Search results for: Stack Generalization

Authors: Madrine King'endo

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This study dealt with the influence of spelling errors on English language performance among learners with dysgraphia in public primary schools in West Embu, Embu County, Kenya. The study purposed to investigate the influence of spelling errors on the English language performance among the class three pupils with dysgraphia in public primary schools. The objectives of the study were to identify the spelling errors that learners with dysgraphia make when writing English words and classify the spelling errors they make. Further, the study will establish how the spelling errors affect the performance of the language among the study participants, and suggest the remediation strategies that teachers could use to address the errors. The study could provide the stakeholders with relevant information in writing skills that could help in developing a responsive curriculum to accommodate the teaching and learning needs of learners with dysgraphia, and probably ensure training of teachers in teacher training colleges is tailored within the writing needs of the pupils with dysgraphia. The study was carried out in Embu county because the researcher did not find any study in related literature review concerning the influence of spelling errors on English language performance among learners with dysgraphia in public primary schools done in the area. Moreover, besides being relatively populated enough for a sample population of the study, the area was fairly cosmopolitan to allow a generalization of the study findings. The study assumed the sampled schools will had class three pupils with dysgraphia who exhibited written spelling errors. The study was guided by two spelling approaches: the connectionist stimulation of spelling process and orthographic autonomy hypothesis with a view to explain how participants with learning disabilities spell written words. Data were collected through interviews, pupils’ exercise books, and progress records, and a spelling test made by the researcher based on the spelling scope set for class three pupils by the ministry of education in the primary education syllabus. The study relied on random sampling techniques in identifying general and specific participants. Since the study used children in schools as participants, voluntary consent was sought from themselves, their teachers and the school head teachers who were their caretakers in a school setting.

Keywords: dysgraphia, writing, language, performance

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Authors: Charles Amoatey, Ebenezer Kumah

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Purpose: The purpose of this paper is to assess the effects of implementation of Distributor Management Solution (DMS) on supply chain performance in the Fast Moving Consumer Goods (FMCG) industry in Ghana. Methodology: A purposive sampling approach was used in selecting the respondents for the study. Data was collected from senior management and field supervisors from sales, distribution and customer service units of the case study firm and its channel members. This study made use of systematic literature review and results of survey data analysis to assess how information system has been used to improve supply chain performance. Findings: Results from the study showed that the critical effect factors from implementation of a DMS include (1) Obtain prompt and reliable feedback from the market; (2) Building the capacity and skills levels of employees as well as 3rd Party Agents; (3) Motivated top management to invest in MIS; and (4) Performance improvement in sales route management. The most critical challenges to an effective and sustainable MIS implementation are lack of enough trained IT employees and high barriers to cultural change especially with distributors. The paper recommends consistent investment in IS infrastructure and development of IT skills. Research limitations/implications: This study contributes to the literature by exploring the effects of distribution management solution implementation and supply chain performance in a developing country context. Considering the fact that this study is based on data from only one case study firm and its channel members, generalization of the results should be treated with caution. Practical implications: The findings have confirmed the benefits of implementing a Management Information System. The result should encourage channel members to allocate adequate resources for building MIS capacity to enhance their supply chain performance. Originality/Value: In this paper, the relationship between DMS/MIS implementation and improvement in supply chain performance, in the Ghanaian context, has been established.

Keywords: distributor management solution, fast-moving consumer goods, supply chain management, information systems, Ghana

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Authors: Osamede Asowata, Christo Pienaar, Johan Bekker

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Photovoltaic (PV) modules may become a trend for future PV systems because of their greater flexibility in distributed system expansion, easier installation due to their nature, and higher system-level energy harnessing capabilities under shaded or PV manufacturing mismatch conditions. This is as compared to the single or multi-string inverters. Novel residential scale PV arrays are commonly connected to the grid by a single DC–AC inverter connected to a series, parallel or series-parallel string of PV panels, or many small DC–AC inverters which connect one or two panels directly to the AC grid. With an increasing worldwide interest in sustainable energy production and use, there is renewed focus on the power electronic converter interface for DC energy sources. Three specific examples of such DC energy sources that will have a role in distributed generation and sustainable energy systems are the photovoltaic (PV) panel, the fuel cell stack, and batteries of various chemistries. A high-efficiency inverter using Metal Oxide Semiconductor Field-Effect Transistors (MOSFETs) for all active switches is presented for a non-isolated photovoltaic and AC-module applications. The proposed configuration features a high efficiency over a wide load range, low ground leakage current and low-output AC-current distortion with no need for split capacitors. The detailed power stage operating principles, pulse width modulation scheme, multilevel bootstrap power supply, and integrated gate drivers for the proposed inverter is described. Experimental results of a hardware prototype, show that not only are MOSFET efficient in the system, it also shows that the ground leakage current issues are alleviated in the proposed inverter and also a 98 % maximum associated driver circuit is achieved. This, in turn, provides the need for a possible photovoltaic panel switching technique. This will help to reduce the effect of cloud movements as well as improve the overall efficiency of the system.

Keywords: grid connected photovoltaic (PV), Matlab efficiency simulation, maximum power point tracking (MPPT), module integrated converters (MICs), multilevel converter, series connected converter

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Authors: Jeff Moussodji, Dominique Drouin

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The highly complex technology requirements of today’s integrated circuits (ICs), lead to the increased use of several materials types such as metal structures, brittle and porous low-k materials which are used in both front end of line (FEOL) and back end of line (BEOL) process for wafer manufacturing. In order to singulate chip from wafer, a critical laser-grooving process, prior to blade dicing, is used to remove these layers of materials out of the dicing street. The combination of laser-grooving and blade dicing allows to reduce the potential risk of induced mechanical defects such micro-cracks, chipping, on the wafer top surface where circuitry is located. It seems, therefore, essential to have a fundamental understanding of the physics involving laser-dicing in order to maximize control of these critical process and reduce their undesirable effects on process efficiency, quality, and reliability. In this paper, the study was based on the convergence of two approaches, numerical and experimental studies which allowed us to investigate the interaction of a nanosecond pulsed laser and BEOL wafer materials. To evaluate this interaction, several laser grooved samples were compared with finite element modeling, in which three different aspects; phase change, thermo-mechanical and optic sensitive parameters were considered. The mathematical model makes it possible to highlight a groove profile (depth, width, etc.) of a single pulse or multi-pulses on BEOL wafer material. Moreover, the heat affected zone, and thermo-mechanical stress can be also predicted as a function of laser operating parameters (power, frequency, spot size, defocus, speed, etc.). After modeling validation and calibration, a satisfying correlation between experiment and modeling, results have been observed in terms of groove depth, width and heat affected zone. The study proposed in this work is a first step toward implementing a quick assessment tool for design and debug of multiple laser grooving conditions with limited experiments on hardware in industrial application. More correlations and validation tests are in progress and will be included in the full paper.

