Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the alignment between the operating room department and the nursing wards : Changing the current operating room scheduling method in order to minimize the variability in bed utilization at the nursing wards

Health care costs are rising, the Dutch population is aging, and the government and health care insurers are cutting costs. These are only a few of the current developments in the Dutch health care sector, which obliges every hospital to take a closer look at their current processes. Medisch Spectrum Twente (MST) is one of the Dutch hospitals that needs to rearrange their processes in order to keep delivering the quality of care they do. The MST hospital found out their processes are too much organized around the specialties and departments instead of around the patient, which results in a lack of alignment between the several steps a patient follows through the hospital. Therefore, the hospital started an efficiency program where every step of the total chain of care, from the first patients visit up to their discharge, needs to be reviewed and improved. This research takes a closer look at the alignment between the most expensive department of the hospital, the operating room (OR) department, and several nursing wards. Personnel members of both departments encounter problems as an effect of a deficiency between the two departments. The OR planning department aims to maximize the OR utilization since these expensive resources need to be used as much as possible. However, they barely have any insight in the consequence of their schedule for the nursing wards. The personnel members at the nursing wards encounter a strongly fluctuating bed utilization at the wards, since the OR scheduling department does not consider the length of stay of the patients they schedule, and to which ward these patients will flow. Therefore, the main research question is: “How can the MST hospital reduce the variability in bed utilization at the nursing wards, while the OR capacity will be used in an efficient way? ” The objective is to propose a new way of scheduling where the OR scheduling department can take into account the patient characteristics in order to minimize the variability at the nursing wards. Moreover, when a forecast can be made for the nursing departments of how many and what type of patients will enter their ward, a more accurate personnel planning can be made.
SEXUAL ASSAULT IN A HIGHLY TRAUMATIZED INNER-CITY POPULATION: PREVALENCE, ASSOCIATED SEQUELAE, AND PSYCHOPHYSIOLOGICAL PHENOTYPES

By ALEX O. ROTHBAUM Sexual assault is unfortunately a common form of interpersonal violence that confers considerable risk for negative psychological and physical outcomes. However, the prevalence and associated sequelae of sexual assault are understudied in key at risk groups like inner city and low-income populations. Using a subset of a large epidemiological sample comprised of 8,533 highly traumatized, low-income African American men and women, the current study examines the prevalence of sexual assault and a range of associated health outcomes among sexual assault survivors. Results indicate nearly 40% experienced sexual assault, primarily in childhood. Associated negative health outcomes include PTSD, Depression, self-harm, substance use, incarceration, and increased trauma exposure, among others. In a translational paradigm conducted on a subset (n=241), dark-enhanced startle yielded no differences between groups, however sexual assault was associated with habituation deficits.
Trustworthy speaker recognition with minimal prior knowledge using neural networks

The performance of speaker recognition systems has considerably improved in the last decade. This is mainly due to the development of Gaussian mixture model-based systems and in particular to the use of i-vectors. These systems handle relatively well noise and channel mismatches and yield a low error rate when confronted with zero-effort impostors, i.e. impostors using their own voice but claiming to be someone else. However, speaker verification systems are vulnerable to more sophisticated attacks, called presentation or spoofing attacks. In that case, the impostors present a fake sample to the system, which can either be generated with a speech synthesis or voice conversion algorithm or can be a previous recording of the target speaker. One way to make speaker recognition systems robust to this type of attack is to integrate a presentation attack detection system. Current methods for speaker recognition and presentation attack detection are largely based on short-term spectral processing. This has certain limitations. For instance, state-of-the-art speaker verification systems use cepstral features, which mainly capture vocal tract system characteristics, although voice source characteristics are also speaker discriminative. In the case of presentation attack detection, there is little prior knowledge that can guide us to differentiate bona fide samples from presentation attacks, as they are both speech signals that carry the same high level information, such as message, speaker identity and information about environment. This thesis focuses on developing speaker verification and presentation attack detection systems that rely on minimal assumptions. Towards that, inspired by recent advances in deep learning, we first develop speaker verification approaches where speaker discriminative information is learned from raw waveforms using convolutional neural networks (CNNs). We show that such approaches are capable of learning both voice source related and vocal tract system related speaker discriminative information and yield performance competitive to state of the art systems, namely i-vectors and x-vectors-based systems. We then develop two high performing approaches for presentation attack detection: one based on long-term spectral statistics and the other based on raw speech modeling using CNNs. We show that these two approaches are complementary and make the speaker verification systems robust to presentation attacks. Finally, we develop a visualization method inspired from the computer vision community to gain insight about the task-specific information captured by the CNNs from the raw speech signals.
Crustal Characteristics in the Subduction Zone of Mexico: Implication of the Tectonostratigraphic Terranes on Slab Tearing

