Search results for: mortality prediction

Authors: Ahmed Z. Mazen, Nejat Rahmanian, Iqbal Mujtaba, Ali Hassanpour

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In oil and gas well drilling, the drill bit is an important part of the Bottom Hole Assembly (BHA), which is installed and designed to drill and produce a hole by several mechanisms. The efficiency of the bit depends on many drilling parameters such as weight on bit, rotary speed, and mud properties. When the bit is pulled out of the hole, the evaluation of the bit damage must be recorded very carefully to guide engineers in order to select the bits for further planned wells. Having a worn bit for hole drilling may cause severe damage to bit leading to cutter or cone losses in the bottom of hole, where a fishing job will have to take place, and all of these will increase the operating cost. The main factor to reduce the cost of drilling operation is to maximize the rate of penetration by analyzing real-time data to predict the drill bit wear while drilling. There are numerous models in the literature for prediction of the rate of penetration based on drilling parameters, mostly based on empirical approaches. One of the most commonly used approaches is Bourgoyne and Young model, where the rate of penetration can be estimated by the drilling parameters as well as a wear index using an empirical correlation, provided all the constants and coefficients are accurately determined. This paper introduces a new methodology to estimate the eight coefficients for Bourgoyne and Young model using the gPROMS parameters estimation GPE (Version 4.2.0). Real data collected form similar formations (12 ¼’ sections) in two different fields in Libya are used to estimate the coefficients. The estimated coefficients are then used in the equations and applied to nearby wells in the same field to predict the bit wear.

Keywords: Bourgoyne and Young model, bit wear, gPROMS, rate of penetration

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Authors: Alexander Heckman, Rohan Goswami, Zachi Attia, Paul Friedman, Peter Noseworthy, Demilade Adedinsewo, Pablo Moreno-Franco, Rickey Carter, Tathagat Narula

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Background: Postoperative atrial fibrillation (POAF) is associated with adverse health consequences, higher costs, and longer hospital stays. Utilizing existing predictive models that rely on clinical variables and circulating biomarkers, multiple societies have published recommendations on the treatment and prevention of POAF. Although reasonably practical, there is room for improvement and automation to help individualize treatment strategies and reduce associated complications. Methods and Results: In this retrospective cohort study of solid organ transplant recipients, we evaluated the diagnostic utility of a previously developed AI-based ECG prediction for silent AF on the development of POAF within 30 days of transplant. A total of 2261 non-cardiac transplant patients without a preexisting diagnosis of AF were found to have a 5.8% (133/2261) incidence of POAF. While there were no apparent sex differences in POAF incidence (5.8% males vs. 6.0% females, p=.80), there were differences by race and ethnicity (p<0.001 and 0.035, respectively). The incidence in white transplanted patients was 7.2% (117/1628), whereas the incidence in black patients was 1.4% (6/430). Lung transplant recipients had the highest incidence of postoperative AF (17.4%, 37/213), followed by liver (5.6%, 56/1002) and kidney (3.6%, 32/895) recipients. The AUROC in the sample was 0.62 (95% CI: 0.58-0.67). The relatively low discrimination may result from undiagnosed AF in the sample. In particular, 1,177 patients had at least 1 AI-ECG screen for AF pre-transplant above .10, a value slightly higher than the published threshold of 0.08. The incidence of POAF in the 1104 patients without an elevated prediction pre-transplant was lower (3.7% vs. 8.0%; p<0.001). While this supported the hypothesis that potentially undiagnosed AF may have contributed to the diagnosis of POAF, the utility of the existing AI-ECG screening algorithm remained modest. When the prediction for POAF was made using the first postoperative ECG in the sample without an elevated screen pre-transplant (n=1084 on account of n=20 missing postoperative ECG), the AUROC was 0.66 (95% CI: 0.57-0.75). While this discrimination is relatively low, at a threshold of 0.08, the AI-ECG algorithm had a 98% (95% CI: 97 – 99%) negative predictive value at a sensitivity of 66% (95% CI: 49-80%). Conclusions: This study's principal finding is that the incidence of POAF is rare, and a considerable fraction of the POAF cases may be latent and undiagnosed. The high negative predictive value of AI-ECG screening suggests utility for prioritizing monitoring and evaluation on transplant patients with a positive AI-ECG screening. Further development and refinement of a post-transplant-specific algorithm may be warranted further to enhance the diagnostic yield of the ECG-based screening.

Keywords: artificial intelligence, atrial fibrillation, cardiology, transplant, medicine, ECG, machine learning

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Authors: Haruna Karamba, Nafisa Muhammad Danyaro

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The study describe the action of natural latex (extract) of two sub-aquatic macrophytes plants i.e., Cassia occidentalis and Physalis angulata which were tested against two water snail species; Bulinus globusus and Lymnaea natalensis, the intermediate host of Bilharziasis (chistosomiasis) in the tropical countries. Bilherziasis is a disease prevalent and endermic to tropical Africa, seriously undermining health status of Nigerian youth. The easiest way to eradicate the disease is to eliminate the secondary host of the pathogen, chistosoma species. Therefore we carried out a research to investigate the molluscicidal effect of the leaf extract of C. occidentalis and P. angulata on mortality rate of B. globusus and L. natalensis water snails using pond water in the laboratory of science laboratory department of Kano State Polytechnic, Nigeria. One hundred and fifty juveniles’ snails were collected from Jakara Dam in the Northeastern part of Kano, Nigeria. The snails were put inside a plastic container and transported immediately to the laboratory where they were transferred into reservoir tank containing pond water and kept for 48 hours to get acclimatized with laboratory environment. Twelve water bathes 2/3 filled with pond water were prepared and kept in the laboratory. Leaf extract of the plants were obtained by blending and homogenizing the leaf tissue from which the extract were obtained and prepared in 10, 20, 30, 40 and 50 ppm, in addition to 0 ppm, which served as control. Ten snails were placed in each of the twelve water bathes. Six water bathes for the species of C. accidentalis extract and other six for P. angulata. The treatment combinations were maintained for 2 days after which the number of living snails present in each water bathes were counted and subsequently at 2 days intervals. The result indicated that extracts from both plants were lethal to the snails as concentration of the extract increases particularly mortality rate was highest at 40 and 50 ppm. Conclusively the toxicity of the extracts from these plants proven lethal to snails and hence can be used as molluscicides for cheap and easy method of eliminating water snails and therefore reducing the incidence of Bilharziasis.

