Search results for: predictive coding

Authors: Osama Zayed

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Aim of the work: The aim of this study is to assess the predictors of pelvic vascular injuries in patients with pelvic fractures from major blunt trauma. Methods: This study was conducted as a tool-assessment study. Forty six patients with pelvic fractures from major blunt trauma will be recruited to the study arriving to department of emergency, Suez Canal University Hospital. Data were collected from questionnaire including; personal data of the studied patients and full medical history, clinical examinations, outcome measures (The Physiological and Operative Severity Score for enumeration of Mortality and morbidity (POSSUM), laboratory and imaging studies. Patients underwent surgical interventions or further investigations based on the conventional standards for interventions. All patients were followed up during conservative, operative and post-operative periods in the hospital for interpretation the predictive scores of vascular injuries. Results: Significant predictors of vascular injuries according to computed tomography (CT) scan include age, male gender, lower Glasgow coma (GCS) scores, occurrence of hypotension, mortality rate, higher physical POSSUM scores, presence of ultrasound collection, type of management, higher systolic blood pressure (SBP) and diastolic blood pressure (DBP) POSSUM scores, presence of abdominal injuries, and poor outcome. Conclusions: There was higher frequency of males than females in the studied patients. There were high probability of morbidity and low probability of mortality among patients. Our study demonstrates that POSSUM score can be used as a predictor of vascular injury in pelvis fracture patients.

Keywords: predictors, pelvic vascular injuries, pelvic fractures, major blunt trauma, POSSUM

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Authors: Dawit Abay Tesfamariam

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Ethiopia’s Climate- Resilient Green Economy strategy focuses mainly on generating and utilization of Renewable Energy (RE). The data collected in 2016 by Ethiopian Electric Power (EEP) indicates that the intermittent RE sources on the grid from solar and wind energy were only 8 % of the total energy produced. On the other hand, the EEP electricity generation plan in 2030 indicates that 36 % of the energy generation share will be covered by solar and wind sources. Thus, a case study was initiated to model and compute the balance and consumption of electricity in three different scenarios: 2016, 2025, and 2030 using the Energy PLAN Model (EPM). Initially, the model was validated using the 2016 annual power-generated data to conduct the EPM analysis for two predictive scenarios. The EPM simulation analysis using EPM for 2016 showed that there was no significant excess power generated. Hence, the model’s results are in line with the actual 2016 output. Thus, the EPM was applied to analyze the role of energy storage in RE in Ethiopian grid systems. The results of the EPM simulation analysis showed there will be excess production of 402 /7963 MW average and maximum, respectively, in 2025. The excess power was dominant in all months except in the three rainy months of the year (June, July, and August). Consequently, based on the validated outcomes of EPM indicates, there is a good reason to think about other alternatives for the utilization of excess energy and storage of RE. Thus, from the scenarios and model results obtained, it is realistic to infer that; if the excess power is utilized with a storage mechanism that can stabilize the grid system; as a result, the extra RE generated can be exported to support the economy. Therefore, researchers must continue to upgrade the current and upcoming energy storage system to synchronize with RE potentials that can be generated from RE.

Keywords: renewable energy, storage, wind, energyplan

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Authors: Irfan Ahmad Afip

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This main research objective is to accurately identify the possibility for a Leptospirosis outbreak severity of a certain area based on its input features into a multivariate regression model. The research question is the possibility of an outbreak in a specific area being influenced by this feature, such as social demographics and hydrometeorological extremes. If the occurrence of an outbreak is being subjected to these features, then the epidemic severity for an area will be different depending on its environmental setting because the features will influence the possibility and severity of an outbreak. Specifically, this research objective was three-fold, namely: (a) to identify the relevant multivariate features and visualize the patterns data, (b) to develop a multivariate regression model based from the selected features and determine the possibility for Leptospirosis outbreak in an area, and (c) to compare the predictive ability of multivariate regression model and machine learning algorithms. Several secondary data features were collected locations in the state of Negeri Sembilan, Malaysia, based on the possibility it would be relevant to determine the outbreak severity in the area. The relevant features then will become an input in a multivariate regression model; a linear regression model is a simple and quick solution for creating prognostic capabilities. A multivariate regression model has proven more precise prognostic capabilities than univariate models. The expected outcome from this research is to establish a correlation between the features of social demographic and hydrometeorological with Leptospirosis bacteria; it will also become a contributor for understanding the underlying relationship between the pathogen and the ecosystem. The relationship established can be beneficial for the health department or urban planner to inspect and prepare for future outcomes in event detection and system health monitoring.

Keywords: geographical information system, hydrometeorological, leptospirosis, multivariate regression

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Authors: A. Kherraf, M. Bouziane, A. Drighil, L. Azzouzi, R. Habbal

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Objective: Myxomas are rare heart tumors most commonly found in the left atrium. The purpose of this observation is to report a rare case of myxoma of the right atrium revealed by pulmonary embolism. Observation: A 34-year-old patient with no history presented to the emergency room with sudden onset dyspnea. Clinical examination showed arterial pressure at 110/70mmHg, tachycardia at 110bpm, and 90% oxygen saturation. The ECG enrolled in incomplete right bundle branch block. The radio-thorax was normal. Echocardiography revealed the presence of a large homogeneous intra-OD mass, contiguous to the inter-atrial septum, prolapsing through the tricuspid valve, and causing mild tricuspid insufficiency, with dilation of the right ventricle and retained systolic function with PAPs estimated at 45mmHg. A chest scan was performed, revealing the presence of right segmental pulmonary embolism. The patient was put under anticoagulant and underwent surgical resection of the mass; its pathological examination concluded to a myxoma. The post-operative consequences were simple, without recurrence of the mass after one year follow-up. Discussion: Myxomas represent 50% of heart tumors. Most often, they originate in the left atrium, and more rarely in the right atrium or the ventricles. Myxoma of the right atrium can be responsible for life-threatening pulmonary embolism. The most predictive factor for embolization remains the morphology of the myxomas; papillary or villous myxomas are the most friable. Surgery is the standard treatment, with regular postoperative follow-up to detect recurrence. Conclusion: Myxomas of the right atrium are a rare location for these tumors. Pulmonary embolism is the main complication and should routinely involve careful study of the right chambers on echocardiography.