Keywords: laser-dicing, nano-second pulsed laser, wafer multi-stack, multiphysics modeling

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Authors: Mochamad Nuruz Zaman, Mangatur Rudolf Nababan, M. A. Djatmika

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Both society and education areas teach to have good communication for building the interpersonal skills up. Everyone has the capacity to understand something new, either well comprehension or worst understanding. Worst understanding makes the language errors when the interactions are done by someone in the first meeting, and they do not know before it because of distance area. “The Space between Us” movie delivers the love-adventure story between Mars Boy and Earth Girl. They are so many missing conversations because of the different climate and environment. As the moviegoer also must be focused on the subtitle in order to enjoy well the movie. Furthermore, Indonesia subtitle and English conversation on the movie still have overlapping understanding in the translation. Translation hereby consists of source language -SL- (English conversation) and target language -TL- (Indonesia subtitle). These research gap above is formulated in research question by how the language errors happened in that movie and their effects on translation quality which is deepest analyzed by translation study toward discourse analysis approach. The research goal is to expand the language errors and their translation qualities in order to create a good atmosphere in movie media. The research is studied by embedded research in qualitative design. The research locations consist of setting, participant, and event as focused determined boundary. Sources of datum are “The Space between Us” movie and informant (translation quality rater). The sampling is criterion-based sampling (purposive sampling). Data collection techniques use content analysis and questioner. Data validation applies data source and method triangulation. Data analysis delivers domain, taxonomy, componential, and cultural theme analysis. Data findings on the language errors happened in the movie are referential, register, society, textual, receptive, expressive, individual, group, analogical, transfer, local, and global errors. Data discussions on their effects to translation quality are concentrated by translation techniques on their data findings; they are amplification, borrowing, description, discursive creation, established equivalent, generalization, literal, modulation, particularization, reduction, substitution, and transposition.

Keywords: discourse analysis, language errors, The Space between Us movie, translation techniques, translation quality instruments

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Authors: Konstantina Georgali

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The present study explores Greek teachers’ understandings of typical language development and of language difficulties. Its core aim was to highlight that teachers need to have a thorough understanding of educational linguistics, that is of how language figures in education. They should also be aware of how language should be taught so as to promote language development for all students while at the same time support the needs of children with language difficulties in an inclusive ethos. The study, thus argued that language can be a dynamic learning mechanism in the minds of all children and a powerful teaching tool in the hands of teachers and provided current research evidence to show that structural and morphological particularities of native languages- in this case, of the Greek language- can be used by teachers to enhance children’s understanding of language and simultaneously improve oral language skills for children with typical language development and for those with language difficulties. The research was based on a Sequential Exploratory Mixed Methods Design deployed in three consecutive and integrative phases. The first phase involved 18 exploratory interviews with teachers. Its findings informed the second phase involving a questionnaire survey with 119 respondents. Contradictory questionnaire results were further investigated in a third phase employing a formal testing procedure with 60 children attending Y1, Y2 and Y3 of primary school (a research group of 30 language impaired children and a comparison group of 30 children with typical language development, both identified by their class teachers). Results showed both strengths and weaknesses in teachers’ awareness of educational linguistics and of language difficulties. They also provided a different perspective of children’s language needs and of language teaching approaches that reflected current advances and conceptualizations of language problems and opened a new window on how best they can be met in an inclusive ethos. However, teachers barely used teaching approaches that could capitalize on the particularities of the Greek language to improve language skills for all students in class. Although they seemed to realize the importance of oral language skills and their knowledge base on language related issues was adequate, their practices indicated that they did not see language as a dynamic teaching and learning mechanism that can promote children’s language development and in tandem, improve academic attainment. Important educational implications arose and clear indications of the generalization of findings beyond the Greek educational context.

Keywords: educational linguistics, inclusive ethos, language difficulties, typical language development

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Authors: Semira Zeru Haileslassie

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Lack of awareness and practices of PEP treatment were observed among respondents, but they had a better attitude towards PEP. To this end, a formal training for all respondents regarding PEP for HIV prior to their clinical attachments is of utmost importance. The training ought to incorporate a brief clarification with respect to the unpleasant impact of non-adherence that essentially incorporate destitute treatment result and most prominent hazard of resistance and few given as a major cause for non-compliance to PEP, common transient side-effects of PEP and its administrations ought to be cloister educated healthcare specialists to diminish its effect on adherence. Besides, the propensity of detailing needle adhere harm was destitute that needs endeavors to progress. Progressing the culture of detailing and making the detailing handle simple is very necessary. In reality, announcing such wounds as early as conceivable will educate others not to commit same issue once more and, for the most part, will empower stakeholders to intercede the issue sometime prior to it re-occur. At long last, as distant as get up and go utilize has cleared out with so numerous bothers, risk decrease is the foremost choice. With this, taking the increased significance of protective barriers so as to decrease the hazard of exposure to HIV, distinctive stakeholders (the healing center hardware supply chain director, the HIV/ Helps clinic, the clinic chief, hardware and supply quality confirmation group, and other authoritative bodies) ought to work together in co-ordination to secure the supply and guarantee the quality of those crucial protective barriers and to advance demand health laborers to continuously wear protective barriers when exposed to HIV hazard components as well as to dispose appropriately once done. At long last, we prescribe future examiners to conduct planned multicenter studies with extra goals (counting indicator investigation) for way better generalization and result. In spite of satisfactory information and favorable state of mind towards PEP for HIV in most of the respondents, this study uncovered that there were delays in starting, low utilization, and fragmented use of the prescribed PEP. So, health care staff need to progress their practice on PEP of HIV through diverse training program related to PEP of HIV.

Keywords: HIV infection, prophylaxis, knowledge, attitude

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Authors: Amina Abubakar

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English like any other language is rich by means of arbitrary, conventional, symbols which lend it to lot of inconsistencies in spelling, phonology, syntax, and morphology. The research examines the irregularities prevalent in the structure and meaning of some ‘er’ lexical items in English and its implication to vocabulary acquisition. It centers its investigation on the derivational suffix ‘er’, which changes the grammatical category of word. English language poses many challenges to Second Language Learners because of its irregularities, exceptions, and rules. One of the meaning of –er derivational suffix is someone or somebody who does something. This rule often confuses the learners when they meet with the exceptions in normal discourse. The need to investigate instances of such inconsistencies in the formation of –er words and the meanings given to such words by the students motivated this study. For this purpose, some senior secondary two (SS2) students in six randomly selected schools in the metropolis were provided a large number of alphabetically selected ‘er’ suffix ending words, The researcher opts for a test technique, which requires them to provide the meaning of the selected words with- er. The marking of the test was scored on the scale of 1-0, where correct formation of –er word and meaning is scored one while wrong formation and meaning is scored zero. The number of wrong and correct formations of –er words meaning were calculated using percentage. The result of this research shows that a large number of students made wrong generalization of the meaning of the selected -er ending words. This shows how enormous the inconsistencies are in English language and how are affect the learning of English. Findings from the study revealed that though students mastered the basic morphological rules but the errors are generally committed on those vocabulary items that are not frequently in use. The study arrives at this conclusion from the survey of their textbook and their spoken activities. Therefore, the researcher recommends that there should be effective reappraisal of language teaching through implementation of the designed curriculum to reflect on modern strategies of teaching language, identification, and incorporation of the exceptions in rigorous communicative activities in language teaching, language course books and tutorials, training and retraining of teachers on the strategies that conform to the new pedagogy.

