Awareness of hypertension guidelines among family physicians in primary health care

Background: Only 14% of patients on treatment achieve the recommended blood pressure target. Guidelines aim to assist clinicians in the management of patients with hypertension.Objectives: The primary purpose of the study was to survey family physicians(FPs) in Kuwait about their awareness, and to understand better their reasons for not implementing specific guidance within the WHO/ISH guidelines.Methods: This study is a cross-sectional survey that was carried out in the five health regions of Kuwait. All PHC physicians who were currently working as FPs were asked to participate in the study. Data were collected using a structured questionnaire of clinically oriented questions formulated on the basis of the 1999 World Health Organization/International Society of Hypertension (WHO/ISH), as standard reference.Results: The study revealed that 49.1% and 42.1% of FPs were very familiar or somewhat familiar with the guidelines respectively, 92.1% were in agreement, and 79.8% indicated that they always or usually follow these guidelines when treating patients. Regarding the correct choice of the guideline statements, only 8.8% of the FPs choose correctly less than ten of the 20 statements, 64% choose 10 to less than 15, and only 27.2% choose > 15 statements. When asked about perceived patient barriers to blood pressure control, 84.0% of the respondents ranked overcrowded clinics as important or most important barrier to blood pressure control while, 87.4% considered lack of patient knowledge as important or most important barrier. Non availability of the drugs in the clinic was considered by 88.4% of the physicians, and poor adherence to antihypertensive drugs by 90.1%.Conclusion: There is a need to establish nationwide educational and quality monitoring programs to facilitate the correct implementation of hypertension guidelines in PHC clinical practices in Kuwait.Keywords:  Family physicians; Awareness; Hypertension; Guideline

Named Entity Disambiguation using Hierarchical Text Categorization

Named entity extraction is an important step in natural language processing. It aims at finding the entities which are present in text such as organizations, places or persons. Named entities extraction is of a paramount importance when it comes to automatic translation as different named entities are translated differently. Named entities are also very useful for advanced search engines which aim at searching for a detailed information regarding a specific entity. Named entity extraction is a difficult problem as it usually requires a disambiguation step as the same word might belong to different named entities depending on the context. This work has been conducted on the ANERCorp named entities database. This Arabic database contains four different named entities: person, organization, location and miscellaneous. The database contains 6099 sentences, out of which 60% are used for training 20% for validation and 20% for testing. Our method for named entity extraction contains two main steps: the first step predicts the list of named entities which are present at the sentence level. The second step predicts the named entity of each word of the sentence. The prediction of the list of named entities at the sentence level is done through separating the document into sentences using punctuation marks. Subsequently, a binary relation between the set of sentences (x) and the set of words (y) is created from the obtained list of sentences. A relation exists between the sentence (x) and the word (y) if, and only if, (x) contains (y). A binary relation is created for each category of named entities (person, organization, location and miscellaneous). If a sentence contains several named entities, it is duplicated in the relation corresponding to each one of them. Our method then extracts keywords from the obtained binary relations using the hyper concept method [1]. This method decomposes the original relation into non-overlapping rectangles and highlights for each rectangle the most representative keyword. The output is a list of keywords sorted in a hierarchical ordering of importance. The obtained keyword list associated with each category of named entities are fed into a random forest classifier of 10000 random trees in order to predict the list of named entities associated with each sentence. The random forest classifier produces for each sentence the list of probabilities corresponding to the existence of each category of named entities within the sentence. Random Forest [sentence(i)] = (P(Person),P(Organization),P(Location),P(miscellaneous)). Subsequently, the sentence is associated with the named entities for which the corresponding probability is larger than a threshold set empirically on the validation set. In the second step, we create a lookup table associating to each word in the database, the list of named entities to which it corresponds in the training set. For unseen sentences of the test set, the list of named entities predicted at the sentence level is produced, and for each word, the list of predicted named entities is also produced using the lookup table previously built. Ultimately, for each word, the intersection between the two predicted lists of named entities (at the sentence and the word level) will give the final predicted named entity. In the case where more than one named entity is produced at this stage, the one with the maximum probability is kept. We obtained an accuracy of 76.58% when only considering lookup tables of named entities produced at the word level. When performing the intersection with the list produced at the sentence level the accuracy reaches 77.96%. In conclusion, the hierarchical named entity extraction leads to improved results over direct extraction. Future work includes the use of other linguist features and larger lookup table in order to improve the results. Validation on other state of the art databases is also considered. Acknowledgements This contribution was made possible by NPRP grant #06-1220-1-233 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.qscienc