Keywords: pulmonary embolism, myxoma, right atrium, heart tumors

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Authors: Emmanuel Nkemakolam Okwuduba, Fransica Chinelo Offiah

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The technique of teaching chemistry to students is one of the determining factors towards their achievement. Thus, the study investigated the relationship between classroom interaction patterns and students’ achievement in Chemistry. The purpose of this study was to identify patterns of interaction in an observed chemistry classroom, determine the amount of teacher talk, student talk and period of silence and to find out the relationship between them and the mean achievement scores of students. Five research questions and three hypotheses guided the study. The study was a correlational survey. The sample consisted of 450 (212males and 238 females) senior secondary one students and 12 (5males and 7 females) chemistry teachers drawn from 12 selected secondary schools in Awka Education Zone of Anambra state. In each of the 12 selected schools, an intact class was used. Science Interaction Category (SIC) and Chemistry Achievement Test (CAT) were developed, validated and used for data collection. Each teacher was observed three times and the interaction patterns coded using a coding sheet containing the Science Interaction Category. At the end of the observational period, the Chemistry Achievement Test (for collection of data on students’ achievement in chemistry) was administered on the students. Frequencies, percentage, mean, standard deviation and Pearson product moment correlation were used for data analysis. The result showed that the percentages of teacher talk, student talk and silence were 59.6%, 37.6% and 2.8% respectively. The Pearson correlation coefficient(r) for teacher talk, student talk and silence were -0.61, 0.76 and-0.18 respectively. The result showed negative and significant relationship between teacher talk and mean achievement scores of students; positive and significant relationship between student talk and mean achievement scores of students but there is no relationship between period of silence and mean achievement scores of students at 0.05 significant levels. The following recommendations were made based on the findings: teachers should establish high level of student talk through initiation and response as it promotes involvement and enhances achievement.

Keywords: academic achievement, chemistry, classroom, interactions patterns

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Authors: Syena Moltaji, Stephanie Posa, Sander Hitzig, Amanda Mayo, Heather Baltzer

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Purpose: To critically assess deficits following thumb amputation and delineate elements of an ideal thumb prosthesis from the end-user perspective. Methods: This was a qualitative study based on grounded theory. End-user stakeholder groups of thumb amputees and prosthetists were interviewed. Transcripts were reviewed whole first for familiarity. Data coding was then performed by two individual authors. Coded units were grouped by similarity and reviewed to reach a consensus. Codes were then analyzed for emergent themes by each author. A consensus meeting was held with all authors to finalize themes. Results: Three patients with traumatic thumb amputation and eight prosthetists were interviewed. Seven themes emerged. First was the significant impact of losing a thumb, in which codes of functional impact, mental impact, and occupational impact were included. The second theme was the unique nature of each thumb amputee, including goals, readiness for prosthesis, nature of the injury, and insurance. The third emergent theme was cost, surrounding government funding, insurability, and prosthetic pricing. The fourth theme was patient frustration, which included mismatches of prosthetic expectations and realities, activity limitations, and causes of devices abandonment. Themes five and six surrounded the strengths and weaknesses of current prosthetics, respectively. Theme seven was the ideal design for a thumb prosthetic, including abilities, suspension, and materials. Conclusions: Representative data from stakeholders mapped the current status of thumb prosthetics. Preferences for an ideal thumb prosthetic emerged, with suggestions for a simple, durable design. The ability to oppose, grasp and sense pressure was reported as functional priorities. Feasible cost and easy fitting emerged as systemic objectives. This data will be utilized in the development of a sensate thumb prosthetic.

Keywords: smart thumb, thumb prosthetic, sensate prosthetic, amputation

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Authors: Neelma Ashraf

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Actinomycetes, particularly genus Streptomyces is of great importance due to their role in the discovery of new natural products, particularly antimicrobial secondary metabolites in the medicinal science and biotechnology industry. Different Streptomyces strains were isolated from Helianthus annuus plants and tested for antibacterial and antifungal activities. The most promising five strains were chosen for further investigation, and growth conditions for antibiotic synthesis were optimised. The supernatants were extracted in different solvents, and the extracted products were analyzed using liquid chromatography-mass spectrometry (LC-MS) and biological testing. From one of the potent strains Streptomyces globusus sp. BR123, a compound lavendamycin was identified using these analytical techniques. In addition, this potent strain also produces a strong antifungal polyene compound with a quasimolecular ion of 2072. Streptomyces sp. BR123 was genome sequenced because of its promising antimicrobial potential in order to identify the gene cluster responsible for analyzed compound “lavendamycin”. The genome analysis yielded candidate genes responsible for the production of this potent compound. The genome sequence of 8.15 Mb of Streptomyces sp. isolate BR123 with a GC content of 72.63% and 8103 protein coding genes was attained. Many antimicrobial, antiparasitic, and anticancerous compounds were detected through multiple biosynthetic gene clusters predicted by in-Silico analysis. Though, the novelty of metabolites was determined through the insignificant resemblance with known biosynthetic gene clusters. The current study gives insight into the bioactive potential of Streptomyces sp. isolate BR123 with respect to the synthesis of bioactive secondary metabolites through genomic and spectrometric analysis. Moreover, the comparative genome study revealed the connection of isolate BR123 with other Streptomyces strains, which could expand the knowledge of this genus and the mechanism involved in the discovery of new antimicrobial metabolites.

