Theoretical and numerical approach to calculate the shear stiffness of corrugated metal deck

Permanent Metal Deck Forms (PMDFs) currently are used in building application as a lateral beam bracing. In the bridge applications, PMDFs are frequently used to support the wet concrete of bridge decks during the construction phase, but they are not relied on as lateral bracing. The girders in the bridge system are subjected to lateral torsional buckling that occurs under the casting of the bridge deck. In order to improve the stabilizing potential of the PMDF system in the bridge system, it is important to estimate the shear rigidity of metal decks that are used as shear diaphragm

Lameness detection in sheep through behavioural sensor data analysis

Lameness is a clinical symptom referring to locomotion changes, resulting in impaired and erratic movements that differ widely from normal gait or posture. Lameness has an adverse impact on both sheep welfare and farm economy, therefore the preclinical detection of lameness will improve both sheep health and, in turn, support farming businesses. A newly developed sensor technology should enable automatic monitoring of animals to determine physiological and behavioural indicators, which would then be subsequently used as inputs into data analysis algorithms. The sensor that will be used to conduct this research is immensely accurate and sensitive. It provides acceleration, angular velocity, orientation, longitude, latitude and the time of reading which can be set up according to the demanded accuracy. This study will develop an automated model to detect lameness in sheep by analysing the data retrieved from a mounted sensor on the neck of the sheep. This model will help the shepherd to detect lame sheep earlier, to prevent trimming or even culling

Process simulation and experimental investigation of biofuel production in a high rate anaerobic digestion process

The non-hazardous waste management hierarchy of the US EPA calls for “Reduce, Reuse, and Recycle” (or the three R’s) of wastes. The anaerobic digestion process is one of the most important methods that used to treat the wastes and at the same time generate energy out of it. The anaerobic digestion process generates a mixture of methane and carbon dioxide gases which is known as biogas. The biogas composition is about 50-70% methane and 10-30% of carbon dioxide and trace amount of other gases like hydrogen and hydrogen sulfide. This biogas can be used in power generation, heating systems and in combined heating and powering systems. Also, it could be upgraded to improve its quality and make is utilized in all equipment used for natural gas with a minimal adjustment due to the lower BTU contents for methane gas. Three papers were written and submitted regarding the biogas production and the liquid waste water treatment. The first paper focuses on developing a process modeling simulation by aspen plus for the anaerobic digestion process and on conducting a sensitivity analysis to investigate the parameters that could upgrade the biogas quality. The second paper focuses on the effect of hydraulic retention time of the substrate on the biogas production. This investigation conducted in a two-stage high rate expanded granular sludge bed reactor under different variables like substrate concentration, organic loading rate, and operating temperature. The third paper focuses on upgrading the biogas quality and quantity in a two-stage expanded granular sludge bed reactor by investigating the effect of injecting the pre-acidification gas (the first stage), which is mainly produced a gas mixture consist of hydrogen and carbon dioxide) into the second stage (the expanded granular sludge bed reactor)--Abstract, page iv

Automatic quality assessment in mammography screening:a deep learning based segmentation method

