Absolute Precision Research Articles

A comparison of pharmacokinetics software for therapeutic drug monitoring of piperacillin in patients with severe infections

ObjectiveTo evaluate the predictive performance of population pharmacokinetic models for piperacillin (PIP) available in the software MwPharm, TDMx and ID-ODs for initial dosing selection and therapeutic drug monitoring (TDM) purposes.MethodsThis...

Trust-aware denoising autoencoder with spatial-temporal activity for cross-domain personalized recommendations

Recently, cross-domain recommendation systems have been very helpful in improving the quality of recommendation and solving the problem of cold start and data sparsity. Cross-domain recommender systems allow the transfer of knowledge from one domain with dense ratings to other domain with sparse ratings. Such transfer of knowledge helps in addressing the data sparseness and cold start issues in traditional recommender systems. Although cross-domain recommendations have evolved significantly, yet employment of various factors, such as time, trust, and location remains a challenge. Most of the existing approaches ignore the important fact that at what specific time the user may be interested in the recommended item. Moreover, a person’s trust level and sentiments may be influenced by the variation in the location and time, thereby affecting the decision making. In this paper, we propose a cross-domain recommender system that not only takes into account the time at finer granularity levels (e.g., hours, days, weeks, etc.), but also considers a persons location, trust level, and sentiment analysis while computing recommendations. Our proposed model, named as, Trust-Aware Spatial-Temporal Activity based Denoising Autoencoder (TSTDAE), employs autoencoder-based deep-learning models to generate top-N recommendations for a given user and addresses the cold-start problem in the cross-domain scenario of ‘User Overlap’. The proposed work is fivefold: i) Filter out the users’ biased profiles based on sentiment analysis. ii) Learn the features using autoencoder and then perform clustering among the users of source and target domains to discover the best neighbors. iii) Compute the trust and ratio of preference bias between active user (the user to whom top-N items are recommended) and their neighbors and grade the neighbors based on unbiased preferences iv) Project the best time for recommending the items to an active user and generate the top-N recommendations. We have evaluated the proposed model on a public dataset of e-commerce retail service ‘AliExpress’ for the evaluation metrics: Precision, Mean Absolute Precision (MAP), Normalized Discounted Cumulative Gain (NDCG), and Hit Ratio (HR). The experimental results showed improved performance of the proposed system over the existing models.

A New Approach to Satellite-Derived Bathymetry: An Exercise in Seabed 2030 Coastal Surveys

ARGANS has years of experience in analysing the factors limiting light transmission in coastal environments around the world. This has led its Satellite-Derived Bathymetry (SDB) team to the conclusion that current satellite instruments and their resolution are unable to provide the absolute precision required for safe navigation according to International Hydrographic Bureau (IHO) standards, except in the clearest of waters characterised by sufficiently well-defined environmental parameters. This limitation is caused by the variability itself of the parameters in the radiative transfer equation (RTE), which is too high to provide results within the strict accuracy ranges of IHO S.44, as shown by scatter plots characterised time and again by large biases and standard deviations of several metres. The radiative transfer equation and Hydrolight simulations are not at fault, but their results must be interpreted with extreme caution if the level of accuracy required for safe navigation over large areas is to be guaranteed. Therefore, ARGANS has developed an innovative, alternative method. This is based on the classification of the full range of images pixels allowing for homogeneous sub-areas to be determined and linked together by artificial intelligence and statistical clustering of similar parameters. Thanks to the sponsorship of the European Space Agency and Seabed 2030, ARGANS has been able to test its Water Column Parameter Estimator (WCPE), first in the challenging waters of Madagascar updating over 1000 km of lead line exploratory surveys, then in the South Pacific coral environment and then in the turbid coastal water of Qatar. The parameters determined by WCPE can be extrapolated to unknown regions of similar environment and propagated from one place to the next, using a chain method somewhat inspired by photogrammetric techniques of old. Further progress and automation can be expected from an improved control of sediment plumes that can obscure or distort all optical methods, whether satellite or LIDAR.

