bluuebunny/crossref_metadata_2025 huggingface dataset:
Structural, Mechanical and Thermal Properties of Cement Effect of Fractional Addition of Rice Husk Ash
Authors:   Department of Physics Div. of Sci.&Tech.Township University of Education Lahore Pakistan., Ahmad Naveed , Qasim M. ,   Department of Physics Div. of Sci.&Tech.Township University of Education Lahore Pakistan., Zafar Ramna ,   Department of Physics Div. of Sci.&Tech.Township University of Education Lahore Pakistan., Naveed Afzal ,   Center for Advance Study of Physics (CASP) G C University Lahore Pakistan, Riaz Saira ,   Center for Solid State Physics New Campus Punjab University Lahore Pakistan, Naseem Shazad ,   Center for Solid State Physics New Campus Punjab University Lahore Pakistan, Tariq Attiq ur Rehman ,   Building and Research Station Govt. of Punjab Lahore Pakistan
Published: May 2023

It has been established that Rice Husk Ash (RHA) contains a large amount of Silica and causes environmental problems if left to burn. An inevitable and preliminary interest of the current work was to investigate the effect of partial replacement of RHA in ordinary Portland cement (OPC) regarding variation in its compressive strength, thermal conductivity and structural properties. The RHA was obtained via controlled burning followed by grinding and replacement with OPC. RHA has been added at various percentages from 10 to 50% with an incremental step size of 10%. The properties of the final product have been investigated after preservation of the samples for 3, 7, 14, 21, 28, 56, and 90 days. The study reveals that samples containing 10% of RHA exhibit maximum compressive strength of 2066.33 Psi with a density of 2.65g/cm3 when cured for 28 days. Due to high relative humidity and temperature after 28 days the strength was slightly lower than the specimen preserved in water. The thermal conductivity measurements revealed that samples with 50% of RHA exhibit minimum thermal conductivity as 0.81 Btu/ft2/hr/0F while maximum thermal conductivity was obtained at 10% RHA replacement. XRD analysis showed a significant transformation in the phase and crystallinity of the mixture. The average crystallite size of pure RHA was 2227.69 Å, however, for the mixture, the crystallite size was observed to be 712.2 and 162.77 Å with 10 and 40 % of the RHA, respectively. This study concluded that partial replacement of RHA in OPC provides a low-cost and insulating building material for the construction industry.
Biodiversity of Rotifers Recorded from the Littoral Zone of a Flood Plain
Authors:   Govt. Islamia Graduate College Civil Lines Lahore, Nadeem Amir , Hayee Sara ,   Government Graduate College for Women, Samanabad, Lahore., Shakoor Sadia ,   Government College University, Lahore., Akhtar Naveed ,   Government Graduate College for Boys, Pattoki, Kasur., Nawaz Faheem ,   Government Khawaja Rafique Shaheed College for Boys, Lahore., Ashfaq Farah ,   Government Graduate College for Women, Samanabad, Lahore., Khan Sulehria Abdul Qayyum ,   Govt. Islamia Graduate College Civil Lines Lahore
Published: May 2023

This study intended to investigate the rotifer density and diversity from a flood plain. For this purpose, Balloki Headworks was selected. It is an area near the Ravi River that was affected by flooding. The sampling was done from April to June 2022. The highest diversity and density were observed in June and the lowest in April. Twelve species of rotifers were obtained from sampling, belonging to four different genera. Eight different physico-chemical parameters of water were measured using respective laboratory meters using standard guidelines. These parameters include the temperature of water and air, conductivity, dissolved oxygen, water pH, Oxygen saturation, salinity, total dissolved solids and turbidity. It was observed that Habrotrocha ligula (Bryce, 1913) and Rotaria rotatoria (Pallas, 1766) were the two most abundant species, while Philodina citrina (Ehrenberg, 1832) and Philodina acuticornis (Murray, 1902) were the least abundant.
Analysis of the Potential Factors Affecting the Higher Education Quality using Soft Computing Technique
Authors:   Department of Electronics, GC University Lahore Pakistan, Khan Imran Javed , Akhlaq Maham ,   Department of Electronics, GC University Lahore Pakistan, Qasim Faheem ,   Department of Electronics, GC University Lahore Pakistan, Ahmad Firdous ,   Department of Electronics, GC University Lahore Pakistan
Published: May 2023

With the society's continual technological improvements and modernity, it is critical to stay up with the advancements in instructional tools. To address this, a comprehensive study was done to investigate the elements influencing the quality of higher education in accordance with contemporary educational systems. The study sought to uncover the precise elements that have a major influence on the progress and improvement of higher education. Soft computing approaches, such as fuzzy logic, were used to anticipate the influence of numerous criteria on higher education quality, such as teacher to student ratio, classroom atmosphere, learning materials, current tools, managerial efforts, and academic assistance. Changes in these elements can improve students' thinking and talents, resulting in a better society with more economic power. This work uses fuzzy logic system in order to found that by enhancing these elements, educational quality may be improved, resulting in high-quality instruction and a better society.
Bir Güvenlikleştirme Pratiği Olarak Terörizm Söylemi ve Etiketi: Eleştirel Terörizm Çalışmaları Perspektifinden Kavramsal Bir Analitik Çerçeve
Authors: ÇONA Ömer
Published: May 2023