Keywords: schistosomiasis, bilharziasis, Bulinus globusus, Lymnea natalensis, Physalis angulata, Cassia occidentalis, Kano

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Authors: Pritam Kitey

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Platelets play a key role in the development of atherothrombosis, a major contributor of cardiovascular evevts. The contributor of platelets to cardiovascular events has been noted for decades. Mean paltelets volume [MPV] is a marker of platelets size that is easily determined on routine automated haemograms and routinely available at low cost. Subjects with higher MPV have larger platelets that are metabolically and enzamatically more active and have greater prothombotic potential than smaller platelets. In fact several studies have demonstrated a significant association between higher MPV and an increased incidence of cerebrovascular events and all-cause mortality.

Keywords: mean paltelets volume (MPV), platelets, cerebrovascular stroke, cardiovascular events

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Authors: H. N. Alyaseen, S. E. Alalawi, T. Cordoba, É. Delisle, C. Cordoba, A. Odobescu

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Sternal dehiscence is defined as the persistent separation of sternal bones that are often complicated with mediastinitis. Etiologies that lead to sternal dehiscence vary, with cardiovascular and thoracic surgeries being the most common. Early diagnosis in susceptible patients is crucial to the management of such cases, as they are associated with high mortality rates. A recent meta-analysis of more than four hundred thousand patients concluded that deep sternal wound infections were the leading cause of mortality and morbidity in patients undergoing cardiac procedures. Long-term complications associated with sternal dehiscence include increased hospitalizations, cardiac infarctions, and renal and respiratory failures. Numerous osteosynthesis methods have been described in the literature. Surgical materials offer enough rigidity to support the sternum and can be flexible enough to allow physiological breathing movements of the chest; however, these materials fall short when managing patients with extensive bone loss, osteopenia, or general poor bone quality, for such cases, flaps offer a better closure system. Early utilization of flaps yields better survival rates compared to delayed closure or to patients treated with sternal rewiring and closed drainage. The utilization of pectoralis major flaps, rectus abdominus, and latissimus muscle flaps have all been described in the literature as great alternatives. Flap selection depends on a variety of factors, mainly the size of the sternal defect, infection, and the availability of local tissues. Free fibular flaps are commonly harvested flaps utilized in reconstruction around the body. In cases regarding sternal reconstruction with free fibular flaps, the literature exclusively discussed the flap applied vertically to the chest wall. We present a different technique applying the free fibular triple barrel flap oriented in a transverse manner, in parallel to the ribs. In our experience, this method could have enhanced results and improved prognosis as it contributes to the normal circumferential shape of the chest wall.

Keywords: sternal dehiscence, management, free fibular flaps, novel surgical techniques

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Authors: Koe Han Beng, Khoo Boo Cheong

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The rising interest of stepped hull design has been led by the demand of more efficient high-speed boat. At the same time, the need of accurate prediction method for stepped planing hull is getting more important. By understanding the flow at high Froude number is the key in designing a practical step hull, the study surrounding stepped hull has been done mainly in the towing tank which is time-consuming and costly for initial design phase. Here the feasibility of predicting hydrodynamics of high-speed planing hull both with and without step using computational fluid dynamics (CFD) with the volume of fluid (VOF) methodology is studied in this work. First the flow around the prismatic body is analyzed, the force generated and its center of pressure are compared with available experimental and empirical data from the literature. The wake behind the transom on the keel line as well as the quarter beam buttock line are then compared with the available data, this is important since the afterbody flow of stepped hull is subjected from the wake of the forebody. Finally the calm water performance prediction of a conventional planing hull and its stepped version is then analyzed. Overset mesh methodology is employed in solving the dynamic equilibrium of the hull. The resistance, trim, and heave are then compared with the experimental data. The resistance is found to be predicted well and the dynamic equilibrium solved by the numerical method is deemed to be acceptable. This means that computational fluid dynamics will be very useful in further study on the complex flow around stepped hull and its potential usage in the design phase.

Keywords: planing hulls, stepped hulls, wake shape, numerical simulation, hydrodynamics

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Authors: Muhammad Luthfi, Sutikno Sutikno, Purhadi Purhadi

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Weather forecast has necessarily been improved to provide the communities an accurate and objective prediction as well. To overcome such issue, the numerical-based weather forecast was extensively developed to reduce the subjectivity of forecast. Yet the Numerical Weather Predictions (NWPs) outputs are unfortunately issued without taking dynamical weather behavior and local terrain features into account. Thus, NWPs outputs are not able to accurately forecast the weather quantities, particularly for medium and long range forecast. The aim of this research is to aid and extend the development of ensemble forecast for Meteorology, Climatology, and Geophysics Agency of Indonesia. Ensemble method is an approach combining various deterministic forecast to produce more reliable one. However, such forecast is biased and uncalibrated due to its underdispersive or overdispersive nature. As one of the parametric methods, Bayesian Model Averaging (BMA) generates the calibrated ensemble forecast and constructs predictive PDF for specified period. Such method is able to utilize ensemble of any size but does not take spatial correlation into account. Whereas space dependencies involve the site of interest and nearby site, influenced by dynamic weather behavior. Meanwhile, Geostatistical Output Perturbation (GOP) reckons the spatial correlation to generate future weather quantities, though merely built by a single deterministic forecast, and is able to generate an ensemble of any size as well. This research conducts both BMA and GOP to generate the calibrated ensemble forecast for the daily temperature at few meteorological sites nearby Indonesia international airport.

Keywords: Bayesian Model Averaging, ensemble forecast, geostatistical output perturbation, numerical weather prediction, temperature

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Authors: Chouahda Salima, Soltani Noureddine

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The present study is a part of biological control against mosquitoes. It aims to assess the impact of two xenobiotics (a selective insect growth regulator: halofenozide and heavy metals: cadmium, more toxic and widespread in the region) in mosquitofish: Gambusia affinis. Several approaches were examined: Acute toxicity of cadmium and halofenozide: The acute toxicity of cadmium and halofenozide was examined in juvenile and adult males and females of G. affinis at different concentrations, cadmium causes mortality of the species studied with a relation dose-response. In laboratory conditions, the impact of cadmium was determined on two biomarkers of environmental stress: glutathione and acetylcholinesterase. The results show that the juvenile followed by adult males are more susceptible than adult females, while the halofenozide does not have any effect on the mortality of juvenile and adult males and females of G.affinis. Chronic toxicity of cadmium and halofenozide: both xenobiotics were added to the water fish raising at different doses tested in juveniles and adults males and females during two months of experience. Growth and metric indices; results show that halofenozide added to the water juveniles of G. affinis has no effect on their growth (length and weight). On the other side, the cadmium at the dose 5 µg/L shows a higher toxicity against juvenile, where he appears to reduce significantly their linear growth and weight. In females, the both xenobiotics have significant effects on metric indices, but these effects are more important on the hepatosomatic index that the gonadosomatic index and the coefficient of condition. Biomarkers; acetylcholinesterase (AChE), glutathione S-transferase (GST) and glutathione (GSH) used in assessing of environmental stress were measured in juveniles and adults males and females. The response of these biomarkers reveals an inhibition of AChE specific activity, an induction of GST activity, and decrease of GSH rates in juveniles in the end of experiment and during chronic treatment adult males and females. The effect of these biomarkers is more pronounced in females compared to males and juveniles. These different biomarkers have a similar profile for the duration of exposure.