The Relationship Between Gadget Usage and Adolescents Eye Health

The changes in public life style cause the raising demand in practical communication media. The data from a survey conducted in 2014 in United States shows that the gadgets ownership in group of people ranged from 13 to 15 years old has increased from 35% to 55%, and tablet ownership has increased twice as much, from 18% to 37%. Referring to the previous study, it is said that teenagers are susceptible to experience the negative effect from the excessive use of gadget, for the gadget screen exposure could induce the release of dopamine hormone which has important role in shaping addictive trait. To prevent such impact, the government of Denpasar City should be having the latest data about the gadget use in teenagers, especially early teenagers. The study aims to determine the relationship between the use of gadgets with eye health in adolescent.
 Methods: This research uses quantitative methods. The research is conducted in SMP Negeri 3, 6, 7, 8 and 9 Denpasar. The data gathering was conducted from July 23 until August 19, 2020. The technique for determining the minimum sample size is the estimation of the proportion in a simple random sample with absolute precision.
 Results: Bivariate analysis with chi-square test showed p-value = 0.000. There is a relationship between gadget use and the eye health on teenager informants.
 Conclusion: There is a positive and significant relationship between the use of gadgets with eye health in adolescent.

LiDAR Platform for Acquisition of 3D Plant Phenotyping Database.

Currently, there are no free databases of 3D point clouds and images for seedling phenotyping. Therefore, this paper describes a platform for seedling scanning using 3D Lidar with which a database was acquired for use in plant phenotyping research. In total, 362 maize seedlings were recorded using an RGB camera and a SICK LMS4121R-13000 laser scanner with angular resolutions of 45° and 0.5° respectively. The scanned plants are diverse, with seedling captures ranging from less than 10 cm to 40 cm, and ranging from 7 to 24 days after planting in different light conditions in an indoor setting. The point clouds were processed to remove noise and imperfections with a mean absolute precision error of 0.03 cm, synchronized with the images, and time-stamped. The database includes the raw and processed data and manually assigned stem and leaf labels. As an example of a database application, a Random Forest classifier was employed to identify seedling parts based on morphological descriptors, with an accuracy of 89.41%.

Impact of Laser Intensity Noise on Dual-Comb Absolute Ranging Precision.

Noise in mode-locked lasers has been a central issue for dual-comb metrological applications. In this work, we investigate the laser intensity noise on dual-comb absolute ranging precision. Two different dual-comb schemes based on linear optical sampling (LOS) and nonlinear asynchronous optical sampling (ASOPS) have been constructed. In the LOS scheme, the ranging precision deteriorates with the increase in laser relative intensity noise (RIN). This effect can be corrected by implementing a balanced photo-detection (BPD). In the ASOPS scheme, the experiment shows that the conversion from laser RIN to dual-comb ranging precision is negligible, making a balanced detection unnecessary for ranging precision improvement. The different manners of RIN’s impact on absolute ranging precision are attributed to the distinct cross-correlation signal patterns and the underlying time-of-flight (TOF) extraction algorithms.

A comparative study on outlier detection techniques for noisy production data from unconventional shale reservoirs

