Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Optimization of k-t Sampling Pattern and Reconstruction of DCE MRI for Pharmacokinetic Parameter Estimation

This work proposes to develop and evaluate a deep learning framework that jointly optimizes k-t sampling patterns and reconstruction for head and neck dynamic contrast-enhanced (DCE) MRI aiming to reduce bias and uncertainty of pharmacokinetic (PK) parameter estimation. 2D Cartesian phase encoding k-space subsampling patterns for a 3D spoiled gradient recalled echo (SPGR) sequence along a time course of DCE MRI were jointly optimized in a deep learning-based dynamic MRI reconstruction network by a loss function concerning both reconstruction image quality and PK parameter estimation accuracy. During training, temporal k-space data sharing scheme was optimized as well. The proposed method was trained and tested by multi-coil complex digital reference objects of DCE images (mcDROs). The PK parameters estimated by the proposed method were compared with two published iterative DCE MRI reconstruction schemes using normalized root mean squared errors (NRMSEs) and Bland-Altman analysis at temporal resolutions of $\Delta \text{t}$ = 2s, 3s, 4s, and 5s, which correspond to undersampling rates of R = 50, 34, 25, and 20. The proposed method achieved low PK parameter NRMSEs at all four temporal resolutions compared with the benchmark methods on testing mcDROs. The Bland-Altman plots demonstrated that the proposed method reduced PK parameter estimation bias and uncertainty in tumor regions at temporal resolution of 2s. The proposed method also showed robustness to contrast arrival timing variations across patients. This work provides a potential way to increase PK parameter estimation accuracy and precision, and thus facilitate the clinical translation of DCE MRI.
A Novel Partial Discharge Detection Method for Power Transformers On Site Adopting Its Component as Ultra-High Frequency Sensor

A novel partial discharge (PD) detection method for transformers on site adopting its component as ultra-high frequency (UHF) sensor is proposed. The bushing tap, iron core, and high-voltage (HV) winding can be used as internal UHF sensor for UHF PD detection. The experiments are carried out on an 80-kV transformer model to prove the validity of this method. The results show that the proposed method has good detection sensitivity and anti-disturbance performance, and can measure the PD signals without additional sensor. It solves the problem of the lack of available unused valves for internal UHF sensor installation while the external sensor is susceptible to interference. It provides a novel method with application prospects for PD detection of transformers in field.
Phylogenetic analysis of Starmerella apis in honey bees (Apis mellifera)

Honey bees are among the most effective pollinators that promote plant reproduction. Bees are highly active in the pollen collection season, which can lead to the transmission of selected pathogens between colonies. The clade Starmerella comprises yeasts that are isolated mainly from bees and their environment. When visiting plants, bees can come into contact with Starmerella spp. The aim of this study was to determine the prevalence and phylogenetic position of S. apis in bee colonies. Bee colonies were collected from nine apiaries in three regions. Ten colonies were sampled randomly from each apiary, and pooled samples were collected from the central part of the hive in each colony. A total of 90 (100%) bee colonies from nine apiaries were examined. Starmerella apis was detected in 31 (34.44%) samples, but related species were not identified. The 18S rRNA amplicon sequences of S. apis were compatible with the GenBank sequences of Starmerella spp. from India, Japan, Syria, Thailand, and the USA. The amplicon sequences of S. apis were also 99.06% homologous with the sequences deposited in GenBank under accession numbers JX515988 and NG067631 . This is the first study to perform a phylogenetic analysis of S. apis in Polish honey bees.
A Synopsis of Artificial Intelligence and its Applications in Surgery

Artificial intelligence (AI) has made steady in-roads into the healthcare scenario over the last decade. While widespread adoption into clinical practice remains elusive, the outreach of this discipline has progressed beyond the physician scientist, and different facets of this technology have been incorporated into the care of surgical patients. New AI applications are developing at rapid pace, and it is imperative that the general surgeon be aware of the broad utility of AI as applicable in his or her day-to-day practice, so that healthcare continues to remain up-to-date and evidence based. This review provides a broad account of the tip of the AI iceberg and highlights it potential for positively impacting surgical care.
Improving rescinnamine as an inducer of MSH 2-dependent apoptosis in cancer treatment

ElShimaa M. N. AbdelHafez, Andrew Diamanduros, Lacramioara Negureanu5 , Yan Luy5 , J. Hayley Bean, Katherine Zielke, Brittany Crowe, Aksana Vasilyeva, Jill E. Clodfelter, Omar M. Aly, Gamal El-Din A. A. Abuo-Rahma, Karin D. Scarpinato, Freddie R. Salsbury Jr., S. Bruce King 1 Department of Chemistry, Wake Forest University, Winston-Salem, NC 2 Department of Biology, Georgia Southern University, Statesboro, GA 3 Department of Cancer Biology, Wake Forest University School of Medicine, WinstonSalem, NC; now: St. Jude Hospital, Memphis, TN
The freighted social histories of HIV and hepatitis C: exploring service providers’ perspectives on stigma in the current epidemics