Keywords: ESL(English as a second language), derivational morpheme, inflectional morpheme, suffixes

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Authors: Narendra Akhadkar, Philippe Delcambre

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In power protection and control products, assembly process variations due to the individual parts manufactured from single or multi-cavity tooling is a major problem. The dimensional and geometrical variations on the individual parts, in the form of manufacturing tolerances and assembly tolerances, are sources of clearance in the kinematic joints, polarization effect in the joints, and tolerance stack-up. All these variations adversely affect the quality of product, functionality, cost, and time-to-market. Variation simulation analysis may be used in the early product design stage to predict such uncertainties. Usually, variations exist in both manufacturing processes and materials. In the tolerance analysis, the effect of the dimensional and geometrical variations of the individual parts on the functional characteristics (conditions) of the final assembled products are studied. A functional characteristic of the product may be affected by a set of interrelated dimensions (functional parameters) that usually form a geometrical closure in a 3D chain. In power protection and control products, the prerequisite is: when a fault occurs in the electrical network, the product must respond quickly to react and break the circuit to clear the fault. Usually, the response time is in milliseconds. Any failure in clearing the fault may result in severe damage to the equipment or network, and human safety is at stake. In this article, we have investigated two important functional characteristics that are associated with the robust performance of the product. It is demonstrated that the experimental data obtained at the Schneider Electric Laboratory prove the very good prediction capabilities of the variation simulation performed using CETOL (tolerance analysis software) in an industrial context. Especially, this study allows design engineers to better understand the critical parts in the product that needs to be manufactured with good, capable tolerances. On the contrary, some parts are not critical for the functional characteristics (conditions) of the product and may lead to some reduction of the manufacturing cost, ensuring robust performance. The capable tolerancing is one of the most important aspects in product and manufacturing process design. In the case of miniature circuit breaker (MCB), the product's quality and its robustness are mainly impacted by two aspects: (1) allocation of design tolerances between the components of a mechanical assembly and (2) manufacturing tolerances in the intermediate machining steps of component fabrication.

Keywords: geometrical variation, product robustness, tolerance analysis, variation simulation

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Authors: K. Mokatse, O. M. Barei, K. Gabanakgosi, P. Matlhabaphiri

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The mining of pit slopes is often associated with some level of deviation from design recommendations, and this may translate to associated changes in the stability of the excavated pit slopes. Therefore slope angle design reconciliations are essential for assessing and monitoring compliance of excavated pit slopes to accepted slope designs. These associated changes in slope stability may be reflected by changes in the calculated factors of safety and/or probabilities of failure. Reconciliations of as-mined and slope design profiles are conducted periodically to assess the implications of these deviations on pit slope stability. Currently, the slope design reconciliation process being implemented in Jwaneng Mine involves the measurement of as-mined and design slope angles along vertical sections cut along the established geotechnical design section lines on the GEOVIA GEMS™ software. Bench retentions are calculated as a percentage of the available catchment area, less over-mined and under-mined areas, to that of the designed catchment area. This process has proven to be both tedious and requires a lot of manual effort and time to execute. Consequently, a new semi-automated mine-to-design reconciliation approach that utilizes laser scanning and GIS-based tools is being proposed at Jwaneng Mine. This method involves high-resolution scanning of targeted bench walls, subsequent creation of 3D surfaces from point cloud data and the derivation of slope toe lines and crest lines on the Maptek I-Site Studio software. The toe lines and crest lines are then exported to the ArcGIS software where distance offsets between the design and actual bench toe lines and crest lines are calculated. Retained bench catchment capacity is measured as distances between the toe lines and crest lines on the same bench elevations. The assessment of the performance of the inter-ramp and overall slopes entails the measurement of excavated and design slope angles along vertical sections on the ArcGIS software. Excavated and design toe-to-toe or crest-to-crest slope angles are measured for inter-ramp stack slope reconciliations. Crest-to-toe slope angles are also measured for overall slope angle design reconciliations. The proposed approach allows for a more automated, accurate, quick and easier workflow for carrying out slope angle design reconciliations. This process has proved highly effective and timeous in the assessment of slope performance in Jwaneng Mine. This paper presents a newly proposed process for assessing compliance to slope angle designs for Jwaneng Mine.

Keywords: slope angle designs, slope design recommendations, slope performance, slope stability

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Authors: Jiri Kysucan, Dusan Klos, Katherine Vomackova, Pavel Koranda, Martin Lovecek, Cestmir Neoral, Roman Havlik

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Aim: The prognosis of patients with pancreatic cancer is poor. The median of survival after establishing diagnosis is 3-11 months without surgical treatment, 13-20 months with surgical treatment depending on the disease stage, 5-year survival is less than 5%. Radical surgical resection remains the only hope of curing the disease. Early diagnosis with valid establishment of tumor resectability is, therefore, the most important aim for patients with pancreatic cancer. The aim of the work is to evaluate the contribution and define the role of 18F-FDG PET/CT in preoperative staging. Material and Methods: In 195 patients (103 males, 92 females, median age 66,7 years, 32-88 years) with a suspect pancreatic lesion, as part of the standard preoperative staging, in addition to standard examination methods (ultrasonography, contrast spiral CT, endoscopic ultrasonography, endoscopic ultrasonographic biopsy), a hybrid 18F-FDG PET/CT was performed. All PET/CT findings were subsequently compared with standard staging (CT, EUS, EUS FNA), with peroperative findings and definitive histology in the operated patients as reference standards. Interpretation defined the extent of the tumor according to TNM classification. Limitations of resectability were local advancement (T4) and presence of distant metastases (M1). Results: PET/CT was performed in a total of 195 patients with a suspect pancreatic lesion. In 153 patients, pancreatic carcinoma was confirmed and of these patients, 72 were not indicated for radical surgical procedure due to local inoperability or generalization of the disease. The sensitivity of PET/CT in detecting the primary lesion was 92.2%, specificity was 90.5%. A false negative finding in 12 patients, a false positive finding was seen in 4 cases, positive predictive value (PPV) 97.2%, negative predictive value (NPV) 76,0%. In evaluating regional lymph nodes, sensitivity was 51.9%, specificity 58.3%, PPV 58,3%, NPV 51.9%. In detecting distant metastases, PET/CT reached a sensitivity of 82.8%, specificity was 97.8%, PPV 96.9%, NPV 87.0%. PET/CT found distant metastases in 12 patients, which were not detected by standard methods. In 15 patients (15.6%) with potentially radically resectable findings, the procedure was contraindicated based on PET/CT findings and the treatment strategy was changed. Conclusion: PET/CT is a highly sensitive and specific method useful in preoperative staging of pancreatic cancer. It improves the selection of patients for radical surgical procedures, who can benefit from it and decreases the number of incorrectly indicated operations.

