Search results for: clinical prediction score

Authors: Sujata Chaturvedi, Ishita Pant, Deepak Kumar Jha, Vinod Kumar Singh Gautam, Chandra Bhushan Tripathi

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Objective: To describe clinical, histopathological and immunohistochemical profile of glioblastoma in patients and to correlate these findings with patient survival. Material and methods: 30 cases of histopathologically diagnosed glioblastomas were included in this study. These cases were analysed in detail for certain clinical and histopathological parameters. Immunohistochemical staining for p53, epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF), mouse double minute 2 homolog (MDM2) and Ki67 was done and scores were calculated. Results of these findings were correlated with patient survival. Results: A retrospective analysis of the histopathology records and clinical case files was done in 30 cases of glioblastoma (WHO grade IV). The mean age of presentation was 50.6 years with a male predilection. The most common involved site was the frontal lobe. Amongst the clinical parameters, age of the patient and extent of surgical resection showed a significant correlation with the patient survival. Histopathological parameters showed no significant correlation with the patient survival, while amongst the immunohistochemical parameters expression of MDM2 showed a significant correlation with the patient survival. Conclusion: In this study incorporating clinical, histopathological and basic panel of immunohistochemistry, age of the patient, extent of the surgical resection and expression of MDM2 showed significant correlation with the patient survival.

Keywords: glioblastoma, p53, EGFR, VEGF, MDM2, Ki67

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Authors: Vadim A. Kuznetsov, Anna M. Soldatova, Tatyana N. Enina, Elena A. Gorbatenko, Dmitrii V. Krinochkin

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The aim of the study was to evaluate potential parameters related with super-response to CRT. Methods: 60 CRT patients (mean age 54.3 ± 9.8 years; 80% men) with congestive heart failure (CHF) II-IV NYHA functional class, left ventricular ejection fraction < 35% were enrolled. At baseline, 1 month, 3 months and each 6 months after implantation clinical, electrocardiographic and echocardiographic parameters, NT-proBNP level were evaluated. According to the best decrease of left ventricular end-systolic volume (LVESV) (mean follow-up period 33.7 ± 15.1 months) patients were classified as super-responders (SR) (n=28; reduction in LVESV ≥ 30%) and non-SR (n=32; reduction in LVESV < 30%). Results: At baseline groups differed in age (58.1 ± 5.8 years in SR vs 50.8 ± 11.4 years in non-SR; p=0.003), gender (female gender 32.1% vs 9.4% respectively; p=0.028), width of QRS complex (157.6 ± 40.6 ms in SR vs 137.6 ± 33.9 ms in non-SR; p=0.044). Percentage of LBBB was equal between groups (75% in SR vs 59.4% in non-SR; p=0.274). All parameters of mechanical dyssynchrony were higher in SR, but only difference in left ventricular pre-ejection period (LVPEP) was statistically significant (153.0 ± 35.9 ms vs. 129.3 ± 28.7 ms p=0.032). NT-proBNP level was lower in SR (1581 ± 1369 pg/ml vs 3024 ± 2431 pg/ml; p=0.006). The survival rates were 100% in SR and 90.6% in non-SR (log-rank test P=0.002). Multiple logistic regression analysis showed that LVPEP (HR 1.024; 95% CI 1.004–1.044; P = 0.017), baseline NT-proBNP level (HR 0.628; 95% CI 0.414–0.953; P=0.029) and age at baseline (HR 1.094; 95% CI 1.009-1.168; P=0.30) were independent predictors for CRT super-response. ROC curve analysis demonstrated sensitivity 71.9% and specificity 82.1% (AUC=0.827; p < 0.001) of this model in prediction of super-response to CRT. Conclusion: Super-response to CRT is associated with better survival in long-term period. Presence of LBBB was not associated with super-response. LVPEP, NT-proBNP level, and age at baseline can be used as independent predictors of CRT super-response.

Keywords: cardiac resynchronisation therapy, superresponse, congestive heart failure, left bundle branch block

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Authors: Nataša Kalajdžija, Strahinja Kovačević, Davor Lončar, Sanja Podunavac Kuzmanović, Lidija Jevrić

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In order to investigate the relationship between retention and structure, a quantitative Structure Retention Relationships (QSRRs) study was applied for the prediction of retention times of a set of 23 secoestrane derivatives in a reversed-phase thin-layer chromatography. After the calculation of molecular descriptors, a suitable set of molecular descriptors was selected by using step-wise multiple linear regressions. Artificial Neural Network (ANN) method was employed to model the nonlinear structure-activity relationships. The ANN technique resulted in 5-6-1 ANN model with the correlation coefficient of 0.98. We found that the following descriptors: Critical pressure, total energy, protease inhibition, distribution coefficient (LogD) and parameter of lipophilicity (miLogP) have a significant effect on the retention times. The prediction results are in very good agreement with the experimental ones. This approach provided a new and effective method for predicting the chromatographic retention index for the secoestrane derivatives investigated.