Keywords: gambusia affinis, insecticide, heavy metal, morphology, biomarkers, chronic toxicity, acute toxicity, pollution

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Authors: Manuela Villani, Anila Xhahysa, Christopher Brooks, Marco Pagani

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The geological structure of Western Balkans is strongly affected by the collision between Adria microplate and the southwestern Euroasia margin, resulting in a considerably active seismic region. The Harmonization of Seismic Hazard Maps in the Western Balkan Countries Project (BSHAP) (2007-2011, 2012-2015) by NATO supported the preparation of new seismic hazard maps of the Western Balkan, but when inspecting the seismic hazard models produced later by these countries on a national scale, significant differences in design PGA values are observed in the border, for instance, North Albania-Montenegro, South Albania- Greece, etc. Considering the fact that the catalogues were unified and seismic sources were defined within BSHAP framework, obviously, the differences arise from the Ground Motion Prediction Equations selection, which are generally the component with highest impact on the seismic hazard assessment. At the time of the project, a modest database was present, namely 672 three-component records, whereas nowadays, this strong motion database has increased considerably up to 20,939 records with Mw ranging in the interval 3.7-7 and epicentral distance distribution from 0.47km to 490km. Statistical analysis of the strong motion database showed the lack of recordings in the moderate-to-large magnitude and short distance ranges; therefore, there is need to re-evaluate the Ground Motion Prediction Equation in light of the recently updated database and the new generations of GMMs. In some cases, it was observed that some events were more extensively documented in one database than the other, like the 1979 Montenegro earthquake, with a considerably larger number of records in the BSHAP Analogue SM database when compared to ESM23. Therefore, the strong motion flat-file provided from the Harmonization of Seismic Hazard Maps in the Western Balkan Countries Project was merged with the ESM23 database for the polygon studied in this project. After performing the preliminary residual analysis, the candidate GMPE-s were identified. This process was done using the GMPE performance metrics available within the SMT in the OpenQuake Platform. The Likelihood Model and Euclidean Distance Based Ranking (EDR) were used. Finally, for this study, a GMPE logic tree was selected and following the selection of candidate GMPEs, model weights were assigned using the average sample log-likelihood approach of Scherbaum.

Keywords: residual analysis, GMPE, western balkan, strong motion, openquake

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Authors: Mohd Shazad, Kamal Kumar Gupta

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Aedes aegypti (Diptera: Culicidae), commonly known as tiger mosquito is the vector of dengue fever, yellow fever, chikungunya and zika virus. In the absence of any effective vaccine against these diseases, control the mosquito population is the only promising mean to prevent the diseases. Currently used chemical insecticides cause environmental contamination, high mammalian toxicity and hazards to non-target organisms, insecticide resistance and vector resurgence. Present research work aimed to explore the potentials of phytochemicals present in the Ocimum sanctum in management of mosquito population. The leaves of Ocimum were extracted with ethanol by ‘cold extraction method’. 0-24h old fourth instar larvae of Aedes aegypti were treated with the extract of concentrations 50ppm, 100ppm, 200ppm and 400ppm for 24h. Survival, growth and development of the treated larvae were evaluated. The adults emerged from the treated larvae were used for the reproductive fitness studies. Our results indicate 77.2% mortality in the larvae exposed to 400 ppm. At lower doses, although there was no significant reduction in the survival after 24h however, it decreased during subsequent days of observations. In control experiments, no mortality was observed. It was also observed that the larvae survived after treatment showed severe growth and developmental abnormalities. There was significant increase in larval duration. In control, fourth instar moulted into pupa after 3 days while larvae treated with 400 ppm extract were moulted after 4.6 days. Larva-pupa intermediates and the pupa-adult intermediates were observed in many cases. The adults emerged from the treated larvae showed impaired mating and oviposition behaviour. The females exhibited longer preoviposition period, reduced oviposition rate and decreased egg output. GCMS analysis of the ethanol extract revealed presence of JH mimics and intermediates of JH biosynthetic pathway. Potentials of Ocimum sanctum in integrated vector management programme of Aedes aegypti were discussed.

Keywords: Aedes aegypti, Ocimum sanctum, oviposition, survival

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Authors: Yan Zhang

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Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: Cristian Pauna

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Trading the financial markets is a widespread activity today. A large number of investors, companies, public of private funds are buying and selling every day in order to make profit. Algorithmic trading is the prevalent method to make the trade decisions after the electronic trading release. The orders are sent almost instantly by computers using mathematical models. This paper will present a price prediction methodology based on a multi-dimensional neural network. Using the Fisher transform, the neural network will be instructed for a low-latency auto-adaptive process in order to predict the price evolution for the next period of time. The model is designed especially for algorithmic trading and uses the real-time price series. It was found that the characteristics of the Fisher function applied at the nodes scale level can generate reliable trading signals using the neural network methodology. After real time tests it was found that this method can be applied in any timeframe to trade the financial markets. The paper will also include the steps to implement the presented methodology into an automated trading system. Real trading results will be displayed and analyzed in order to qualify the model. As conclusion, the compared results will reveal that the neural network methodology applied together with the Fisher transform at the nodes level can generate a good price prediction and can build reliable trading signals for algorithmic trading.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, neural network