Keywords: cancer, PET/CT, staging, surgery

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Authors: Xinyu Zhang, Olga Torres-Hostench

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This article presents a comparative study evaluating and comparing the quality of machine translation (MT) output of Chinese gastronomy nomenclature. Chinese gastronomic culture is experiencing an increased international acknowledgment nowadays. The nomenclature of Chinese gastronomy not only reflects a specific aspect of culture, but it is related to other areas of society such as philosophy, traditional medicine, etc. Chinese dish names are composed of several types of cultural references, such as ingredients, colors, flavors, culinary techniques, cooking utensils, toponyms, anthroponyms, metaphors, historical tales, among others. These cultural references act as one of the biggest difficulties in translation, in which the use of translation techniques is usually required. Regarding the lack of Chinese food-related translation studies, especially in Chinese-Spanish translation, and the current massive use of MT, the quality of the MT output of Chinese dish names is questioned. Fifty Chinese dish names with different types of cultural components were selected in order to complete this study. First, all of these dish names were translated by three different MT tools (Google Translate, Baidu Translate and Bing Translator). Second, a questionnaire was designed and completed by 12 Chinese online users (Chinese graduates of a Hispanic Philology major) in order to find out user preferences regarding the collected MT output. Finally, human translation techniques were observed and analyzed to identify what translation techniques would be observed more often in the preferred MT proposals. The result reveals that the MT output of the Chinese gastronomy nomenclature is not of high quality. It would be recommended not to trust the MT in occasions like restaurant menus, TV culinary shows, etc. However, the MT output could be used as an aid for tourists to have a general idea of a dish (the main ingredients, for example). Literal translation turned out to be the most observed technique, followed by borrowing, generalization and adaptation, while amplification, particularization and transposition were infrequently observed. Possibly because that the MT engines at present are limited to relate equivalent terms and offer literal translations without taking into account the whole context meaning of the dish name, which is essential to the application of those less observed techniques. This could give insight into the post-editing of the Chinese dish name translation. By observing and analyzing translation techniques in the proposals of the machine translators, the post-editors could better decide which techniques to apply in each case so as to correct mistakes and improve the quality of the translation.

Keywords: Chinese dish names, cultural references, machine translation, translation techniques

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Authors: Spencer Liu, William Chan, Marlena Komatz, Tommy Ramos, Mark Trentalange, Faye Rim, Dae Kim, Mary Kelly, Samuel Schuessler, Roberta Stack, Justas Lauzadis, Kathryn DelPizzo, Seth Waldman, Alexandra Sideris

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Background & Significance: The increasing prevalence of cannabis use in the United States has important safety considerations in the perioperative setting, as chronic or heavy preoperative cannabis use may increase the risk of intraoperative complications, postoperative nausea and vomiting (PONV), increased postoperative pain levels, and acute side effects associated with cannabis use cessation. In this retrospective chart review study, we sought to determine the prevalence of self-reported cannabis use in the past 5-years at a single institution in New York City. We hypothesized that there is an increasing prevalence of preoperative self-reported cannabis use among adult and adolescent patients undergoing orthopedic surgery. Methods: After IRB approval for this retrospective study, surgical cases performed on patients 12 years of age and older at the hospital’s main campus and two ambulatory surgery centers between January 1st, 2018, and December 31st, 2023, with preoperatively self-disclosed cannabis use entered in the social history intake form were identified using the tool SlicerDicer in Epic. Case and patient characteristics were extracted, and trends in utilization over time were assessed by the Cochran-Armitage trend test. Results: Overall, the prevalence of self-reported cannabis use increased from 6.6% in 2018 to 10.6% in 2023. By age group, the prevalence of self-reported cannabis use among adolescents remained consistently low (2018: 2.6%, 2023: 2.6%) but increased with significant evidence for a linear trend (p < 0.05) within every adult age group. Among adults, patients who were 18-24 years old (2018: 18%, 2023: 20.5%) and 25-34 years old (2018: 15.9%, 2023: 24.2%) had the highest prevalences of disclosure, whereas patients who were 75 years of age or older had the lowest prevalence of disclosure (2018: 1.9%, 2023: 4.6%). Patients who were 25-34 years old had the highest percent difference in disclosure rates of 8.3%, which corresponded to a 52.2% increase from 2018 to 2023. The adult age group with the highest percent change was patients who were 75 years of age or older, with a difference of 2.7%, which corresponded to a 142.1% increase from 2018 to 2023. Conclusions: These trends in preoperative self-reported cannabis use among patients undergoing orthopedic surgery have important implications for perioperative care and clinical outcomes. Efforts are underway to refine and standardize cannabis use data capture at our institution.

Keywords: orthopedic surgery, cannabis, postoperative pain, postoperative nausea

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Authors: Nareshkumar Harale, B. B. Meshram

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The continued exponential growth of successful cyber intrusions against today’s businesses has made it abundantly clear that traditional perimeter security measures are no longer adequate and effective. We evolved the network trust architecture from trust-untrust to Zero-Trust, With Zero Trust, essential security capabilities are deployed in a way that provides policy enforcement and protection for all users, devices, applications, data resources, and the communications traffic between them, regardless of their location. Information exchange over the Internet, in spite of inclusion of advanced security controls, is always under innovative, inventive and prone to cyberattacks. TCP/IP protocol stack, the adapted standard for communication over network, suffers from inherent design vulnerabilities such as communication and session management protocols, routing protocols and security protocols are the major cause of major attacks. With the explosion of cyber security threats, such as viruses, worms, rootkits, malwares, Denial of Service attacks, accomplishing efficient and effective intrusion detection and prevention is become crucial and challenging too. In this paper, we propose a design and analysis model for next generation network intrusion detection and protection system as part of layered security strategy. The proposed system design provides intrusion detection for wide range of attacks with layered architecture and framework. The proposed network intrusion classification framework deals with cyberattacks on standard TCP/IP protocol, routing protocols and security protocols. It thereby forms the basis for detection of attack classes and applies signature based matching for known cyberattacks and data mining based machine learning approaches for unknown cyberattacks. Our proposed implemented software can effectively detect attacks even when malicious connections are hidden within normal events. The unsupervised learning algorithm applied to network audit data trails results in unknown intrusion detection. Association rule mining algorithms generate new rules from collected audit trail data resulting in increased intrusion prevention though integrated firewall systems. Intrusion response mechanisms can be initiated in real-time thereby minimizing the impact of network intrusions. Finally, we have shown that our approach can be validated and how the analysis results can be used for detecting and protection from the new network anomalies.

