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This work deals with the synthesis, structural characterization and applications of N-halamine glycinate functionalized silica-mesoporous silica coated ZnO-NPs/AC composite for water disinfection. Several nanocomposite materials were obtained: ZnO-NPs/AC, ZnO-NPs/AC@SiO2, ZnO-NPs/AC@SiO2@mSiO2, ZnO-NPs@SiO2@mSiO2-Gly and ZnO-NPs@SiO2@mSiO2-N-halamine-Gly. These nanocomposite materials were fully characterized via different physiochemical techniques including: FTIR, TGA, XPS, XRD, SEM, TEM and BET. XRD indicated a predominance of crystalline pattern of ZnO-NPs impregnated into activated carbon (AC) and their silica and m-mesoporous silica coating precursors. The FTIR spectra confirmed an immense combination between ZnO-NPs and AC of ZnO-NPs/AC nanocomposite as well as its interactions with coated silica precursors. SEM, TEM images illustrated that the fabricated ZnO-NPs/AC nanocomposites are well coated with silica-mesoporous silica functionalized N-halamine. The distinctive surface area has decreased from 800 m2/g for pristine AC to 772 m2/g for ZnO-NPs/AC and to 282 m2/g for ZnO-NPs/AC@SiO2 and to 139 m2/g for ZnO-NPs/AC@SiO2@mSiO2 and to 15.4 m2/g for ZnO-NPs@SiO2@mSiO2-N-Gly. All those nanocomposites showed good efficacy against all four bacterial species, with higher inhibition zones for the 2 g-positive bacteria than that of the 2 g-negative ones. The ZnO@SiO2@mSiO2-N-halamine-Gly exhibited the high zone inhibition against all tested bacteria except for E. Coli.

Jingyi Ma, Mariam Issa, Devesh Varma, Iqbal IK Ahmed Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, CanadaCorrespondence: Iqbal IK Ahmed, Ophthalmology and Vision Sciences, University of Toronto, 2201 Bristol Circle, Suite 100, Oakville, Ontario, L6H 0J8, Canada, Tel +1 (905) 456-3937, Email ikeahmed@mac.comPurpose: We aimed to evaluate the effectiveness and safety of a virtual eye assessment triage system implemented in response to COVID-19.Patients and Methods: We conducted a retrospective cross-sectional study using a consecutive sample of all virtual assessments conducted from March 24 to June 7, 2020 at a single ophthalmology center in Toronto, ON, Canada. Visual acuity and smartphone photographs were uploaded to an electronic assessment website. All patients were virtually triaged to an email or phone consult. Patient outcomes and satisfaction were assessed with a quality assurance survey. Primary outcome measures were the incidence of unplanned additional in-person visits and changes in treatment.Results: We performed 1535 virtual assessments. Of the triage pathways, 15% received an email consult only and 85% received a phone consult. Subsequently, 15% required an in-person assessment, 3% were referred elsewhere, and 0.1% were sent to the emergency. Presentations were most commonly cornea (52%) and retina (25%). They were non-urgent in 68% of cases and no pharmacologic treatment was required for 49%. Of 397 patients that responded out of 653 patients surveyed, 4% had an unplanned additional visit to the emergency, after which two patients underwent urgent retinal surgery and one patient underwent urgent glaucoma surgery. Two patients (0.5%) had a minor change in treatment.Conclusion: As routine regular in-person visits were not possible during the COVID-19 lockdown, virtual eye assessments provided an opportunity to triage patients. Virtual assessments have the potential to reduce in-person visits, but caution must be exercised to not miss vision-threatening conditions.Keywords: virtual care, telemedicine, COVID-19, medical education