Bu çalışmada söylemsel bir pratik olarak “teröristleştirme” retoriğinin, politik muhaliflerin meşruiyetini baltalamak amacıyla iktidarlar tarafından bir güvenlikleştirme stratejisi olarak kullanıldığı tartışması yapılmıştır. Makalenin teorik çerçevesi, siyasi liderlerin güvenlik konularını nasıl çerçevelediğini açıklamaya matuf bir kuramsallaştırma biçimi olan güvenlikleştirme teorisidir. Terörizmin nesnel bir gerçeklik fakat sosyal bir inşa olduğunu vurgulayan makale, terörizmin ontolojik güvenliğe bir tehdit oluşundan ötürü kamuoyunda etiketi alan kişilerin ne şekilde ve hangi amaçlar için itibarsızlaştırıldığına dikkat çekmeye çalışmaktadır. Terör teriminin istikrarsız doğası ve belirli tarihsel süreçlerdeki farklılaşan anlam dünyasına Eleştirel Terörizm Çalışmaları’nın yaklaşımlarına dayanarak vurgu yapan makale, politik elitlerin terörist etiketini kullanmasının arkasında yatan çıkarları ortaya koymak için okuyucuya kavramsal bir analitik çerçeve çizmeyi amaçlamaktadır. Makale, terörizmin politik olarak muhalifleri şeytanlaştırmak ve halk nazarında itibarsızlaştırmak amacıyla kullanılan aşağılayıcı bir etiket olduğunu öne sürerek, okuyucuya siyasal şiddet literatüründe kullanılan terimlere daima şüpheyle yaklaşarak eleştirel akıl süzgecinden geçirmesini teklif etmektedir.
Fatores que Influenciam a Inovação nos Serviços da Administração Pública Local: O Caso dos Municípios da Comunidade Intermunicipal das Beiras e Serra da Estrela
Authors: Marques Sara Raquel , Natário Maria Manuela Santos , Rosa Maria Cecília
Published: May 2023

O presente estudo centra-se na análise da inovação nas Instituições Públicas Locais, nomeadamente nos municípios da Comunidade Intermunicipal das Beiras e Serra da Estrela (CIM BSE). O objetivo é identificar padrões de inovação nestas instituições e perceber os fatores que influenciam a inovação nos serviços da Administração Pública Local (APL). Para o efeito, utilizou-se como metodologia a Análise de Cluster, para analisar o posicionamento dos diferentes Municípios em termos de inovação, e o teste de diferenças de médias de Scheffe, para identificar as principais características associadas aos melhores desempenhos de inovação nestas instituições. A recolha de informação foi feita por meio de um inquérito dirigido às 15 Autarquias da CIM BSE. O estudo contribui para um melhor conhecimento da temática e permitiu concluir que o número de funcionários e as suas habilitações académicas não influenciam o comportamento mais inovador das Autarquias da BSE. A dinâmica de inovação das instituições da APL está associada aquelas cujos objetivos para inovar assentam no aumento da eficiência e da qualidade dos serviços, na melhoria dos serviços online, na satisfação do Munícipe e na melhoria das condições de trabalho; à liderança ativa e aos custos elevados para inovar como principal barreira à inovação.
THE USE OF MODERN TECHNOLOGIES IN THE DEVELOPMENT OF EMOTIONAL INTELLIGENCE IN ENGLISH LESSONS
Authors:   Shoqan Ualikhanov Kokshetau University, Kokshetau, The Republic of Kazakhstan, Nemchenko N.F. , Gluchshenko Y.Y. ,   Shoqan Ualikhanov Kokshetau University, Kokshetau, The Republic of Kazakhstan
Published: May 2023

This article deals with general aspects of emotional intelligence and modern technologies in teaching English. The methodological model is represented by several assignments that can be used for developing emotional intelligence in English lessons. The article presents the survey results, concerning the problem of emotional intelligence development in secondary school, among Kokshetau teachers and the experimental research of the methodological model approbation among high school students in Kokshetau. Keywords: pedagogical technology, emotional intelligence, emotional intelligence components, empathy, motivation, self-awareness, self-regulation, social skills.
РОМАН ЖӘНЕ ӨМІРДІҢ ШЫТЫРМАН ШЫНДЫҒЫ (ІЛИЯС ЖАНСҮГІРОВТІҢ «ЖОЛДАСТАР» РОМАНЫНЫҢ ИДЕЯЛЫҚ-ТАҚЫРЫПТЫҚ, ТІЛДІК-КӨРКЕМДІК ЖӘНЕ ЖАНРЛЫҚ-СТИЛЬДІК ЕРЕКШЕЛІКТЕРІНІҢ БЕЛГІБАЙ ШАЛАБАЕВ ЗЕРТТЕУЛЕРІНДЕ КӨРІНІС ТАБУЫ)
Authors:   Ш. Уәлиханов атындағы Көкшетау университеті, Көкшетау қ., Қазақстан Республикасы, Қажыбай А.Т.
Published: May 2023