A virus has a social history. In the case of the hepatitis C virus (HCV) and HIV, this history is one involving stigma and discrimination, advocacy and activism, and recent dramatic improvements in treatment. These social histories influence the experience of people who live with the viruses, and those who work with them. One aspect of this is the impact of social changes on the biographical disruption and integration brought about by illness. Healthcare practitioners who see significant improvements in the effectiveness of treatment for a condition over the course of their professional life will incorporate those changes into their own history and their relationship to that condition. This article is based on a study of the experiences of serodiscordance, or mixed infection status, in families living with HIV and two types of viral hepatitis, hepatitis B and hepatitis C. The article explores the perspectives of healthcare workers who work with people affected by these viruses, who were asked about their experiences in working with serodiscordance in families. Interviews revealed that changing social meanings given to bloodborne viruses, and changes to treatment over time, held a significant place in the accounts that service providers gave of their work. In asking them to describe their work with HIV and HCV, we were also asking about work that has been shaped by changing patterns and sources of stigma, and recently reshaped by changes in treatment and outcomes. While typically the experiences of patients and their families are used to investigate the social histories of diagnosis and stigma, the professional perspectives and life stories of the service providers who work with them are also revealing. We heard accounts in which histories as well as current regimes were prominent, illuminated further by insights from the sociology of health on narrative and biographical disruption.
Development and Testing of the Meta-Heuristic Hybrid DEEPSO

The majority of stochastic optimization methods, such as Evolutionary Algorithms (EA), have a range of adjustable parameters that need to be adjusted, like mutation rates, crossover probabilities and communication factors, among others. At the moment of presentation of the algorithms to the scientific community, EA developers normally refer an optimal range of parameters values based on only a few experiments made. A poor algorithm parameterization hinders the discovery of good solutions. The parameter values needed for optimal algorithm performance are known to be problem specific that requires some attention on developing of new EA. That is how that the needed for a parameter tuning procedure appears. Due to its difficulty, the issue of controlling values of various parameters of an evolutionary algorithm is one of the most important and promising areas of research in evolutionary computation. To answer this problem, a statistical based methodology is proposed in this dissertation to precede a correct tuning and comparison of EA. The statistical methods, as Analysis of Variance (ANOVA), based on Design of experiments, which was firstly introduced by Ronald A. Fisher, seems to have a great potential in this field, and for this reason it was incorporated into this systematic procedure purposed. Two distinct heuristic methods are the basis to application of the developed methodology, the Evolutionary Particle Swarm Optimization, EPSO, and its hybrid called the Differential Particle Swarm Optimization Evolutionary, DEEPSO. Three versions of DEEPSO are tested, of those, two out of four have already been presented, that use the uniform recombination in sampling procedure, and another one that has been proposed here. Throughout this document, DEEPSO and EPSO approaches will be applied to a set of different optimization problems. Firstly, in order to validate the proposed methodology, they have been applied in six well-known benchmark test functions and then, the case study, where EPSO and DEEPSO variants have been applied in an Optimal Power Flow (OPF) problem using two systems cases with different complexity and dimension (IEEE 57-bus system and IEEE 188 bus system). The analyzed results are focused in the DEEPSO versions. EPSO is only used as comparison with DEEPSO. However, it is also subjected to the whole process involving the tuning and comparison methodology. The proposed methodologies have been successful applied for both application cases (benchmark functions and OPF problem). The general overview of comparison results shows that DEEPSO Sg−rnd performs better in more complex and larger dimension optimization problems, showing significant superiority to EPSO in these cases.
A review on location, detection and fault diagnosis in induction machines

: In this work a careful review describing different types of failures in electrical machines, their characteristic signals generated and diagnosis methods is performed. Additionally, a comparison of the advantages between the known failure detection methods based on the information required for diagnosis, the occurrence and importance of failures detection, the effectiveness for anticipating a malfunction or failure and the final diagnosis accuracy is also made. Particularly, this review will help to provide a straightforward up date about the most recent work and research in this field.
Insights, Solutions and Empowerment: a framework for evaluating participatory design