Objectives: This study aimed to evaluate the effectiveness of slow deep breathing relaxation exercise (SDBRE) in reducing patients’ pain levels during chest tube removal (CTR) post coronary artery bypass grafting (CABG) surgery. Methods: In 2019, fifty post-CABG patients were conveniently selected from a cardiac intensive care unit in Jordan’s major referral heart institute. The patients were randomly assigned to either an intervention group or a control group. A total of 25 patients were assigned into the experimental group who received slow deep breathing relaxation Exercise (SDBRE) alongside the conventional care before CTR. The remaining 25 patients constituted the control group (50%) that had CTR following conventional care. The Visual Analogue Scale (VAS) was used to measure the participants’ pain levels during three phases: before CTR (Time 1), 5-min post CTR (Time 2), and 15-min post CTR (Time 2) to compare the intervention effect between the two groups. Results: The data analysis findings for the control and intervention group of patients showed that there was a statistically significant decline in their pain level across time for both groups (H = 32.71, P

In this paper, we present an asthma risk prediction tool based on machine learning (ML). The entire tool is implemented on a smartphone as a mobile-health (m-health) application using the resources of Internet-of-Things (IoT). Peak Expiratory Flow Rates (PEFR) are commonly measured using external instruments such as peak flow meters and are well known asthama risk predictors. In this work, we find a correlation between the particulate matter (PM) found indoors and the outside weather with the PEFR. The PEFR results are classified into three categories such as ‘Green’ (Safe), ‘Yellow’ (Moderate Risk) and ‘Red’ (High Risk) conditions in comparison to the best peak flow value obtained by each individual. Convolutional neural network (CNN) architecture is used to map the relationship between the indoor PM and weather data to the PEFR values. The proposed method is compared with the state-of-the-art deep neural network (DNN) based techniques in terms of the root mean square and mean absolute error accuracy measures. These performance measures are better for the proposed method than other methods discussed in the literature. The entire setup is implemented on a smartphone as an app. An IoT system including a Raspberry Pi is used to collect the input data. This assistive tool can be a cost-effective tool for predicting the risk of asthma attacks.

Direction-of-arrival (DOA) estimation is a fundamental technique in array signal processing due to its wide applications in beamforming, speech enhancement and many other assistive speech processing technologies. In this paper, we devise a novel DOA technique based on randomized singular value decomposition (RSVD) to improve the performance of non-uniform non-linear microphone arrays (NUNLA). The accurate and efficient singular value decomposition of large data matrices is computationally challenging, and randomization provides an effective tool for performing matrix approximation, therefore, the developed DOA estimation utilizes a modified dictionary-based RSVD method for localizing single speech sources under low signal-to-noise ratios (SNR). Unlike previous methods developed for uniform linear microphone arrays, the proposed approach with L-shaped three microphone setup has no ‘left-right’ ambiguity. We present the performance of our proposed method in comparison to other techniques. The demonstrated experiments shows at-least 20% performance improvement using simulated data and 25% performance improvement using real data when compared with similar DoA estimation techniques for NUNLA. The proposed method exploits frame-based online time delay of arrival (TDOA) measurements which facilitates the proposed algorithm to run on real-time devices. We also show an efficient real-time implementation of the proposed method on a Pixel 3 Android smartphone using its built-in three microphones for hearing aid applications.

Abstract Aim Discharge planning (DP) guides patients' transition to out‐hospital services. This systematic review investigates nurses' knowledge, perception and practices of discharge planning. Design We conducted a systematic review following PRISMA guidelines. Methods Search terms were used to identify research studies published between 1990–2020 across six databases: CINAHL, MEDLINE, PubMed, Complete Academic search, Science Direct and Google Scholar. A total of nine studies met the inclusion criteria. Results Nine articles revealed nurses' knowledge, perspectives and practices of discharge planning. Obstacles included low‐level knowledge of patients' activities and discharge; inability to define DP; debates over the timing of beginning, implementing and preparing discharge; patients and their family members' negative attitudes towards DP; and perceiving DP as excessive, time‐consuming paperwork for which the physician is responsible. Better time management during work improves DP in acute care settings.