Keywords: lipophilicity, QSRR, RP TLC retention, secoestranes

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Authors: S. Moustafa El Shanawany, I. Labib Salem, F. Mohamed Magdy Badr El Dine, H. Tag El Deen Abd Allah

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Thermal injuries remain a global health problem and a common issue encountered in forensic pathology. They are a devastating cause of morbidity and mortality in children and adults especially in developing countries, causing permanent disfigurement, scarring and grievous hurt. Burns have always been a matter of legal concern in cases of suicidal burns, self-inflicted burns for false accusation and homicidal attempts. Assessment of burn injuries as well as rating permanent disabilities and disfigurement following thermal injuries for the benefit of compensation claims represents a challenging problem. This necessitates the development of reliable scoring systems to yield an expected likelihood of permanent disability or fatal outcome following burn injuries. The study was designed to identify the risk factors of mortality in acute burn patients and to evaluate the applicability of FLAMES (Fatality by Longevity, APACHE II score, Measured Extent of burn, and Sex) and BOBI (Belgian Outcome in Burn Injury) model scores in predicting the outcome. The study was conducted on 100 adult patients with acute burn injuries admitted to the Burn Unit of Alexandria Main University Hospital, Egypt from October 2014 to October 2015. Victims were examined after obtaining informed consent and the data were collected in specially designed sheets including demographic data, burn details and any associated inhalation injury. Each burn patient was assessed using both BOBI and FLAMES scoring systems. The results of the study show the mean age of patients was 35.54±12.32 years. Males outnumbered females (55% and 45%, respectively). Most patients were accidently burnt (95%), whereas suicidal burns accounted for the remaining 5%. Flame burn was recorded in 82% of cases. As well, 8% of patients sustained more than 60% of total burn surface area (TBSA) burns, 19% of patients needed mechanical ventilation, and 19% of burnt patients died either from wound sepsis, multi-organ failure or pulmonary embolism. The mean length of hospital stay was 24.91±25.08 days. The mean BOBI score was 1.07±1.27 and that of the FLAMES score was -4.76±2.92. The FLAMES score demonstrated an area under the receiver operating characteristic (ROC) curve of 0.95 which was significantly higher than that of the BOBI score (0.883). A statistically significant association was revealed between both predictive models and the outcome. The study concluded that both scoring systems were beneficial in predicting mortality in acutely burnt patients. However, the FLAMES score could be applied with a higher level of accuracy.

Keywords: BOBI, burns, FLAMES, scoring systems, outcome

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Authors: A. Senthil Kumar, V. Murali Bhaskaran

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In the information technology ground, people are using various tools and software for their official use and personal reasons. Nowadays, people are worrying to choose data accessing and extraction tools at the time of buying and selling their products. In addition, worry about various quality factors such as price, durability, color, size, and availability of the product. The main purpose of the research study is to find solutions to these unsolved existing problems. The proposed algorithm is a Multidirectional Rank Prediction (MDRP) decision making algorithm in order to take an effective strategic decision at all the levels of data extraction, uses a real time textile dataset and analyzes the results. Finally, the results are obtained and compared with the existing measurement methods such as PCC, SLCF, and VSS. The result accuracy is higher than the existing rank prediction methods.

Keywords: Knowledge Discovery in Database (KDD), Multidirectional Rank Prediction (MDRP), Pearson’s Correlation Coefficient (PCC), VSS (Vector Space Similarity)

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Authors: Vasiliki Markantoni, Eftychia Platsidaki, Georgios Chaidemenos, Georgios Kontochristopoulos

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Introduction: Bromhidrosis, also known as osmidrosis, is a common distressing condition with a significant negative effect on patient’s quality of life. Its etiology is multifactorial. It usually affects axilla, genital skin, breasts and soles, areas where apocrine glands are mostly distributed. Therapeutic treatments include topical antibacterial agents, antiperspirants and neuromuscular blocker agents-toxins. In this study, we aimed to evaluate the efficacy and possible complications of topical glycopyrrolate, an anticholinergic agent, for treatment of bromhidrosis. Glycopyrrolate, applied topically as a cream, solution or spray at concentrations between 0,5% and 4%, has been successfully used to treat different forms of focal hyperhidrosis. Materials and Methods: Twenty patients, six males and fourteen females, meeting the criteria for bromhidrosis were treated with topical glycopyrrolate for two months. The average age was 36. Eleven patients had bromhidrosis located to the axillae, four to the soles, four to both axillae and soles and one to the genital folds. Glycopyrrolate was applied topically as a cream at concentration 2%, formulated in Fitalite. During the first month, patients were using the cream every night and thereafter twice daily. The degree of malodor was assessed subjectively by patients and scaled averagely as ‘none’, ‘mild’, ‘moderate’, and ‘severe’ with corresponding scores of 0, 1, 2, and 3, respectively. The modified Dermatology Life Quality Index (DLQI) was used to assess the quality of life. The clinical efficacy was graded by the patient scale of excellent, good, fair and poor. In the end, patients were given the power to evaluate whether they were totally satisfied with, partially satisfied or unsatisfied and possible side effects during the treatment were recorded. Results: All patients were satisfied at the end of the treatment. No patient defined the response as no improvement. The subjectively assessed score level of bromhidrosis was remarkably improved after the first month of treatment and improved slightly more after the second month. DLQI score was also improved to all patients. Adverse effects were reported in 2 patients. In the first case, topical irritation was reported. This was classed as mild (erythema and desquamation), appeared during the second month of treatment and was treated with low-potency topical corticosteroids. In the second case, mydriasis was reported, that recovered without specific treatment, as soon as we insisted to the importance of careful hygiene after cream application so as not to contaminate the periocular skin or ocular surface. Conclusions: Dermatologists often encounter patients with bromhidrosis, therefore should be aware of treatment options. To the best of our knowledge, this is the first study to evaluate the use of topical glycopyrrolate as a therapeutic approach for bromhidrosis. Our findings suggest that topical glycopyrrolate has an excellent safety profile and demonstrate encouraging results for the management of this distressful condition.