Keywords: network intrusion detection, network intrusion prevention, association rule mining, system analysis and design

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Authors: Tassadit Issaadi Hamitouche, Nicolas Gillard, Jean Petit, Valerie Lavaste, Celine Mayousse

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Water is a fundamental resource for the industry. It is taken from the environment either from municipal distribution networks or from various natural water sources such as the sea, ocean, rivers, aquifers, etc. Once used, water is discharged into the environment, reprocessed at the plant or treatment plants. These withdrawals and discharges have a direct impact on natural water resources. These impacts can apply to the quantity of water available, the quality of the water used, or to impacts that are more complex to measure and less direct, such as the health of the population downstream from the watercourse, for example. Based on the analysis of data (meteorological, river characteristics, physicochemical substances), we wish to predict water stress episodes and anticipate prefectoral decrees, which can impact the performance of plants and propose improvement solutions, help industrialists in their choice of location for a new plant, visualize possible interactions between companies to optimize exchanges and encourage the pooling of water treatment solutions, and set up circular economies around the issue of water. The development of a system for the collection, processing, and use of data related to water resources requires the functional constraints specific to the latter to be made explicit. Thus the system will have to be able to store a large amount of data from sensors (which is the main type of data in plants and their environment). In addition, manufacturers need to have 'near-real-time' processing of information in order to be able to make the best decisions (to be rapidly notified of an event that would have a significant impact on water resources). Finally, the visualization of data must be adapted to its temporal and geographical dimensions. In this study, we set up an infrastructure centered on the TICK application stack (for Telegraf, InfluxDB, Chronograf, and Kapacitor), which is a set of loosely coupled but tightly integrated open source projects designed to manage huge amounts of time-stamped information. The software architecture is coupled with the cross-industry standard process for data mining (CRISP-DM) data mining methodology. The robust architecture and the methodology used have demonstrated their effectiveness on the study case of learning the level of a river with a 7-day horizon. The management of water and the activities within the plants -which depend on this resource- should be considerably improved thanks, on the one hand, to the learning that allows the anticipation of periods of water stress, and on the other hand, to the information system that is able to warn decision-makers with alerts created from the formalization of prefectoral decrees.

Keywords: data mining, industry, machine Learning, shortage, water resources

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Authors: M. A. C. S. Sampath Fernando, James M. Curran, Renate Meyer

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Forensic DNA analysis has received much attention over the last three decades, due to its incredible usefulness in human identification. The statistical interpretation of DNA evidence is recognised as one of the most mature fields in forensic science. Peak heights in an Electropherogram (EPG) are approximately proportional to the amount of template DNA in the original sample being tested. A stutter is a minor peak in an EPG, which is not masking as an allele of a potential contributor, and considered as an artefact that is presumed to be arisen due to miscopying or slippage during the PCR. Stutter peaks are mostly analysed in terms of stutter ratio that is calculated relative to the corresponding parent allele height. Analysis of mixture profiles has always been problematic in evidence interpretation, especially with the presence of PCR artefacts like stutters. Unlike binary and semi-continuous models; continuous models assign a probability (as a continuous weight) for each possible genotype combination, and significantly enhances the use of continuous peak height information resulting in more efficient reliable interpretations. Therefore, the presence of a sound methodology to distinguish between stutters and real alleles is essential for the accuracy of the interpretation. Sensibly, any such method has to be able to focus on modelling stutter peaks. Bayesian nonparametric methods provide increased flexibility in applied statistical modelling. Mixture models are frequently employed as fundamental data analysis tools in clustering and classification of data and assume unidentified heterogeneous sources for data. In model-based clustering, each unknown source is reflected by a cluster, and the clusters are modelled using parametric models. Specifying the number of components in finite mixture models, however, is practically difficult even though the calculations are relatively simple. Infinite mixture models, in contrast, do not require the user to specify the number of components. Instead, a Dirichlet process, which is an infinite-dimensional generalization of the Dirichlet distribution, is used to deal with the problem of a number of components. Chinese restaurant process (CRP), Stick-breaking process and Pólya urn scheme are frequently used as Dirichlet priors in Bayesian mixture models. In this study, we illustrate an infinite mixture of simple linear regression models for modelling stutter ratio and introduce some modifications to overcome weaknesses associated with CRP.

Keywords: Chinese restaurant process, Dirichlet prior, infinite mixture model, PCR stutter

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Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

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Authors: Yulii D. Shikhmurzaev

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The modelling of the non-isothermal free-surface flows coupled with the solidification process has become the topic of intensive research with the advent of additive manufacturing, where complex 3-dimensional structures are produced by successive deposition and solidification of microscopic droplets of different materials. The issue is that both the spreading of liquids over solids and the propagation of the solidification front into the fluid and along the solid substrate pose fundamental difficulties for their mathematical modelling. The first of these processes, known as ‘dynamic wetting’, leads to the well-known ‘moving contact-line problem’ where, as shown recently both experimentally and theoretically, the contact angle formed by the free surfac with the solid substrate is not a function of the contact-line speed but is rather a functional of the flow field. The modelling of the propagating solidification front requires generalization of the classical Stefan problem, which would be able to describe the onset of the process and the non-equilibrium regime of solidification. Furthermore, given that both dynamic wetting and solification occur concurrently and interactively, they should be described within the same conceptual framework. The present work addresses this formidable problem and presents a mathematical model capable of describing the key element of additive manufacturing in a self-consistent and singularity-free way. The model is illustrated simple examples highlighting its main features. The main idea of the work is that both dynamic wetting and solidification, as well as some other fluid flows, are particular cases in a general class of flows where interfaces form and/or disappear. This conceptual framework allows one to derive a mathematical model from first principles using the methods of irreversible thermodynamics. Crucially, the interfaces are not considered as zero-mass entities introduced using Gibbsian ‘dividing surface’ but the 2-dimensional surface phases produced by the continuum limit in which the thickness of what physically is an interfacial layer vanishes, and its properties are characterized by ‘surface’ parameters (surface tension, surface density, etc). This approach allows for the mass exchange between the surface and bulk phases, which is the essence of the interface formation. As shown numerically, the onset of solidification is preceded by the pure interface formation stage, whilst the Stefan regime is the final stage where the temperature at the solidification front asymptotically approaches the solidification temperature. The developed model can also be applied to the flow with the substrate melting as well as a complex flow where both types of phase transition take place.

Keywords: dynamic wetting, interface formation, phase transition, solidification

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Authors: Umedov Mekhroz, Griaznova Svetlana

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In connection with the growth of oncological diseases, the design of centers for diagnostics and the production of radiopharmaceuticals is the most relevant area of healthcare facilities. The design of new nuclear medicine centers should be carried out from the standpoint of solving the following tasks: the availability of medical care, functionality, environmental friendliness, sustainable development, improving the safety of drugs, the use of which requires special care, reducing the rate of environmental pollution, ensuring comfortable conditions for the internal microclimate, adaptability. The purpose of this article is to substantiate architectural and planning solutions, formulate recommendations and principles for the design of nuclear medicine centers and determine the connections between the production and medical functions of a building. The advantages of combining the production of radiopharmaceuticals and the department of medical care: less radiation activity is accumulated, the cost of the final product is lower, and there is no need to hire a transport company with a special license for transportation. A medical imaging department is a structural unit of a medical institution in which diagnostic procedures are carried out in order to gain an idea of the internal structure of various organs of the body for clinical analysis. Depending on the needs of a particular institution, the department may include various rooms that provide medical imaging using radiography, ultrasound diagnostics, and the phenomenon of nuclear magnetic resonance. The production of radiopharmaceuticals is an object intended for the production of a pharmaceutical substance containing a radionuclide and intended for introduction into the human body or laboratory animal for the purpose of diagnosis, evaluation of the effectiveness of treatment, or for biomedical research. The research methodology includes the following subjects: study and generalization of international experience in scientific research, literature, standards, teaching aids, and design materials on the topic of research; An integrated approach to the study of existing international experience of PET / CT scan centers and the production of radiopharmaceuticals; Elaboration of graphical analysis and diagrams based on the system analysis of the processed information; Identification of methods and principles of functional zoning of nuclear medicine centers. The result of the research is the identification of the design principles of nuclear medicine centers with the functions of the production of radiopharmaceuticals and the department of medical imaging. This research will be applied to the design and construction of healthcare facilities in the field of nuclear medicine.