ABSTRACT Participatory design (PD) has become a popular approach used in a range of applications to generate a range of different outcomes. The defining strength of this approach is that it has potential to generate appropriate solutions while also empowering the involved community. This strength has resulted in PD being used as a method for developing humanitarian technology in developing countries. However, this diversity has made it difficult to evaluate the effectiveness of each project, leading to criticisms that PD lacks empirically demonstrable benefits over traditional approaches. This article presents a new outcome-focused framework to assist in the evaluation of PD projects. The Insights, Solutions and Empowerment Framework draws from extant literature, and first-hand case studies in rural Cambodia, and attempts to provide a holistic view of the value PD offers, and important cross-cutting values to reflect on.
Recent Developments in Solar Thermal Desalination Technologies: A Review

Fresh water resources are depleting rapidly as the water demand around the world continues to increase. Fresh water resources are also not equally distributed geographically worldwide. The best way to tackle this situation is to use solar energy for desalination to not only cater for the water needs of humanity, but also to offset some detrimental environmental effects of desalination. A comprehensive review of the latest literature on various desalination technologies utilizing solar energy is presented here. This paper also highlights the environmental impacts of desalination technologies along with an economic analysis and cost comparison of conventional desalination methods with different solar energy based technologies. This review is part of an investigation into integration of solar thermal desalination into existing grid infrastructure in the Australian context.
An Inventive Method for Eco-Efficient Operation of Home Energy Management Systems

A demand response (DR) based home energy management systems (HEMS) synergies with renewable energy sources (RESs) and energy storage systems (ESSs). In this work, a three-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of HEMS. The proposed method provides the trade-off between the net cost of energy ( C E n e t ) and the time-based discomfort ( T B D ) due to shifting of home appliances (HAs). At step-1, primary trade-offs for C E n e t , T B D and minimal emissions T E M i s s are generated through a heuristic method. This method takes into account photovoltaic availability, the state of charge, the related rates for the storage system, mixed shifting of HAs, inclining block rates, the sharing-based parallel operation of power sources, and selling of the renewable energy to the utility. The search has been driven through multi-objective genetic algorithm and Pareto based optimization. A filtration mechanism (based on the trends exhibited by T E M i s s in consideration of C E n e t and T B D ) is devised to harness the trade-offs with minimal emissions. At step-2, a constraint filter based on the average value of T E M i s s is used to filter out the trade-offs with extremely high values of T E M i s s . At step-3, another constraint filter (made up of an average surface fit for T E M i s s ) is applied to screen out the trade-offs with marginally high values of T E M i s s . The surface fit is developed using polynomial models for regression based on the least sum of squared errors. The selected solutions are classified for critical trade-off analysis to enable the consumer choice for the best options. Furthermore, simulations validate our proposed method in terms of aforementioned objectives.
Novel Predictive Stator Flux Control Techniques for PMSM Drives

In this paper, a predictive stator flux control (PSFC) algorithm for permanent magnet synchronous motor (PMSM) drives is proposed, which can eliminate the influence of flux linkage parameter perturbation and rotor position error. First, the sensitivity of conventional predictive current control (PCC) to the flux linkage parameter and rotor position is analyzed. Then, the novel composite discrete time sliding mode observer (SMO) based on stator flux state is designed, which can estimate the flux linkage parameter perturbation, rotor position error, and load torque simultaneously. Finally, a novel PSFC method is developed, which can enhance the robustness of PCC against flux linkage parameter perturbation and rotor position error by using composite discrete time SMO. Simulation and experimental results indicate that the proposed PSFC can achieve low stator current harmonics, low torque ripple, and excellent steady-state performance under the flux linkage parameter perturbation and rotor position error.
Cyber-victimization Trends in Trinidad & Tobago: The Results of Cyber-victimization Trends in Trinidad & Tobago: The Results of An Empirical Research An Empirical Research

: Cybertechnology has brought benefits to the Caribbean in the form of new regional economic and social growth. In the last years, Ca - ribbean countries have also become attractive targets for cybercrime due to increased economic success and online presence with a low level of cyber resilience. This study examines the online-related activities that affect cybercrime victimization by using the Routine Activity Theory (RAT). The present study seeks to identify activities that contribute to different forms of cybercrime victimization and develop risk models for these crimes, particularly the understudied cyber-dependent crimes of Hacking and Malware. It also aims to explore if there are similarities or differences in factors leading to victimization, which correlate to the classification of crimes as ei - ther cyber-dependent or cyber-enabled. The data analysis suggests that there is significant applicability for RAT in explaining Online Harassment victimization, while the usability of the RAT for predicting Malware victimization proved to be minimal, with only two significant variables being identified, with both being associated with Capable Guardianship.
Arithmetic progressions in sparse sets