Мақалада Ілияс Жансүгіровтің «Жолдастар романының идеялық-көркемдік, композициялық-тақырыптық, тілінің поэтикалығы және жанрлық-стильдік ерекшеліктерінің әдебиетші ғалым Белгібай Шалабаев зерттеулерінде көрініс табуы жан-жақты жүйелі зерттеле келіп, жазушының бұл романы алғашқы қазақ романдарын толықтыра келген, қайшылыққа толы халық тағдырындағы тарихи процесті бейнелеуде романның ауыр жүгін көтере отырып, өз үлесін қосқан туындылардың бірі екендігі тұжырымдалады. «Жолдастар» романының идеялық-тақырыптық ерекшеліктері, тілдік-көркемдік қолтаңбасы қарастырылып, романның ХХ ғасырдың 20-30 жылдардағы қазақ романдарымен ұқсастығы мен өзгешелігі, қазақ прозасынан алатын орны, жазушы шеберлігі егжей-тегжейлі талданған. Ғалым Б.Шалабаев, қаламгер Ілиястың романындағы табиғат пейзаждарын, адам портреттері мен мінез қалыптарын ашуда реалистік биікке көтерілгендігін дәлелдей келе, юморлық, сатиралық кейіпкерлерде көркемдік қуат танытуда шығарманың барымта, жұт сценаларын, билер дауы, молдалар айтысын, жұмысшылар ортасын бейнелеуден анық көрінедігін тілге тиек етеді. Дегенмен, қазақтың ауызекі әңгіме айту дәстүріндегі шеберлікті еркін игерген Илияс жеке бөлімдердің байланысы, композициялық тұтастық, типтендіру принциптеріне келгенде еуропалық үлгілерді терең меңгермегендігін, проза саласындағы, әсіресе роман жазуда тәжірибесіздігін аңғартып қоятындығын Б.Шалабаев өткір сынға алады. Дей тұрғанмен, қазақ ауылындағы әлеуметтік-таптық өзгерістерді, өндіріс, мәдениет, техникаға бой ұрған адамдарды бейнелеу талабындағы халық өмірінің кең панорамасын жасауда мақсат еткен қалың оқиғалы, қою тартысты, көп кейіпкерлі «Жолдастар» романы – қазақ әдебиетіндегі әлеуметтік-психологиялық роман жазу барысындағы тұңғыш сәтті тәжірибелердің бірі, өмірлік мазмұны терең, эстетикалық әсері күшті сапалы шығарма екендігі даусыз ақиқат. Кілт сөздер: роман, идея, тақырып, сюжет, композиция, кейіпкерлер образы, табиғат пейзаждары, геройлар психологиясы, жазушы шеберлігі
Application of Nanoparticles in Medicine
Authors: Pandya Maharshi , Saran Raghaw
Published: May 2023

Nanomaterials due to their size (ranging from 0.1-100 nm, at least in one dimension) and higher ratio of surface area to volume display dominant quantum effects causing drastic changes in their chemical reactivity as well as optical, elastic, electrical and magnetic properties. The electrons due to their wave nature move very easily without scattering in nanomaterials and allow their use as biological sensors. Nano wires, semiconducting in nature, act as a versatile optoelectronic component in photodetectors sensitive to polarization and arrays with sub wavelength resolution. The wide applicability of nanomaterials in medicines emerge from the similarity in size of biomolecule moieties of metabolic processes occurring at nano levels. Optical properties of quantum dots allow their use as biomarkers subsequent to coating with a material able to bind selectively with certain biological structures like cancer cells by fluorescent absorption followed by emission of electrons known as functionalised quantum dots. Nanomaterials on combining with biomolecules develop ability to recognize sensitive diagnostic and regulated drug delivery processes with appreciably better performances and may be used as tissue substitutes. The properties produced in organic solvents make them hydrophobic and incompatible to biological molecules. At the same time, they may be converted into water soluble form and made biocompatible through different techniques like ligand exchange, encapsulation, polymer coating (with functional groups attached to the surface) providing reactive site for bio conjugation through different processes keeping limitations of the processes in view. Nanomaterials play prominent role in medicines as obviated by growing global market for them in the field expected to reach to USD 182.3 billion by 2027 at a compounded annual growth rate of 19.9% from 2021.
A debate on some methods for measuring intellectual capital
Authors:   Politehnica University Timişoara, Gogan Luminița-Maria
Published: May 2023

In Europe, the growing concern for environmental protection, along with expanding environmental legislation led to taking into account environmental audit in small and medium size enterprises (SME). This awareness coincides with that of various bodies involved in the production process, so that it became necessary to introduce environmental issues related to daily decision-making process. It is about developing an appropriate policy regarding environmental agreement in principle with the social, can turn into a powerful marketing tool if used correctly.
Taking the Leap to Agile Manufacturing: From Intention to a Successful Paradigm Shift
Authors:   Simens Continental Automotive Timisoara, Romania, PhD. at Politehnica University Timisoara, Canda Alexandru
Published: May 2023

The paper presents the agile principles with the needs origin analysis for the definition and design of a new business management paradigm (convergent to a business model) combining two dimensions: (1) organizational behavior related to their competitiveness on the market and (2) the internal environment characteristics focused on human resources management. In addition, there are presented the implementation phases of the proposed business model (as strategic options at the organizational level) and challenges for the organization`s project team, by underlining the leadership aspects as key issues in a successful implementation of agility throughout the company.
"Environmental Audit within Romanian Small and Medium Sized Enterprises"
Authors:   Politehnica University Timişoara, Artene Alin Emanuel , Duran Vasile ,   Politehnica University Timişoara
Published: May 2023