Keywords: streptomyces, secondary metabolites, genome, biosynthetic gene clusters, high performance liquid chromatography, mass spectrometry

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Authors: Sara Ataei, Maryam Taghizadeh-Ghehi, Amir Sarayani, Asieh Ashouri, Amirhossein Moslehi, Molouk Hadjibabaie, Kheirollah Gholami

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Background: Acute kidney injury (AKI) is common in hematopoietic stem cell transplantation (HSCT) patients with an incidence of 21–73%. Prevention and early diagnosis reduces the frequency and severity of this complication. Predictive biomarkers are of major importance to timely diagnosis. Neutrophil gelatinase associated lipocalin (NGAL) is a widely investigated novel biomarker for early diagnosis of AKI. However, no study assessed NGAL for AKI diagnosis in HSCT patients. Methods: We performed further analyses on gathered data from our recent trial to evaluate the performance of urine NGAL (uNGAL) as an indicator of AKI in 72 allogeneic HSCT patients. AKI diagnosis and severity were assessed using Risk–Injury–Failure–Loss–End-stage renal disease and AKI Network criteria. We assessed uNGAL on days -6, -3, +3, +9 and +15. Results: Time-dependent Cox regression analysis revealed a statistically significant relationship between uNGAL and AKI occurrence. (HR=1.04 (1.008-1.07), P=0.01). There was a relation between uNGAL day +9 to baseline ratio and incidence of AKI (unadjusted HR=.1.047(1.012-1.083), P<0.01). The area under the receiver-operating characteristic curve for day +9 to baseline ratio was 0.86 (0.74-0.99, P<0.01) and a cut-off value of 2.62 was 85% sensitive and 83% specific in predicting AKI. Conclusions: Our results indicated that increase in uNGAL augmented the risk of AKI and the changes of day +9 uNGAL concentrations from baseline could be of value for predicting AKI in HSCT patients. Additionally uNGAL changes preceded serum creatinine rises by nearly 2 days.

Keywords: acute kidney injury, hemtopoietic stem cell transplantation, neutrophil gelatinase-associated lipocalin, Receiver-operating characteristic curve

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Authors: S. Da Costa, D. Páez, F. Sánchez

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We examine the causal role of positive affect on creativity, the association of creativity or innovation in the ideation phase with functional emotional regulation, successful adjustment to stress and dispositional emotional creativity, as well as the predictive role of creativity for positive emotions and social adjustment. The study examines the effects of modification of positive affect on creativity. Participants write three poems, narrate an infatuation episode, answer a scale of personal growth after this episode and perform a creativity task, answer a flow scale after creativity task and fill a dispositional emotional creativity scale. High and low positive effect was induced by asking subjects to write three poems about high and low positive connotation stimuli. In a neutral condition, tasks were performed without previous affect induction. Subjects on the condition of high positive affect report more positive and less negative emotions, more personal growth (effect size r = .24) and their last poem was rated as more original by judges (effect size r = .33). Mediational analysis showed that positive emotions explain the influence of the manipulation on personal growth - positive affect correlates r = .33 to personal growth. The emotional creativity scale correlated to creativity scores of the creative task (r = .14), to the creativity of the narration of the infatuation episode (r = .21). Emotional creativity was also associated, during performing the creativity task, with flow (r = .27) and with affect balance (r = .26). The mediational analysis showed that emotional creativity predicts flow through positive affect. Results suggest that innovation in the phase of ideation is associated with a positive affect balance and satisfactory performance, as well as dispositional emotional creativity is adaptive.

Keywords: affectivity, creativity, induction, innovation, psychological factors

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Authors: Cheng Zeng, George Michailidis, Hitoshi Iyatomi, Leo L. Duan

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The conditional density characterizes the distribution of a response variable y given other predictor x and plays a key role in many statistical tasks, including classification and outlier detection. Although there has been abundant work on the problem of Conditional Density Estimation (CDE) for a low-dimensional response in the presence of a high-dimensional predictor, little work has been done for a high-dimensional response such as images. The promising performance of normalizing flow (NF) neural networks in unconditional density estimation acts as a motivating starting point. In this work, the authors extend NF neural networks when external x is present. Specifically, they use the NF to parameterize a one-to-one transform between a high-dimensional y and a latent z that comprises two components [zₚ, zₙ]. The zₚ component is a low-dimensional subvector obtained from the posterior distribution of an elementary predictive model for x, such as logistic/linear regression. The zₙ component is a high-dimensional independent Gaussian vector, which explains the variations in y not or less related to x. Unlike existing CDE methods, the proposed approach coined Augmented Posterior CDE (AP-CDE) only requires a simple modification of the common normalizing flow framework while significantly improving the interpretation of the latent component since zₚ represents a supervised dimension reduction. In image analytics applications, AP-CDE shows good separation of 𝑥-related variations due to factors such as lighting condition and subject id from the other random variations. Further, the experiments show that an unconditional NF neural network based on an unsupervised model of z, such as a Gaussian mixture, fails to generate interpretable results.

Keywords: conditional density estimation, image generation, normalizing flow, supervised dimension reduction

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Authors: Lota Largavista

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This study entitled, ‘The Organizational Structure, Development Features, and Metadiscoursal Elements in the Expository Writing of Freshman College Writers’ aimed to examine essays written by college students. It seeks to examine the organizational structure and development features of the essays and describe their defining characteristics, the linguistic elements at both macrostructural and microstructural discourse levels and the types of textual and interpersonal metadiscourse markers that are employed in order to negotiate meanings with their prospective readers. The different frameworks used to analyze the essays include Toulmin’s ( 1984) model for argument structure, Olson’s ( 2003) three-part essay structure; Halliday and Matthiesen (2004) in Herriman (2011) notions of thematic structure, Danes (1974) thematic progression or method of development, Halliday’s (2004) concept of grammatical and lexical cohesion ;Hyland’s (2005) metadiscourse strategies; and Chung and Nation’s( 2003) four-step scale for technical vocabulary. This descriptive study analyzes qualitatively and quantitatively how freshman students generally express their written compositions. Coding of units is done to determine what linguistic features are present in the essays. Findings revealed that students’ expository essays observe a three-part structure having all three moves, the Introduction, the Body and the Conclusion. Stance assertion, stance support, and emerging moves/strategies are found to be employed in the essays. Students have more marked themes on the essays and also prefer constant theme progression as their method of development. The analysis of salient linguistic elements reveals frequently used cohesive devices and metadiscoursal strategies. Based on the findings, an instructional learning plan is being proposed. This plan is characterized by a genre approach that focuses on expository and linguistic conventions.