Decline curve analyses (DCA) and rate transient analyses (RTA) are widely used to characterize the fluid flow through porous media and forecast future production. Oil and gas production data are routinely analyzed for history matching and optimizing the well stimulation methods in hydrocarbon exploration and production lifecycle. However, outliers add significant uncertainty and non-uniqueness to results from production data analysis. This study provides a structured and comprehensive overview of five widely used outlier detection (OD) techniques for identifying and removing outliers in production data. Each OD technique measures deviation differently and, therefore, has a different outcome even when applied to the same dataset, creating the need to test several methods and find the optimal technique for identifying and removing outliers from production data. First, we generated production data from a typical multi-fractured horizontal well using a numerical reservoir simulator and added random noise to the data. Then, we used five different OD techniques to identify the prelabeled outliers from the synthetic production data. Finally, we identified the best-performing OD algorithm by comparing the various evaluation metrics such as the mean absolute error (MAE), precision, sensitivity, and F1 score. Results showed that the angle-based OD (ABOD) had the best MAE, precision, sensitivity, and F1 score of 8%, 85%, 98%, and 0.90, respectively. The next best-performing OD technique was distance-based OD (DBOD), with MAE, precision, sensitivity, and F1 score of 16%, 71%, 100%, and 0.83, respectively. We tested the ABOD method on several field production datasets by assuming different outlier thresholds (fraction of the data points likely to be outliers). Visual inspection of the processed data showed that the ABOD method effectively identified and removed the outliers from a relatively clean dataset (outlier threshold of 20%) and a highly noisy dataset (outlier threshold of 80%). This algorithm is intuitive and can effectively identify and remove outliers from the field production data to improve production forecasting, reserves estimation, and rate transient analysis for multi-fractured horizontal oil and gas reservoirs.

Fetal Macrosomia Among Non-diabetic Women: Our Experience in a Developing Country.

BackgroundThe prevalence of fetal macrosomia varies worldwide. Its trend has increased over the past decades in many developed nations. It is associated with various maternal and fetal complications. The information regarding the frequency of fetal macrosomia among non-diabetic women is limited in resource-limited countries such as Pakistan. Therefore, this study aimed to determine the number of fetal macrosomia cases among non-diabetic women.MethodologyThis was a cross-sectional study conducted in a tertiary care hospital in Peshawar, Pakistan. A total of 119 pregnant women were enrolled in the study. All pregnant women aged 15 to 45 years who had singleton pregnancies with any parity or gravida and a gestational age of ≥37 weeks were included in the study. Pregnant women with underlying chronic systemic disorders such as diabetes mellitus, gestational diabetes mellitus, hypertension, renal or cardiac disorders, and sickle cell anemia were excluded from the study. Women who did not consent to participate and those with a gestational age of ≥42 weeks at the time of delivery were also excluded from our study. Based on a 5.2% prevalence of fetal macrosomia in the general population, the sample size was calculated using the World Health Organization calculator at a confidence interval of 95%, absolute precision of 0.05 with anticipated population proportion, and a 4% margin of error. The required sample size was calculated at 119. The chi-square test was applied. P-values of ≤0.05 were considered significant.ResultsOut of 119 participants, fetal macrosomia among non-diabetic women was seen in 10 (8.4%) cases. The mean age of patients in our study was 29.80 ± 4.33 years. The mean gestational age was 36.05 ± 1.31 weeks, whereas the mean body mass index of participants was 29.17 ± 2.36 kg/m2. Post-stratification, spontaneous vaginal delivery was the only significant variable with a P-value of <0.05 in our study.ConclusionsThe number of fetal macrosomia among non-diabetic women in our study was 10 (8.4%). Because this was a single-center, hospital-based, cross-sectional study, we need to conduct large multi-centered randomized controlled studies to identify the actual prevalence of fetal macrosomia in non-diabetic women in our population.

Integrating Deep Learning-Based IoT and Fog Computing with Software-Defined Networking for Detecting Weapons in Video Surveillance Systems.