In Europe, the growing concern for environmental protection, along with expanding environmental legislation led to taking into account environmental audit in small and medium size enterprises (SME). This awareness coincides with that of various bodies involved in the production process, so that it became necessary to introduce environmental issues related to daily decision-making process. It is about developing an appropriate policy regarding environmental agreement in principle with the social, can turn into a powerful marketing tool if used correctly.
Commodities in Current Macroeconomic Outlook. A Possible Hedge against Inflation
Authors:   Sucden Geneva, PhD research analyst, Covrig Claudiu
Published: May 2023

The recent global economic recession pushed investment funds and other players in the investment world closer to tangible assets, luring them back to commodities and especially to metals and agricultural markets. As the present macro environment supports commodities investing – especially investments in agriculture, thanks to very low interest rates, weaker currencies, a commodity boom, and as a hedge against global inflation - the level of funds invested in commodities increased by over 220% in the 2006-2011 period when compared to the previous five years’ time (2000-2005). Meanwhile, estimates of an increasing global population, rather extreme weather patterns observed in recent years, decreasing crop yields and the extraordinary growth in global ethanol and biodiesel markets, focused the attention on cereals, sugar cane, rapeseed and soy, as concerns of a possible very tight supply situation might happen any time.
"Debate on the Cultural Style Indicators. The Case of Cross-Cultural Teams "
Authors:   Politehnica University Timişoara, Popescu Anca-Diana
Published: May 2023

This article aims to analyze the Cultural Style Indicator using Peterson Cultural Style Indicator™. Starting from the conceptual framework definition of the organizational culture, the paper will present then a possible research approach for investigating organizational culture dimensions using Peterson Cultural Style Indicator™ (the cultural intelligence and the cross-cultural differences are defined by considering dimensions as: management, strategy, planning, personnel communication and reasoning). In addition, this article presents the research results done in the university. The pilot research results achieved, can offer the possibility of a comparison between the data obtained and the data already collected by Dr. Peterson.
On representativeness, informative sampling, nonignorable nonresponse,
semiparametric prediction and calibration
Authors:   Department of Mathematics,
            Al-Quds University, Abu-Dees Campus, Al-Quds, Palestine, Eideh Abdulhakeem
Published: May 2023

Informative sampling refers to a sampling design for which the sample selection
probabilities depend on the values of the model outcome variable. In such cases the
model holding for the sample data is different from the model holding for the population
data. Similarly, nonignorable nonresponse refers to a nonresponse mechanism in which the
response probability depends on the value of a missing outcome variable. For such a
nonresponse mechanism the model holding for the response data is different from the
model holding for the population data. In this paper, we study, within a modelling
framework, the semi-parametric prediction of a finite population total by specifying the
probability distribution of the response units under informative sampling and
nonignorable nonresponse. This is the most general situation in surveys and other
combinations of sampling informativeness and response mechanisms can be considered as
special cases. Furthermore, based on the relationship between response distribution and
population distribution, we introduce a new measure of the representativeness of a
response set and a new test of nonignorable nonresponse and informative sampling,
jointly. Finally, a calibration estimator is obtained when the sampling design is
informative and the nonresponse mechanism is nonignorable.
Development of Pavement Condition Index for Local Street of Pune
Authors:   Civil Department, JSPM’s Imperial College of Engineering and Research, Maharashtra, India., S.S Kori , Patil Supriya , Borse Jui , Suryawanshi Priyanka , Borhade Manisha
Published: May 2023

The project titled "Development of Pavement Condition Index for Local Streets of Pune" aims to assess the condition of local streets in Pune and develop a comprehensive and reliable index to measure pavement condition. The study involves field surveys to collect data on pavement distress and distress severity, followed by data analysis and the development of a new pavement condition index (PCI) specific to the local streets in Pune. The proposed index is expected to provide a standardized and objective approach for evaluating pavement conditions and prioritizing maintenance and repair activities. The study is expected to contribute to the development of more efficient and effective pavement management strategies for local streets in Pune, ultimately leading to improved road safety, reduced maintenance costs, and increased user satisfaction. Key Word: Pavment Condition index (PCI).
Smart Cart
Authors:   Computer Science and Engineering, Jeppiaar Institute of Technology, Tamilnadu, India., S Naren , R Ajay , S Harikrishna , P Thanajjayan
Published: May 2023

This project is on a web-based application “Smart Cart” which ensures quality control in businesses that handle transactions revolving around consumer goods. Without proper inventory control, a large retail store may run out of stock on an important item. This system will alert the retailer when it is time to reorder. Smart Cart automates the process of managing inventory at a general store. It manages the sales, supplies and inventory of the general store. It keeps track of all the customers, the purchases made during a given period and the inventory of the items. This application is basically managed by the administrator. The administrator can login through the system. He also can view the details of the customers. A database is maintained which has the inventory of all the items. The system also keeps track of supplier details and sales. It stores all purchase order details and invoice details in respective tables in the database. This web-based application also has a POS system. The main function of a POS system is to process sales transactions quickly and accurately. When a customer makes a purchase, the system records the transaction, deducts the amount from the inventory, and calculates the total cost including taxes and discounts. Key Word: Inventory management, data storage architecture, product recommendations, invoice generation.
Real-Time Drowsiness Identification Based On Eye State Analysis
Authors:   the kavery Engineering College, Mecheri, Tamilnadu, India., J Vignesh , K.S Gowri Shankar , K Dhavasi , M Thiyagu , C Soundharya
Published: May 2023