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Authors: Raptis Sotirios

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Health and social care (HSc) services planning and scheduling are facing unprecedented challenges due to the pandemic pressure and also suffer from unplanned spending that is negatively impacted by the global financial crisis. Data-driven can help to improve policies, plan and design services provision schedules using algorithms assist healthcare managers’ to face unexpected demands using fewer resources. The paper discusses services packing using statistical significance tests and machine learning (ML) to evaluate demands similarity and coupling. This is achieved by predicting the range of the demand (class) using ML methods such as CART, random forests (RF), and logistic regression (LGR). The significance tests Chi-Squared test and Student test are used on data over a 39 years span for which HSc services data exist for services delivered in Scotland. The demands are probabilistically associated through statistical hypotheses that assume that the target service’s demands are statistically dependent on other demands as a NULL hypothesis. This linkage can be confirmed or not by the data. Complementarily, ML methods are used to linearly predict the above target demands from the statistically found associations and extend the linear dependence of the target’s demand to independent demands forming, thus groups of services. Statistical tests confirm ML couplings making the prediction also statistically meaningful and prove that a target service can be matched reliably to other services, and ML shows these indicated relationships can also be linear ones. Zero paddings were used for missing years records and illustrated better such relationships both for limited years and in the entire span offering long term data visualizations while limited years groups explained how well patients numbers can be related in short periods or can change over time as opposed to behaviors across more years. The prediction performance of the associations is measured using Receiver Operating Characteristic(ROC) AUC and ACC metrics as well as the statistical tests, Chi-Squared and Student. Co-plots and comparison tables for RF, CART, and LGR as well as p-values and Information Exchange(IE), are provided showing the specific behavior of the ML and of the statistical tests and the behavior using different learning ratios. The impact of k-NN and cross-correlation and C-Means first groupings is also studied over limited years and the entire span. It was found that CART was generally behind RF and LGR, but in some interesting cases, LGR reached an AUC=0 falling below CART, while the ACC was as high as 0.912, showing that ML methods can be confused padding or by data irregularities or outliers. On average, 3 linear predictors were sufficient, LGR was found competing RF well, and CART followed with the same performance at higher learning ratios. Services were packed only if when significance level(p-value) of their association coefficient was more than 0.05. Social factors relationships were observed between home care services and treatment of old people, birth weights, alcoholism, drug abuse, and emergency admissions. The work found that different HSc services can be well packed as plans of limited years, across various services sectors, learning configurations, as confirmed using statistical hypotheses.

Keywords: class, cohorts, data frames, grouping, prediction, prob-ability, services

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Authors: Erika Yamaguchi, Sirawadee Arunyanrt, Shunichi Ohmori, Kazuho Yoshimoto

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In this paper, we suggest a simulation tool to make a decision of monthly production planning for maximizing a profit of Specialty store retailer of Private label Apparel (SPA) firms. Most of SPA firms are fabless and make outsourcing deals for productions with factories of their subcontractors. Every month, SPA firms make a booking for production lines and manpower in the factories. The booking is conducted a few months in advance based on a demand prediction and a monthly production planning at that time. However, the demand prediction is updated month by month, and the monthly production planning would change to meet the latest demand prediction. Then, SPA firms have to change the capacities initially booked within a certain range to suit to the monthly production planning. The booking system is called “capacity-booking”. These days, though it is an issue for SPA firms to make precise monthly production planning, many firms are still conducting the production planning by empirical rules. In addition, it is also a challenge for SPA firms to match their products and factories with considering their demand predictabilities and regulation abilities. In this paper, we suggest a model for considering these two issues. An objective is to maximize a total profit of certain periods, which is sales minus costs of production, inventory, and capacity-booking penalty. To make a better monthly production planning at SPA firms, these points should be considered: demand predictabilities by random trends, previous and next month’s production planning of the target month, and regulation abilities of the capacity-booking. To decide matching products and factories for outsourcing, it is important to consider seasonality, volume, and predictability of each product, production possibility, size, and regulation ability of each factory. SPA firms have to consider these constructions and decide orders with several factories per one product. We modeled these issues as a linear programming. To validate the model, an example of several computational experiments with a SPA firm is presented. We suppose four typical product groups: basic, seasonal (Spring / Summer), seasonal (Fall / Winter), and spot product. As a result of the experiments, a monthly production planning was provided. In the planning, demand predictabilities from random trend are reduced by producing products which are different product types. Moreover, priorities to produce are given to high-margin products. In conclusion, we developed a simulation tool to make a decision of monthly production planning which is useful when the production planning is set every month. We considered the features of capacity-booking, and matching of products and factories which have different features and conditions.

Keywords: capacity-booking, SPA, monthly production planning, linear programming

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Authors: Aid Abdelkrim

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A method of fatigue damage accumulation based upon application of energy parameters of the fatigue process is proposed in the paper. Using this model is simple, it has no parameter to be determined, it requires only the knowledge of the curve W–N (W: strain energy density N: number of cycles at failure) determined from the experimental Wöhler curve. To examine the performance of nonlinear models proposed in the estimation of fatigue damage and fatigue life of components under random loading, a batch of specimens made of 6082 T 6 aluminium alloy has been studied and some of the results are reported in the present paper. The paper describes an algorithm and suggests a fatigue cumulative damage model, especially when random loading is considered. This work contains the results of uni-axial random load fatigue tests with different mean and amplitude values performed on 6082T6 aluminium alloy specimens. The proposed model has been formulated to take into account the damage evolution at different load levels and it allows the effect of the loading sequence to be included by means of a recurrence formula derived for multilevel loading, considering complex load sequences. It is concluded that a ‘damaged stress interaction damage rule’ proposed here allows a better fatigue damage prediction than the widely used Palmgren–Miner rule, and a formula derived in random fatigue could be used to predict the fatigue damage and fatigue lifetime very easily. The results obtained by the model are compared with the experimental results and those calculated by the most fatigue damage model used in fatigue (Miner’s model). The comparison shows that the proposed model, presents a good estimation of the experimental results. Moreover, the error is minimized in comparison to the Miner’s model.

Keywords: damage accumulation, energy model, damage indicator, variable loading, random loading

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Authors: Pampita Chakraborty, Sukumar Mukherjee

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People with diabetes mellitus are more susceptible to developing infections, as high blood sugar levels can weaken the patient's immune system defences. People with diabetes are more adversely affected when they get an infection than someone without the disease, because you have weakened immune defences in diabetes. People who have minimally elevated blood sugar levels experience worse outcomes with infections. Diabetic patients in hospitals do not necessarily have a higher mortality rate due to infections, but they do face longer hospitalisation and recovery times. A study was done in a tertiary care unit in eastern India. Patients with type 2 diabetes mellitus infection were recruited in the study. A total of 520 cases of Type 2 Diabetes Mellitus were recorded out of which 200 infectious cases was included in the study. All subjects underwent detailed history & clinical examination. Microbiological samples were collected from respective site of the infection for microbial culture and antibiotic sensitivity test. Out of the 200 infectious cases urinary tract infection(UTI) was found in majority of the cases followed by diabetic foot ulcer (DFU), respiratory tract infection(RTI) and sepsis. It was observed that Escherichia coli was the most commonest pathogen isolated from UTI cases and Staphylococcus aureus was predominant in foot ulcers followed by other organisms. Klebsiella pneumonia was the major organism isolated from RTI and Enterobacter aerogenes was commonly observed in patients with sepsis. Isolated bacteria showed differential sensitivity pattern against commonly used antibiotics. The majority of the isolates were resistant to several antibiotics that are usually prescribed on an empirical basis. These observations are important, especially for patient management and the development of antibiotic treatment guidelines. It is recommended that diabetic patients receive pneumococcal and influenza vaccine annually to reduce morbidity and mortality. Appropriate usage of antibiotics based on local antibiogram pattern can certainly help the clinician in reducing the burden of infections.