Alert signals like sirens and home alarms are important as they warn people of precarious situations. This work presents the detection and separation of these acoustically important alert signals, not to be attenuated as noise, to assist the hearing impaired listeners. The proposed method is based on convolutional neural network (CNN) and convolutional-recurrent neural network (CRNN). The developed method consists of two blocks, the detector block, and the separator block. The entire setup is integrated with speech enhancement (SE) algorithms, and before the compression stage, used in a hearing aid device (HAD) signal processing pipeline. The detector recognizes the presence of alert signal in various noisy environments. The separator block separates the alert signal from the mixture of noisy signals before passing it through SE to ensure minimal or no attenuation of the alert signal. It is implemented on a smartphone as an application that seamlessly works with HADs in real-time. This smartphone assistive setup allows the hearing aid users to know the presence of the alert sounds even when these are out of sight. The algorithm is computationally efficient with a low processing delay. The key contribution of this paper includes the development and integration of alert signal separator block with SE and the realization of the entire setup on a smartphone in real-time. The proposed method is compared with several state-of-the-art techniques through objective measures in various noisy conditions. The experimental analysis demonstrates the effectiveness and practical usefulness of the developed setup in real-world noisy scenarios.

In this article, we present a real-time convolutional neural network (CNN)-based Speech source localization (SSL) algorithm that is robust to realistic background acoustic conditions (noise and reverberation). We have implemented and tested the proposed method on a prototype (Raspberry Pi) for real-time operation. We have used the combination of the imaginary-real coefficients of the short-time Fourier transform (STFT) and Spectral Flux (SF) with delay-and-sum (DAS) beamforming as the input feature. We have trained the CNN model using noisy speech recordings collected from different rooms and inference on an unseen room. We provide quantitative comparison with five other previously published SSL algorithms under several realistic noisy conditions, and show significant improvements by incorporating the Spectral Flux (SF) with beamforming as an additional feature to learn temporal variation in speech spectra. We perform real-time inferencing of our CNN model on the prototyped platform with low latency (21 milliseconds (ms) per frame with a frame length of 30 ms) and high accuracy (i.e. 89.68% under Babble noise condition at 5dB SNR). Lastly, we provide a detailed explanation of real-time implementation and on-device performance (including peak power consumption metrics) that sets this work apart from previously published works. This work has several notable implications for improving the audio-processing algorithms for portable battery-operated Smart loudspeakers and hearing improvement (HI) devices.

Objective: Perioperative poor glycemic control in diabetic patients undergoing Coronary Artery Bypass Graft (CABG) surgery has been associated with infectious complications, particularly surgical site infections that are linked with adverse health surgical outcomes. The purpose of this study was to investigate the effect of two different intraoperative glycemic control protocol, tight and conventional, on thirty-day postoperative surgical site infection (SSI) rates among diabetic patients undergoing CABG surgery. Design: A randomized controlled trial (RCT) design was employed in the study, with a convenience sample of 144 adult patients who were scheduled to undergo coronary artery bypass grafting surgery. Setting: A main referral heart institute in Amman, Jordan. Participants: Subjects were randomly assigned to either the tight glycemic control group (n = 72), which maintained an intraoperative blood glucose level of 110–149 mg/dl via continuous intravenous insulin infusion, or the conventional glycemic control group (n = 72), which maintained an intraoperative blood glucose level of 150–180 mg/dl via continuous intravenous insulin infusion. The postoperative SSIs among both groups were evaluated and compared by independent blinded physicians. Results: The primary findings of this study indicated no statistically significant difference between the two treatment groups in terms of SSI rates and their potential adverse surgical outcomes (p = 0.512). Conclusion: Nurses should consider the glycemic stability and glycemic control approach to minimize adverse surgical outcomes post CABG surgery. Healthcare providers should also carefully consider diabetic patients who have undergone CABG surgery and are at risk of developing postoperative SSIs. Clinicaltrials.gov identifier: NCT04451655 was retrospectively registered in 30/06/2020.