Keywords: Bromhidrosis, glycopyrrolate, topical treatment, osmidrosis

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Authors: Henok Hailemariam, Frank Wuttke

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Collapsible soils are weak soils that appear to be stable in their natural state, normally dry condition, but rapidly deform under saturation (wetting), thus generating large and unexpected settlements which often yield disastrous consequences for structures unwittingly built on such deposits. In this study, a prediction model for the relative subsidence of stressed collapsible soils based on dielectric permittivity measurement is presented. Unlike most existing methods for soil subsidence prediction, this model does not require moisture content as an input parameter, thus providing the opportunity to obtain accurate estimation of the relative subsidence of collapsible soils using dielectric measurement only. The prediction model is developed based on an existing relative subsidence prediction model (which is dependent on soil moisture condition) and an advanced theoretical frequency and temperature-dependent electromagnetic mixing equation (which effectively removes the moisture content dependence of the original relative subsidence prediction model). For large scale sub-surface soil exploration purposes, the spatial sub-surface soil dielectric data over wide areas and high depths of weak (collapsible) soil deposits can be obtained using non-destructive high frequency electromagnetic (HF-EM) measurement techniques such as ground penetrating radar (GPR). For laboratory or small scale in-situ measurements, techniques such as an open-ended coaxial line with widely applicable time domain reflectometry (TDR) or vector network analysers (VNAs) are usually employed to obtain the soil dielectric data. By using soil dielectric data obtained from small or large scale non-destructive HF-EM investigations, the new model can effectively predict the relative subsidence of weak soils without the need to extract samples for moisture content measurement. Some of the resulting benefits are the preservation of the undisturbed nature of the soil as well as a reduction in the investigation costs and analysis time in the identification of weak (problematic) soils. The accuracy of prediction of the presented model is assessed by conducting relative subsidence tests on a collapsible soil at various initial soil conditions and a good match between the model prediction and experimental results is obtained.

Keywords: collapsible soil, dielectric permittivity, moisture content, relative subsidence

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Authors: Ho Sy Nam, Tang Ha Nam Anh

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Background: Rotator cuff tears are a common problem in the aging population. The prevalence of massive rotator cuff tears varies in some studies from 10% to 40%. Of irreparable rotator cuff tears (IRCTs), which are mostly associated with massive tear size, 79% are estimated to have recurrent tears after surgical repair. Recent studies have shown that superior capsule reconstruction (SCR) in massive rotator cuff tears can be an efficient technique with optimistic clinical scores and preservation of stable glenohumeral stability. Superior capsule reconstruction techniques most commonly use either fascia lata autograft or dermal allograft, both of which have their own benefits and drawbacks (such as the potential for donor site issues, allergic reactions, and high cost). We propose a simple technique for superior capsule reconstruction that involves using the long head of the biceps tendon as a local autograft; therefore, the comorbidities related to graft harvesting are eliminated. The long head of the biceps tendon proximal portion is relocated to the footprint and secured as the SCR, serving to both stabilize the glenohumeral joint and maintain vascular supply to aid healing. Objective: The purpose of this study is to assess the clinical outcomes of patients with large to massive RCTs treated by SCR using LHBT. Materials and methods: A study was performed of consecutive patients with large to massive RCTs who were treated by SCR using LHBT between January 2022 and December 2022. We use one double-loaded suture anchor to secure the long head of the biceps to the middle of the footprint. Two more anchors are used to repair the rotator cuff using a single-row technique, which is placed anteriorly and posteriorly on the lateral side of the previously transposed LHBT. Results: The 3 men and 5 women had an average age of 61.25 years (range 48 to 76 years) at the time of surgery. The average follow-up was 8.2 months (6 to 10 months) after surgery. The average preoperative ASES was 45.8, and the average postoperative ASES was 85.83. The average postoperative UCLA score was 29.12. VAS score was improved from 5.9 to 1.12. The mean preoperative ROM of forward flexion and external rotation of the shoulder was 720 ± 160 and 280 ± 80, respectively. The mean postoperative ROM of forward flexion and external rotation were 1310 ± 220 and 630 ± 60, respectively. There were no cases of progression of osteoarthritis or rotator cuff muscle atrophy. Conclusion: SCR using LHBT is considered a treatment option for patients with large or massive RC tears. It can restore superior glenohumeral stability and function of the shoulder joint and can be an effective procedure for selected patients, helping to avoid progression to cuff tear arthropathy.