Keywords: antimicrobial resistance, diabetic foot ulcer, respiratory tract infection, urinary tract infection

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Authors: Mustafa Salman, Nurcan Cetinkaya, Zehra Selcuk, Bugra Genc

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The objectives of the present study were to estimate the microbial-N flow to the small intestine and to predict the digestible organic matter intake (DOMI) in grazing Karayaka sheep based on urinary excretion of purine derivatives (xanthine, hypoxanthine, uric acid, and allantoin) by the use of spot urine sampling under field conditions. In the trial, 10 Karayaka sheep from 2 to 3 years of age were used. The animals were grazed in a pasture for ten months and fed with concentrate and vetch plus oat hay for the other two months (January and February) indoors. Highly significant linear and cubic relationships (P<0.001) were found among months for purine derivatives index, purine derivatives excretion, purine derivatives absorption, microbial-N and DOMI. Through urine sampling and the determination of levels of excreted urinary PD and Purine Derivatives / Creatinine ratio (PDC index), microbial-N values were estimated and they indicated that the protein nutrition of the sheep was insufficient. In conclusion, the prediction of protein nutrition of sheep under the field conditions may be possible with the use of spot urine sampling, urinary excreted PD and PDC index. The mean purine derivative levels in spot urine samples from sheep were highest in June, July and October. Protein nutrition of pastured sheep may be affected by weather changes, including rainfall. Spot urine sampling may useful in modeling the feed consumption of pasturing sheep. However, further studies are required under different field conditions with different breeds of sheep to develop spot urine sampling as a model.

Keywords: Karayaka sheep, spot sampling, urinary purine derivatives, PDC index, microbial-N, feed intake

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Authors: V. Ibujes, A. Guevara, P. Barreto

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Concentration of arsenic represents a serious threat to human health. It is a bioaccumulable toxic element and is transferred through the food chain. In Ecuador, values of 0.0423 mg/kg As are registered in potatoes of the skirts of the Tungurahua volcano. The increase of arsenic contamination in Ecuador is mainly due to mining activity, since the process of gold extraction generates toxic tailings with mercury. In the Province of Azuay, due to the mining activity, the soil reaches concentrations of 2,500 to 6,420 mg/kg As whereas in the province of Tungurahua it can be found arsenic concentrations of 6.9 to 198.7 mg/kg due to volcanic eruptions. Since the contamination by arsenic, the present investigation is directed to the remediation of the soils in the provinces of Azuay and Tungurahua by phytoremediation technique and the definition of a methodology of extraction by means of analysis of arsenic in the system soil-plant. The methodology consists in selection of two types of plants that have the best arsenic removal capacity in synthetic solutions 60 μM As, a lower percentage of mortality and hydroponics resistance. The arsenic concentrations in each plant were obtained from taking 10 ml aliquots and the subsequent analysis of the ICP-OES (inductively coupled plasma-optical emission spectrometry) equipment. Soils were contaminated with synthetic solutions of arsenic with the capillarity method to achieve arsenic concentration of 13 and 15 mg/kg. Subsequently, two types of plants were evaluated to reduce the concentration of arsenic in soils for 7 weeks. The global variance for soil types was obtained with the InfoStat program. To measure the changes in arsenic concentration in the soil-plant system, the Rhizo and Wenzel arsenic extraction methodology was used and subsequently analyzed with the ICP-OES (optima 8000 Pekin Elmer). As a result, the selected plants were bluegrass and llanten, due to the high percentages of arsenic removal of 55% and 67% and low mortality rates of 9% and 8% respectively. In conclusion, Azuay soil with an initial concentration of 13 mg/kg As reached the concentrations of 11.49 and 11.04 mg/kg As for bluegrass and llanten respectively, and for the initial concentration of 15 mg/kg As reached 11.79 and 11.10 mg/kg As for blue grass and llanten after 7 weeks. For the Tungurahua soil with an initial concentration of 13 mg/kg As it reached the concentrations of 11.56 and 12.16 mg/kg As for the bluegrass and llanten respectively, and for the initial concentration of 15 mg/kg As reached 11.97 and 12.27 mg/kg Ace for bluegrass and llanten after 7 weeks. The best arsenic extraction methodology of soil-plant system is Wenzel.

Keywords: blue grass, llanten, phytoremediation, soil of Azuay, soil of Tungurahua, synthetic arsenic solution

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Authors: Janani Selvaraj, M. Prashanthi Devi, P. B. Harathi

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Epidemiologic studies conducted over several decades have provided evidence to suggest that long-term exposure to elevated ambient levels of particulate air pollution is associated with increased mortality. Air quality risk management is significant in developing countries and it highlights the need to understand the role of ecologic covariates in the association between air pollution and mortality. Several new methods show promise in exploring the geographical distribution of disease and the identification of high risk areas using epidemiological maps. However, the addition of the temporal attribute would further give us an in depth idea of the disease burden with respect to forecasting measures. In recent years, new methods developed in the reanalysis were useful for exploring the spatial structure of the data and the impact of spatial autocorrelation on estimates of risk associated with exposure to air pollution. Based on this, our present study aims to explore the spatial and temporal distribution of the lung cancer cases in the Coimbatore district of Tamil Nadu in relation to air pollution risk areas. A spatio temporal moving average method was computed using the CrimeStat software and visualized in ArcGIS 10.1 to document the spatio temporal movement of the disease in the study region. The random walk analysis performed showed the progress of the peak cancer incidences in the intersection regions of the Coimbatore North and South taluks that include major commercial and residential regions like Gandhipuram, Peelamedu, Ganapathy, etc. Our study shows evidence that daily exposure to high air pollutant concentration zones may lead to the risk of lung cancer. The observations from the present study will be useful in delineating high risk zones of environmental exposure that contribute to the increase of cancer among daily commuters. Through our study we suggest that spatially resolved exposure models in relevant time frames will produce higher risks zones rather than solely on statistical theory about the impact of measurement error and the empirical findings.

