Miracle-dz/UniFAR-FTUs huggingface dataset:
Carbohydrate counting in type 1 diabetes mellitus: dietitians’ perceptions, training and barriers to use

Objectives A study was undertaken to determine the perceptions, training and barriers regarding the use of carbohydrate counting in the dietary management of type 1 diabetes mellitus (T1DM) among dietitians in KwaZulu-Natal (KZN). Design A cross-sectional, descriptive study was conducted. Setting Dietitians who were registered with the Health Professions Council of South Africa (HPCSA), and working in the province of KZN at the time of the study, were invited to participate. Methods Data were collected using a self-administered electronic questionnaire distributed through SurveyMonkey, an Internet-based survey programme. Results Dietitians agreed that carbohydrate counting was a useful dietary management approach for diabetes (p < 0.05) and that it was essential to manage T1DM (p < 0.05). However, they felt it was a difficult concept for patients with T1DM to understand (p = 0.001) and teaching it to patients was time consuming (p < 0.05). Although dietitians believed that there was a strong evidence base for teaching carbohydrate counting to patients with T1DM (p < 0.05), they indicated a need for further training or education in it (p < 0.05). Barriers to using carbohydrate counting included a lack of training, confidence and experience, financial resources, time, blood glucose records and poor patient motivation and patient illiteracy (p < 0.05). Conclusions Overall, dietitians who participated in the study had a positive perception towards the use of carbohydrate counting in the management of T1DM. However, further training needs to be addressed for carbohydrate counting to be used with confidence by dietitians in KZN to optimize their management of T1DM.
A multi-stakeholder partnership to fight food waste in the hospitality industry: a contribution to the United Nations Sustainable Development Goals 12 and 17

Abstract One of society’s greatest challenges, reflected in United Nations Sustainable Development, is the issue of food waste. To address this, the hospitality industry must develop and implement multifaceted solutions from a range of stakeholders in the food supply chain. The paper describes a multi-stakeholder partnership set up to help the Dutch hospitality industry reduce food waste by means of a “Food Waste Challenge”. It reports findings from the challenge’s first edition, a large-scale field experiment with 172 participating restaurants, showing that after implementing a range of behavioural interventions, food waste decreased by 21% on average at participating restaurants with complete measurements. This research builds on scarce literature on multi-stakeholder partnerships and “green” behavioural interventions in tourism in order to battle societal problems such as food waste and thereby contribute to the United Nations Sustainable Development Goals 12 and 17 and build a more sustainable hospitality industry.
The Impact of Better Access to Improved Water Sources on Health, Schooling, and Water Collection of Girls and Boys in Rural Zambia

Abstract This paper examines the short-term impact of better access to improved water available at newly built boreholes on children’s health, schooling, and time allocation in rural Zambia. We employed a difference-in-differences estimation using a dataset collected under a quasi-experimental setting. We observed significant effects of better access to improved water sources on the reduced incidence of diarrhea for pre-school children but not for school-age children. We found no significant effect on school attendance. To understand the mechanism behind this pattern, we examined any changes in time use by children who had better access to improved water sources. For girls, particularly those who lived near the boreholes, we found a significant decrease in time spent on schooling and homework and a significant increase in time spent on water-related household chores including fetching water. We did not find any significant changes for boys. Alongside a significant decrease in time spent on water-related chores by female adults, better access to improved water supply shifts the burden of water-related household chores from female adults to girls while the net burden of water collection alone for girls was unchanged.
A Target Attention Model for Neural Machine Translation

Neural Machine Translation (NMT) with an attention mechanism has shown promising results by utilizing word alignments between the source and target sentences. Typically, training of NMT proceeds token-by-token on the target side, where each token is predicted using only a vector representing the current hidden-state, and the previous token. However, this strategy has serious shortcomings originating the lack of information about the partial target sequence hypothesis; speciﬁcally, this can lead to source tokens being translated multiple times or remaining untranslated. To alleviate this problem, we introduce a target-side attention mechanism to exploit the generated target sequence of tokens more effectively. We calculate a target-side context vector using a recurrent neural network and feed it to an attention mechanism so that the decoder can pay more or less attention to each token in the partially generated target sequence when predicting the next target token. Experiments on three different English-to-Japanese translation tasks show improvements of 0.6-1.5 BLEU points.
A Neural Network Transliteration Model in Low Resource Settings