Awareness of hypertension guidelines among family physicians in primary health care

AbstractBackgroundOnly 14% of patients on treatment achieve the recommended blood pressure target. Guidelines aim to assist clinicians in the management of patients with hypertension.ObjectivesThe primary purpose of the study was to survey family physicians(FPs) in Kuwait about their awareness, and to understand better their reasons for not implementing specific guidance within the WHO/ISH guidelines.MethodsThis study is a cross-sectional survey that was carried out in the five health regions of Kuwait. All PHC physicians who were currently working as FPs were asked to participate in the study. Data were collected using a structured questionnaire of clinically oriented questions formulated on the basis of the 1999 World Health Organization/International Society of Hypertension (WHO/ISH), as standard reference.ResultsThe study revealed that 49.1% and 42.1% of FPs were very familiar or somewhat familiar with the guidelines respectively, 92.1% were in agreement, and 79.8% indicated that they always or usually follow these guidelines when treating patients. Regarding the correct choice of the guideline statements, only 8.8% of the FPs choose correctly less than ten of the 20 statements, 64% choose 10 to less than 15, and only 27.2% choose ⩾15 statements. When asked about perceived patient barriers to blood pressure control, 84.0% of the respondents ranked overcrowded clinics as important or most important barrier to blood pressure control while, 87.4% considered lack of patient knowledge as important or most important barrier. Non availability of the drugs in the clinic was considered by 88.4% of the physicians, and poor adherence to antihypertensive drugs by 90.1%.ConclusionThere is a need to establish nationwide educational and quality monitoring programs to facilitate the correct implementation of hypertension guidelines in PHC clinical practices in Kuwait

Can the word superiority effect be modulated by serial position and prosodic structure?

In this study, we examined the word superiority effect in Arabic and English, two languages with significantly different morphological and writing systems. Thirty-two Arabic-English bilingual speakers performed a post-cued letter-in-string identification task in words, pseudo-words, and non-words. The results established the presence of the word superiority effect in Arabic and a robust effect of context in both languages. However, they revealed that, compared to the non-word context, word and pseudo-word contexts facilitated letter identification more in Arabic than in English. In addition, the difference between word and pseudo-word contexts was smaller in Arabic compared to English. Finally, there was a consistent first-letter advantage in English regardless of the context, while this was more consistent only in the word and pseudo-word contexts in Arabic. We discuss these results in light of previous findings and argue that the differences between the patterns reported for Arabic and English are due to the qualitative difference between word morphophonological representations in the two languages.Open Access funding provided by the Qatar National Library

Numerical Analysis of Transient Response in a Single-Pass Cross flow Heat Exchanger

Crossflow heat exchangers are commonly employed in process critical applications in industries. A heat exchanger can be subjected to flow or temperature perturbation during its regular operation. It should be noted that a heat exchanger would require a finite time to overcome such perturbations. In this study, the transient response of a single-pass crossflow heat exchanger with variable inlet temperatures and mass flow rates was determined. Herein, the analysis of the transient performance of cross flow heat exchangers is conducted on a dimensionless basis. In every instance, the energy balance equations were solved using an explicit finite difference method. Numerical predictions were obtained for cases where both the fluids were subjected to step changes in inlet temperature, coupled with step mass flow rate change of the fluids. The analysis can be readily extended to study different flow circuiting and inlet conditions

Software-Defined GPU-CPU Empowered Efficient Wireless Federated Learning With Embedding Communication Coding for Beyond 5G