Abstract. Mammography is an imaging method used as a main tool to detect breast cancer at early stages. Images (mammograms) are examined by radiologists, who aim to identify cancerous findings. However, in order to do that, the mammograms need to be of diagnostic quality, which can sometimes be insufficient, and thus the quality of diagnosis also suffers. Radiology technicians (radiographers) are trained to take mammography images, but not in every healthcare center a strict quality control process is established, which may substantially affect the patients. The most common defects in mammograms are positioning defects, which are seen in the images as skin-foldings or non-imaged parts of the breast. The major issue at a process level is that the described positioning issues are noticed late, already at the diagnostic phase. If a radiologist decides that the mammogram is a non-diagnostic quality, the patient needs to revisit the imaging center. If quality control could be automated and standardized, unnecessary patient recalls could be avoided, thus, reducing the costs of the mammographic process. To date, there is a lack of automatic general quality control tools for mammography screening. Looking at the recent advances in artificial intelligence, it may be possible to automate this process. The goal of this thesis was to develop an automatic system for quality assessment of mammograms. The author used Deep learning to develop an automatic framework for automatic segmentation of defects in mammograms using a dataset of 512 mammographic images extracted from the Oulu University Hospital archive. The second stage of the developed method performed quality assessment by analyzing the presence and location of different tissues in the images from the predicted segmentations. The developed segmentation model yielded a Dice coefficient over 0.90 for the whole breast, breast, and pectoral muscle, and over 0.60 for skin-foldings and nipple. The developed method is the first to tackle automatic segmentation of all major positioning issues in mammography. Ultimately, the developed technology has a potential to improve the mammography workflows and, eventually, patient outcomes.Automaattinen laadunarviointi mammografian kuvauksessa : syväoppimispohjainen segmentointimenetelmä. Tiivistelmä. Mammografiaa on kuvantamismenetelmä, jota käytetään päävälineenä rintasyövän havaitsemiseksi varhaisessa vaiheessa. Radiologien on tutkittava mammogrammit ja päätettävä sitten, onko pahanlaatusia löydöksiä, ja tätä varten mammografiakuvien on oltava diagnostisesti laadukkaita. Ammattilaiset koulutetaan mammografiakuvien ottamiseksi, mutta ei kaikissa terveyskeskuksissa on otettu käyttöön tiukka laadunvalvontaprosessi, joka voi vaikuttaa merkittävästi potilaisiin. Kuvissa voi olla virheitä, jotka tekevät kuvista ei-diagnostisen laadukkaan mammogrammin, ja ne voivat vaikuttaa diagnostiikkatuloksiin. Yksi näistä vioista ovat paikannusvirheet, joissa näkyvät kuvissa ihon taitoksina ja jotkut rinnan osat eivät näy. Suurin ongelma prosessitasolla on, että kuvatut paikannusvirheet havaitaan myöhässä, jo diagnoosivaiheessa. Jos radiologit päättävät, että mammografiakuva ei ole diagnostisesti laadukas, potilaan on palattava kuvantamiskeskukseen ja tutkittava uudelleen, mikä voi lisätä kustannuksia ja työmäärää. Jos laadunvalvonta voidaan automatisoida ja standardoida, voidaan välttää tarpeetonta potilaan palauttamista ja vähentää siten mammografiaprosessin kustannuksia. Tähän mennessä mammografiaseulonnassa ei ole automaattista yleistä laadunvalvontaa. Kun tarkastellaan tekoälyn viimeaikaisia edistystä, tämän prosessin automatisointi voi olla mahdollista. Tämän projektin tarkoituksena oli todistaa diagnostisten ja ei-diagnostisten laatumammogrammien automaattisen erottamisen toteutettavuus. Kirjoittaja käytti syvää oppimista automatisoidun kehyksen luomisessa käyttämällä 512 mammografiakuvaa, jotka otettiin Oulun yliopistollisen sairaalan arkistosta. Automaattisen menetelmän ensimmäisessä vaiheessa suoritettiin rintakudosten ja ihon taittumien segmentointi. Toisessa vaiheessa suoritettiin laadunarviointi analysoimalla eri kudosten läsnäolo ja sijainti kuvissa. Kehitetyllä segmentointimallilla saavutettiin merkittäviä tuloksia, kun koko rinnan ja rintalihasten segmentoinnin onnistumisen hyvyttä mittaava Dice-kerroin oli yli 0,90, ja ihon taittumiselle ja nännille yli 0,60. Kehitetty menetelmä on ensimmäinen, joka käsittelee mammografian kaikkien tärkeimpien paikannusvirheiden automaattista segmentointia. Sillä on potentiaalia myötävaikuttaa mammografian työnkulkujen ja potilastulosten parantamiseen