We investigate arithmetic progressions in sparse sets. We further develop the theory of arithmetic progressions in M-lacunary sequences. In particular, there is a finite constant K , where N N 1 3 , such that if A is a finite M-lacunary sequence and the sum of the reciprocals of the elements of A is greater than or equal to K , then A contains N consecutive terms in arithmetic N progression. An investigation of arithmetic progressions in geometric progressions {f (n) 1 = {cn) , where c > 1 is a real number and n is a non-negative integer is macTe. The topological structure of the set of all such c , where {cn) contains a 3-term arithmetic progression is discussed. Arithmetic progressions in quadratics {f (n) = {an2 + Bn + y}, where a > 0 are also studied. 2 In particular, necessary and sufficient conditions for ((an + b) ) to contain a three term arithmetic progression are given for most cases of a and b , where a > b 1 0 and (arb) = 1. Some unsolved problems are mentioned.
FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders

Simultaneous Localization and Mapping (SLAM) is an active area of robot research. SLAM with a laser range finder (LRF) is effective for localization and navigation. However, commercial robots usually have to use low-cost LRF sensors, which result in lower resolution and higher noise. Traditional scan-matching algorithms may often fail while the robot is running too quickly in complex environments. In order to enhance the stability of matching in the case of large pose differences, this paper proposes a new method of scan-matching mainly based on Fast Fourier Transform (FFT) as well as its application with a low-cost LRF sensor. In our method, we change the scan data within a range of distances from the laser to various images. FFT is applied to the images to determine the rotation angle and translation parameters. Meanwhile, a new kind of feature based on missing data is proposed to determine the rough estimation of the rotation angle under some representative scenes, such as corridors. Finally, Iterative Closest Point (ICP) is applied to determine the best match. Experimental results show that the proposed method can improve the scan-matching and SLAM performance for low-cost LRFs in complex environments.
Laboratory Mix Design of Cold Bitumen Emulsion Mixtures Incorporating Reclaimed Asphalt and Virgin Aggregates

Bitumen emulsion asphalts, especially those incorporating marginal and secondary aggregates, are energy efficient, environment friendly, and sustainable alternatives to hot-mix asphalts. This study set out to compare engineering properties of a bitumen emulsion asphalt composed entirely of virgin aggregates with another composed of 55% reclaimed asphalt and 45% virgin aggregates. The aggregates were bound with a slow setting cationic bitumen emulsion composed of 65% base bitumen and 35% water. Marshall specimens molded at varying pre-mix water and bitumen emulsion contents were cured in molds for 24 h before being de-molded and cured for a further 72 h at 40 °C. Dry densities, porosities, and indirect tensile strengths for the cured specimens were determined in dry and soaked states. Virgin aggregate mix, at an optimum binder content of 6.1%, had a tensile strength ratio of 1.3 with corresponding air voids and moisture absorption values of 10.1% and 0.92%, respectively. Similarly, reclaimed asphalt mix at an optimum binder content of 6.2% had a tensile strength ratio of 1.03, with corresponding air voids and moisture absorption values of 7.9% and 0.38%, respectively. Compared to virgin mix, reclaimed asphalt mix had lower air voids and lower moisture absorption values with the overall benefit of enhanced resistance to moisture damage.
Exploring Making in Schools: A Maker-Framework for Teachers in K12

In recent years, the Maker movement has surged in popularity and has become attractive for didactic research. Exploration, creativity, open learning spaces, and problem-orientation are the foundations for successful Maker-Education. While the relevance of Making in higher education is slowly being recognized, it is still uncharted territory for most schools. As part of the “Making at School” project, the Zurich University of Teacher Education, together with the University of Zurich and the CreativeLab Zurich, is committed to creating interdisciplinary modules for Making, e.g., in biology, computer science, or handicraft. The main goal is to help teachers to develop a Maker mindset by providing ideas for designing their own projects. This paper presents a preliminary planning framework that is evaluated by (inter-)national experts in Making. This first assessment shows that the framework is described as useful and appealing, but still too complex to be used in schools.
Prototyping Products using Web-based AI Tools: Designing a Tangible Programming Environment with Children

A wide variety of children’s products such as mobile apps, toys, and assistant systems now have integrated smart features. Designing such AI-powered products with children, the users, is essential. Using high-fidelity prototypes can be a means to reveal children’s needs and behaviors with AI-powered systems. Yet, a prototype that can show unpredictable features similar to the final AI-powered product can be expensive. A more manageable and inexpensive solution is using web-based AI prototyping tools such as Teachable Machine. In this work, we developed a Teachable Machine-powered game-development environment to inform our tangible programming environment’s design decisions. Using this kind of an AI-powered high-fidelity prototype in the research process allowed us to observe children in a very similar setting to our final AI-powered product and extract design considerations. This paper reports our experience of prototyping AI-powered solutions with children and shares our design considerations for children’s self-made tangible representations.