Due to the widespread proliferation of multimedia traffic resulting from Internet of Things (IoT) applications and the increased use of remote multimedia-based applications, as a consequence of COVID-19, there is an urgent need to develop intelligent adaptive techniques that improve the Quality of Service (QoS) perceived by end-users. In this work, we investigate the integration of deep learning techniques with Software-Defined Network (SDN) architecture to support delay-sensitive applications in IoT environments. Weapon detection in real-time video surveillance applications is deployed as our case study upon which multiple deep learning-based models are trained and evaluated for detection using precision, recall, and mean absolute precision. The deep learning model with the highest performance is then deployed within a proposed artificial intelligence model at the edge to extract the first detected video frames containing weapons for quick transmission to authorities, thus helping in the early detection and prevention of different kinds of crimes, and at the same time decreasing the bandwidth requirements by offloading the communication network from massive traffic transmission. Performance improvement is achieved in terms of delay, throughput, and bandwidth requirements by dynamically programming the network to provide different QoS based on the type of offered traffic and current traffic load, and based on the destination of the traffic. Performance evaluation of the proposed model was carried out using the mininet emulator, which revealed improvement of up to 75.0% in terms of average throughput, up to 14.7% in terms of mean jitter, and up to 32.5% in terms of packet loss.

An optimized recommendation framework exploiting textual review based opinion mining for generating pleasantly surprising, novel yet relevant recommendations

Serendipity is a critical factor in the Recommender Systems (RS) in delivering pleasantly surprising, novel, yet contextually relevant recommendations. Most existing methods improve serendipity in RS by learning user preferences based on item popularity or similarity. However, the effectiveness of these methods in mitigating popularity bias and generating novel and unexpected item recommendations remains poorly understood. Recent studies suggest improvement in user preference by incorporating textual opinion provided by the user on an item. Additionally, the trade-off relationship between serendipity's conflicting components, including relevance, novelty, and unexpectedness, warrants further investigation to improve the quality of top-n recommendations. Hence, this research proposes an opinion mining-based approach to learn the users' personalized preferences from the textual reviews and incorporate both rating and reviews preferences to improve the quality of the recommendation list. Next, we design a new Two-Fold Algorithmic (TFA) approach-based objective function for serendipity to mitigate the popularity bias by aggregating uncertainty based on item popularity and item similarity to user preferences. Lastly, a multi-objective evolutionary algorithm-based Serendipity Objective Optimization-based Recommendation Framework(SOORF) is designed to optimize the serendipity's conflicting components. Extensive simulations are conducted over four benchmark datasets. The Mean Absolute Precision(MAP)@n and [email protected] based evaluation findings of SOORF and TFA demonstrate an improvement of at least 8.10% and 58.48%, respectively. The [email protected] and [email protected] based evaluations on different dataset sparsity conditions are observed with an improvement of at least 13.59% and 27.73% over baseline models. The Pareto front shows the models' ability to generate surprising, novel, yet relevant recommendations.

The prevalence of Strongylida/strongyles in small ruminants in Manatuto Municipality in central region of Timor-Leste

&lt;p class="MDPI17abstract"&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This research aimed to identify the prevalence of Strongyles in small ruminants (sheep and goats) in the central region of Timor-Leste.&lt;/p&gt;&lt;p class="MDPI17abstract"&gt;&lt;strong&gt;Methods: &lt;/strong&gt;There were two methods used in this research, i.e. &lt;em&gt;multistage sampling&lt;/em&gt; to determine research sites and &lt;em&gt;purposive sampling &lt;/em&gt;to collect samples. The total samples collected were 192. From the 192 samples collected, 96 samples were from goats and another 96 samples from sheep. The sample size was determined based on 50% design prevalence and 95% confidence level&lt;em&gt; &lt;/em&gt;and absolute precision of 10%. Samples examined were fresh fecal samples from sheep and goats. The samples were tested using a native smear to identify the presence of worms’ eggs in the feces. The 95% confidence interval of the prevalence was calculated using the exact binomial method.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p class="MDPI17abstract"&gt;&lt;strong&gt;Results&lt;/strong&gt;&lt;strong&gt;: &lt;/strong&gt;Based on laboratory examination, it was found that the prevalence of&lt;strong&gt; &lt;/strong&gt;&lt;em&gt;Strongylida/strongyles&lt;/em&gt; in sheep was 21.88% (14.1-31.5%) and in goats was 20.83% (13.2-30.3%). Based on sex, the highest prevalence of &lt;em&gt;Strongylida/strongyles &lt;/em&gt;of sheep was found in ram 25% (13.6-39.6%) compared to 18.75% (8.9-32.6%) in the ewe. In goats, however, the prevalence of &lt;em&gt;Strongylida/strongyles &lt;/em&gt;was 25% (13.6-39.6%) in doe and 16.67% (7.5-30.2%) in bucks. Based on age, the highest prevalence in sheep was found in the age group of 9-12 months old 31.25% (16.1-50.0%), whereas in goats, the highest prevalence was found in the age group of 0-4 months old 28.13% (13.7-46.7%).&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p class="MDPI17abstract"&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Based on this research&lt;strong&gt;, &lt;/strong&gt;it was identified that the prevalence of &lt;em&gt;Strongylida/strongyles&lt;/em&gt; in small ruminants (sheep and goats) in the central region of Timor-Leste was 21.35% (15.8-27.8%).&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;