During the past years, significant work has been done for studying the crustal anisotropy and state of stress of the Mexican subduction zone. At the same time, there is new evidence of the geometry of the subducted slab proposing subduction tearing. Here, we present a study of the Earth crust using three different methods: azimuthal anisotropy based on ambient noise, shear-wave splitting of tectonic tremors, and moment tensor inversions of the earthquakes of 7 September 2017 Mw 8.2 Tehuantepec, Mexico. This earthquake initiated a seismic sequence that triggered shallow seismicity and aftershocks. The shallow earthquakes fall into a region where there were few published focal mechanism higher than Mw 4.5. Two slab tearings: in the Michoacán–Guerrero border and in central Oaxaca, best represent the slab geometry of the Mexican subduction zone. At the Michoacán–Guerrero, the subducted slab is subhorizontal, whereas in central Oaxaca the plate is characterized by northeast vergence. We interpret that the mantle’s flow in this part of the subducted slab produces multiple alignments in the crust and differentiates the tectonostratigraphic terranes of the southern region of Mexico.
Hybrid of Reinforcement and Imitation Learning for Human-Like Agents

SUMMARY Reinforcement learning methods achieve performance superior to humans in a wide range of complex tasks and uncertain environments. However, high performance is not the sole metric for practical use such as in a game AI or autonomous driving. A highly e ﬃ cient agent performs greedily and selﬁshly, and is thus inconvenient for surrounding users, hence a demand for human-like agents. Imitation learning reproduces the behavior of a human expert and builds a human-like agent. However, its performance is limited to the expert’s. In this study, we propose a training scheme to construct a human-like and e ﬃ cient agent via mixing reinforcement and imitation learning for discrete and continuous action space problems. The proposed hybrid agent achieves a higher performance than a strict imitation learning agent and exhibits more human-like behavior, which is measured via a human sensitivity test.
Security‐constrained transmission expansion planning using linear sensitivity factors

Formulating power flow equations with linear sensitivity factors (LSFs) reduces the number of variables and constraints, and consequently, the computational burden of power systems’ optimisation problems. This study proposes a transformative, computationally efficient model for transmission expansion planning (TEP). While the existing TEP models use bus voltage angles, the proposed TEP takes advantages of LSFs to formulate an optimisation. LSFs allow to omit voltage angles from the formulation and replace all nodal power balance constraints by one equivalent constraint. Thus, the proposed model includes less number of variables and constraints compared with the classical angle-based model. These features significantly reduce computational costs of TEP and enhance its scalability, especially for large-scale systems. Load and generation uncertainties are modelled using a data-driven approach, and N − 1 security criteria are taken into account to ensure system security. All equations under normal and N − 1 conditions are considered using data of the complete network graph. Simulation results show that the proposed model provides the same results as the conventional angle-based model while being much faster (more than 58% based on the authors’ case studies) and computationally more efficient.
Benign hematological disorders in India: The status

Hemato-oncological disorders notwithstanding, the load of benign hematolgical disorders in India is alarming. Benign hematolgical disorders can be broadly divided into those with acquired and inherited causes. Acquired causes due to iron and vitamin defi ciency indeed exceed the congenital causes. These are more common in women than in men. Women get iron defi ciency more often due to excessive bleeding during menstruation and child birth. Worm infestation and piles contribute to iron defi ciency anemia both among men and women. Occult malignancies which cause bleeding are also well-known causes for iron defi ciency anemia. Nutritional defi ciency, multiple pregnancies, and malabsorption contribute to dimorphic anemia — a combination of iron and folate/ vitamin B12 defi ciency.
Consequences prevalence of anaemia and dietary habits of lactating women of Ambedkar Nagar

Anaemia is a serious nutrition problem affecting million in developing countries and remains a major challenge for human health and social and economic development this indicates that the quality of breast milk is maintained at the expense of maternal stores. Postpartum anaemia is highest in mothers who are anaemic during pregnancy lactating mothers being the postnatal period after having iron depleted through the continuum from pregnancy to childbearing in a mater-analysis of observational and intervention trials, Ross and Thomas found that 20% of maternal mortality in sub-Saharan Africa and South and Asia was attributable to anaemia that was primarily the result of iron deficiency. This study was performed to determine the Consequences prevalence of anaemia and dietary habits of lactating women of Ambedkar Nagar. For the research survey method was used for the collection of data. The sample sizes of 100 anaemic respondent’s were randomly selected. The survey schedule consisted of question on general profile anthropometric measurement in which height (cm) weight (kg) and BMI. Result show that maximum womens were anaemic because the dietary habit was not good in the family.
Supporting Customers with Limited Budget in Data Marketplaces