Keywords: superior capsule reconstruction, large or massive rotator cuff tears, the long head of the biceps, stabilize the glenohumeral joint

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Authors: Emel Bayraktar, Gulengun Turk

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Introduction and Objective: Safe administration of medicines is one of the main responsibilities of nurses. Intramuscular drug administration is among the most common methods used by nurses among all drug applications. This study was carried out in order to determine determine the effect of education given on injection in ventrogluteal area on the level of knowledge of nurses on this subject. Methods: The sample of the study consisted of 20 nurses who agreed to participate in the study between 01 October and 31 December 2019. The research is a pretest-posttest comparative, quasi-experimental type pilot study. The nurses were given a 4-hour training prepared on injection into the ventrogluteal area. The training consisted of two hours of theoretical and two hours of laboratory practice. Before the training and 4 weeks after the training, a questionnaire form containing questions about their knowledge and practices regarding the injection of the ventrogluteal region was applied to the nurses. Results: The average age of the nurses is 26.55 ± 7.60, 35% (n = 7) of them are undergraduate and 30% (n = 6) of them work in intensive care units. Before the training, 35% (n = 7) of the nurses stated that the most frequently used intramuscular injection site was the ventrogluteal area, and 75% (n = 15) stated that the safest area was the rectus femoris muscle. After the training, 55% (n = 11) of the nurses stated that they most frequently used the ventrogluteal area and 100% (n = 20) of them stated that the ventrogluteal area was the safest area. The average score the nurses got from the premises before the training is 14.15 ± 6.63 (min = 0, max = 20), the total score is 184. The average score obtained after the training was determined as 18.69 ± 2.35 (min = 12, max = 20), and the total score was 243. Conclusion: As a result of the research, it was determined that the training given on the injection of ventrogluteal area increased the knowledge level of the nurses. It is recommended to organize in-service trainings for all nurses on the injection of ventrogluteal area.

Keywords: safe injection, knowledge level, nurse, intramuscular injection, ventrogluteal area

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Authors: Yuwaratu Syafira, Wahyu K. Y. Putra, Kusharisupeni Djokosujono

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Background/Objective: Body Mass Index (BMI) serves various purposes, including measuring the prevalence of obesity in a population, and also in formulating a patient’s diet at a hospital, and can be calculated with the equation = body weight (kg)/body height (m)². However, the BMI of an individual with difficulties in carrying their weight or standing up straight can not necessarily be measured. The aim of this study was to form a prediction model for the BMI of young adult students of Public Health Faculty of University of Indonesia. Subject/Method: This study used a cross sectional design, with a total sample of 132 respondents, consisted of 58 males and 74 females aged 21- 30. The dependent variable of this study was BMI, and the independent variables consisted of sex and anthropometric measurements, which included ulna length, arm length, tibia length, knee height, mid-upper arm circumference, and calf circumference. Anthropometric information was measured and recorded in a single sitting. Simple and multiple linear regression analysis were used to create the prediction equation for BMI. Results: The male respondents had an average BMI of 24.63 kg/m² and the female respondents had an average of 22.52 kg/m². A total of 17 variables were analysed for its correlation with BMI. Bivariate analysis showed the variable with the strongest correlation with BMI was Mid-Upper Arm Circumference/√Ulna Length (MUAC/√UL) (r = 0.926 for males and r = 0.886 for females). Furthermore, MUAC alone also has a very strong correlation with BMI (r = 0,913 for males and r = 0,877 for females). Prediction models formed from either MUAC/√UL or MUAC alone both produce highly accurate predictions of BMI. However, measuring MUAC/√UL is considered inconvenient, which may cause difficulties when applied on the field. Conclusion: The prediction model considered most ideal to estimate BMI is: Male BMI (kg/m²) = 1.109(MUAC (cm)) – 9.202 and Female BMI (kg/m²) = 0.236 + 0.825(MUAC (cm)), based on its high accuracy levels and the convenience of measuring MUAC on the field.

Keywords: body mass index, mid-upper arm circumference, prediction model, ulna length

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Authors: Ejaz Ahmed, Huang Yong

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The operation of aero engines has a critical importance in the vicinity of lean blowout (LBO) limits. Lefebvre’s model of LBO based on empirical correlation has been extended to flame volume concept by the authors. The flame volume takes into account the effects of geometric configuration, the complex spatial interaction of mixing, turbulence, heat transfer and combustion processes inside the gas turbine combustion chamber. For these reasons, flame volume based LBO predictions are more accurate. Although LBO prediction accuracy has improved, it poses a challenge associated with Vf estimation in real gas turbine combustors. This work extends the approach of flame volume prediction previously based on fuel iterative approximation with cold flow simulations to reactive flow simulations. Flame volume for 11 combustor configurations has been simulated and validated against experimental data. To make prediction methodology robust as required in the preliminary design stage, reactive flow simulations were carried out with the combination of probability density function (PDF) and discrete phase model (DPM) in FLUENT 15.0. The criterion for flame identification was defined. Two important parameters i.e. critical injection diameter (Dp,crit) and critical temperature (Tcrit) were identified, and their influence on reactive flow simulation was studied for Vf estimation. Obtained results exhibit ±15% error in Vf estimation with experimental data.

Keywords: CFD, combustion, gas turbine combustor, lean blowout

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Authors: Yuban Sebastian Cuartas-Agudelo, Sandra Marcela López-Hincapié, Vivianna Alexandra Garrido-Altamar, María de los Ángeles Rodríguez-Gázquez, Camilo Ruiz-Mejía, Lina María Martínez-Sánchez, Gloria Inés Martínez-Domínguez, Luis Eduardo Contreras, Felipe Eduardo Marino-Isaza

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Introduction: Doping is a prohibited practice in competitive sports with potential adverse effects; therefore, it is crucial to describe the attitudes of athletes towards this behavior and to determine which o these increase the susceptibility to carry out this practice. Objective: To determine the attitude of high-performance athletes towards doping in a sports institute in the city of Medellin, Colombia. Methods: We performed a cross-sectional study during 2016, with a sample taken to convenience consisting of athletes over 18 years old enrolled in a sports institute of the city of Medellin (Colombia). The athletes filled by themselves the Petroczi and Aidman questionnaire: Performance Enhancement Attitude Scale (PEAS) adapted to the Spanish language by Morente-Sánchez et al. This scale has 17 items with likert answer options, with a score ranging from 1 to 6, with a higher score indicating a stronger tendency towards doping practices. Results: 112 athletes were included with an average age of 21.6 years old, a 60% of them were male and the most frequent sports were karate 17%, judo 12.5% and athletics 9.8%. The average score of the questionnaire was 35.5 points of a 102 possible points. The lowest score was obtained in the following items: Is Doping necessary 1,4 and Doping isn’t cheating, everyone does it 1,5. Conclusion: In our population, there is a low tendency towards doping practices.