Keywords: architectural planning solutions, functional zoning, nuclear medicine, PET/CT scan, production of radiopharmaceuticals, radiotherapy

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Authors: Miguel Contreras, David Long, Will Bachman

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Background: Microscopy plays a central role in cell and developmental biology. In particular, fluorescence microscopy can be used to visualize specific cellular components and subsequently quantify their morphology through development of virtual-cell models for study of effects of mechanical forces on cells. However, there are challenges with these imaging experiments, which can make it difficult to quantify cell morphology: inconsistent results, time-consuming and potentially costly protocols, and limitation on number of labels due to spectral overlap. To address these challenges, the objective of this project is to develop an automatic computational machine learning pipeline to predict cellular components morphology for virtual-cell generation based on fluorescence cell membrane confocal z-stacks. Methods: Registered confocal z-stacks of nuclei and cell membrane of endothelial cells, consisting of 20 images each, were obtained from fluorescence confocal microscopy and normalized through software pipeline for each image to have a mean pixel intensity value of 0.5. An open source machine learning algorithm, originally developed to predict fluorescence labels on unlabeled transmitted light microscopy cell images, was trained using this set of normalized z-stacks on a single CPU machine. Through transfer learning, the algorithm used knowledge acquired from its previous training sessions to learn the new task. Once trained, the algorithm was used to predict morphology of nuclei using normalized cell membrane fluorescence images as input. Predictions were compared to the ground truth fluorescence nuclei images. Results: After one week of training, using one cell membrane z-stack (20 images) and corresponding nuclei label, results showed qualitatively good predictions on training set. The algorithm was able to accurately predict nuclei locations as well as shape when fed only fluorescence membrane images. Similar training sessions with improved membrane image quality, including clear lining and shape of the membrane, clearly showing the boundaries of each cell, proportionally improved nuclei predictions, reducing errors relative to ground truth. Discussion: These results show the potential of pre-trained machine learning algorithms to predict cell morphology using relatively small amounts of data and training time, eliminating the need of using multiple labels in immunofluorescence experiments. With further training, the algorithm is expected to predict different labels (e.g., focal-adhesion sites, cytoskeleton), which can be added to the automatic machine learning pipeline for direct input into Principal Component Analysis (PCA) for generation of virtual-cell mechanical models.

Keywords: cell morphology prediction, computational machine learning, fluorescence microscopy, virtual-cell models

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Authors: Khalid Omari, Rene Chenier, Enrique Blondel, Ryan Ahola

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Northern Canadian coasts have vulnerable and very dynamic intertidal zones with very high tides occurring in several areas. The impact of climate change presents challenges not only for maintaining this biodiversity but also for navigation safety adaptation due to the high sediment mobility in these coastal areas. Thus, frequent mapping of shorelines and intertidal changes is of high importance. To help in quantifying the changes in these fragile ecosystems, remote sensing provides practical monitoring tools at local and regional scales. Traditional methods based on high-resolution optical sensors are often used to map intertidal areas by benefiting of the spectral response contrast of intertidal classes in visible, near and mid-infrared bands. Tidal areas are highly reflective in visible bands mainly because of the presence of fine sand deposits. However, getting a cloud-free optical data that coincide with low tides in intertidal zones in northern regions is very difficult. Alternatively, the all-weather capability and daylight-independence of the microwave remote sensing using synthetic aperture radar (SAR) can offer valuable geophysical parameters with a high frequency revisit over intertidal zones. Multi-polarization SAR parameters have been used successfully in mapping intertidal zones using incoherence target decomposition. Moreover, the crustal displacements caused by ocean tide loading may reach several centimeters that can be detected and quantified across differential interferometric synthetic aperture radar (DInSAR). Soil moisture change has a significant impact on both the coherence and the backscatter. For instance, increases in the backscatter intensity associated with low coherence is an indicator for abrupt surface changes. In this research, we present primary results obtained following our investigation of the potential of the fully polarimetric Radarsat-2 data for mapping an inter-tidal zone located on Tasiujaq on the south-west shore of Ungava Bay, Quebec. Using the repeat pass cycle of Radarsat-2, multiple seasonal fine quad (FQ14W) images are acquired over the site between 2016 and 2018. Only 8 images corresponding to low tide conditions are selected and used to build an interferometric stack of data. The observed displacements along the line of sight generated using HH and VV polarization are compared with the changes noticed using the Freeman Durden polarimetric decomposition and Touzi degree of polarization extrema. Results show the consistency of both approaches in their ability to monitor the changes in intertidal zones.

Keywords: SAR, degree of polarization, DInSAR, Freeman-Durden, polarimetry, Radarsat-2

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Authors: Zheng Wang, Zhenhong Li, Jon Mills

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Ground-based synthetic aperture radar (GBSAR) represents a powerful remote sensing tool for deformation monitoring towards various geohazards, e.g. landslides, mudflows, avalanches, infrastructure failures, and the subsidence of residential areas. Unlike spaceborne SAR with a fixed revisit period, GBSAR data can be acquired with an adjustable temporal resolution through either continuous or discontinuous operation. However, challenges arise from processing high temporal-resolution continuous GBSAR data, including the extreme cost of computational random-access-memory (RAM), the delay of displacement maps, and the loss of temporal evolution. Moreover, repositioning errors between discontinuous campaigns impede the accurate measurement of surface displacements. Therefore, a versatile package with two complete chains is developed in this study in order to process both continuous and discontinuous GBSAR data and address the aforementioned issues. The first chain is based on a small-baseline subset concept and it processes continuous GBSAR images unit by unit. Images within a window form a basic unit. By taking this strategy, the RAM requirement is reduced to only one unit of images and the chain can theoretically process an infinite number of images. The evolution of surface displacements can be detected as it keeps temporarily-coherent pixels which are present only in some certain units but not in the whole observation period. The chain supports real-time processing of the continuous data and the delay of creating displacement maps can be shortened without waiting for the entire dataset. The other chain aims to measure deformation between discontinuous campaigns. Temporal averaging is carried out on a stack of images in a single campaign in order to improve the signal-to-noise ratio of discontinuous data and minimise the loss of coherence. The temporal-averaged images are then processed by a particular interferometry procedure integrated with advanced interferometric SAR algorithms such as robust coherence estimation, non-local filtering, and selection of partially-coherent pixels. Experiments are conducted using both synthetic and real-world GBSAR data. Displacement time series at the level of a few sub-millimetres are achieved in several applications (e.g. a coastal cliff, a sand dune, a bridge, and a residential area), indicating the feasibility of the developed GBSAR data processing package for deformation monitoring of a wide range of scientific and practical applications.