Radio network management in cognitive LTE-Femtocell Systems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.There is a strong uptake of femtocell deployment as small cell application platforms in the upcoming LTE networks. In such two-tier networks of LTEfemtocell base stations, a large portion of the assigned spectrum is used sporadically leading to underutilisation of valuable frequency resources. Novel spectrum access techniques are necessary to solve these current spectrum inefficiency problems. Therefore, spectrum management solutions should have the features to improve spectrum access in both temporal and spatial manner. Cognitive Radio (CR) with the Dynamic Spectrum Access (DSA) is considered to be the key technology in this research in order to increase the spectrum efficiency. This is an effective solution to allow a group of Secondary Users (SUs) to share the radio spectrum initially allocated to the Primary User (PUs) at no interference. The core aim of this thesis is to develop new cognitive LTE-femtocell systems that offer a 4G vision, to facilitate the radio network management in order to increase the network capacity and further improve spectrum access probabilities. In this thesis, a new spectrum management model for cognitive radio networks is considered to enable a seamless integration of multi-access technology with existing networks. This involves the design of efficient resource allocation algorithms that are able to respond to the rapid changes in the dynamic wireless environment and primary users activities. Throughout this thesis a variety of network upgraded functions are developed using application simulation scenarios. Therefore, the proposed algorithms, mechanisms, methods, and system models are not restricted in the considered networks, but rather have a wider applicability to be used in other technologies. This thesis mainly investigates three aspects of research issues relating to the efficient management of cognitive networks: First, novel spectrum resource management modules are proposed to maximise the spectrum access by rapidly detecting the available transmission opportunities. Secondly, a developed pilot power controlling algorithm is introduced to minimise the power consumption by considering mobile position and application requirements. Also, there is investigation on the impact of deploying different numbers of femtocell base stations in LTE domain to identify the optimum cell size for future networks. Finally, a novel call admission control mechanism for mobility management is proposed to support seamless handover between LTE and femtocell domains. This is performed by assigning high speed mobile users to the LTE system to avoid unnecessary handovers. The proposed solutions were examined by simulation and numerical analysis to show the strength of cognitive femtocell deployment for the required applications. The results show that the new system design based on cognitive radio configuration enable an efficient resource management in terms of spectrum allocation, adaptive pilot power control, and mobile handover. The proposed framework and algorithms offer a novel spectrum management for self organised LTE-femtocell architecture. Eventually, this research shows that certain architectures fulfilling spectrum management requirements are implementable in practice and display good performance in dynamic wireless environments which recommends the consideration of CR systems in LTE and femtocell networks

Deep learning-based lower back pain classification and detection from T2-weighted magnetic resonance images