Absolute precision confidence intervals for unstandardized mean differences using sequential stopping rules.

Societies and journals in psychology encourage use of confidence intervals (CIs) on effect sizes. Gaining a maximum of precision of the CI at a minimum cost is desirable. Methods are available to calculate a sample size to provide some percent "assurance" that the final CI will be no wider than a desired value under the fixed-sample rule (FSR), in which a sample size must be decided a priori. Such assurance is expensive, and still subject to failure. The desired width can be specified either in standardized or unstandardized units, and this article focuses on unstandardized widths. A sequential stopping rule (SSR) can generate a CI for a mean difference that is always the desired width, and the average use of subjects is about the same as the FSR sample size without assurance. Improper use of sequential sampling can lead to a CI that has degraded coverage - the interval may contain the population value only 90% of the time for a nominal 95% CI. SSR methods are available to deliver CIs that are within a certain tolerance of the nominal coverage or that are at least the nominal coverage on average. These methods can be assessed by simulations, and they involve a manipulation of the minimum sample size, the desired width, or the practical confidence coefficient used to calculate CIs during the SSR experiment. As with FSR, more exact CIs are generated when the population standard deviation can be estimated accurately.

Enhanced Single Shot Small Object Detector for Aerial Imagery Using Super-Resolution, Feature Fusion and Deconvolution.

One common issue of object detection in aerial imagery is the small size of objects in proportion to the overall image size. This is mainly caused by high camera altitude and wide-angle lenses that are commonly used in drones aimed to maximize the coverage. State-of-the-art general purpose object detector tend to under-perform and struggle with small object detection due to loss of spatial features and weak feature representation of the small objects and sheer imbalance between objects and the background. This paper aims to address small object detection in aerial imagery by offering a Convolutional Neural Network (CNN) model that utilizes the Single Shot multi-box Detector (SSD) as the baseline network and extends its small object detection performance with feature enhancement modules including super-resolution, deconvolution and feature fusion. These modules are collectively aimed at improving the feature representation of small objects at the prediction layer. The performance of the proposed model is evaluated using three datasets including two aerial images datasets that mainly consist of small objects. The proposed model is compared with the state-of-the-art small object detectors. Experiment results demonstrate improvements in the mean Absolute Precision (mAP) and Recall values in comparison to the state-of-the-art small object detectors that investigated in this study.

The lived experiences of radiotherapists treating paediatric patients: Gauteng, South Africa