The emergence of mutant K13-mediated artemisinin (ART) resistance in Plasmodium falciparum malaria parasites has led to widespread treatment failures across Southeast Asia. In Africa, K13-propeller genotyping confirms the emergence of the R561H mutation in Rwanda and highlights the continuing dominance of wild-type K13 elsewhere. Using gene editing, we show that R561H, along with C580Y and M579I, confer elevated in vitro ART resistance in some African strains, contrasting with minimal changes in ART susceptibility in others. C580Y and M579I cause substantial fitness costs, which may slow their dissemination in high-transmission settings, in contrast with R561H that in African 3D7 parasites is fitness neutral. In Cambodia, K13 genotyping highlights the increasing spatio-temporal dominance of C580Y. Editing multiple K13 mutations into a panel of Southeast Asian strains reveals that only the R561H variant yields ART resistance comparable to C580Y. In Asian Dd2 parasites C580Y shows no fitness cost, in contrast with most other K13 mutations tested, including R561H. Editing of point mutations in ferredoxin or mdr2, earlier associated with resistance, has no impact on ART susceptibility or parasite fitness. These data underline the complex interplay between K13 mutations, parasite survival, growth and genetic background in contributing to the spread of ART resistance.

This paper presents a Speech Enhancement (SE) technique based on multi-objective learning convolutional neural network to improve the overall quality of speech perceived by Hearing Aid (HA) users. The proposed method is implemented on a smartphone as an application that performs real-time SE. This arrangement works as an assistive tool to HA. A multi-objective learning architecture including primary and secondary features uses a mapping-based convolutional neural network (CNN) model to remove noise from a noisy speech spectrum. The algorithm is computationally fast and has a low processing delay which enables it to operate seamlessly on a smartphone. The steps and the detailed analysis of real-time implementation are discussed. The proposed method is compared with existing conventional and neural network-based SE techniques through speech quality and intelligibility metrics in various noisy speech conditions. The key contribution of this paper includes the realization of CNN SE model on a smartphone processor that works seamlessly with HA. The experimental results demonstrate significant improvements over the state-of-the-art techniques and reflect the usability of the developed SE application in noisy environments.

In this paper, we present a real-time convolutional neural network (CNN) based approach for speech source localization (SSL) using Android-based smartphone and its two built-in microphones under noisy conditions. We propose a new input feature set - using real and imaginary parts of the short-time Fourier transform (STFT) for CNN-based SSL. We use simulated noisy data from popular datasets that was augmented with few hours of real recordings collected on smartphones to train our CNN model. We compare the proposed method to recent CNN-based SSL methods that are trained on our dataset and show that our CNN-based SSL method offers higher accuracy on identical test datasets. Another unique aspect of this work is that we perform real-time inferencing of our CNN model on an Android smartphone with low latency (14 milliseconds(ms) for single frame-based estimation, 180 ms for multi frame-based estimation and frame length is 20 ms for both cases) and high accuracy (i.e. 88.83% at 0dB SNR). We show that our CNN model is rather robust to smartphone hardware mismatch, hence we may not need to retrain the entire model again for use with different smartphones. The proposed application provides a `visual' indication of the direction of a talker on the screen of Android smartphones for improving the hearing of people with hearing disorders.

Objectives: This study aims to determine if patients with acute myocardial infarction differ in illness perception and secondary prevention outcomes depending on the treatment they received. Methods: A repeated measures design was used to compare patients with acute myocardial infarction receiving three different treatment modalities: ST-elevation myocardial infarction treated by primary percutaneous coronary intervention, ST-elevation myocardial infarction treated by thrombolytic therapy, and non ST-elevation myocardial infarction treated by medication. A convenient sampling technique was used to recruit 206 patients with acute myocardial infarction who agreed to participate in the current study. Patients' illness perception, physical activity, and demographical and clinical data were collected during hospital admission and again at 6 months. Results: A total of 186 patients completed the study. Results showed that the primary percutaneous coronary intervention group perceived their illness as acute rather than chronic (P = 0.034) and has lower personal control (P = 0.032), higher treatment control (P = 0.025), and higher perception of illness coherence (P = 0.022) compared with patients receiving thrombolytic therapy and treated after non-ST segment infarction. Moreover, they report low control of their blood pressure (P = 0.013) and less physical activity (P = 0.001). Conclusion: The results of this study revealed that patients' treated with primary percutaneous coronary intervention had negative illness perception and limited behavioral changes 6 months after hospitalization in comparison with other treatment modalities such as percutaneous coronary intervention and thrombolytic treatment. Further research is recommended to confirm this association with longer follow-up study and among different cultures.