Currently, with the widespread of the intelligent Internet of Things (IoT) in beyond 5G, wireless federated learning (WFL) has attracted a lot of attention to enable knowledge construction and sharing among a huge amount of distributed edge devices. However, under unstable wireless channel conditions, existing WFL schemes exist the following challenges: First, learning model parameters will be disturbed by bit errors because of interference and noise during wireless transmission, which will affect the training accuracy and the loss of the learning model. Second, traditional edge devices with CPU acceleration are inefficient due to the low throughout computation, especially in accelerating the encoding and decoding process during wireless transmission. Third, current hardware-level GPU acceleration methods cannot optimize complex operations, for instance, complex wireless coding in the WFL environment. To address the above challenges, we propose a software-defined GPU-CPU empowered efficient WFL architecture with embedding LDPC communication coding. Specifically, we embed wireless channel coding into the server weight aggregation and the client local training process respectively to resist interruptions in the learning process and design a GPU-CPU acceleration scheme for this architecture. The experimental results show its anti-interference ability and GPU-CPU acceleration ability during wireless transmission, which is 10 times the error control capability and 100 times faster than existing WFL schemes

Energy-Efficient End-to-End Security for Software Defined Vehicular Networks

One of the most promising application areas of the Industrial Internet of Things (IIoT) is Vehicular Ad hoc NETworks (VANETs). VANETs are largely used by Intelligent Transportation Systems (ITS) to provide smart and safe road transport. To reduce the network burden, Software Defined Networks (SDNs) acts as a remote controller. Motivated by the need for greener IIoT solutions, this paper proposes an energy-efficient end-to-end security solution for Software Defined Vehicular Networks (SDVN). Besides SDN’s flexible network management, network performance, and energy-efficient end-toend security scheme plays a significant role in providing green IIoT services. Thus, the proposed SDVN provides lightweight end-to-end security. The end-to-end security objective is handled in two levels: i) In RSU-based Group Authentication (RGA) scheme, each vehicle in the RSU range receives a group id-key pair for secure communication and ii) In private-Collaborative Intrusion Detection System (p-CIDS), SDVN detects the potential intrusions inside the VANET architecture using collaborative learning that guarantees privacy through a fusion of differential privacy and homomorphic encryption schemes. The SDVN is simulated in NS2 & MATLAB, and results show increased energy efficiency with lower communication and storage overhead than existing frameworks. In addition, the p-CIDS detects the intruder with an accuracy of 96.81% in the SDV

Deep information fusion-driven POI scheduling for Mobile Social Networks

With the growing importance of green wireless communications, point-of-interest (POI) scheduling in the mobile social network (MSN) environment has become important in addressing the high demand for innovative scheduling solutions. To enhance feature expressions for the complicated structures in MSNs, this article explores a deep information, fusion-based POI scheduling system of the MSN environment via the implementation of an edge-cloud deep hybrid sensing (PS-MSN) framework. Cloud sensing modules utilize the explicit contextual real-time information for each user, while edge sensing modules detect the real-time implicit linkages among users. Based on these two types of modules, a deep representation scheme is embedded into the hybrid sensing framework to improve its feature expression abilities. As a result, this type of framework is able to integrate multisource information so that more fine-grained feature spaces are built. In this work, two groups of experiments are conducted on a real-world dataset to evaluate the efficiency, as well as stability, of the designed PS-MSN. Using three benchmark methods to make comparisons, the excellent overall performance of PS-MSN is properly verified

Energy-Efficient Random Access for LEO Satellite-Assisted 6G Internet of Remote Things

Satellite communication system is expected to play a vital role for realizing various remote internet of things (IoT) applications in 6G vision. Due to unique characteristics of satellite environment, one of the main challenges in this system is to accommodate massive random access (RA) requests of IoT devices while minimizing their energy consumptions. In this paper, we focus on the reliable design and detection of RA preamble to effectively enhance the access efficiency in high-dynamic low-earth-orbit (LEO) scenarios. To avoid additional signaling overhead and detection process, a long preamble sequence is constructed by concatenating the conjugated and circularly shifted replicas of a single root Zadoff-Chu (ZC) sequence in RA procedure. Moreover, we propose a novel impulse-like timing metric based on length-alterable differential cross-correlation (LDCC), that is immune to carrier frequency offset (CFO) and capable of mitigating the impact of noise on timing estimation. Statistical analysis of the proposed metric reveals that increasing correlation length can obviously promote the output signal-to-noise power ratio, and the first-path detection threshold is independent of noise statistics. Simulation results in different LEO scenarios validate the robustness of the proposed method to severe channel distortion, and show that our method can achieve significant performance enhancement in terms of timing estimation accuracy, success probability of first access, and mean normalized access energy, compared with the existing RA methods