Keywords: air pollution, cancer, spatio-temporal analysis, India

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Authors: M. D. Haneef, R. B. Randall, Z. Peng

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Journal bearings used in IC engines are prone to premature failures and are likely to fail earlier than the rated life due to highly impulsive and unstable operating conditions and frequent starts/stops. Vibration signature extraction and wear debris analysis techniques are prevalent in the industry for condition monitoring of rotary machinery. However, both techniques involve a great deal of technical expertise, time and cost. Limited literature is available on the application of these techniques for fault detection in reciprocating machinery, due to the complex nature of impact forces that confounds the extraction of fault signals for vibration based analysis and wear prediction. This work is an extension of a previous study, in which an engine simulation model was developed using a MATLAB/SIMULINK program, whereby the engine parameters used in the simulation were obtained experimentally from a Toyota 3SFE 2.0 litre petrol engines. Simulated hydrodynamic bearing forces were used to estimate vibrations signals and envelope analysis was carried out to analyze the effect of speed, load and clearance on the vibration response. Three different loads 50/80/110 N-m, three different speeds 1500/2000/3000 rpm, and three different clearances, i.e., normal, 2 times and 4 times the normal clearance were simulated to examine the effect of wear on bearing forces. The magnitude of the squared envelope of the generated vibration signals though not affected by load, but was observed to rise significantly with increasing speed and clearance indicating the likelihood of augmented wear. In the present study, the simulation model was extended further to investigate the bearing wear behavior, resulting as a consequence of different operating conditions, to complement the vibration analysis. In the current simulation, the dynamics of the engine was established first, based on which the hydrodynamic journal bearing forces were evaluated by numerical solution of the Reynold’s equation. Also, the essential outputs of interest in this study, critical to determine wear rates are the tangential velocity and oil film thickness between the journal and bearing sleeve, which if not maintained appropriately, have a detrimental effect on the bearing performance. Archard’s wear prediction model was used in the simulation to calculate the wear rate of bearings with specific location information as all determinative parameters were obtained with reference to crank rotation. Oil film thickness obtained from the model was used as a criterion to determine if the lubrication is sufficient to prevent contact between the journal and bearing thus causing accelerated wear. A limiting value of 1 µm was used as the minimum oil film thickness needed to prevent contact. The increased wear rate with growing severity of operating conditions is analogous and comparable to the rise in amplitude of the squared envelope of the referenced vibration signals. Thus on one hand, the developed model demonstrated its capability to explain wear behavior and on the other hand it also helps to establish a correlation between wear based and vibration based analysis. Therefore, the model provides a cost-effective and quick approach to predict the impending wear in IC engine bearings under various operating conditions.

Keywords: condition monitoring, IC engine, journal bearings, vibration analysis, wear prediction

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Authors: Muhsin Kizhisseri, Jorg Schluter, Saleh Gharie

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Atherosclerosis is the main reason of stroke, which is one of the deadliest diseases in the world. The carotid artery in the brain is the prominent location for atherosclerotic progression, which hinders the blood flow into the brain. The inclusion of computational fluid dynamics (CFD) into the diagnosis cycle to understand the hemodynamics of the patient-specific carotid artery can give insights into stroke prediction. Realistic outlet boundary conditions are an inevitable part of the numerical simulations, which is one of the major factors in determining the accuracy of the CFD results. The Windkessel model-based outlet boundary conditions can give more realistic characteristics of the distal vascular branches of the carotid artery, such as the resistance to the blood flow and compliance of the distal arterial walls. This study aims to find the most influential distal branches of the carotid artery by using the Windkessel model parameters in the outlet boundary conditions. The parametric study approach to Windkessel model parameters can include the geometrical features of the distal branches, such as radius and length. The incorporation of the variations of the geometrical features of the major distal branches such as the middle cerebral artery, anterior cerebral artery, and ophthalmic artery through the Windkessel model can aid in identifying the most influential distal branch in the carotid artery. The results from this study can help physicians and stroke neurologists to have a more detailed and accurate judgment of the patient's condition.

Keywords: stroke, carotid artery, computational fluid dynamics, patient-specific, Windkessel model, distal vascular branches

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Authors: Woldegerima G. Medhin, Tadele Araya

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Background: Rabies is acute viral encephalitis affecting mainly carnivores and insectivorous but can affect any mammal. Case fatality rate is 100% once clinical signs appear. Rabies has a worldwide distribution in continental regions of Asia and Africa. Globally, rabies is responsible for more than 61000 human deaths annually. An estimation of human mortality rabies in Asia and Africa annually exceed 35172 and 21476 respectively. Ethiopia approximately 2900 people were estimated to die of rabies annually, Tigary region approximately 98 people were estimated to die annually. The aim of this study is to analyze trends, describe, and evaluate the ten years rabies data in Tigray, Ethiopia. Methods: We conducted descriptive epidemiological study from 15-30 February, 2023 of rabies exposure and death in humans by reviewing the health management information system report from Tigray Regional Health Bureau and vaccination coverage of dog population from 2013 to 2022. We used case definition, suspected cases are those bitten by the dogs displaying clinical signs consistent with rabies and confirmed cases were deaths from rabies at time of the exposure. Results: A total 21031 dog bites and 375 deaths report of rabies and 18222 post exposure treatments for humans in Tigray region were used. A suspected rabies patients had shown an increasing trend from 2013 to 2015 and 2018 to 2019. Overall mortality rate was 19/1000 in Tigray. Majority of suspected patients (45%) were age <15 years old. An estimated by Agriculture Bureau of Tigray Region about 12000 owned and 2500 stray dogs are available in the region, but yearly dog vaccination remains low (50%). Conclusion: Rabies is a public health problem in Tigray region. It is highly recommended to vaccinate individually owned dogs and concerned sectors should eliminate stray dogs. Surveillance system should strengthen for estimating the real magnitude, launch preventive and control measures.

