generalize this conclusion to other clinical survey datasets.

Keywords: rough set, imputation, clinical survey data simulation, genuine missing data, predictive index

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Authors: Jasvinder Singh, Manjinder Singh

Abstract:

Welding of dissimilar materials is a complicated process due to the difference in melting point of two materials. Thermal conductivity and coefficient of thermal expansion of dissimilar materials also different; therefore, residual stresses produced in the weldment and base metal are the most critical problem associated with the joining of dissimilar materials. Tensile strength and impact toughness also reduced due to the residual stresses. In the present research work, an attempt has been made to weld SS304 and MS1018 dissimilar materials by submerged arc welding (SAW). By conducting trail, runs most effective parameters welding current, Arc voltage, welding speed and nozzle to plate distance were selected to weld these materials. The fractional factorial technique was used to optimize the welding parameters. Effect on tensile strength (TS), fracture toughness (FT) and microhardness of weldment were studied. It was concluded that by optimizing welding current, voltage and welding speed the properties of weldment can be enhanced.

Keywords: SAW, Tensile Strength (TS), fracture toughness, micro hardness

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Authors: Zbigniew Szklarz, Aldona Garbacz-Klempka, Magdalena Bisztyga-Szklarz

Abstract:

Typical metallic alloys bases on one major alloying component, where the addition of other elements is intended to improve or modify certain properties, most of all the mechanical properties. However, in 1995 a new concept of metallic alloys was described and defined. High Entropy Alloys (HEA) contains at least five alloying elements in an amount from 5 to 20 at.%. A common feature this type of alloys is an absence of intermetallic phases, high homogeneity of the microstructure and unique chemical composition, what leads to obtaining materials with very high strength indicators, stable structures (also at high temperatures) and excellent corrosion resistance. Hence, HEA can be successfully used as a substitutes for typical metallic alloys in various applications where a sufficiently high properties are desirable. For fabricating HEA, a few ways are applied: 1/ from liquid phase i.e. casting (usually arc melting); 2/ from solid phase i.e. powder metallurgy (sintering methods preceded by mechanical synthesis) and 3/ from gas phase e.g. sputtering or 4/ other deposition methods like electrodeposition from liquids. Application of different production methods creates different microstructures of HEA, which can entail differences in their properties. The last two methods also allows to obtain coatings with HEA structures, hereinafter referred to as High Entropy Films (HEF). With reference to above, the crucial aim of this work was the optimization of the manufacturing process of the multi-component metallic layers (HEF) by the low- and high temperature electrochemical deposition ( ED). The low-temperature deposition process was crried out at ambient or elevated temperature (up to 100 ᵒC) in organic electrolyte. The high-temperature electrodeposition (several hundred Celcius degrees), in turn, allowed to form the HEF layer by electrochemical reduction of metals from molten salts. The basic chemical composition of the coatings was CoCrFeMnNi (known as Cantor’s alloy). However, it was modified by other, selected elements like Al or Cu. The optimization of the parameters that allow to obtain as far as it possible homogeneous and equimolar composition of HEF is the main result of presented studies. In order to analyse and compare the microstructure, SEM/EBSD, TEM and XRD techniques were employed. Morover, the determination of corrosion resistance of the CoCrFeMnNi(Cu or Al) layers in selected electrolytes (i.e. organic and non-organic liquids) was no less important than the above mentioned objectives.

Keywords: high entropy alloys, electrodeposition, corrosion behavior, microstructure

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Authors: Vinayaka Gude Divya Sampath

Abstract:

The research introduces an integrated prediction model to assess the susceptibility of roads in a future flooding event. The model consists of deep learning algorithm for forecasting gauge height data and Flood Inundation Mapper (FIM) for spatial flooding. An optimal architecture for Long short-term memory network (LSTM) was identified for the gauge located on Tangipahoa River at Robert, LA. Dropout was applied to the model to evaluate the uncertainty associated with the predictions. The estimates are then used along with FIM to identify the spatial flooding. Further geoprocessing in ArcGIS provides the susceptibility values for different roads. The model was validated based on the devastating flood of August 2016. The paper discusses the challenges for generalization the methodology for other locations and also for various types of flooding. The developed model can be used by the transportation department and other emergency response organizations for effective disaster management.

Keywords: deep learning, disaster management, flood prediction, urban flooding

Procedia PDF Downloads 134