Keywords: metadiscourse, organization, theme progression, structure

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Authors: Metrek Almetrek, Naser Alqahtani, Eisa Ghazwani, Mona Asiri, Mohammed Alqahtani, Magboolah Balobaid

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Background: Advocacy of potential for community coalitions to positively address social determinants of health and quality of life, little is known about the views of stakeholders involved in such efforts. This study sought to assess the provinces leaders’ perspectives about social determinants related to the Health Neighborhood Initiative (HNI), a new county effort to support community coalitions. Method and Subjects: We used a descriptive, qualitative study using personal interviews in 2022. We conducted it in the community coalition's “main cities committees” set across service planning areas that serve vulnerable groups located in the seven registered healthy cities to WHO (Abha, Tareeb, Muhayel, Balqarn, Alharajah, Alamwah, and Bisha). We conducted key informant interviews with 76 governmental, profit, non-profit, and community leaders to understand their perspectives about the HNI. As part of a larger project, this study focused on leaders’ views about social determinants of health related to the HNI. All interviews were audio-recorded and transcribed. An inductive approach to coding was used, with text segments grouped by social determinant categories. Results: Provinces leaders described multiple social determinants of health and quality of life that were relevant to the HNI community coalitions: housing and safety, community violence, economic stability, city services coordination and employment and education. Leaders discussed how social determinants were interconnected with each other and the need for efforts to address multiple social determinants simultaneously to effectively improve health and quality of life. Conclusions: Community coalitions have an opportunity to address multiple social determinants of health and quality of life to meet the social needs of vulnerable groups. Future research should examine how community coalitions, like those in the HNI, can actively engage with community members to identify needs and then deliver evidence-based care.

Keywords: social determinants, health and quality of life, vulnerable groups, qualitative research

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Authors: Gabriella Notarianni Burk

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The present study investigates the Aspect Hypothesis (AH) in Italian child language in the production of frog story narratives from the CHILDES database. The AH is based on the assumption that children initially encode aspectual and lexical distinctions rather than temporal relations. Children from a variety of first languages have been shown to mark past initially with achievements and accomplishments (telic predicates) and in later stages with states and activities (atelic predicates). Aspectual distinctions in Romance languages are obligatorily and overtly encoded in the inflectional morphology. In Italian the perfective viewpoint is realized by the passato prossimo, which expresses a temporal and aspectual meaning of pastness and perfectivity, whereas the imperfective viewpoint in the past tense is realized by the imperfetto. The aim of this study is to assess the role of lexical aspect in the acquisition of tense and aspect morphology and to understand if Italian children’s mapping of aspectual and temporal distinctions follows consistent developmental patterns across languages. The research methodology aligns with the cross-linguistic designs, tasks and coding procedures previously developed in the frog story literature. Results from two-factor ANOVA show that Italian children (age range: 4-6) exhibited a statistically significant distinction between foregrounded perfective and backgrounded imperfective marking. However, a closer examination of the sixty narratives reveals an idiosyncratic production pattern for Italian children, whereby the marking of imperfetto deviates from the tenets of AH and emerges as deictic tense to entail completed and bounded events in foreground clauses. Instances of ‘perfective’ uses of imperfetto were predominantly found in the four-year old narratives (25%). Furthermore, the analysis of the perfective marking suggests that morphological articulation and diatopic variation may influence the child production of formal linguistic devices in discourse.

Keywords: actionality, aspect, grounding, temporal reference

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Authors: P. R. Prabhu, S. M. Kulkarni, S. S. Sharma, K. Jagannath, Achutha Kini U.

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In the present study, response surface methodology has been used to optimize turn-assisted deep cold rolling process of AISI 4140 steel. A regression model is developed to predict surface hardness and surface roughness using response surface methodology and central composite design. In the development of predictive model, deep cold rolling force, ball diameter, initial roughness of the workpiece, and number of tool passes are considered as model variables. The rolling force and the ball diameter are the significant factors on the surface hardness and ball diameter and numbers of tool passes are found to be significant for surface roughness. The predicted surface hardness and surface roughness values and the subsequent verification experiments under the optimal operating conditions confirmed the validity of the predicted model. The absolute average error between the experimental and predicted values at the optimal combination of parameter settings for surface hardness and surface roughness is calculated as 0.16% and 1.58% respectively. Using the optimal processing parameters, the hardness is improved from 225 to 306 HV, which resulted in an increase in the near surface hardness by about 36% and the surface roughness is improved from 4.84µm to 0.252 µm, which resulted in decrease in the surface roughness by about 95%. The depth of compression is found to be more than 300µm from the microstructure analysis and this is in correlation with the results obtained from the microhardness measurements. Taylor Hobson Talysurf tester, micro Vickers hardness tester, optical microscopy and X-ray diffractometer are used to characterize the modified surface layer.

Keywords: hardness, response surface methodology, microstructure, central composite design, deep cold rolling, surface roughness

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Authors: Petra Borilova Linhartova, Michaela Ruckova, Sabina Sevcikova, Natalie Mlcuchova, Jan Bohm, Katerina Zukalova, Monika Vlachova, Jiri Dolina, Lumir Kunovsky, Radek Kroupa, Zdenek Pavlovsky, Zdenek Danek, Tereza Deissova, Lydie Izakovicova Holla, Ondrej Slaby, Zdenek Kala