BackgroundPaediatric cancers are rare and more complex than adult cancers. Consequently, treatment for paediatric cancers is complex, requiring absolute precision and accuracy in delivering the prescribed dose. For radiotherapists, the complexity of performing paediatric radiotherapy treatment is augmented by its rarity, time and pressure constraints, the patient's lack of cooperation, anxiety and emotional challenges. These multiple challenges may affect the quality of patient care if they are not recognised and reduced. Therefore, this study explored radiotherapists experience of treating paediatric patients. ObjectiveThe purpose of this study was to explore and describe the lived experiences of radiotherapists treating paediatric patients. MethodsThis qualitative phenomenological study purposively sampled qualified radiotherapists involved in paediatric treatment planning and delivery in Gauteng, South Africa. Data were collected through 17 individual, in-depth interviews and underwent thematic analysis. ResultsThematic analysis identified the emotional context in which radiotherapists work when treating paediatric patients and how radiotherapists’ familiarity affects their experience of treating paediatric patients. The closeness of contact with the paediatric patient, parents and family dimension impacted participants positively and negatively. ConclusionThe study confirmed that radiotherapists treating paediatric patients work within a highly intense emotional context. The importance of gaining familiarity and experience in paediatric radiotherapy to enhance competency, was identified. Radiotherapist’s experienced both positive and negative implications due to their close contact with the paediatric patient, parents and family dimension. The necessity to advance the practical infrastructural areas and strive for enhanced professionalism to improve paediatric radiotherapy was thus emphasised.

Identification of Risk Factors and Development of Predictive Risk Score Model for Mortality after Open Ruptured Abdominal Aortic Aneurysm Repair.

Background and Objectives: Despite the relatively large number of publications concerning the validation of these models, there is currently no solid evidence that they can be used with absolute precision to predict survival. The goal of this study is to identify preoperative factors that influenced 30-day mortality and to create a predictive model after open ruptured abdominal aortic aneurysm (RAAA) repair. Materials and Methods: This was a retrospective single-center cohort study derived from a prospective collected database, between 1 January 2009 and 2016. Multivariate logistic regression analysis was used to identify all significant predictive factors. Variables that were identified in the multivariate analysis were dichotomized at standard levels, and logistic regression was used for the analysis. To ensure that dichotomized variables were not overly simplistic, the C statistic was evaluated for both dichotomized and continuous models. Results: There were 500 patients with complete medical data included in the analysis during the study period. Of them, 37.6% were older than 74 years, and 83.8% were males. Multivariable logistic regression showed five variables that were predictive of mortality: age > 74 years (OR = 4.01, 95%CI 2.43–6.26), loss of consciousness (OR = 2.21, 95%CI 1.11–4.40), previous myocardial infarction (OR = 2.35, 95%CI 1.19–4.63), development of ventricular arrhythmia (OR = 4.54, 95%CI 1.75–11.78), and DAP < 60 mmHg (OR = 2.32, 95%CI 1.17–4.62). Assigning 1 point for each variable, patients were stratified according to the preoperative RAAA mortality risk score (range 0–5). Patients with 1 point suffered 15.3% mortality and 3 points 68.2% mortality, while all patients with 5 points died. Conclusions: This preoperative RAAA score identified risk factors readily assessed at the bedside and provides an accurate prediction of 30-day mortality after open repair of RAAA.