As the competitiveness and dynamics of current markets intensify, companies and organizations see opportunities to optimize their strategies and increase their business advantage in data-driven decision-making. This has led to an emergence of data marketplaces, where providers can sell data, while consumers can purchase it. However, the process of acquiring data from a marketplace involves issuing queries with an associated monetary cost, and data consumers often struggle to purchase the targeted data set of appropriate volume and content within their budget. Two issues need to be considered: One is querying itself, which may require API calls, structured queries written in SQL, graph queries written in Neo4J, or any other language framework. Querying is often a stepwise process that starts from generic queries and gets refined as the user learns about the data that results. The other issue is the cost involved, which consists of the price a consumer has to pay for the data and that of processing the various queries. In this paper, the second issue is studied from a computational perspective; in particular, we propose a novel framework for data-purchase support that considers data purchase from a marketplace as a sequence of interactions between the data provider (or the marketplace) and the consumer. This allows us to deal with scenarios in which the consumer has a limited budget, insufficient to embrace the complete data set he or she targets. We formalize the problem setting and the characteristics of available queries offered by the data provider so that efficient (approximation) algorithms can be devised. Our empirical results demonstrate that intelligent algorithms can aid the data consumer with near-optimum solutions that consider her preferences about the queries to be issue to the data provider.
Awareness of breast cancer among south indian women: a survey

Aim:-To create an awareness on breast cancer among South Indian women. Objective:-The objective of this study is to test the efficacy of women with the knowledge, skills, confidence and motivation to detect symptoms and seek help promptly, with the aim of promoting early presentation with breast cancer symptoms. Background:-Breast cancer is one of the most common cancers among females worldwide. Global statistics show the annual incidence of breast cancer is increasing and this is occurring more rapidly in countries with a low incidence rate of breast cancer. It has been reported that each year over 1.15 million women worldwide are diagnosed with breast cancer and 502,000 die from the disease. Early detection of breast cancer plays the leading role in reducing mortality rates and improving the patients prognosis. Method:The survey was carried out by preparing a set of questionnaires.These questionnaires were circulated among women to determine their awareness about breast cancer.The sample size for this survey was 200,which includes various age group.Data collected was then analysed statistically with the help of SPSS online software. Result:Awareness for breast cancer is very much necessary among women.Our present study came out with a result of 81.9% awareness among South Indian women population.Media is interpreted to be a boon for creating awareness of 88% among the population.37. 2% of the subjects believe that formation of any lumps or thickening in one or both the breast is the major symptoms for breast cancer.Almost half of the subjects (56.1%) were not aware that overweight or obesity can cause the risk for breast cancer and also not aware that non cancerous breast lumps/cyst or fibrocystic breast cancer disease can also increase the risk for breast cancer (51.3%). 27.6% of the population believe that all the breast cyst or lumps to be cancerous.But our study shows 30.8% were not aware about the screening programs, while those who were aware (69.2%), reported that Mammography (38.3%) followed by BSP(Breast screening program) (22.2%), BSE (Breast self examination) (18.6%). 32.5% and 29.3% were aware only about surgery and chemotherapy procedures. Conclusion: Our study found high awareness of breast cancer among South Indian women. The subjects had a moderate level of knowledgeabout breast cancer risk factors and signs and symptoms.Our study also shows media plays a vital role in educating the people about breast cancer. BSE practices were very less among the population.To spread on more awareness about the risk factors, symptoms and BSE ,campaigns can be launched.
Factors Affecting Export Performance in the Context of Strategic Management: Proposal of a Theoretical Model

Exporting attracts attention as an international sales activity, which has been studied by the country’s economic managements, firm managers and academic researchers as it reduces the current deficit by providing foreign exchange inflow in economy and has positive effects on the profitability indicators at the firm level. Though there are many studies on the factors that affect export performance of firms, in the literature, most of the studies are not theoretical. For firms, export activity is the most common strategic option to access to the international markets. Considering the export activity from the strategic management perspective provides a high prediction in terms of understanding the factors that affect export performance. In this study, the factors that affect export activity of the firms are examined from the strategic management perspective within the scope of the theoretical approaches presented in the literature. In this regard, the effects of competitive strategies within the scope of the industrial organization theory, and of firm resources and capabilities within the scope of the resource-based view on export performance are discussed, and a theoretical model has been presented.
On enhancing the security of time constrained mobile contactless transactions