As per the previous year’s report concerning to road crashes indicates that the principal cause of such a fatal road accidents is because of negligence behavior as well as drowsiness of driver. This problem reveals the requirement of such a system that can recognize drowsiness state of driver and gives alert signal to the driver before the occurrence of any accidents. Therefore, this proposed work has established drowsy detection as well as accident avoidance system based on the eye blink duration. Here, first the open and close state of eye are detected based on the eye aspect ratio (EAR). Further, the blink duration or count during the changes of eye state from open to close are analyzed. Then, it identifies the state of drowsiness, when blink duration becomes more than a certain limits and sends the alert message to the driver through the alarm. Our developed system has shown the accuracy of 92.5 % approx on yawning dataset (YawDD). Key Word: Eye blink detection; Drowsiness Detection; Eye Aspect Ratio (EAR)
Design & Fabrication of Electro Magnetic Clutch System for Four-Wheeler
Authors:   Mechanical Engineering, Sri Venkateshwara Hi-Tech Engineering College Gobi,TamilNadu,India., P Lingeswaran , J Nagaraj
Published: May 2023

The aim of the project is to Electro Magnetic Clutch System for Four-Wheeler. The hand operated clutch is operated by motor power. Here we are using for DC motor. The motor is connected with the belt. Due to continuous power supply the motor runs. The clutch employed here is electromagnetic clutch (EMC). A switch is provided on the gear lever. Whenever the switch is pressed to shift the gear, that makes the clutch to operate. The driver circuit is constructed with transistor which acts as switch to control the relay. The relay output is directly connected to the electromagnetic clutch which engages the clutch. The objective of the project is to develop a hand operated clutch system which can be useful for handicapped persons while driving the four wheelers. This project is mainly used in Vehicles either two or four wheelers. If this project is used in real time, we can avoid so many accidents. Key Word: Electromagnetic Clutch, DC Motor, Micro Controller.
Securing Access to Applications with IAP and Compute Engine
Authors:   Department of Computer Science and Engineering, KSR Institute For Engineering and Technology, R Deebika , B Gobi , G Logeshwaran , G Sinthana , M Shaheen
Published: May 2023

Cloud Identity- Aware Proxy (IAP) is a security service handed by Google Cloud Platform that allows directors to authenticate and authorize access to web operations and VMs running on Google Cloud. This service provides a fresh subcaste of security to operations and VMs by vindicating the stoner's identity and determining if they've authorization to pierce the resource. Setting up Cloud IAP for Compute Engine involves configuring access programs, OAuth2.0 customer IDs, and firewall rules. Once configured, IAP allows only authorized druggies to pierce the operation, grounded on their Google identity and class in specific Google groups. In this process, directors must produce an access policy, which determines which druggies or groups can pierce the operation. They must also produce an OAuth2.0 customer ID, which is used for authentication with IAP. Eventually, the director must modernize the Compute Engine case firewall rules to allow business from IAP. The benefits of setting up Cloud IAP for Compute Engine include enhanced security for the operation and the capability to manage access to coffers centrally. It also eliminates the need to manage access control within the operation and simplifies the process of granting or repealing access for druggies. While setting up Cloud IAP for Compute Engine can be a complex process, following the recommended way ensures that the operation is secure and only accessible to authorized druggies.
Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome
Authors: Chotzoglou Elisa , Day Thomas , Tan Jeremy , Matthew Jacqueline , Lloyd David , Razavi Reza , Simpson John , Kainz Bernhard
Published: May 2023

Congenital heart disease is considered as one the most common groups of congenital malformations which affects 6 − 11 per 1000 newborns. In this work, an automated framework for detection of cardiac anomalies during ultrasound screening is proposed and evaluated on the example of Hypoplastic Left Heart Syndrome (HLHS), a sub-category of congenital heart disease. We propose an unsupervised approach that learns healthy anatomy exclusively from clinically confirmed normal control patients. We evaluate a number of known anomaly detection frameworks together with a new model architecture based on the α-GAN network and find evidence that the proposed model performs significantly better than the state-of-the-art in image-based anomaly detection, yielding average 0.81 AUC and a better robustness towards initialisation compared to previous works.
Uncertainty quantification in non-rigid image registration via stochastic gradient Markov chain Monte Carlo
Authors: Grzech Daniel , Azampour Mohammad Farid , Qiu Huaqi , Glocker Ben , Kainz Bernhard , Le Folgoc Loïc
Published: May 2023