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Esophageal adenocarcinoma (EAC) is a highly aggressive malignancy that frequently develops from Barrett's esophagus (BE), a premalignant pathologic change occurring in the lower end of the esophagus. Specific microRNAs (miRNAs), small non-coding RNAs that function as posttranscriptional regulators of gene expression, were repeatedly proved to play key roles in the pathogenesis of these diseases. This pilot study aimed to analyze four selected miRNAs in esophageal tissues from healthy controls (HC) and patients with reflux esophagitis (RE)/BE/EAC, as well as to compare expression at the site of Barrett's mucosa/adenocarcinoma and healthy esophageal tissue outside the area of the main pathology in patients with BE/EAC. In this pilot study, 22 individuals (3 HC, 8 RE, 5 BE, 6 EAC) were included and endoscopically examined. RNA was isolated from the fresh-frozen esophageal tissue (stored in the RNAlater™ Stabilization Solution −70°C) using the AllPrep DNA/RNA/miRNA Universal Kit. Subsequent RT-qPCR analysis was performed using selected TaqMan MicroRNA Assays for miR-21, miR-34a, miR-196a, miR-196b, and endogenous control (RNU44). While the expression of miR-21 in the esophageal tissue with the main pathology was decreased in BE and EAC patients in comparison to the group of HC and RE patients (p=0.01), the expression of miR-196a was increased in the BE and EAC patients (p<0.01). Correlations between those miRNAs expression in tissue and severity of diagnosis were observed (p<0.05). In addition, miR-196a was significantly more expressed at the site with the main pathology than in paired adjacent esophageal tissue in BE and EAC patients (p<0.01). In conclusion, our pilot results showed that miR-196a, which regulates the proliferation, invasion, and migration (and was previously associated with esophageal squamous cell carcinoma and marked as a potential therapeutic target), could be a diagnostic tissue biomarker for BE and EAC as well.

Keywords: microRNA, barrett´s esophagus, esophageal adenocarcinoma, biomarker

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Authors: Yuechao Lei, Lei Zhang

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The interface shear between asphalt overlay and existing rigid airport pavements occurs due to differences in the mechanical properties of materials subjected to aircraft loading. Interlayer contact influences the mechanical characteristics of the asphalt overlay directly. However, the effective interlayer relative displacement obtained accurately using existing displacement sensors of the loading apparatus remains challenging. This study aims to utilize digital image correlation technology to enhance the accuracy of interfacial contact parameters by obtaining effective interlayer relative displacements. Composite structure specimens were prepared, and fixtures for interlayer shear tests were designed and fabricated. Subsequently, a digital image recognition scheme for required markers was designed and optimized. Effective interlayer relative displacement values were obtained through image recognition and calculation of surface markers on specimens. Finite element simulations validated the mechanical response of composite specimens with interlayer shearing. Results indicated that an optimized marking approach using the wall mending agent for surface application and color coding enhanced the image recognition quality of marking points on the specimen surface. Further image extraction provided effective interlayer relative displacement values during interlayer shear, thereby improving the accuracy of interface contact parameters. For composite structure specimens utilizing Styrene-Butadiene-Styrene (SBS) modified asphalt as the tack coat, the corresponding maximum interlayer shear stress strength was 0.6 MPa, and fracture energy was 2917 J/m2. This research provides valuable insights for investigating the impact of interlayer contact in composite pavement structures on the mechanical characteristics of asphalt overlay.

Keywords: interlayer contact, effective relative displacement, digital image correlation technology, composite pavement structure, asphalt overlay

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Authors: Zsuzsanna Schnell, Francesca Ervas

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Background: the experimental pragmatic study confirms a basic tenet in the Relevance theoretical views in language philosophy. It draws up a developmental trajectory of the maxims, revealing the cognitive difficulty of their interpretation, their relative place to each other, and the order they may follow in development. A central claim of the present research is that social-cognitive skills play a significant role in inferential meaning construction. Children passing the False Belief Test are significantly more successful in tasks measuring the recognition of the infringement of conversational maxims. Aims and method: Preschoolers’ conversational skills and pragmatic competence is examined in view of their mentalization skills. In doing so it use a measure of linguistic tasks, containing 5 short scenarios for each Gricean maxim. it measure preschoolers’ ToM performance with a first- and a second order ToM task and compare participants’ ability to recognize the infringement of the Gricean maxims in view of their social cognitive skills. Results: Findings suggest that Theory of Mind has a predictive force of 75% concerning the ability to follow Gricean maxims efficiently. ToM proved to be a significant factor in predicting the group’s performance and success rates in 3 out of 4 maxim infringement recognition tasks: in the Quantity, Relevance and Manner conditions, but not in the Quality trial. Conclusions: the results confirm that children’s communicative competence in social contexts requires the development of higher-order social-cognitive reasoning, and reveal the cognitive effort needed for the recognition of the infringement of each maxim, yielding a continuum of their cognitive difficulty and trajectory of development.

Keywords: maxim infringement recognition, social cognition, Gricean maxims, developmental pragmatics

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Authors: Adrian Hang Yue Siu, Matthew Holyland, Sharon Carey, Daniel Steffens, Nabila Ansari, Cherry E. Koh

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Introduction: Peritoneal malignancies are challenging cancers to manage. While cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS and HIPEC) may offer a cure, it’s considered radical and morbid. Pre-emptive identification of deconditioned patients for optimization may mitigate the risks of surgery. However, the difficulty lies in the scarcity of validated predictive tools to identify high-risk patients. In recent times, there has been growing interest in sarcopenia, which can occur as a result of malnutrition and malignancies. Therefore, the purpose of this study was to assess the utility of sarcopenia in predicting post-operative outcomes. Methods: A single quaternary-center retrospective study of CRS and HIPEC patients between 2017-2020 was conducted to determine the association between pre-operative sarcopenia and post-operative outcomes. Lumbar CT images were analyzed using Slice-o-matic® to measure sarcopenia. Results : Cohort (n=94) analysis found that 40% had sarcopenia, with a majority being female (53.2%) and a mean age of 55 years. Sarcopenia was statistically associated with decreased weight compared to non-sarcopenia patients, 72.7kg vs. 82.2kg (p=0.014) and shorter overall survival, 1.4 years vs. 2.1 years (p=0.032). Post-operatively, patients with sarcopenia experienced more post-operative complications (p=0.001). Conclusion: Complex procedures often require optimization to prevent complications and improve survival. While patient biomarkers – BMI and weight – are used for optimization, this research advocates for the identification of sarcopenia status for pre-operative planning. Sarcopenia may be an indicator of advanced disease requiring further treatment and is an emerging area of research. Larger studies are required to confirm these findings and to assess the reversibility of sarcopenia after surgery.