Keywords: sports, doping in sports, athletic performance, attitude

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Authors: Aasheesh Raturi, Vimal Kothiyal, P. D. Semalty

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We studied mechanical properties of Eucalyptus tereticornis using FT-NIR spectroscopy. Firstly, spectra were pre-processed to eliminate useless information. Then, prediction model was constructed by partial least squares regression. To study the influence of pre-processing on prediction of mechanical properties for NIR analysis of wood samples, we applied various pretreatment methods like straight line subtraction, constant offset elimination, vector-normalization, min-max normalization, multiple scattering. Correction, first derivative, second derivatives and their combination with other treatment such as First derivative + straight line subtraction, First derivative+ vector normalization and First derivative+ multiplicative scattering correction. The data processing methods in combination of preprocessing with different NIR regions, RMSECV, RMSEP and optimum factors/rank were obtained by optimization process of model development. More than 350 combinations were obtained during optimization process. More than one pre-processing method gave good calibration/cross-validation and prediction/test models, but only the best calibration/cross-validation and prediction/test models are reported here. The results show that one can safely use NIR region between 4000 to 7500 cm-1 with straight line subtraction, constant offset elimination, first derivative and second derivative preprocessing method which were found to be most appropriate for models development.

Keywords: FT-NIR, mechanical properties, pre-processing, PLS

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Authors: Keerthie Dissanayake, Chandrika Jayasinghe, Priyani Wanigasekara, Jayampathy Dissanayake, Ajith Sominanda

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The routine use of Disease Activity Score-28 (DAS28) to assess the disease activity in rheumatoid arthritis (RA) is limited due to its dependency on laboratory investigations and the complex calculations involved. In contrast, the clinical disease activity index (CDAI) is simple to calculate, which makes the "treat to target" strategy for the management of RA more practical. We aimed to assess the validity of CDAI compared to DAS28 in RA patients in Sri Lanka. A total of 103 newly diagnosed RA patients were recruited, and their disease activity was calculated using DAS 28 and CDAI during the first visit to the clinic (0 months) and re-assessed at 4 and 9 months of the follow-up visits. The validity of the CDAI, compared to DAS 28, was evaluated. Patients had a female preponderance (6:1) and a short symptom duration (mean = 6.33 months). The construct validity of CDAI, as assessed by Cronbach's α test, was 0.868. Convergent validity was assessed by correlation and Kappa statistics. Strong positive correlations were observed between CDAI and DAS 28 at the baseline (0 months), 4, and 9 months of evaluation (Spearman's r = 0.9357, 0.9354, 0.9106, respectively). Moderate-good inter-rater agreements between the DAS-28 and CDAI were observed (Weighted kappa of 0.660, 0.519, and 0.741 at 0, 4, and 9 months respectively). Discriminant validity, as assessed by ROC curves at 0, 4th, and 9th months of the evaluation, showed the area under the curve (AUC) of 0.958, 0.985, and 0.914, respectively. The suggested cut-off points for different CDAI disease activity categories according to ROC curves were ≤ 2 (Remission), >2 to ≤ 5 (low), >5 to ≤ 18 (moderate), > 18 (high). These findings indicate that the CDAI has good concordance with DAS 28 in assessing the disease activity in RA patients in this study sample.

Keywords: rheumatoid arthritis, CDAI, disease activity, Sri Lanka, validation

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Authors: Tomas Vampola, Michael Valášek

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On the basis of simulation-generated failure states of structural elements of a turboprop engine suitable for the busy-jet class of aircraft, an algorithm for early prediction of damage or reduction in functionality of structural elements of the engine is designed and verified with real data obtained at dynamometric testing facilities of aircraft engines. Based on an expanding database of experimentally determined data from temperature and pressure sensors during the operation of turboprop engines, this strategy is constantly modified with the aim of using the minimum number of sensors to detect an inadmissible or deteriorated operating mode of specific structural elements of an aircraft engine. The assembled algorithm for the early prediction of reduced functionality of the aircraft engine significantly contributes to the safety of air traffic and to a large extent, contributes to the economy of operation with positive effects on the reduction of the energy demand of operation and the elimination of adverse effects on the environment.

Keywords: detectability of malfunction, dynamometric testing, prediction of damage, turboprop engine

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Authors: Kefaya Qaddoum

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Most of greenhouse growers desire a determined amount of yields in order to accurately meet market requirements. The purpose of this paper is to model a simple but often satisfactory supervised classification method. The original naive Bayes have a serious weakness, which is producing redundant predictors. In this paper, utilized regularization technique was used to obtain a computationally efficient classifier based on naive Bayes. The suggested construction, utilized L1-penalty, is capable of clearing redundant predictors, where a modification of the LARS algorithm is devised to solve this problem, making this method applicable to a wide range of data. In the experimental section, a study conducted to examine the effect of redundant and irrelevant predictors, and test the method on WSG data set for tomato yields, where there are many more predictors than data, and the urge need to predict weekly yield is the goal of this approach. Finally, the modified approach is compared with several naive Bayes variants and other classification algorithms (SVM and kNN), and is shown to be fairly good.