ASSESSMENT OF FRONTLINE HEALTH WORKERS KNOWLEDGE AND ATTITUDE TOWARDS TUBERCULOSIS SCREENING IN OYO STATE

Background: A good knowledge of Tuberculosis (TB) and favourable attitudinal disposition of frontline health workers (FLHWs) would be required to improve TB diagnosis, treatment, and prevention and to reach global eradication targets. This study aims to assess the level of knowledge and attitudinal disposition of frontline health workers towards Tuberculosis screening and case finding in Oyo State, Nigeria. Methods: A cross-sectional study was conducted among FLHWs in Oyo State, Nigeria. Respondents were randomly selected, health workers at DOTS and ART units were excluded from the study. A sample size of 102 was calculated based on results of previous study (Global Fund, 2018). A confidence interval of 95% and absolute precision of 0.05 were used. Eighteen (18) Local Government Areas (LGAs) out of 33 in the State were randomly selected and 5 FLHWs meeting the inclusion criteria across 5 health facilities were selected at each LGA. The study was approved by the Babcock university health research ethical committee (BUHREC). Signed consents were also obtained from the respondents. A semi-structured questionnaire comprising of 43 questions covering socio-demographic information (13), knowledge (15) and attitude (15) of the respondents. It was developed based on WHO Advocacy, communication and social mobilization for TB control: A guide to developing knowledge, attitude and practice surveys instrument(World Health Organization, 2008). Data was analyzed using Statistical Package for Social Sciences (SPSS), version 20. Results were presented in percentages and proportions. The association between knowledge and attitude was tested using chi-square. Significance was considered at a p-value &lt; 0.05. Results Socio-demographic data shows that 71% (71/100) of the respondents were aged 40 years and above, 88% (88/100) were females and the CHOs were the most prominent cadre- 64% (64/100). Total knowledge score ranged from 7 to 13 with an average total knowledge score of 10.7 (SD 1.4) with only 34% (34/100) having good knowledge of Tuberculosis. Attitudinal scores ranged from 5 to 13, mean attitudinal score was 8.59 (SD 2.225). Only 11 percent (11/100) of the respondents had a good attitude towards TB screening, a larger proportion (80%) had fair attitude while 9% had poor attitude. Chi-Square test of association shows a significant relationship between knowledge and attitude X2 (2, N=100) = 9.992, p = .007, effect size was however low 0.25. Conclusion: Knowledge and attitudinal disposition of frontline health workers to TB and TB screening in Oyo State is sub-optimal. Further studies might be needed to examine the effect of structured training towards improving health workers attitude towards TB services including screening.

Applications of an improved Dixon elimination method for the inverse kinematics of 6R manipulators

Variable elimination of high dimensional polynomials is an essential problem for robot inverse kinematics, but it is hard to approach both high efficiency and precision simultaneously. This study proposes a unique elimination method for vector polynomial systems established for kinematic modelling of six-revolute serial manipulators, and then applies it to derive analytical inverse kinematic solutions in real time with high absolute precision. To begin, the kinematic modelling equations are parameterized into a non-redundant vector polynomial system, so as to make full use of vector properties in elimination processing. After that, an improved elimination method based on the Dixon resultant is proposed as a solution to the polynomial computation issue. Since there exist some duplicate terms in the Dixon polynomial, a linear constrained Dixon matrix is created by extracting linear terms from variable sequences. Furthermore, the inverse kinematic problem for six-revolute manipulators is addressed based on the elimination method, wherein the configurations of general decoupling manipulators are designed considering both precise machining and fast solutions. The proposed formulations are ported into the core algorithm of professional software, demonstrating that they are highly accurate and real-time required for precise manipulators of automatic use.