Keywords: rabies, Virus, transmision, prevalence

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Authors: Amina Akhtar, Rory O'Connor

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Introduction: There remains significant morbidity and mortality in young pedestrians hit by motor vehicles, even in the era of pedestrian crossings and speed limits. The aim of this study was to compare incidence and injury severity of motor vehicle-related pedestrian trauma according to time of day and season in a young population, based on the supposition that injuries would be more prevalent during dusk and dawn and during autumn and winter. Methods: Data was retrieved for patients between 10-25 years old from the National Trauma Audit and Research Network (TARN) database who had been involved as pedestrians in motor vehicle accidents between 2015-2020. The incidence of injuries, their severity (using the Injury Severity Score [ISS]), hospital transfer time, and mortality were analysed according to the hours of daylight, darkness, and season. Results: The study identified a seasonal pattern, showing that autumn was the predominant season and led to 34.9% of injuries, with a further 25.4% in winter in comparison to spring and summer, with 21.4% and 18.3% of injuries, respectively. However, visibility alone was not a sufficient factor as 49.5% of injuries occurred during the time of darkness, while 50.5% occurred during daylight. Importantly, the greatest injury rate (number of injuries/hour) occurred between 1500-1630, correlating to school pick-up times. A further significant relationship between injury severity score (ISS) and daylight was demonstrated (p-value= 0.0124), with moderate injuries (ISS 9-14) occurring most commonly during the day (72.7%) and more severe injuries (ISS>15) occurred during the night (55.8%). Conclusion: We have identified a relationship between time of day and the frequency and severity of pedestrian trauma in young people. In addition, particular time groupings correspond to the greatest injury rate, suggesting that reduced visibility coupled with school pick-up times may play a significant role. This could be addressed through a targeted public health approach to implementing change. We recommend targeted public health measures to improve road safety that focus on these times and that increase the visibility of children combined with education for drivers.

Keywords: major trauma, paediatric trauma, road traffic accidents, diurnal pattern

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Authors: Jesryl B. Paulite, Irene Alcantara-Papa, Teofila O. Zulaybar, Jocelyn T. Zarate, Virgie Ugay

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Banana is one of the major fruits in the Philippines in terms of volume of production and export earnings. The Philippines export of fresh Cavendish banana ranked No.1 with 22% share. One major threat to the industry is Fusarium wilt caused by Fusarium oxysporum f. sp. cubense. It tops as a major concern today affecting the Philippine banana industry since 2002 up to the present in Mindanao. Because of environmental and health issues concerning the use of chemical pesticides in the control of diseases, utilization of microorganisms has been significant in recent years as a promising alternative. This study aims to evaluate the potential of actinomycetes to control Fusarium wilt in Cavendish banana. The in-vitro experiments was carried out in Complete Randomized Design (CRD) while field experiment was laid out in a Randomized Complete Block Design (RCBD) with three treatments and three replications. Actinomycetes were isolated from mangrove soils in areas in Quezon and Bataan, Philippines. A total of 199 actinomycetes were isolated and 82 actinomycetes showed activity against the local Fusarium oxysporum (Foc) by agar plug assay. The test for antagonisms (AQ6, AQ30, and AQ121) of three best isolates Foc to were selected inhibiting Foc by 21.0mm, 22.0mm and 20.5mm, respectively. The same actinomycetes inhibited well Foc Tropical Race 4 showing 24.6 mm, 20.2mm and 19.0 mm zones of inhibition by agar plug assay, respectively. Combinations of the three isolates yielded an inhibition of 13.5 mm by cup cylinder assay. These findings led to the formulation of the mixed actinomycetes as biocontrol agents against Foc. A field experiment to evaluate the formulated mixed actinomycetes against Foc in a Foc infested field in Kinamayan, Sto Tomas, Davao Del Norte, Philippines. was conducted. Results showed that preventive method of application of the mixed actinomycetes against Foc showed promising results. A 56.66% mortality was observed in control set-up (no biocontrol agent added) compared to 33.33% mortality in preventive method. Further validation of the effectiveness of the mixed actinomycetes as biocontrol agent is presently being conducted in Asuncion, Davao Del Norte, Philippines.

Keywords: actinomycetes, biocontrol agents, cavendish banana, Fusarium oxysporum f. sp. cubense

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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: Lule Basha, Eralda Gjika

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This study is focused on analyzing climate change in Albania and its potential effects on cereal yields. Initially, monthly temperature and rainfalls in Albania were studied for the period 1960-2021. Climacteric variables are important variables when trying to model cereal yield behavior, especially when significant changes in weather conditions are observed. For this purpose, in the second part of the study, linear and nonlinear models explaining cereal yield are constructed for the same period, 1960-2021. The multiple linear regression analysis and lasso regression method are applied to the data between cereal yield and each independent variable: average temperature, average rainfall, fertilizer consumption, arable land, land under cereal production, and nitrous oxide emissions. In our regression model, heteroscedasticity is not observed, data follow a normal distribution, and there is a low correlation between factors, so we do not have the problem of multicollinearity. Machine-learning methods, such as random forest, are used to predict cereal yield responses to climacteric and other variables. Random Forest showed high accuracy compared to the other statistical models in the prediction of cereal yield. We found that changes in average temperature negatively affect cereal yield. The coefficients of fertilizer consumption, arable land, and land under cereal production are positively affecting production. Our results show that the Random Forest method is an effective and versatile machine-learning method for cereal yield prediction compared to the other two methods.

Keywords: cereal yield, climate change, machine learning, multiple regression model, random forest

Procedia PDF Downloads 83

Authors: Engin Berber, Nurettin Canakoglu, Ibrahim Sozdutmaz, Merve Caliskan, Shaikh Terkis Islam Pavel, Hazel Yetiskin, Aykut Ozdarendeli

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Crimean-Congo hemorrhagic fever virus (CCHFV) is a tick-borne virus in the family Bunyaviridae, genus Nairovirus. The CCHFV genome consists of three molecules of negative-sense single-stranded RNA, each encapsulated separately. The virion particle contains viral RNA polymerase (L segment), surface glycoproteins Gn and Gc (Msegment), and a nucleocapsid protein NP (S segment). CCHF is characterized by high case mortality, occurring in Asia, Africa, the Middle East and Eastern Europe. Clinical CCHF was first recognized in Turkey in 2002. The numbers of CCHF cases have gradually increased in Turkey making the virus a public health concern. Between 2002 and 2014, more than 8000 the CCHF cases have been reported in Turkey and mortality rate is around 5%. So, Turkey is one of the countries where the epidemy has become spread to the wider geography and the biggest outbreaks of CCHF have occurred in the world. We have recently developed an inactivated cell-culture based vaccine against CCHF. We have showed that the Balb/c mice immunized with the CCHF vaccine induced the high level of neutralizing antibodies. In this study, we aimed to determine the immunodominant regions of nucleoprotein (NP) CCHFV Kelkit06 strain which stimulate T cells. For this purpose, pools of overlapping NP were used for an IFN- γ ELISPOT assay. Balb/c mice were divided into two groups for the experiment. Two groups (n = 10 each) were immunized via the intraperitoneal route with 5, or 10μg of the cell culture-based vaccine. The control group (n = 6) was mock immunized with PBS. Booster injections with the same formulation were given on days 21 and 42 after the first immunization. The higher reactivity against the CCHFV NP pools 31-40 and 80-90 was determined in the two dose groups. In order to analyze the vaccine-induced T cell responses in Balb/c mice immunized with varying doses of the vaccine, we have been also currently working on CD4+, CD8+ and CD3 + T cells by flow cytometry.