Transliteration is the process of converting a text in one script to another, guided by phonetic clues. This conversion requires an important set of rules defined by expert linguists to determine how the phonemes are aligned and to take into account the phonology system of the target language. The problem with under-resourced language pairs remains the lack of linguistic resources. In this research, we present a recurrent neural network based approach to overcome the transliteration problem for a low-resource language pair, with an application on the French-Vietnamese language pair. Our system requires a small bilingual learning dataset. We obtained promising results with a large gain of BLEU-score and a reduction in translation errors rate (TER) and phonemes errors rate (PER), compared to other systems.
Bootstrap testing and approximate finite sample distributions for tests of linear restrictions on cointegrating vectors

This dissertation contains five essays in the field of time series econometrics. The main issue discussed is the lack of coherence between small sample and asymptotic inference. Frequently, in modern econometrics distributional results are strictly only valid for a hypothetical infinite sample. Studies show that the attained actual level of a test may be considerable different from the nominal significance level, and as a concequence, too many true null hypotheses will falsely be rejected. This leads, in the extension, to applied users that too often reject evidence in the data for theoretical predictions. In large, the thesis discusses how computer intensive methods may be used to adjust the test distribution, such that the actual significance level will coincide with the desired nominal level. The first two essays focus on how to improve testing for persistence in data, through a bootstrap procedure within a univariate framework. The remaining three essays are studies of multivariate time series models. The third essay considers the identification problem of the basic stationary vector autoregressive model, which is also the basic-line econometric specification for maximum likelihood cointegration analysis. In the fourth essay the multivariate framework is expanded to allow for components of different integrating order and in this setting the paper discusses how fractional cointegration affects the inference in maximum likelihood cointegration analysis. The fifth essay consider once again the bootstrap testing approach, now in a multivariate application, to correct inference on long-run relations in maximum likelihood cointegration analysis.
Zero-Shot Translation for Indian Languages with Sparse Data

Neural Machine Translation (NMT) is a recently-emerged paradigm for Machine Translation (MT) that has shown promising results as well as a great potential to solve challenging MT tasks. One such a task is how to provide good MT for languages with sparse training data. In this paper we investigate a Zero Shot Translation (ZST) approach for such language combinations. ZST is a multilingual translation mechanism which uses a single NMT engine to translate between multiple languages, even such languages for which no direct parallel data was provided during training. After assessing ZST feasibility, by training a proof-of-concept engine ZST on French $ English and Italian $ English data, we focus on languages with sparse training data. In particular, we address the Tamil $ Hindi language pair. Our analysis shows the potential and effectiveness of ZST in such scenarios. To train and translate with ZST engines, we extend the training and translation pipelines of a commercial MT provider – KantanMT – with ZST capabilities, making this technology available to all users of the platform.
Empirical Study of Dropout Scheme for Neural Machine Translation

Dropout has lately been recognized as an effective method to relieve over-ﬁtting when training deep neural networks. However, there has been little work studying the optimal dropout scheme for neural machine translation (NMT). NMT models usually contain attention mechanisms and multiple recurrent layers, thus applying dropout becomes a non-trivial task. This paper approached this problem empirically through experiments where dropout were applied to different parts of connections using different dropout rates. The work in this paper not only leads to an improvement over an established baseline, but also provides useful heuristics about using dropout effectively to train NMT models. These heuristics include which part of connections in NMT models have higher priority for dropout than the others, and how to correctly enhance the effect of dropout for difﬁcult translation tasks.
Monitoring Yearly Change Patterns of the Surface Tidal Trail (STT) in Tidal Flats: A Novel Morphological Indicator Extracted from a Near-Infrared Terrestrial Laser Scanner

ABSTRACT Zhan, Y. ; Aarninkhof, S.G. J.; Wang, Z. , and Zhou, Y. , 2022. Monitoring yearly change patterns of the surface tidal trail (STT) in tidal flats: A novel morphological indicator extracted from a near-infrared terrestrial laser scanner. Journal of Coastal Research, 38(5), 1011–1020. Coconut Creek (Florida), ISSN 0749-0208. Tidal flats play an important role in the geomorphological and biological dynamics of coasts. Research on the morphological evolution of tidal flats constitutes one of the key research issues pertaining to the sustainability of coastal ecosystems and related coastal defense issues. In this work, a novel indicator, the surface tidal trail (STT), was extracted from a near infrared terrestrial laser scanner and studied. The results show that the area intensity and size of STTs decline yearly. Meanwhile, the position shift of the peak value on the STT curves presents a similar pattern of hydrodynamic force in response to the seawall, which has been studied in previous works. Although no direct correlation between the STT intensity and the deposition rate was found, the corresponding hydrodynamic force data were not available in this work. The change process of STTs still provides a possible speculation that hydrodynamic force and the softness of tidal surfaces are two main factors that form and influence STTs. For future research, establishing the direct quantitative relationships among hydrodynamic force, topography, and STTs on different temporal and spatial scales would help to better understand this novel indicator.
Pretargeting: A Path Forward for Radioimmunotherapy