We develop a new Bayesian model for non-rigid registration of three-dimensional medical images, with a focus on uncertainty quantification. Probabilistic registration of large images with calibrated uncertainty estimates is difficult for both computational and modelling reasons. To address the computational issues, we explore connections between the Markov chain Monte Carlo by backpropagation and the variational inference by backpropagation frameworks, in order to efficiently draw samples from the posterior distribution of transformation parameters. To address the modelling issues, we formulate a Bayesian model for image registration that overcomes the existing barriers when using a dense, high-dimensional, and diffeomorphic transformation parametrisation. This results in improved calibration of uncertainty estimates. We compare the model in terms of both image registration accuracy and uncertainty quantification to VoxelMorph, a state-of-the-art image registration model based on deep learning.
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics
Authors: Perkonigg Matthias , Hofmanninger Johannes , Herold Christian , Prosch Helmut , Langs Georg
Published: May 2023

Machine learning in medical imaging during clinical routine is impaired by changes in scanner protocols, hardware, or policies resulting in a heterogeneous set of acquisition settings. When training a deep learning model on an initial static training set, model performance and reliability suffer from changes of acquisition characteristics as data and targets may become inconsistent. Continual learning can help to adapt models to the changing environment by training on a continuous data stream. However, continual manual expert labelling of medical imaging requires substantial effort. Thus, ways to use labelling resources efficiently on a well chosen sub-set of new examples is necessary to render this strategy feasible. Here, we propose a method for continual active learning operating on a stream of medical images in a multi-scanner setting. The approach automatically recognizes shifts in image acquisition characteristics – new domains –, selects optimal examples for labelling and adapts training accordingly. Labelling is subject to a limited budget, resembling typical real world scenarios. In order to avoid catastrophic forgetting while learning on new domains the proposed method utilizes a rehearsal memory. To demonstrate generalizability, we evaluate the effectiveness of our method on three tasks: cardiac segmentation, lung nodule detection and brain age estimation. Results show that the proposed approach outperforms other active learning methods on a continuous data stream with domain shifts.
Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection
Authors: Akrami Haleh , Joshi Anand , Aydore Sergul , Leahy Richard
Published: May 2023

Despite impressive state-of-the-art performance on a wide variety of machine learning tasks in multiple applications, deep learning methods can produce over-confident predictions, particularly with limited training data. Therefore, quantifying uncertainty is particularly important in critical applications such as lesion detection and clinical diagnosis, where a realistic assessment of uncertainty is essential in determining surgical margins, disease status and appropriate treatment. In this work, we propose a novel approach that uses quantile regression for quantifying aleatoric uncertainty in both supervised and unsupervised lesion detection problems. The resulting confidence intervals can be used for lesion detection and segmentation. In the unsupervised settings, we combine quantile regression with the Variational AutoEncoder (VAE). The VAE is trained on lesion-free data, so when presented with an image with a lesion, it tends to reconstruct a lesion-free version of the image. To detect the lesion, we then compare the input (lesion) and output (lesion-free) images. Here we address the problem of quantifying uncertainty in the images that are reconstructed by the VAE as the basis for principled outlier or lesion detection. The VAE models the output as a conditionally independent Gaussian characterized by its mean and variance. Unfortunately, joint optimization of both mean and variance in the VAE leads to the well-known problem of shrinkage or underestimation of variance. Here we describe an alternative Quantile-Regression VAE (QR-VAE) that avoids this variance shrinkage problem by directly estimating conditional quantiles for the input image. Using the estimated quantiles, we compute the conditional mean and variance for input image from which we then detect outliers by thresholding at a false-discovery-rate corrected p-value. In the supervised setting, we develop binary quantile regression (BQR) for the supervised lesion segmentation task. We show how BQR can be used to capture uncertainty in lesion boundaries in a manner that characterizes expert disagreement.
Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations
Authors: Zhao Zipei , Pang Fengqian , Liu Yaou , Liu Zhiwen , Ye Chuyang
Published: May 2023

Cell detection in histopathology images is of great interest to clinical practice and research, and convolutional neural networks (CNNs) have achieved remarkable cell detection results. Typically, to train CNN-based cell detection models, every positive instance in the training images needs to be annotated, and instances that are not labeled as positive are considered negative samples. However, manual cell annotation is complicated due to the large number and diversity of cells, and it can be difficult to ensure the annotation of every positive instance. In many cases, only incomplete annotations are available, where some of the positive instances are annotated and the others are not, and the classification loss term for negative samples in typical network training becomes incorrect. In this work, to address this problem of incomplete annotations, we propose to reformulate the training of the detection network as a positive-unlabeled learning problem. Since the instances in unannotated regions can be either positive or negative, they have unknown labels. Using the samples with unknown labels and the positively labeled samples, we first derive an approximation of the classification loss term corresponding to negative samples for binary cell detection, and based on this approximation we further extend the proposed framework to multi-class cell detection. For evaluation, experiments were performed on four publicly available datasets. The experimental results show that our method improves the performance of cell detection in histopathology images given incomplete annotations for network training.
Semi-Supervised Federated Peer Learning for Skin Lesion Classification
Authors: Bdair Tariq , Navab Nassir , Albarqouni Shadi
Published: May 2023