Keywords: tomato yield prediction, naive Bayes, redundancy, WSG

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Authors: Zhao Jiashen, Serene Goh, Jerry Goo, Anthony Li, Lim Woan Wui, Paul Drakeford, Chen Qing Yan

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Introduction: Emergency laparotomy (EL) is one of the most common surgeries done in Singapore to treat acute abdominal pathologies. A significant proportion of these surgeries are performed in the geriatric population (65 years and older), who tend to have the highest postoperative morbidity, mortality, and highest utilization of intensive care resources. Frailty, the state of vulnerability to adverse outcomes from an accumulation of physiological deficits, has been shown to be associated with poorer outcomes after surgery and remains a strong driver of healthcare utilization and costs. To date, there is little understanding of the impact it has on emergency laparotomy outcomes. The objective of this study is to examine the impact of frailty on postoperative morbidity, mortality, and length of stay after EL. Methods: A retrospective study was conducted in two tertiary centres in Singapore, Tan Tock Seng Hospital and Khoo Teck Puat Hospital the period from January to December 2019. Patients aged 65 years and above who underwent emergency laparotomy for intestinal obstruction, perforated viscus, bowel ischaemia, adhesiolysis, gastrointestinal bleed, or another suspected acute abdomen were included. Laparotomies performed for trauma, cholecystectomy, appendectomy, vascular surgery, and non-GI surgery were excluded. The Clinical Frailty Score (CFS) developed by the Canadian Study of Health and Aging (CSHA) was used. A score of 1 to 4 was defined as non-frail and 5 to 7 as frail. We compared the clinical outcomes of elderly patients in the frail and non-frail groups. Results: There were 233 elderly patients who underwent EL during the study period. Up to 26.2% of patients were frail. Patients who were frail (CFS 5-9) tend to be older, 79 ± 7 vs 79 ± 5 years of age, p <0.01. Gender distribution was equal in both groups. Indication for emergency laparotomies, time from diagnosis to surgery, and presence of consultant surgeons and anaesthetists in the operating theatre were comparable (p>0.05). Patients in the frail group were more likely to receive postoperative geriatric assessment than in the non-frail group, 49.2% vs. 27.9% (p<0.01). The postoperative complications were comparable (p>0.05). The length of stay in the critical care unit was longer for the frail patients, 2 (IQR 1-6.5) versus 1 (IQR 0-4) days, p<0.01. Frailty was found to be an independent predictor of 90-day mortality but not age, OR 2.9 (1.1-7.4), p=0.03. Conclusion: Up to one-fourth of the elderly who underwent EL were frail. Patients who were frail were associated with a longer length of stay in the critical care unit and a 90-day mortality rate of more than three times that of their non-frail counterparts. PPOSSUM was a better predictor of 90-day mortality in the non-frail group than in the frail group. As frailty scoring was a significant predictor of 90-day mortality, its integration into acute surgical units to facilitate shared decision-making and discharge planning should be considered.

Keywords: frailty elderly, emergency, laparotomy

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Authors: Riznaldi Akbar

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In this study, we compared the performance of the Artificial Neural Network (ANN) model with back-propagation algorithm in correctly predicting in-sample and out-of-sample external debt crisis in Indonesia. We found that exchange rate, foreign reserves, and exports are the major determinants to experiencing external debt crisis. The ANN in-sample performance provides relatively superior results. The ANN model is able to classify correctly crisis of 89.12 per cent with reasonably low false alarms of 7.01 per cent. In out-of-sample, the prediction performance fairly deteriorates compared to their in-sample performances. It could be explained as the ANN model tends to over-fit the data in the in-sample, but it could not fit the out-of-sample very well. The 10-fold cross-validation has been used to improve the out-of-sample prediction accuracy. The results also offer policy implications. The out-of-sample performance could be very sensitive to the size of the samples, as it could yield a higher total misclassification error and lower prediction accuracy. The ANN model could be used to identify past crisis episodes with some accuracy, but predicting crisis outside the estimation sample is much more challenging because of the presence of uncertainty.

Keywords: debt crisis, external debt, artificial neural network, ANN

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Authors: Phawichsak Prapassornpitaya, Wanida Jinsart

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Daily monitoring PM₁₀ and PM₂.₅ data from 2007 to 2017 were analyzed to provide baseline data for prediction of the air pollution in Bangkok in the period of 2018 -2020. Two statistical models, Autoregressive Integrated Moving Average model (ARIMA) were used to evaluate the trends of pollutions. The prediction concentrations were tested by root means square error (RMSE) and index of agreement (IOA). This evaluation of the traffic PM₂.₅ and PM₁₀ were studied in association with the regulatory control and emission standard changes. The emission factors of particulate matter from diesel vehicles were decreased when applied higher number of euro standard. The trends of ambient air pollutions were expected to decrease. However, the Bangkok smog episode in February 2018 with temperature inversion caused high concentration of PM₂.₅ in the air environment of Bangkok. The impact of traffic pollutants was depended upon the emission sources, temperature variations, and metrological conditions.