Pretargeted radioimmunodiagnosis and radioimmunotherapy aim to efficiently combine antitumor antibodies and medicinal radioisotopes for high-contrast imaging and high–therapeutic-index (TI) tumor targeting, respectively. As opposed to conventional radioimmunoconjugates, pretargeted approaches separate the tumor-targeting step from the payload step, thereby amplifying tumor uptake while reducing normal-tissue exposure. Alongside contrast and TI, critical parameters include antibody immunogenicity and specificity, availability of radioisotopes, and ease of use in the clinic. Each of the steps can be optimized separately; as modular systems, they can find broad applications irrespective of tumor target, tumor type, or radioisotopes. Although this versatility presents enormous opportunity, pretargeting is complex and presents unique challenges for clinical translation and optimal use in patients. The purpose of this article is to provide a brief historical perspective on the origins and development of pretargeting strategies in nuclear medicine, emphasizing 2 protein delivery systems that have been extensively evaluated (i.e., biotin–streptavidin and hapten-bispecific monoclonal antibodies), as well as radiohaptens and radioisotopes. We also highlight recent innovations, including pretargeting with bioorthogonal chemistry and novel protein vectors (such as self-assembling and disassembling proteins and Affibody molecules). We caution the reader that this is by no means a comprehensive review of the past 3 decades of pretargeted radioimmunodiagnosis and pretargeted radioimmunotherapy. But we do aim to highlight major developmental milestones and to identify benchmarks for success with regard to TI and toxicity in preclinical models and clinically. We believe this approach will lead to the identification of key obstacles to clinical success, revive interest in the utility of radiotheranostics applications, and guide development of the next generation of pretargeted theranostics.
A Survey of 3D Ear Recognition Techniques

Human recognition with biometrics is a rapidly emerging area of computer vision. Compared to other well-known biometric features such as the face, fingerprint, iris, and palmprint, the ear has recently received considerable research attention. The ear recognition system accepts 2D or 3D images as input. Since pose, illumination, and scale all affect 2D ear images, it is evident that they all impact recognition performance; therefore, 3D ear images are employed to address these issues. The geometric shapes of 3D ears are utilized as rich features to improve recognition accuracy. We present recent advances in several areas relevant to 3D ear recognition and provide directions for future research. To the best of our knowledge, no comprehensive review has been conducted on using 3D ear images in human recognition. This review focuses on three primary categories of 3D ear recognition techniques: (1) registration-based recognition, (2) local and global feature-based recognition, and (3) a combination of (1) and (2). Based on the above categorization and publicly available 3D ear datasets, this article reviews existing 3D ear recognition techniques.
Elastically Augmenting the Control-path Throughput in SDN to Deal with Internet DDoS Attacks

Distributed denial of service (DDoS) attacks have been prevalent on the Internet for decades. Albeit various defenses, they keep growing in size, frequency, and duration. The new network paradigm, Software-defined networking (SDN), is also vulnerable to DDoS attacks. SDN uses logically centralized control, bringing the advantages in maintaining a global network view and simplifying programmability. When attacks happen, the control path between the switches and their associated controllers may become congested due to their limited capacity. However, the data plane visibility of SDN provides new opportunities to defend against DDoS attacks in the cloud computing environment. To this end, we conduct measurements to evaluate the throughput of the software control agents on some of the hardware switches when they are under attacks. Then, we design a new mechanism, called Scotch, to enable the network to scale up its capability and handle the DDoS attack traffic. In our design, the congestion works as an indicator to trigger the mitigation mechanism. Scotch elastically scales up the control plane capacity by using an Open vSwitch-based overlay. Scotch takes advantage of both the high control plane capacity of a large number of vSwitches and the high data plane capacity of commodity physical switches to increase the SDN network scalability and resiliency under abnormal (e.g., DDoS attacks) traffic surges. We have implemented a prototype and experimentally evaluated Scotch. Our experiments in the small-scale lab environment and large-scale GENI testbed demonstrate that Scotch can elastically scale up the control channel bandwidth upon attacks.
The Pandemic and the Elderly: Navigating Age and the COVID-19 Policies of India