Abstract. Lower back pain (LBP) is a common physiological condition that affects 50–80% of the adult population at some point in their lives. For example, the economic load of LBP in Sweden was estimated to be approx. at C740 million in 2011. In LBP diagnostics, magnetic resonance imaging (MRI) is often used. MRI is used to visualize the structures in the lumbar region of the spine such as disks, bones, and spaces between the vertebral bones where nerves pass through. The lumbar spine refers to the lowest five vertebrae and intervertebral discs of the spine. MRI provides a detailed picture of the lumbar spine to get visual confirmation of any abnormalities potentially related to LBP to support the diagnosis process. The goal of this thesis was to investigate visual patterns related to LBP in T2-weighted MR images measured with a fast spin-echo sequence on a GE Healthcare Signa HDxt 1.5 T MRI system. A convolutional neural network was used to classify MRIs into symptomatic and asymptomatic cases and to develop a fully automated pain prediction process. A total of 526 MRI examinations with supporting pain questionnaires from the Northern Finland Birth Cohort 1966 (NFBC1966) were used. Three different datasets were created for the experiments: i) a dataset with mid-sagittal slices from the center of the spine from each examination, ii) a dataset with mid-sagittal slices and its immediate neighboring slices, and similarly, iii) a dataset with five middle-most sagittal slices. In each dataset, individual slices were considered as independent samples, i.e., inputs for the classification method. The developed classification method yielded the best results when the input dataset comprised of three middle-most slices (Balanced Accuracy score (BACC) of 0.709 ± 0.011, Average Precision (AP) of 0.467 ± 0.025, and Area Under Receiver Operating Characteristic curve (ROC-AUC) of 0.740 ± 0.008). The baseline model trained using only the mid-sagittal slice for classification yielded the lowest classification scores (BACC of 0.546 ± 0.032, AP of 0.403 ± 0.007, and ROC-AUC of 0.667 ± 0.008) followed by the model trained with the dataset with five middle-most slices (BACC of 0.675 ± 0.008, AP of 0.369 ± 0.009, and ROCAUC of 0.619 ± 0.011). To conclude, this work suggests that the developed deep learning-based classification pipeline could be used for LBP diagnostics of lumbar spine MRI. LBP diagnostics is heavily based on degenerative MRI findings and deep learning has the potential to supplement these visual assessments objectively. The developed method could be helpful, for example, in identifying negative cases in order to enhance the workflow of routine diagnostic imaging tasks.Alaselkäkivun luokittelu ja havainnointi T2-painotetuista magneettikuvista syväoppimista hyödyntäen. Tiivistelmä. Alaselkäkipu on yleinen fysiologinen tila, joka vaikuttaa 50:stä 80:een %:iin aikuisväestöstä jossain vaiheessa heidän elämäänsä. Ruotsissa alaselkäkipuun liittyvän taloudellisen kuormituksen on arvioitu olleen noin 740 miljoonaa euroa vuonna 2011. Alaselkäkivun syyn etsimiseen käytetään tyypillisesti magneettikuvausta (MRI). MRI:tä käytetään lannerangan alueen rakenteiden, kuten levyjen, luiden ja selkärangan luiden välisten tilojen, joissa hermot kulkevat, visualisoimiseen. Lannerangalla tarkoitetaan selkärangan viittä alinta nikamaa ja levyä. MRI tarjoaa diagnoosin tukemiseksi yksityiskohtaisen kuvan lannerangasta mahdollistaen alaselkäkipuun mahdollisesti liittyvien poikkeamien visuaalisen tarkastelun. Tämän opinnäytetyön tavoitteena oli tutkia alaselkäkipuun liityviä muutoksia T2-painotetuissa magneettikuvissa, jotka kuvattiin GE Healthcare Signa HDxt 1,5 T magneettikuvauslaitteistolla nopeaa spin-kaikusekvenssiä käyttäen. Kuvien luokitteluun käytettiin konvoluutioneuroverkkoja oireellisiin ja oireettomiin tapauksiin täysautomatisen kivun ennustusmenetelmän kehittämiseksi. Aineistona käytettiin yhteensä 526 tutkimusta Pohjois-Suomen syntymäkohortista 1966 (NFBC1966). Testejä varten luotiin kolme erilaista aineistoa: i) keskisagittaaliset viipalekuvat, ii) keskisagittaaliset viipalekuvat ja niiden naapuriviipaleet, sekä vastaavasti iii) viisi keskimmäisintä viipalekuvaa, joita hyödynnettiin itsenäisinä näytteinä, eli luokitusmenetelmän syötteinä. Kehitetty luokitusmenetelmä tuotti parhaat tulokset kun syötejoukkona olivat keskisagittaaliset viipalekuvat ja niiden naapuriviipaleet (Balanced Accuracy score (BACC) 0,709 ± 0,011, Average Precision (AP) 0,467 ± 0,025, ja Area Under Receiver Operating Characteristic curve (ROC-AUC) 0,740 ± 0,008). Keskisagittaalisten viipalekuvien avulla koulutettu vertailumalli tuotti alhaisimmat luokittelutulokset (BACC 0.546 ± 0.032, AP 0.403 ± 0.007, and ROC-AUC 0.667 ± 0.008), ja seuraavaksi paras malli oli viidellä keskimmäisellä viipalekuvalla koulutettu malli (BACC 0.675 ± 0.008, AP 0.369 ± 0.009, and ROC-AUC 0.619 ± 0.011). Tämä työ antaa viitteitä siitä, että syväoppimiseen perustuvaa menetelmää voitaisiin käyttää lannerangan MRI-aineistosta suoritettavaan alaselkäkivun diagnosointiin. Alaselkäkivun diagnostiikka perustuu vahvasti MRIrappeumalöydöksiin, ja syväoppimisella on edellytyksiä täydentää objektiivisella tavalla näitä visuaalisia arvioita. Kehitetystä menetelmästä voisi olla apua esimerkiksi negatiivisten tapausten tunnistamisessa rutiininomaisten diagnostisten kuvantamistehtävien työnkulun tehostamiseksi