Globally, Skin carcinoma is among the most lethal diseases. Millions of people are diagnosed with this cancer every year. Sill, early detection can decrease the medication cost and mortality rate substantially. The recent improvement in automated cancer classification using deep learning methods has reached a human-level performance requiring a large amount of annotated data assembled in one location, yet, finding such conditions usually is not feasible. Recently, federated learning (FL) has been proposed to train decentralized models in a privacy-preserved fashion depending on labeled data at the client-side, which is usually not available and costly. To address this, we propose FedPerl, a semi-supervised federated learning method. Our method is inspired by peer learning from educational psychology and ensemble averaging from committee machines. FedPerl builds communities based on clients' similarities. Then it encourages communities' members to learn from each other to generate more accurate pseudo labels for the unlabeled data. We also proposed the peer anonymization (PA) technique to anonymize clients. As a core component of our method, PA is orthogonal to other methods without additional complexity, and reduces the communication cost while enhances performance. Finally, we propose a dynamic peer learning policy that controls the learning stream to avoid any degradation in the performance, especially for the individual clients. Our experimental setup consists of 71,000 skin lesion images collected from 5 publicly available datasets. We test our method in four different scenarios in SSFL. With few annotated data, FedPerl is on par with a state-of-the-art method in skin lesion classification in the standard setup while outperforming SSFLs and the baselines by 1.8% and 15.8%, respectively. Also, it generalizes better to an unseen client while being less sensitive to noisy ones.
Distributional Gaussian Processes Layers for Out-of-Distribution Detection
Authors: Popescu Sebastian G. , Sharp David J. , Cole James H. , Kamnitsas Konstantinos , Glocker Ben
Published: May 2023

Machine learning models deployed on medical imaging tasks must be equipped with out-of-distribution detection capabilities in order to avoid erroneous predictions. It is unsure whether out-of-distribution detection models reliant on deep neural networks are suitable for detecting domain shifts in medical imaging. Gaussian Processes can reliably separate in-distribution data points from out-of-distribution data points via their mathematical construction. Hence, we propose a parameter efficient Bayesian layer for hierarchical convolutional Gaussian Processes that incorporates Gaussian Processes operating in Wasserstein-2 space to reliably propagate uncertainty. This directly replaces convolving Gaussian Processes with a distance-preserving affine operator on distributions. Our experiments on brain tissue-segmentation show that the resulting architecture approaches the performance of well-established deterministic segmentation algorithms (U-Net), which has not been achieved with previous hierarchical Gaussian Processes. Moreover, by applying the same segmentation model to out-of-distribution data (i.e., images with pathology such as brain tumors), we show that our uncertainty estimates result in out-of-distribution detection that outperforms the capabilities of previous Bayesian networks and reconstruction-based approaches that learn normative distributions.
Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements
Authors: Jallais Maëliss , Rodrigues Pedro L. C. , Gramfort Alexandre , Wassermann Demian
Published: May 2023

Effective characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in diffusion MRI (dMRI). Solving the problem of relating the dMRI signal with cytoarchitectural characteristics calls for the definition of a mathematical model that describes brain tissue via a handful of physiologically-relevant parameters and an algorithm for inverting the model. To address this issue, we propose a new forward model, specifically a new system of equations, requiring a few relatively sparse b-shells. We then apply modern tools from Bayesian analysis known as likelihood-free inference (LFI) to invert our proposed model. As opposed to other approaches from the literature, our algorithm yields not only an estimation of the parameter vector θ that best describes a given observed data point x0 , but also a full posterior distribution p(θ|x0) over the parameter space. This enables a richer description of the model inversion, providing indicators such as credible intervals for the estimated parameters and a complete characterization of the parameter regions where the model may present indeterminacies. We approximate the posterior distribution using deep neural density estimators, known as normalizing flows, and fit them using a set of repeated simulations from the forward model. We validate our approach on simulations using dmipy and then apply the whole pipeline on two publicly available datasets.
Label fusion and training methods for reliable representation of inter-rater uncertainty
Authors: Lemay Andreanne , Gros Charley , Naga Karthik Enamundram , Cohen-Adad Julien
Published: May 2023

Medical tasks are prone to inter-rater variability due to multiple factors such as image quality, professional experience and training, or guideline clarity. Training deep learning networks with annotations from multiple raters is a common practice that mitigates the model’s bias towards a single expert. Reliable models generating calibrated outputs and reflecting the inter-rater disagreement are key to the integration of artificial intelligence in clinical practice. Various methods exist to take into account different expert labels. We focus on comparing three label fusion methods: STAPLE, average of the rater’s segmentation, and random sampling of each rater’s segmentation during training. Each label fusion method is studied using both the conventional training framework and the recently published SoftSeg framework that limits information loss by treating the segmentation task as a regression. Our results, across 10 data splittings on two public datasets (spinal cord gray matter challenge, and multiple sclerosis brain lesion segmentation), indicate that SoftSeg models, regardless of the ground truth fusion method, had better calibration and preservation of the inter-rater rater variability compared with their conventional counterparts without impacting the segmentation performance. Conventional models, i.e., trained with a Dice loss, with binary inputs, and sigmoid/softmax final activate, were overconfident and underestimated the uncertainty associated with inter-rater variability. Conversely, fusing labels by averaging with the SoftSeg framework led to underconfident outputs and overestimation of the rater disagreement. In terms of segmentation performance, the best label fusion method was different for the two datasets studied, indicating this parameter might be task-dependent. However, SoftSeg had segmentation performance systematically superior or equal to the conventionally trained models and had the best calibration and preservation of the inter-rater variability. SoftSeg has a low computational cost and performed similarly in terms of uncertainty to ensembles which require multiple models and forward passes. Our code is available at https://ivadomed.org.
Cross Attention Transformers for Multi-modal Unsupervised Whole-Body PET Anomaly Detection
Authors: Patel Ashay , Tudiosu Petru-Danial , Pinaya Walter H.L. , Cook Gary , Goh Vicky , Ourselin Sebastien , Cardoso M. Jorge
Published: May 2023