English

BACKGROUND Cirrhosis is an immunocompromised state that predisposes patients to spontaneous bacterial infections, hospital-acquired infections, and a variety of infections from uncommon pathogens. The incidence of antibiotic resistant pathogens has been increasingly detected, especially in healthcare-associated settings. In this era of highly effective antibiotics, infections are still found to be major cause for hospitalisation and mortality in patient with CLD (chronic liver disease). Viral infections and viral translocations in cirrhosis needs more data and research to comment at present. No recent data available about bacterial infections and resistance pattern in cirrhotic patients in our region including Kerala region (South India). There is a need to produce data regarding this, which can help to identify the burden and need for newer strategies. METHODS This is a retrospective study conducted in Department of Gastroenterology, Pushpagiri Medical College, Thiruvalla, from September 2018 to January 2019. A preliminary chart review of 30 patients showed that 46% had SBP. Sample size calculated was 95 for alpha error of 1.96 with an absolute precision of 10%. Consecutive 100 patients (approximated sample size) admitted with culture proven infections (except in skin and soft tissue infections) were evaluated between September 2018 to January 2019. Infections were classified according to source and drug resistance. Diagnosis based on culture positivity and standard diagnostic criteria. Skin and soft tissue infections diagnosed after meticulous clinical examinations, blood investigations, cultures and ruling out other possible mimics. In view of all the variables are nominal or ordinal, significance calculated as proportions and percentages. RESULTS In present study, common infections were SBP (40%), urinary tract infection (UTI) (28%), skin and soft tissue infections (SSTI) (18%) and lower respiratory infection (LRTI) (12%). Enterobacteriaceae (E. coli and klebsiella) was found to be commonest among all infections followed by enterococcus, streptococcus, staphylococcus and candida. Pseudomonas aeruginosa (second isolate) isolated from patient with SSTI and aeromonas species (second isolate) was isolated from one patient with SBP. Multidrug resistance (MDR) was reported in 44 cases out of 96 total culture positive cases. Approximately about 46% of the study sample reported as MDR. CONCLUSIONS Most common infection found to be SBP followed by UTI, SSTIs and LRTI. Most prevalent organisms were E. coli and klebsiella. Prevalence of SSTI was found to be higher compared to reported literature. Drug resistance especially in klebsiella infections found to be increased compared to available studies. KEY WORDS CLD: Chronic Liver Disease, SBP: Spontaneous Bacterial Peritonitis, UTI: Urinary Tract Infection, LRTI: Lower Respiratory Tract Infection, SSTI: Skin and Soft Tissue Infection, MDR: Multi Drug Resistance.

A Map-Based Recommendation System and House Price Prediction Model for Real Estate

In 2015, global real estate was worth $217 trillion, which is approximately 2.7 times the global GDP; it also accounts for roughly 60% of all conventional global resources, making it one of the key factors behind any country’s economic growth and stability. The accessibility of spatial big data will help real estate investors make better judgement calls and earn additional profit. Since location is deemed necessary for real estate and consequent decision-making, digital maps have become a prime resource for real estate purchases, planning and development. Personalisation can assist in making judgments by identifying user desires and inclinations, which can then be recorded or captured as a user performs some interactions with a digital map. A personalised real estate portal can use this information to suggest properties, assist homeowners and provide valuable real estate analytics. This article presents a novel framework for recommending real estate to users. By monitoring user interactions through an online real estate portal, the framework can make personalised recommendations of real estate based on content, collaboration and location. The effectiveness of the recommendations was tested by the user feedback mechanism through a method of mean absolute precision, and the results show that 79% precise suggestions were generated, i.e., out of 5 recommendations produced, users were interested in at least 3. Along with that, a separate house price prediction model based on neural networks and classical regression techniques was also implemented to assist users in making an informed decision regarding prospects of real estate purchase.

On the evaluation of the eigendecomposition of the Airy integral operator

The distributions of the $k$-th largest level at the soft edge scaling limit of Gaussian ensembles are some of the most important distributions in random matrix theory, and their numerical evaluation is a subject of great practical importance. One numerical method for evaluating the distributions uses the fact that they can be represented as Fredholm determinants involving the so-called Airy integral operator. When the spectrum of the integral operator is computed by discretizing it directly, the eigenvalues are known to at most absolute precision. Remarkably, the Airy integral operator is an example of a so-called bispectral operator, which admits a commuting differential operator that shares the same eigenfunctions. In this manuscript, we develop an efficient numerical algorithm for evaluating the eigendecomposition of the Airy integral operator to full relative precision, using the eigendecomposition of the commuting differential operator. This allows us to rapidly evaluate the distributions of the $k$-th largest level to full relative precision rapidly everywhere except in the left tail, where they are computed to absolute precision. In addition, we characterize the eigenfunctions of the Airy integral operator, and describe their extremal properties in relation to an uncertainty principle involving the Airy transform. We observe that the Airy integral operator is fairly universal, and we describe a separate application to Airy beams in optics. Using the eigenfunctions, we compute a finite-energy Airy beam that is optimal, in the sense that the beam is both maximally concentrated, and maximally non-diffracting and self-accelerating.