Several media reports have highlighted how various sections of India’s population have been coping with the pandemic. Extending this debate, many academic articles have focussed on how the pandemic has led to multiple challenges for medical professionals and impacted rising unemployment rates and low female labour force participation in India. But India’s elderly who have been grappling with significant problems as a result of COVID-19 appear to be missing from both academic research as well as the policy narrative. Based on qualitative interviews, this article demonstrates how the elderly in an urban city of India dealt with the pandemic as well as the nationwide lockdown. Additionally, this article will also discuss the policies that should be put into place to address the needs of the elderly population in the country. In this regard, this article will discuss the impact of vaccine policies on the elderly and illustrate intersections between later life experiences and a global disaster such as a pandemic in urban India.
Solving the p-Median Problem with Insights from Discrete Vector Quantization

The goals of this paper are twofold. First, we formally equate the p-median problem from facility location to the optimal design of a vector quantizer. Second, we use the equivalence to show that the Maranzana Algorithm can be interpreted as a projected Lloyd Algorithm, a fact that improves complexity. Numerical results verify significant improvements in run-time.
Separating Respiration and Perfusion in EIT: Harmonic Analysis on 2D-Thorax Simulation

Abstract Electrical impedance tomography (EIT) has the potential for monitoring perfusion in addition to respiration on the bedside. Several separation methods were reported, e.g. Filtering, ECG-gating and PCA. However, the separation is not trivial, which is why harmonic analysis was introduced in EIT data analysis. It is based on several assumptions and therefore prove of plausibility is necessary. In this contribution a two-dimensional thorax simulation is introduced, which includes simplified lungs and heart with changing shapes and conductivities due to volume variations and lung perfusion. Harmonic analysis was applied on the simulated data. The separation results are in good agreement with the simulation settings. Further investigations will be required due to limitations of the simulation. Nevertheless, harmonic analysis delivered precise results in ideal and strictly defined conditions.
Comparison of assignment algorithms with applications to the passive sensor data association problem

The problem of tracking multiple targets with passive electronic or sonar sensors requires at least three sensors for correct measurement-target association. Mathematically, the measurement -target association problem leads to a generalization of the multi-dimensional assignment problem, which is known to be NP-complete when the number of sensors S /spl ges/ 3, i.e., the complexity of an optimal algorithm increases exponentially with the size of the problem. We consider a three sensor case and solve the 3-dimensional assignment problem using a Lagrangian relaxation algorithm that successively solves a series of generalized two-dimensional assignment subproblems with the worst case complexity of O(k n/sup 3/), where n is the number of reports from each sensor, and k is the number of dual iterations. This paper investigates the computational efficiency of three recently developed assignment algorithms - RELAX II, generalized Auction and generalized Signature methods --- as part of the dual relaxation method. The computational efficiency of the algorithms is evaluated for various target spacing/sensor accuracy ratios, false alarm and missed detection probabilities. It is concluded that the generalized Auction is 4-5 times faster than either RELAX-II or the generalized signature method.
Bioengineering of Tubes, Rings and Panels for Guided Bone and Vascular Regeneration

Abstract The development of three dimensional geometries by electrospinning of nonwoven, bioresorbable PDLLA nanoand microfibers is a prerequisite for multimodal growth factor delivery by biohybrids. Electrospinning of 3D tubes is a formidable task since the deposited nanofibers on the rotating mandrel adhere so strongly that newly formed tubes cannot be removed. A specific electrospinning instrument setup with a "removable mandrel sleeve" technique facilitated the reproducible synthesis of tubes for guided bone and future vascular applications. It is shown, that this technique not only allows the preparation of tubes but also of self-supported 3D rings and sandwich panels which can be functionally doped by growth factors such as rhBMP-2 to biohybrids.
Model Specification between Parametric and Nonparametric Cointegration

This paper considers a general model specification between a parametric co-integrating model and a nonparametric co-integrating model in a multivariate regression model, which involves a univariate integrated time series regressor and a vector of stationary time series regressors. A new and simple test is proposed and the resulting asymptotic theory is established. The test statistic is constructed based on a natural distance function between a nonparametric estimate and a smoothed parametric counterpart. The asymptotic distribution of the test statistic under the parametric specification is proportional to that of a local-time random variable with a known distribution. In addition, the finite sample performance of the proposed test is evaluated through using both simulated and real data examples.