Keywords: Crimean-Congo hemorrhagic fever virus, immunodominant regions of NP, T cell response, vaccine

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Authors: M. Manzoor, J. N. Ahmad, R. M. Giblin Davis, N. Javed, M. S. Haider

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The invasive Red Palm Weevil (RPW) (Rhynchophorus ferrugineus [Olivier] (Coleoptera: Curculionidae) is one of the most destructive palm pests in the world. Synthetic pesticides are environmentally hazardous pest control strategies being used in the past with emerging need of eco-friendly biological approaches including microbial entomopathogens for RPW management. The sublethal effects of a single entomopathogenic fungus (EPF) Beauveria bassiana (WG-11) (Ascomycota: Hypocreales) and two entomopathogenic nematode (EPN) species Heterorhabditis bacteriophora (Poinar) and Steinernema carpocapsae (Weiser) (Nematoda: Rhabditida) were evaluated in various combinations against laboratory-reared 3rd, 5th and 8th instar larvae of RPW in laboratory assays. Individual and combined effects of both entomopathogens (EP) were observed after the pre-application of B. bassiana fungus at 1-2-week intervals. A number of parameters were measured after the application of sub-lethal doses of EPF such as diet consumption, development, frass production, mortality, and weight gain. Combined treatments were tested for additive and synergistic effects. Synergism was more frequently observed in B. bassiana and S. carpocapsae combined treatments than in B. bassiana and H. bacteriophora combinations. Early instar larvae of RPW were more susceptible than older instars. Synergistic effects were observed in the 3rd and 5th instars exposed to B. bassiana and S. carpocapsae at 0, 7 and 14-day intervals. Whereas, in 8th instar larvae, the synergistic effect was observed only in B. bassiana and S. carpocapsae treatments after 0 and 7 days intervals. EPN treatments decreased pupation, egg hatching and emergence of adults. Lethal effects of nematodes were also observed in all growth stages of R. ferrugineus. Reduced larval weight, increased larval, pre-pupal and pupal duration, reduced adult weight and life span were observed. Sub-lethal concentrations of both entomopathogens induced variations in the different developmental stages and reduced food consumption, frass production, growth, and weight gain. So, on the basis of results, it is concluded that synthetic pesticides should be replaced with environmentally friendly sustainable biopesticides.

Keywords: H. bacteriophora, S. carpocapsae, B. bassiana, mortality

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Authors: Peter Park, Alfonso Ayala, Douglas Saeks, Jordan Miller, Carmen Flores, Karen Nelson

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Introduction Hip fractures account for more than 300,000 hospital admissions every year. Many present as fragility fractures in geriatric patients with multiple medical comorbidities. Standardized protocols for the multidisciplinary management of this patient population have been shown to improve patient outcomes. A hip fracture protocol was implemented at a Level I Trauma center with a focus on pre-operative medical optimization and early surgical care. This study evaluates the efficacy of that protocol, including the early transition period. Methods A retrospective review was performed of all patients ages 60 and older with isolated hip fractures who were managed surgically between 2020 and 2022. This included patients 1 year prior and 1 year following the implementation of a hip fracture protocol at a Level I Trauma center. Results 530 patients were identified: 249 patients were treated before, and 281 patients were treated after the protocol was instituted. There was no difference in mean age (p=0.35), gender (p=0.3), or Charlson Comorbidity Index (p=0.38) between the cohorts. Following the implementation of the protocol, there were observed increases in time to surgery (27.5h vs. 33.8h, p=0.01), hospital length of stay (6.3d vs. 9.7d, p<0.001), and ED LOS (5.1h vs. 6.2h, p<0.001). There were no differences in in-hospital mortality (2.01% pre vs. 3.20% post, p=0.39) and complication rates (25% pre vs 26% post, p=0.76). A trend towards improved outcomes was seen after the early transition period but failed to yield statistical significance. Conclusion Early medical management and surgical intervention are key determining factors affecting outcomes following fragility hip fractures. The implementation of a hip fracture protocol at this institution has not yet significantly affected these parameters. This could in part be due to the restrictions placed at this institution during the COVID-19 pandemic. Despite this, the time to OR pre-and post-implementation was quicker than figures reported elsewhere in literature. Further longitudinal data will be collected to determine the final influence of this protocol. Significance/Clinical Relevance Given the increasing number of elderly people and the high morbidity and mortality associated with hip fractures in this population finding cost effective ways to improve outcomes in the management of these injuries has the potential to have enormous positive impact for both patients and hospital systems.

Keywords: hip fracture, geriatric, treatment algorithm, preoperative optimization

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Authors: Marc Solé, Francesc Giné, Magda Valls, Nina Bijedic

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What people say on social media has turned into a rich source of information to understand social behavior. Specifically, the growing use of Twitter social media for political communication has arisen high opportunities to know the opinion of large numbers of politically active individuals in real time and predict the global political tendencies of a specific country. It has led to an increasing body of research on this topic. The majority of these studies have been focused on polarized political contexts characterized by only two alternatives. Unlike them, this paper tackles the challenge of forecasting Spanish political trends, characterized by multiple political parties, by means of analyzing the Twitters Users political tendency. According to this, a new strategy, named Tweets Analysis Strategy (TAS), is proposed. This is based on analyzing the users tweets by means of discovering its sentiment (positive, negative or neutral) and classifying them according to the political party they support. From this individual political tendency, the global political prediction for each political party is calculated. In order to do this, two different strategies for analyzing the sentiment analysis are proposed: one is based on Positive and Negative words Matching (PNM) and the second one is based on a Neural Networks Strategy (NNS). The complete TAS strategy has been performed in a Big-Data environment. The experimental results presented in this paper reveal that NNS strategy performs much better than PNM strategy to analyze the tweet sentiment. In addition, this research analyzes the viability of the TAS strategy to obtain the global trend in a political context make up by multiple parties with an error lower than 23%.

Keywords: political tendency, prediction, sentiment analysis, Twitter

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