Cancer is a highly heterogeneous condition that can occur almost anywhere in the human body. [18F]fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) is a imaging modality commonly used to detect cancer due to its high sensitivity and clear visualisation of the pattern of metabolic activity. Nonetheless, as cancer is highly heterogeneous, it is challenging to train general-purpose discriminative cancer detection models, with data availability and disease complexity often cited as a limiting factor. Unsupervised learning methods, more specifically anomaly detection models, have been suggested as a putative solution. These models learn a healthy representation of tissue and detect cancer by predicting deviations from the healthy norm, which requires models capable of accurately learning long-range interactions between organs, their imaging patterns, and other abstract features with high levels of expressivity. Such characteristics are suitably satisfied by transformers, which have been shown to generate state-of-the-art results in unsupervised anomaly detection by training on normal data. This work expands upon such approaches by introducing multi-modal conditioning of the transformer via cross-attention i.e. supplying anatomical reference information from paired CT images to aid the PET anomaly detection task. Furthermore, we show the importance and impact of codebook sizing within a Vector Quantized Variational Autoencoder, on the ability of the transformer network to fulfill the task of anomaly detection. Using 294 whole-body PET/CT samples containing various cancer types, we show that our anomaly detection method is robust and capable of achieving accurate cancer localization results even in cases where normal training data is unavailable. In addition, we show the efficacy of this approach on out-of-sample data showcasing the generalizability of this approach even with limited training data. Lastly, we propose to combine model uncertainty with a new kernel density estimation approach, and show that it provides clinically and statistically significant improvements in accuracy and robustness, when compared to the classic residual-based anomaly maps. Overall, a superior performance is demonstrated against leading state-of-the-art alternatives, drawing attention to the potential of these approaches.
Deep Learning for Ultrasound Speed-of-Sound Reconstruction: Impacts of Training Data Diversity on Stability and Robustness
Authors: Khun Jush Farnaz , Biele Markus , Dueppenbecker Peter M. , Maier Andreas
Published: May 2023

Ultrasound b-mode imaging is a qualitative approach and diagnostic quality strongly depends on operators’ training and experience. Quantitative approaches can provide information about tissue properties; therefore, can be used for identifying various tissue types, e.g., speed-of-sound in the tissue can be used as a biomarker for tissue malignancy, especially in breast imaging. Recent studies showed the possibility of speed-of-sound reconstruction using deep neural networks that are fully trained on simulated data. However, because of the ever-present domain shift between simulated and measured data, the stability and performance of these models in real setups are still under debate. In prior works, for training data generation, tissue structures were modeled as simplified geometrical structures which does not reflect the complexity of the real tissues. In this study, we proposed a new simulation setup for training data generation based on Tomosynthesis images. We combined our approach with the simplified geometrical model and investigated the impacts of training data diversity on the stability and robustness of an existing network architecture. We studied the sensitivity of the trained network to different simulation parameters, e.g., echogenicity, number of scatterers, noise, and geometry. We showed that the network trained with the joint set of data is more stable on out-of-domain simulated data as well as measured phantom data.
Automated Cardiac Resting Phase Detection Targeted on the Right Coronary Artery
Authors: Yoon Seung Su , Preuhs Elisabeth , Schmidt Michaela , Forman Christoph , Chitiboi Teodora , Sharma Puneet , Fernandes Juliano L. , Tillmanns Christoph , Wetzl Jens , Maier Andreas
Published: May 2023

Static cardiac imaging such as late gadolinium enhancement, mapping, or 3-D coronary angiography require prior information, e.g., the phase during a cardiac cycle with least motion, called resting phase (RP). The purpose of this work is to propose a fully automated framework that allows the detection of the right coronary artery (RCA) RP within CINE series. The proposed prototype system consists of three main steps. First, the localization of the regions of interest (ROI) is performed. Second, the cropped ROI series are taken for tracking motions over all time points. Third, the output motion values are used to classify RPs. In this work, we focused on the detection of the area with the outer edge of the cross-section of the RCA as our target. The proposed framework was evaluated on 102 clinically acquired dataset at 1.5T and 3T. The automatically classified RPs were compared with the reference RPs annotated manually by a expert for testing the robustness and feasibility of the framework. The predicted RCA RPs showed high agreement with the experts annotated RPs with 92.7% accuracy, 90.5% sensitivity and 95.0% specificity for the unseen study dataset. The mean absolute difference of the start and end RP was 13.6 ± 18.6 ms for the validation study dataset (n=102). In this work, automated RP detection has been introduced by the proposed framework and demonstrated feasibility, robustness, and applicability for static imaging acquisitions.