Keywords: fine particulate matter, ARIMA, RMSE, Bangkok

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Authors: S. M. Gupta, Vanita Aggarwal, Som Nath Sachdeva

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The work reported through this paper is an experimental work conducted on High Performance Concrete (HPC) with super plasticizer with the aim to develop some models suitable for prediction of compressive strength of HPC mixes. In this study, the effect of varying proportions of fly ash (0% to 50% at 10% increment) on compressive strength of high performance concrete has been evaluated. The mix designs studied were M30, M40 and M50 to compare the effect of fly ash addition on the properties of these concrete mixes. In all eighteen concrete mixes have been designed, three as conventional concretes for three grades under discussion and fifteen as HPC with fly ash with varying percentages of fly ash. The concrete mix designing has been done in accordance with Indian standard recommended guidelines i.e. IS: 10262. All the concrete mixes have been studied in terms of compressive strength at 7 days, 28 days, 90 days and 365 days. All the materials used have been kept same throughout the study to get a perfect comparison of values of results. The models for compressive strength prediction have been developed using Linear Regression method (LR), Artificial Neural Network (ANN) and Leave One Out Validation (LOOV) methods.

Keywords: high performance concrete, fly ash, concrete mixes, compressive strength, strength prediction models, linear regression, ANN

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Authors: Suzan Anwar, Utkarsh Goyal

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Heart rate (HR) is a predictor of cardiovascular, cerebrovascular, and all-cause mortality in the general population, as well as in patients with cardio and cerebrovascular diseases. Machine learning (ML) significantly improves the accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment while avoiding unnecessary treatment of others. This research examines relationship between the individual's various heart health inputs like age, sex, cp, trestbps, thalach, oldpeaketc, and the likelihood of developing heart disease. Machine learning techniques like logistic regression and decision tree, and Python are used. The results of testing and evaluating the model using the Heart Failure Prediction Dataset show the chance of a person having a heart disease with variable accuracy. Logistic regression has yielded an accuracy of 80.48% without data handling. With data handling (normalization, standardscaler), the logistic regression resulted in improved accuracy of 87.80%, decision tree 100%, random forest 100%, and SVM 100%.

Keywords: heart rate, machine learning, SVM, decision tree, logistic regression, random forest

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Keywords: DCOP, multi-agent reasoning, Bayesian reasoning, swarm intelligence

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Authors: Cristy Stagnar, Nina Hubig, Diana Ivankovic

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The epigenome is a compelling candidate for mediating long-term responses to environmental effects modifying disease risk. The main goal of this research is to develop a machine learning-based DNA methylation score, which will be valuable in delineating the unique contribution of paternal epigenetic modifications to the germline impacting childhood health outcomes. It will also be a useful tool in validating self-reports of nonsmoking and in adjusting epigenome-wide DNA methylation association studies for this early-life exposure. Using secondary data from two population-based methylation profiling studies, our DNA methylation score is based on CpG DNA methylation measurements from cord blood gathered from children whose fathers smoked pre- and peri-conceptually. Each child’s mother and father fell into one of three class labels in the accompanying questionnaires -never smoker, former smoker, or current smoker. By applying different machine learning algorithms to the accessible resource for integrated epigenomic studies (ARIES) sub-study of the Avon longitudinal study of parents and children (ALSPAC) data set, which we used for training and testing of our model, the best-performing algorithm for classifying the father smoker and mother never smoker was selected based on Cohen’s κ. Error in the model was identified and optimized. The final DNA methylation score was further tested and validated in an independent data set. This resulted in a linear combination of methylation values of selected probes via a logistic link function that accurately classified each group and contributed the most towards classification. The result is a unique, robust DNA methylation score which combines information on DNA methylation and early life exposure of offspring to paternal smoking during pregnancy and which may be used to examine the paternal contribution to offspring health outcomes.

Keywords: epigenome, health outcomes, paternal preconception environmental exposures, supervised machine learning

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Authors: Mikhail Gritskevich, Sebastian Hohenstein

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The paper presents the results of a detailed assessment of several modern Reynolds Averaged Navier-Stokes (RANS) turbulence models for prediction of C3X vane film cooling at various injection regimes. Three models are considered, namely the Shear Stress Transport (SST) model, the modification of the SST model accounting for the streamlines curvature (SST-CC), and the Explicit Algebraic Reynolds Stress Model (EARSM). It is shown that all the considered models face with a problem in prediction of the adiabatic effectiveness in the vicinity of the cooling holes; however, accounting for the Reynolds stress anisotropy within the EARSM model noticeably increases the solution accuracy. On the other hand, further downstream all the models provide a reasonable agreement with the experimental data for the adiabatic effectiveness and among the considered models the most accurate results are obtained with the use EARMS.