Keywords: sarcopaenia, cytoreductive surgery, hyperthermic intraperitoneal chemotherapy, surgical oncology

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Authors: Louise Moles, Steve Riley, Sarah D. Lichenstein, Marzieh Babaeianjelodar, Robert Kohler, Annie Cheng, Corey Horien Abigail Greene, Wenjing Luo, Jonathan Ahern, Bohan Xu, Yize Zhao, Chun Chieh Fan, R. Todd Constable, Sarah W. Yip

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Background: Risks for depression are myriad and include both genetic and brain-based factors. However, relationships between these systems are poorly understood, limiting understanding of disease etiology, particularly at the developmental level. Methods: We use a data-driven machine learning approach connectome-based predictive modeling (CPM) to identify functional connectivity signatures associated with polygenic risk scores for depression (DEP-PRS) among youth from the Adolescent Brain and Cognitive Development (ABCD) study across diverse brain states, i.e., during resting state, during affective working memory, during response inhibition, during reward processing. Results: Using 10-fold cross-validation with 100 iterations and permutation testing, CPM identified connectivity signatures of DEP-PRS across all examined brain states (rho’s=0.20-0.27, p’s<.001). Across brain states, DEP-PRS was positively predicted by increased connectivity between frontoparietal and salience networks, increased motor-sensory network connectivity, decreased salience to subcortical connectivity, and decreased subcortical to motor-sensory connectivity. Subsampling analyses demonstrated that model accuracies were robust across random subsamples of N’s=1,000, N’s=500, and N’s=250 but became unstable at N’s=100. Conclusions: These data, for the first time, identify neural networks of polygenic depression risk in a large sample of youth before the onset of significant clinical impairment. Identified networks may be considered potential treatment targets or vulnerability markers for depression risk.

Keywords: genetics, functional connectivity, pre-adolescents, depression

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Authors: O. B. Iskender, K. V. Ling, V. Dubanchet, L. Simonini

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This paper presents a Guidance and Control (G&C) strategy to address spacecraft maneuvering problem for future Rendezvous and Docking (RVD) missions. The proposed strategy allows safe and propellant efficient trajectories for space servicing missions including tasks such as approaching, inspecting and capturing. This work provides the validation test results of the G&C laws using a Hardware-In-the-Loop (HIL) setup with two robotic mockups representing the chaser and the target spacecraft. Through this paper, the challenges of the relative motion control in space are first summarized, and in particular, the constraints imposed by the mission, spacecraft and, onboard processing capabilities. Second, the proposed algorithm is introduced by presenting the formulation of constrained Model Predictive Control (MPC) to optimize the fuel consumption and explicitly handle the physical and geometric constraints in the system, e.g. thruster or Line-Of-Sight (LOS) constraints. Additionally, the coupling between translational motion and rotational motion is addressed via dual quaternion based kinematic description and accordingly explained. The resulting convex optimization problem allows real-time implementation capability based on a detailed discussion on the computational time requirements and the obtained results with respect to the onboard computer and future trends of space processors capabilities. Finally, the performance of the algorithm is presented in the scope of a potential future mission and of the available equipment. The results also cover a comparison between the proposed algorithms with Linear–quadratic regulator (LQR) based control law to highlight the clear advantages of the MPC formulation.

Keywords: autonomous vehicles, embedded optimization, real-time experiment, rendezvous and docking, space robotics

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Authors: Van-Thanh Ho, Jaiyoung Ryu

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In soft robotics, the optimization of fluid dynamics through pneumatic methods plays a pivotal role in enhancing operational efficiency and reducing energy loss. This is particularly crucial when replacing conventional techniques such as cable-driven electromechanical systems. The pneumatic model employed in this study represents a sophisticated framework designed to efficiently channel pressure from a high-pressure reservoir to various muscle locations on the robot's body. This intricate network involves a branching system of tubes. The study introduces a comprehensive pneumatic model, encompassing the components of a reservoir, tubes, and Pneumatically Actuated Muscles (PAM). The development of this model is rooted in the principles of shock tube theory. Notably, the study leverages experimental data to enhance the understanding of the interplay between the PAM structure and the surrounding fluid. This improved interactive approach involves the use of morphing motion, guided by a contraction function. The study's findings demonstrate a high degree of accuracy in predicting pressure distribution within the PAM. The model's predictive capabilities ensure that the error in comparison to experimental data remains below a threshold of 10%. Additionally, the research employs a machine learning model, specifically a surrogate model based on the Kriging method, to assess and quantify uncertainty factors related to the initial reservoir pressure and tube diameter. This comprehensive approach enhances our understanding of pneumatic soft robotics and its potential for improved operational efficiency.

Keywords: pneumatic artificial muscles, pressure drop, morhing motion, branched network, surrogate model

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Authors: Bryce Benson, Sooin Lee, Ashwin Belle

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Unrecognized or delayed identification of patient deterioration is a key cause of in-hospitals adverse events. Clinicians rely on vital signs monitoring to recognize patient deterioration. However, due to ever increasing nursing workloads and the manual effort required, vital signs tend to be measured and recorded intermittently, and inconsistently causing large gaps during patient monitoring. Additionally, during deterioration, the body’s autonomic nervous system activates compensatory mechanisms causing the vital signs to be lagging indicators of underlying hemodynamic decline. This study analyzes the predictive efficacy of the Analytic for Hemodynamic Instability (AHI) system, an automated tool that was designed to help clinicians in early identification of deteriorating patients. The lead time analysis in this retrospective observational study assesses how far in advance AHI predicted deterioration prior to the start of an episode of hemodynamic instability (HI) becoming evident through vital signs? Results indicate that of the 362 episodes of HI in this study, 308 episodes (85%) were correctly predicted by the AHI system with a median lead time of 57 minutes and an average of 4 hours (240.5 minutes). Of the 54 episodes not predicted, AHI detected 45 of them while the episode of HI was ongoing. Of the 9 undetected, 5 were not detected by AHI due to either missing or noisy input ECG data during the episode of HI. In total, AHI was able to either predict or detect 98.9% of all episodes of HI in this study. These results suggest that AHI could provide an additional ‘pair of eyes’ on patients, continuously filling the monitoring gaps and consequently giving the patient care team the ability to be far more proactive in patient monitoring and adverse event management.