Second Order Effects in Concrete Insulated Wall Panels

Insulated concrete panels (CIPs) have been used for over 60 years for their both structural and thermal resistance. The flexural behavior of CIPs has been investigated in recent years. The CIPs are rapidly becoming ubiquitous in the precast industry, and the tilt-up industry is initiating a study to determine their effectiveness as a tilt-up product. There are several known challenges associated with a tilt-up version of a partially composite wall and likely other unknown challenges. This study was initiated to investigate the behavior of load-bearing concrete insulated wall panels for use in tilt-up construction. The primary objective was to understand the inelastic behavior of these panels so that engineers could perform a proper second-order analysis for combined axial and out-of-plane loading. This dissertation contains information related to the testing of solid and partially composite insulated wall panels that integrated proprietary wythe connection systems. Since tilt-up panel testing of similar scope had not been done since the 1980s on panels of lower height, there were several goals for this testing. This represented an opportunity to validate the current ACI code alternate slender wall analysis method and provide a set of control panels for testing solid tilt-up panel behavior. Testing solid panels and CIPs of 40 ft span, a length typical of contemporary construction, was critical to observe such slenderness ratios and identify significant second-order panel behavior. In addition, 24 small-scale panels of 11 ft-long were monitored for shrinkage and tested under flexural load to determine the effect of shrinkage and the reinforcement ratio on the cracking moment and tension stiffening model. Using the unique experimental information herein, The Shear Flow method and a new method termed The Shear Slip Method were evaluated to estimate horizontal shear failure. The Shear Flow method, when used properly, results in perhaps an overly conservative prediction of horizontal shear strength but does not match the observed data well. The Shear Slip Method relies on an assumption of the failure slip mechanism (as observed from these and other experiments) and the double shear data to determine a maximum horizontal shear strength while incorporating the ductility of the connectors. This method was found to predict horizontal shear failure both accurately and conservatively. A Modified Slender Wall Method was developed to estimate the contribution of connector slip to the shear deformations in a straightforward way. When using this method to predict the deformations at failure for panels that experienced flexural failure it produced accurate and conservative results. For panels that are controlled by horizontal shear failure, this method can be overly conservative for flexural deformations because it is intentionally simplified. Another method termed the K123 method was demonstrated that can better predict panel deflections and horizontal shear failures using matrix analysis or other methods. The results from the experimental program and the panels from the literature were used to calibrate the OpenSees model. A large parametric study consisted of 648 models using OpenSees to evaluate different lengths of CIPs and connector types that were outside of the experimental program scope to demonstrate the effect of the slenderness and axial load. The parametric study confirms that the proposed methods agree well with Opensees models and confirm the findings from the experimental program. This project’s findings serve as a foundation for developing a design guide for Tilt-up CIP

Improving Code Review with GitHub Issue Tracking

Software quality is an important problem for technology companies, since it substantially impacts the efficiency, usefulness, and maintainability of the final product; hence, code review is a must-do activity for software developers. During the code review process, senior engineers monitor other developers' work to spot possible problems and enforce coding standards. One of the most widely used open-source software platforms, GitHub, attracts millions of developers who use it to store their projects. This study aims to analyze code quality on GitHub from the standpoint of code reviews. We examined the code review process using GitHub's Issues Tracker, which allows team members to evaluate, discuss, and share their opinions on the proposed code before it is approved. Based on our analysis, we present a novel approach for improving the code review process by promoting regularity and community involvement.Comment: To appear in the International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2022

THERMODYNAMIC INVESTIGATION OF THE BINDING OF PLATINUM BASED COMPLEXES WITH 10 BASEPAIR DNA/RNA STRUCTURES

The thermal stability of platinum based chemotherapeutic agents Cisplatin, Oxaliplatin and Carboplatin interaction with 10 basepair DNA/RNA duplexes and hybrids was investigated. UV-Spectroscopy was used to measure the enthalpy, entropy, melting temperature, free energy, and binding stoichiometry of DNA/RNA duplexes and hybrids denaturation in the existence and the absence of Cisplatin, Oxaliplatin and Carboplatin. Isothermal Titration Calorimetry was used to measure the enthalpy, entropy, free energy, association/dissociation constants and stoichiometry of DNA/RNA duplexes and hybrids formation in the presence and the absence of Cisplatin, Oxaliplatin and Carboplatin