Keywords: discrete holes film cooling, Reynolds Averaged Navier-Stokes (RANS), Reynolds stress tensor anisotropy, turbulent heat transfer

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Authors: Eun Jeong Yu, Inn Soo Kang, Tae Ki Yoon, Mi Kyoung Koong

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Advanced female age is associated with higher cycle cancelation rates, lower clinical pregnancy rate, increased miscarriage and aneuploidy rates in IVF (In Vitro Fertilization) cycles. This retrospective cohort study was conducted at a Cha Fertility Center, Seoul Station. All fresh non-donor IVF cycles performed in women aged 40 years and above from January 2016 to December 2016 were reviewed. Donor/recipient treatment, PGD/PGS (Preimplantation Genetic Diagnosis/ Preimplantation Genetic Screening) were excluded from analysis. Of the 1,166 cycles from 753 women who completed ovulation induction, 1,047 were appropriate for the evaluation according to inclusion and exclusion criterion. IVF cycles were categorized according to age and grouped into the following 1-year age groups: 40, 41, 42, 43, 44, 45 and > 46. The mean age of patients was 42.4 ± 1.8 years. The median AMH (Anti-Mullerian Hormone) level was 1.2 ± 1.5 ng/mL. The mean number of retrieved oocytes was 4.9 ± 4.3. The clinical pregnancy rate and live birth rate in women > 40 years significantly decreased with each year of advancing age (p < 0.001). The clinical pregnancy rate decreased from 21% at the age of 40 years to 0% at ages above 45 years. Live birth rate decreased from 12.3% to 0%, respectively. There were no clinical pregnancy outcomes among 95 patients aged above 45 years of age. The overall miscarriage rate was 40.7% (range, 36.7%-70%). The transfer of at least one good quality embryo was associated with about 4-9% increased chance of a clinical pregnancy rate. Therefore, IVF in old age women less than 46 had a reasonable chance for successful pregnancy outcomes especially when good quality embryo is transferred.

Keywords: advanced maternal age, in vitro fertilization, pregnancy rate, live birth rate

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Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

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In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

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Authors: Jana Atteah, Wid Jan, Yara AlHibshi, Rahaf AlRougi

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Anyword is an AI copywriting tool that helps marketers create effective campaigns for specific audiences. It offers a wide range of templates for various platforms, brand voice guidelines, and valuable analytics insights. Anyword is used by top global companies and has been recognized as one of the "Fastest Growing Products" in the 2023 software awards. A recent study examined the utilization and impact of AI-powered writing tools, specifically focusing on the adoption of AI in writing pursuits and the use of the Anyword platform. The results indicate that a majority of respondents (52.17%) had not previously used Anyword, but those who had were generally satisfied with the platform. Notable productivity improvements were observed among 13% of the participants, while an additional 34.8% reported a slight increase in productivity. A majority (47.8%) maintained a neutral stance, suggesting that their productivity remained unaffected. Only a minimal percentage (4.3%) claimed that their productivity did not improve with the usage of Anyword AI. In terms of the quality of written content generated, the participants responded positively. Approximately 91% of participants gave Anyword AI a score of 5 or higher, with roughly 17% giving it a perfect score. A small percentage (approximately 9%) gave a low score between 0-2. The mode result was a score of 7, indicating a generally positive perception of the quality of content generated using Anyword AI. These findings suggest that AI can contribute to increased productivity and positively influence the quality of written content. Further research and exploration of AI tools in writing pursuits are warranted to fully understand their potential and limitations.

Keywords: artificial intelligence, marketing platforms, productivity, user interface

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Authors: A. Hakimiyan, H. Namazi

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Human behavior is defined as a range of behaviors exhibited by humans who are influenced by different internal or external sources. Human behavior is the subject of much research in different areas of psychology and neuroscience. Despite some advances in studies related to forecasting of human behavior, there are not many researches which consider the effect of the time delay between the presence of stimulus and the related human response. Analysis of EEG signal as a fractal time series is one of the major tools for studying the human behavior. In the other words, the human brain activity is reflected in his EEG signal. Artificial Neural Network has been proved useful in forecasting of different systems’ behavior especially in engineering areas. In this research, a time delay neural network is trained and tested in order to forecast the human EEG signal and subsequently human behavior. This neural network, by introducing a time delay, takes care of the lagging time between the occurrence of the stimulus and the rise of the subsequent action potential. The results of this study are useful not only for the fundamental understanding of human behavior forecasting, but shall be very useful in different areas of brain research such as seizure prediction.

Keywords: human behavior, EEG signal, time delay neural network, prediction, lagging time

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Authors: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Hatem A. El-Mezayen, Ahmed Abdelmajeed, Fatehya Metwally, Usama Elsaly, Salwa Atef

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Tumor metastasis involves the dissemination of malignant cells into the basement membrane and vascular system contributes to the circulating pool of these markers. In this context our aim has been focused on development of a non-invasive. Circulating tumor cells (CTCs) represent a unique liquid biopsy carrying comprehensive biological information of the primary tumor. Herein, we sought to develop a novel score based on the combination of the most significant CTCs biomarkers with and routine laboratory tests for accurate detection of metastatic breast cancer. Methods: Cytokeratin 18 (CK18), Cytokeratin 19 (CK19), and CA15.3 were assayed in metastatic breast cancer (MBC) patients (75), non-MBC patients (50) and healthy control (20). Results: Areas under receiving operating curve (AUCs) were calculated and used for construction on novel score. A novel score named MBC-CTCs = CA15.3 (U/L) × 0.08 + CK 18 % × 2.9 + CK19 × 3.1– 510. That function correctly classified 87% of metastatic breast cancer at cut-off value = 0.55. (i.e great than 0.55 indicates patients with metastatic breast cancer and less than 0.55 indicates patients with non-metastatic breast cancer). Conclusion: MBC-CTCs is a novel, non-invasive and simple can applied to discriminate patients with metastatic breast cancer.

Keywords: metastatic breast cancer, circulating tumor cells, cytokeratin, EpiCam

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