Keywords: clinical deterioration prediction, decision support system, early warning system, hemodynamic status, physiologic monitoring

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Authors: McKenna Halverson, Allison Karpyn

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Introduction: The COVID-19 pandemic posed unprecedented challenges for families with young children in the United States. The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), a federal nutrition assistance program that provides low-income mothers and young children with access to healthy foods (e.g., infant formula, milk, and peanut butter), mitigated some financial challenges for families. However, the U.S. experienced a national infant formula shortage and rising inflation rates during the pandemic, which likely impacted WIC participants’ shopping experiences and well-being. As such, this study aimed to characterize how the COVID-19 pandemic and related events impacted Delaware WIC participants’ in-store benefit redemption experiences and overall well-being. Method: The authors conducted semi-structured interviews with 51 WIC participants in Wilmington, Delaware. Survey measures included demographic questions and open-ended questions regarding participants’ experiences with WIC benefit redemption during the COVID-19 pandemic. Data were analyzed using a hybrid inductive and deductive coding approach. Findings: The COVID-19 pandemic significantly impacted WIC participants’ shopping experiences and well-being. Specifically, participants were forced to alter their shopping behaviors to account for rising food prices (e.g., used coupons, bought less food, used food banks). Additionally, WIC participants experienced significant distress during the national infant formula shortage resulting from difficulty finding formula to feed their children. Participants also struggled with in-store benefit redemption due to inconsistencies in shelf labelling, the WIC app, and low stock of WIC foods. These findings highlight the need to reexamine WIC operations and emergency food response policy in the United States during times of crisis to optimize public health and ensure federal nutrition assistance programs meeting the needs of low-income families with young children.

Keywords: benefit redemption, COVID-19 pandemic, infant formula shortage, inflation, shopping, WIC

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Authors: Delna Shroff

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Merchant shipping is an occupation with a high rate of fatal injuries caused by organizational accidents and maritime disasters. In most accident investigations, the leader’s actions are under scrutiny and point out the necessity to investigate the leader’s decisions in critical conditions. While several leadership studies have been carried out in the past, there is a tendency for most research to focus on holders of formal positions. The unit of analysis in most studies has been the ‘individual.’ A need is, therefore, felt to adopt a practice-based perspective of leadership, understand how leadership emerges to affect maritime safety. This paper explores the phenomenon of distributed leadership among seafarers more holistically. It further examines the role of one form of distributed leadership, that is, planfully aligned leadership in the emergency response of the team. A mixed design will be applied. In the first phase, the data gathered by way of semi-structured interviews will be used to explore the seafarer’s implicit understanding of leadership. The data will be used to develop a conceptual framework of distributed leadership, specific to the maritime context. This framework will be used to develop a simulation. Experimental design will be used to examine the relationship between planfully aligned leadership and emergency response of the team members during navigation. Findings show that planfully aligned leadership significantly and positively predicts the emergency response of team members. Planfully aligned leadership leads to a better emergency response of the team members as compared to authoritarian leadership. In the third qualitative phase, additional data will be gathered through semi-structured interviews to further validate the findings to gain a more complete understanding of distributed leadership and its relation to emergency response. Above are the predictive results; the study expects to be a cornerstone of safety leadership research and has important implications for leadership development and training within the maritime industry.

Keywords: authoritarian leadership, distributed leadership, emergency response , planfully aligned leadership

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Authors: Chia-Ying Lin

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This paper explains how to enhance doctor-patient communication and nurse-patient communication through multiple intelligence signing methods and user-centered. It is hoped that through the implementation of the "electronic consent", the problems faced by the paper consent can be solved: storage methods, resource utilization, convenience, correctness of information, integrated management, statistical analysis and other related issues. Make better use and allocation of resources to provide better medical quality. First, invite the ｍedical records department to assist in the inventory of paper consent in the hospital: organising, classifying, merging, coding, and setting. Second, plan the electronic consent configuration file: set the form number, consent form group, fields and templates, and the corresponding doctor's order code. Next, Summarize four types of rapid methods of electronic consent: according to the doctor's order, according to the medical behavior, according to the schedule, and manually generate the consent form. Finally, system promotion and adjustment: form an "electronic consent promotion team" to improve, follow five major processes: planning, development, testing, release, and feedback, and invite clinical units to raise the difficulties faced in the promotion, and make improvements to the problems. The electronic signature rate of the whole hospital will increase from 4% in January 2022 to 79% in November 2022. Use the saved resources more effectively, including: reduce paper usage (reduce carbon footprint), reduce the cost of ink cartridges, re-plan and use the space for paper medical records, and save human resources to provide better services. Through the introduction of information technology and technology, the main spirit of "lean management" is implemented. Transforming and reengineering the process to eliminate unnecessary waste is also the highest purpose of this project.

Keywords: smart signature, electronic consent, electronic medical records, user-centered, doctor-patient communication, nurse-patient communication

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Authors: Natascha C. D'Amico, Enzo Grossi, Giovanni Valbusa, Ala Malasevschi, Gianpiero Cardone, Sergio Papa

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Purpose: To characterize, through a radiomic approach, the nature of areas classified PI-RADS (Prostate Imaging Reporting and Data System) 3/5, recognized in multiparametric prostate magnetic resonance with T2-weighted (T2w), diffusion and perfusion sequences with paramagnetic contrast. Methods and Materials: 24 cases undergoing multiparametric prostate MR and biopsy were admitted to this pilot study. Clinical outcome of the PI-RADS 3/5 was found through biopsy, finding 8 malignant tumours. The analysed images were acquired with a Philips achieva 1.5T machine with a CE- T2-weighted sequence in the axial plane. Semi-automatic tumour segmentation was carried out on MR images using 3DSlicer image analysis software. 45 shape-based, intensity-based and texture-based features were extracted and represented the input for preprocessing. An evolutionary algorithm (a TWIST system based on KNN algorithm) was used to subdivide the dataset into training and testing set and select features yielding the maximal amount of information. After this pre-processing 20 input variables were selected and different machine learning systems were used to develop a predictive model based on a training testing crossover procedure. Results: The best machine learning system (three-layers feed-forward neural network) obtained a global accuracy of 90% ( 80 % sensitivity and 100% specificity ) with a ROC of 0.82. Conclusion: Machine learning systems coupled with radiomics show a promising potential in distinguishing benign from malign tumours in PI-RADS 3/5 areas.