Keywords: ground-based synthetic aperture radar, interferometry, small baseline subset algorithm, deformation monitoring

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Authors: Saeed Nazari, Masoom Nazari, Ali Hejazi, Siamak Sanoobari Ghazi Jahani, Mohammad Dehghani, Javad Vakili

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More than ever, digitization is an imperative for businesses to keep their competitive advantages, foster innovation and reduce paperwork. To design and successfully implement digital transformation initiatives within process safety management system, employees need to be equipped with the right tool, frameworks, and best practices. we developed a unique full stack application so-called SHEQTool which is entirely dynamic based on our extensive expertise, experience, and client feedback to help business processes particularly operations safety management. We use our best knowledge and scientific methodologies published by CCPS and OSHA Guidelines to streamline operations and integrated them into task management within Petrochemical Companies. We digitalize their main process safety management system elements and their sub elements such as hazard identification and risk management, training and communication, inspection and audit, critical changes management, contractor management, permit to work, pre-start-up safety review, incident reporting and investigation, emergency response plan, personal protective equipment, occupational health, and action management in a fully customizable manner with no programming needs for users. We review the feedback from main actors within petrochemical plant which highlights improving their business performance and productivity as well as keep tracking their functions’ key performance indicators (KPIs) because it; 1) saves time, resources, and costs of all paperwork on our businesses (by Digitalization); 2) reduces errors and improve performance within management system by covering most of daily software needs of the organization and reduce complexity and associated costs of numerous tools and their required training (One Tool Approach); 3) focuses on management systems and integrate functions and put them into traceable task management (RASCI and Flowcharting); 4) helps the entire enterprise be resilient to any change of your processes, technologies, assets with minimum costs (through Organizational Resilience); 5) reduces significantly incidents and errors via world class safety management programs and elements (by Simplification); 6) gives the companies a systematic, traceable, risk based, process based, and science based integrated management system (via proper Methodologies); 7) helps business processes complies with ISO 9001, ISO 14001, ISO 45001, ISO 31000, best practices as well as legal regulations by PDCA approach (Compliance).

Keywords: process, safety, digitalization, management, risk, incident, SHEQTool, OSHA, CCPS

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Authors: Morena Paulisic, Marli Gonan Bozac

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Business organization and management in theory are typically presented as gender- neutral. Although in practice female contribution to corporation is not questionable, gender diversity in top management of corporation is and that especially in emerging countries like Croatia. This paper brings insights into obstacles and problems which should be overcome. Furthermore, gives an introspective view on the most important promotion and motivation factors of powerful female CEOs in Croatia. The goal was to clarify perception and performance of female CEOs that contributed to their success and to determine mutual characteristics of women in corporate entrepreneurship regarding the motivation. For our study we used survey instrument that was developed for this research. The research methods used were: table research, field research, generalization method, comparative method, and statistical method (descriptive statistics and Pearson’s Chi-square test). Some result showed us that today even more women in corporations are not likely to accept more engagement at work if it harms their families (2003 – 31.9% in 2013 – 33.8%) although their main motivating factor is still interested job (2003 – 95.8%; in 2013-100%). It is also significant that 78.8 % of Croatian top managers (2013) think that women managers in Croatia are insufficiently spoken and written about, and that the reasons for this are that: (1) the society underestimates their ability (37.9%); (2) women underestimate themselves (22.4%); (3) the society still mainly focuses on male managers (20.7%) and (4) women managers avoid interviews and appearing on front pages (19%). The environment still “blocks” the natural course of advancement of women managers in organisations (entrepreneurship in general) and the main obstacle is that women must always or almost always be more capable than men in order to succeed (96.6%). Based on survey results on longitudinal research conducted in 2003 (return rate 30,8%) and 2013 (return rate 29,2%) in Croatia we expand understanding of determination indicators of corporate female entrepreneurship. Theoretically in practice gender structure at the management level (executive management, management board and supervisory board) throw years (2011- 2014) have positive score but still women remain significantly underrepresented at those positions. Findings from different sources have shown that diversity at the top of corporations’ correlates with better performance. In this paper, we have contributed to research on gender in corporate entrepreneurship by offering experiences from successful female CEOs and explanation why in social responsible society women with their characteristics can support needed changes and construct different way forward for corporations. Based on research result we can conclude that in future underrepresentation of female in corporate entrepreneurship should be overcome.

Keywords: Croatia, female entrepreneurship, glass ceiling, motivation

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Authors: Zhijie Li, Wenhui Peng, Zelong Yuan, Jianchun Wang

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Turbulence is a complex phenomenon that plays a crucial role in various fields, such as engineering, atmospheric science, and fluid dynamics. Predicting and understanding its behavior over long time scales have been challenging tasks. Traditional methods, such as large-eddy simulation (LES), have provided valuable insights but are computationally expensive. In the past few years, machine learning methods have experienced rapid development, leading to significant improvements in computational speed. However, ensuring stable and accurate long-term predictions remains a challenging task for these methods. In this study, we introduce the implicit U-net enhanced Fourier neural operator (IU-FNO) as a solution for stable and efficient long-term predictions of the nonlinear dynamics in three-dimensional (3D) turbulence. The IU-FNO model combines implicit re-current Fourier layers to deepen the network and incorporates the U-Net architecture to accurately capture small-scale flow structures. We evaluate the performance of the IU-FNO model through extensive large-eddy simulations of three types of 3D turbulence: forced homogeneous isotropic turbulence (HIT), temporally evolving turbulent mixing layer, and decaying homogeneous isotropic turbulence. The results demonstrate that the IU-FNO model outperforms other FNO-based models, including vanilla FNO, implicit FNO (IFNO), and U-net enhanced FNO (U-FNO), as well as the dynamic Smagorinsky model (DSM), in predicting various turbulence statistics. Specifically, the IU-FNO model exhibits improved accuracy in predicting the velocity spectrum, probability density functions (PDFs) of vorticity and velocity increments, and instantaneous spatial structures of the flow field. Furthermore, the IU-FNO model addresses the stability issues encountered in long-term predictions, which were limitations of previous FNO models. In addition to its superior performance, the IU-FNO model offers faster computational speed compared to traditional large-eddy simulations using the DSM model. It also demonstrates generalization capabilities to higher Taylor-Reynolds numbers and unseen flow regimes, such as decaying turbulence. Overall, the IU-FNO model presents a promising approach for long-term dynamics prediction in 3D turbulence, providing improved accuracy, stability, and computational efficiency compared to existing methods.

Keywords: data-driven, Fourier neural operator, large eddy simulation, fluid dynamics

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Authors: Soheila Sadeghi

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In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.

Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes

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Authors: Anna V. Zaporozhchenko, Dmytro Kutnyakhov, Katherina Medjanik, Christian Tusche, Hans-Joachim Elmers, Olena Fedchenko, Sergey Chernov, Martin Ellguth, Sergej A. Nepijko, Gerd Schoenhense

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Graphene reveals a unique electronic structure that predetermines many intriguing properties such as massless charge carriers, optical transparency and high velocity of fermions at the Fermi level, opening a wide horizon of future applications. Hence, a detailed investigation of the electronic structure of graphene is crucial. The method of choice is angular resolved photoelectron spectroscopy ARPES. Here we present experiments using time-of-flight (ToF) momentum microscopy, being an alternative way of ARPES using full-field imaging of the whole Brillouin zone (BZ) and simultaneous acquisition of up to several 100 energy slices. Unlike conventional ARPES, k-microscopy is not limited in simultaneous k-space access. We have recorded the whole first BZ of graphene on Ir(111) including all six Dirac cones. As excitation source we used synchrotron radiation from BESSY II (Berlin) at the U125-2 NIM, providing linearly polarized (both polarizations p- and s-) VUV radiation. The instrument uses a delay-line detector for single-particle detection up the 5 Mcps range and parallel energy detection via ToF recording. In this way, we gather a 3D data stack I(E,kx,ky) of the full valence electronic structure in approx. 20 mins. Band dispersion stacks were measured in the energy range of 14 eV up to 23 eV with steps of 1 eV. The linearly-dispersing graphene bands for all six K and K’ points were simultaneously recorded. We find clear features of hybridization with the substrate, in particular in the linear dichroism in the angular distribution (LDAD). Recording of the whole Brillouin zone of graphene/Ir(111) revealed new features. First, the intensity differences (i.e. the LDAD) are very sensitive to the interaction of graphene bands with substrate bands. Second, the dark corridors are investigated in detail for both, p- and s- polarized radiation. They appear as local distortions of photoelectron current distribution and are induced by quantum mechanical interference of graphene sublattices. The dark corridors are located in different areas of the 6 Dirac cones and show chirality behaviour with a mirror plane along vertical axis. Moreover, two out of six show an oval shape while the rest are more circular. It clearly indicates orientation dependence with respect to E vector of incident light. Third, a pattern of faint but very sharp lines is visible at energies around 22eV that strongly remind on Kikuchi lines in diffraction. In conclusion, the simultaneous study of all six Dirac cones is crucial for a complete understanding of dichroism phenomena and the dark corridor.

Keywords: band structure, graphene, momentum microscopy, LDAD

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Authors: Yuliya Huseva

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To investigate the contents of tears to determine latent blood. Methods: Tear samples from 72 women were studied with the microscopy of tears aspirated with a capillary and stained by Nocht and with a chemical method of test strips with chromogen. Statistical data processing was carried out using statistical packages Statistica 10.0 for Windows, calculation of Pearson's chi-square test, Yule association coefficient, the method of determining sensitivity and specificity. Results:, In 30.6% (22) of tear samples erythrocytes were revealed microscopically. Correlations between the presence of erythrocytes in the tear and the phase of the menstrual cycle has been discovered. In the follicular phase of the cycle, erythrocytes were found in 59.1% (13) people, which is significantly more (x2=4.2, p=0.041) compared to the luteal phase - in 40.9% (9) women. In the first seven days of the follicular phase of the menstrual cycle the erythrocytes were predominanted of in the tears of women examined testifies in favour of the vicarious bleeding from the mucous membranes of extragenital organs in sync with menstruation. Of the other cellular elements in tear samples with latent haemolacria, neutrophils prevailed - in 45.5% (10), while lymphocytes were less common - in 27.3% (6), because neutrophil exudation is accompanied by vasodilatation of the conjunctiva and the release of erythrocytes into the conjunctival cavity. It was found that the prognostic significance of the chemical method was 0.53 of the microscopic method. In contrast to microscopy, which detected blood in tear samples from 30.6% (22) of women, blood was detected chemically in tears of 16.7% (12). An association between latent haemolacria and endometriosis was found (k=0.75, p≤0.05). Microscopically, in the tears of patients with endometriosis, erythrocytes were detected in 70% of cases, while in healthy women without endometriosis - in 25% of cases. The proportion of women with erythrocytes in tears, determined by a chemical method, was 41.7% among patients with endometriosis, which is significantly more (x2=6.5, p=0.011) than 11.7% among women without endometriosis. The data obtained can be explained by the etiopathogenesis of the extragenital endometriosis which is caused by hematogenous spread of endometrial tissue into the orbit. In endometriosis, erythrocytes are found against the background of accumulations of epithelial cells. In the tear samples of 4 women with endometriosis, glandular cuboidal epithelial cells, morphologically similar to endometrial cells, were found, which may indicate a generalization of the disease. Conclusions: Single erythrocytes can normally be found in the tears, their number depends on the phase of the menstrual cycle, increasing in the follicular phase. Erythrocytes found in tears against the background of accumulations of epitheliocytes and their glandular atypia may indicate a manifestation of extragenital endometriosis. Both used methods (microscopic and chemical) are informative in revealing latent haemolacria. The microscopic method is more sensitive, reveals intact erythrocytes, and besides, it provides information about other cells. At the same time, the chemical method is faster and technically simpler, it determines the presence of haemoglobin and its metabolic products, and can be used as a screening.

Keywords: tear, blood, microscopy, epitheliocytes

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Authors: Chunhua Liao, Jinfei Wang, Bo Shan, Yang Song, Yongjun He, Taifeng Dong

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Winter wheat is one of the main crops in Canada. Forecasting of within-field variability of yield in winter wheat at the early stages is essential for precision farming. However, the crop yield modelling based on high spatial resolution satellite data is generally affected by the lack of continuous satellite observations, resulting in reducing the generalization ability of the models and increasing the difficulty of crop yield forecasting at the early stages. In this study, the correlations between Sentinel-2 data (vegetation indices and reflectance) and yield data collected by combine harvester were investigated and a generalized multivariate linear regression (MLR) model was built and tested with data acquired in different years. It was found that the four-band reflectance (blue, green, red, near-infrared) performed better than their vegetation indices (NDVI, EVI, WDRVI and OSAVI) in wheat yield prediction. The optimum phenological stage for wheat yield prediction with highest accuracy was at the growing stages from the end of the flowering to the beginning of the filling stage. The best MLR model was therefore built to predict wheat yield before harvest using Sentinel-2 data acquired at the end of the flowering stage. Further, to improve the ability of the yield prediction at the early stages, three simple unsupervised domain adaptation (DA) methods were adopted to transform the reflectance data at the early stages to the optimum phenological stage. The winter wheat yield prediction using multiple vegetation indices showed higher accuracy than using single vegetation index. The optimum stage for winter wheat yield forecasting varied with different fields when using vegetation indices, while it was consistent when using multispectral reflectance and the optimum stage for winter wheat yield prediction was at the end of flowering stage. The average testing RMSE of the MLR model at the end of the flowering stage was 604.48 kg/ha. Near the booting stage, the average testing RMSE of yield prediction using the best MLR was reduced to 799.18 kg/ha when applying the mean matching domain adaptation approach to transform the data to the target domain (at the end of the flowering) compared to that using the original data based on the models developed at the booting stage directly (“MLR at the early stage”) (RMSE =1140.64 kg/ha). This study demonstrated that the simple mean matching (MM) performed better than other DA methods and it was found that “DA then MLR at the optimum stage” performed better than “MLR directly at the early stages” for winter wheat yield forecasting at the early stages. The results indicated that the DA had a great potential in near real-time crop yield forecasting at the early stages. This study indicated that the simple domain adaptation methods had a great potential in crop yield prediction at the early stages using remote sensing data.

Keywords: wheat yield prediction, domain adaptation, Sentinel-2, within-field scale

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

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