Relay attacks are passive man-in-the-middle attacks during which an attacker is extending the communication distance of two genuine devices by relaying communication messages between them, without the legitimate user’s consent. In the field of smart cards, distance bounding protocols have been proposed as an effective countermeasure. For smartphones, distance bounding protocols may not work due to the multitude of hardware vendors and background processes. Instead, sensing the natural ambient environment has been proposed. However, previously proposed solutions may not be applicable in scenarios where industry imposed time constraints apply, e.g. in the cases of EMV contactless payments and transport ticketing, where a transaction should typically complete within 300–500ms. In this thesis, the applicability of the natural ambient sensing as a Proximity and Relay Attack Detection (PRAD) mechanism in time-restricted contactless transactions (up to 500ms) is initially investigated. The use of an Artificial Ambient Environment (AAE) is proposed as a potentially more effective PRAD mechanism. Infrared light and vibration have been examined as AAE actuators. Furthermore, two PRAD techniques that are not based on the ambient environment are proposed. First, comparing subsequent button press and release timings, performed by the genuine user on the smartphone during a transaction, and recorded by both transaction devices simultaneously. Finally, in order to protect against relay attacks initiated through a malicious application, installed on the user’s smartphone, a repackaged application detection technique is proposed, based on similarity comparison of application names and icons. Repackaged applications have been found in the past to be responsible for the distribution of as much as 86% of all Android malware. The effectiveness of all the proposed techniques has been empirically evaluated through field trials. Analysis of the collected data, using thresholdand/or machine learning-based techniques, indicates the high effectiveness of all the proposed solutions as PRAD mechanisms.
A New Higher-Order Iterative Scheme for the Solutions of Nonlinear Systems

Many real-life problems can be reduced to scalar and vectorial nonlinear equations by using mathematical modeling. In this paper, we introduce a new iterative family of the sixth-order for a system of nonlinear equations. In addition, we present analyses of their convergences, as well as the computable radii for the guaranteed convergence of them for Banach space valued operators and error bounds based on the Lipschitz constants. Moreover, we show the applicability of them to some real-life problems, such as kinematic syntheses, Bratu’s, Fisher’s, boundary value, and Hammerstein integral problems. We finally wind up on the ground of achieved numerical experiments, where they perform better than other competing schemes.
The Case of the Missing

Everything we see, hear, imagine, or think about is linked to neural responses somewhere in the brain. The brain, with all its 100 billion neurons, allows us to do incredible things like learn and speak several languages, or build superfast computers or send people to the moon and so on. Yet, despite this amazing capacity, we routinely can’t remember where we put our keys, why we went to the other room and so on. This obvious contradiction in functionality leads us to the question of why we forget some things but remember others.
Investigation of The Influence of Sharia Principles Implementation on Business Development and Customer Loyalty

This study aims to examine and analyse the influence of the Implementation of Sharia Principles on Business Development, Application of Sharia Principles on Customer Loyalty, and Sharia Business Development on Customer Loyalty. This research was conducted in Pekanbaru City which is the capital of Riau Province. Riau Province is the closest province to Malaysia and Singapore which is incorporated in Growth Triangle. The number of samples in this study consists of 60 SMEs in which the business is focused on food business with Purposive sample technique. Data analysis was conducted by using Partial Least Square. The results showed that the implementation of Sharia Principles has an effect on encouraging the increase of Business Development, and the Implementation of Sharia Principles do not influence the increase of Customer Loyalty, and the Sharia Business Development influenced the increase of Customer Loyalty.
Overview on Key Technologies of Acceleration Slip Regulation for Four-wheel-independently-actuated Electric Vehicles

Four-wheel-independently-actuated electric vehicles have attracted tremendous attention due to its potential for enhancing vehicle dynamics stability and handling performance. Acceleration slip regulation (ASR) is of great importance for improving the driveability of FWIA EVs under the low-friction road surface. This paper presents a comprehensive overview on the key techniques of ARS for FWIA EVs with the primary goal to summarize recent progress and spark innovative ideas for further research advance. The state-of-the-art vehicle speed estimation, road identification and ASR control algorithms are systematically and thoroughly surveyed. Especially, the kinematicsand dynamics-based estimation methods for vehicle speed estimation are compared, and multi-sensor fusion technique represents the most promising direction in this aspect. The experimentaland model-based road identification algorithms are also overviewed with the emphases on the road adhesion coefficient and road surface identification. The slip rate-based and torque-based ASR controllers are covered with detailed analysis on the factors that need consideration in practical implementations. Finally, the prospects for key technologies for ASR are discussed.