Keywords: machine learning, MR prostate, PI-Rads 3, radiomics

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Authors: Daryl Clement Knutt, Neela Badrie, Marsha Singh

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Quality assurance and product labelling are vital in the food and drink industry, as a tactical tool in a competitive environment. The food label is a principal marketing tool which also serves as a regulatory mechanism in the safeguarding of consumer well –being. The objective of this study was to evaluate the level of consumers’ use and understanding of food labeling information and knowledge pertaining to food quality assurance systems. The study population consisted of Trinidadian adults, who were over the age of 18 (n=384). Data collection was conducted via a self-administered questionnaire, which contained 31 questions, comprising of four sections: I. socio demographic information; II. food quality and quality assurance; III. use of Labeling information; and IV. laws and regulations. Sampling was conducted at six supermarkets, in five major regions of the country over a period of three weeks in 2014. The demographic profile of the shoppers revealed that majority was female (63.6%). The gender factor and those who were concerned about the nutrient content of their food, were predictive indicators of those who read food labels. Most (93.1%) read food labels before purchase, 15.4% ‘always’; 32.5% ‘most times’ and 45.2% ‘sometimes’. Some (42%) were often satisfied with the information presented on food labels, whilst 35.7% of consumers were unsatisfied. When the respondents were questioned on their familiarity with terms ‘food quality’ and ‘food quality assurance’, 21.3% of consumers replied positively - ‘I have heard the terms and know a lot’ whilst 37% were only ‘somewhat familiar’. Consumers were mainly knowledgeable of the International Standard of Organization (ISO) (51.5%) and Good Agricultural Practices GAP (38%) as quality tools. Participants ranked ‘nutritional information’ as the number one labeling element that should be better presented, followed by ‘allergy notes’ and ‘best before date’. Females were more inclined to read labels being the household shoppers. The shoppers would like better presentation of the food labelling information so as to guide their decision to purchase a product.

Keywords: food labels, food quality, nutrition, marketing, Trinidad, Tobago

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Authors: Nehal Adel Khalil, Amel Foad Ketat, Fairouz Elsayed Mohamed Ali, Nahla Abdelmoneim Hamid, Hazem Farag Manaa

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Background: Chronic myeloid leukemia (CML) represents 15% of adult leukemias. Imatinib Mesylate (IM) is the gold standard treatment for new cases of CML. Treatment with IM results in improvement of the majority of cases. However, about 25% of cases may develop resistance. Sensitive and specific early predictors of IM resistance in CML patients have not been established to date. Aim: To investigate the value of miR-451 in CML as an early predictor for IM resistance in Egyptian CML patients. Methods: The study employed Real time Polymerase Reaction (qPCR) technique to investigate the leucocytic expression of miR-451 in fifteen newly diagnosed CML patients (group I), fifteen IM responder CML patients (group II), fifteen IM resistant CML patients (group III) and fifteen healthy subjects of matched age and sex as a control group (group IV). The response to IM was defined as < 10% BCR-ABL transcript level after 3 months of therapy. The following parameters were assessed in subjects of all the studied groups: 1- Complete blood count (CBC). 2- Measurement of plasma level of miRNA 451 using real-time Polymerase Chain Reaction (qPCR). 3- Detection of BCR-ABL gene mutation in CML using qPCR. Results: The present study revealed that miR-451 was significantly down-regulated in leucocytes of newly diagnosed CML patients as compared to healthy subjects. IM responder CML patients showed an up-regulation of miR- 451 compared with IM resistant CML patients. Conclusion: According to the data from the present study, it can be concluded that leucocytic miR- 451 expression is a useful additional follow-up marker for the response to IM and a promising prognostic biomarker for CML.

Keywords: chronic myeloid leukemia, imatinib resistance, microRNA 451, Polymerase Chain Reaction

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Authors: Wu Peng, Anders Liljerehn, Martin Magnevall

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In metal cutting, tool wear gradually changes the micro geometry of the cutting edge. Today there is a significant gap in understanding the impact these geometrical changes have on the cutting forces which governs tool deflection and heat generation in the cutting zone. Accurate models and understanding of the interaction between the work piece and cutting tool leads to improved accuracy in simulation of the cutting process. These simulations are useful in several application areas, e.g., optimization of insert geometry and machine tool monitoring. This study aims to develop an extended Kienzle force model to account for the effect of rake angle variations and tool flank wear have on the cutting forces. In this paper, the starting point sets from cutting force measurements using orthogonal turning tests of pre-machined flanches with well-defined width, using triangular coated inserts to assure orthogonal condition. The cutting forces have been measured by dynamometer with a set of three different rake angles, and wear progression have been monitored during machining by an optical measuring collaborative robot. The method utilizes the measured cutting forces with the inserts flank wear progression to extend the mechanistic cutting forces model with flank wear as an input parameter. The adapted cutting forces model is validated in a turning process with commercial cutting tools. This adapted cutting forces model shows the significant capability of prediction of cutting forces accounting for tools flank wear and different-rake-angle cutting tool inserts. The result of this study suggests that the nonlinear effect of tools flank wear and interaction between the work piece and the cutting tool can be considered by the developed cutting forces model.

Keywords: cutting force, kienzle model, predictive model, tool flank wear

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Authors: Baladitya Swaika, Rahul Khatry

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Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

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