The mapping market set up and analysis for food process cooperatives, small and little organizations within the West Bank is a vital prep for Palestinian enterprises within the Palestine to develop the family firms’ performance. The most objective of this paper is to assist the family sector enterprises in causative to the growth of small-scale food organizations. The paper expectations purpose to supply the micro and small businesses (MS) with the required analysis in building their capability in terms of structure, development promotion and market access. The targeted population was family cooperatives and organizations that work have in food process within the geographic region. (MS) represents 14 % of all the Palestinian corporations, whereas the targeted sample was seven organizations that investigated through empirical observation exploitation the qualitative analysis methodology. The finding show that there are many shortages in terms of methods inventions and implementations, promoting systems match with procedures, policies, and diversifications with internal weaknesses and external threats that effects the corporations skills to survive, sustain and sustaining the performance.

Objective The aim of this study was to assess the baseline clinical characteristics, coronary angiographic features, and adverse cardiovascular events during hospitalization and at 1 year of follow-up in obese patients compared with overweight and normal/underweight patients. Methods A prospective, multicenter study of consecutive patients undergoing percutaneous coronary intervention was performed. Results Of 2425 enrolled patients, 699 (28.8%) were obese, 1178 (48.6%) were overweight, and 548 (22.6%) were normal/underweight. Obese patients were more likely to be female and to have a higher prevalence of diabetes, hypertension, hypercholesterolemia, or previous percutaneous coronary intervention. Acute coronary syndrome was the indication for percutaneous coronary intervention in 77.0% of obese, 76.4% of overweight, and 77.4% of normal/underweight patients. No significant differences in the prevalence of multi-vessel coronary artery disease or multi-vessel percutaneous coronary intervention were found among the three groups. Additionally, no significant differences were found in stent thrombosis, readmission bleeding rates, or cardiac mortality among the three groups during hospitalization, at 1 month, and at 1 year. Conclusion The major adverse cardiovascular event rate was the same among the three groups throughout the study period. Accordingly, body mass index is considered a weak risk factor for cardiovascular comorbidities in Arab Jordanian patients.

Cloud computing is an emerging technology paradigm that migrates current technological and computing concepts into utility-like solutions similar to electricity and water systems. Clouds bring out a wide range of benefits including configurable computing resources, economic savings, and service flexibility. However, security and privacy concerns are shown to be the primary obstacles to a wide adoption of clouds. The new concepts that clouds introduce, such as multi-tenancy, resource sharing and outsourcing, create new challenges to the security community. Addressing these challenges requires, in addition to the ability to cultivate and tune the security measures developed for traditional computing systems, proposing new security policies, models, and protocols to address the unique cloud security challenges. In this work, we provide a comprehensive study of cloud computing security and privacy concerns. We identify cloud vulnerabilities, classify known security threats and attacks, and present the state-of-the-art practices to control the vulnerabilities, neutralize the threats, and calibrate the attacks. Additionally, we investigate and identify the limitations of the current solutions and provide insights of the future security perspectives. Finally, we provide a cloud security framework in which we present the various lines of defense and identify the dependency levels among them. We identify 28 cloud security threats which we classify into five categories. We also present nine general cloud attacks along with various attack incidents, and provide effectiveness analysis of the proposed countermeasures.

Summary: Background: Few studies have been conducted in Jordan related to health care associated bloodstream infections (HCABSIs). This study aims to examine epidemiology of HCABSIs among hospitalized adult patients in Jordan. Methods: A cohort study (N = 570; 445 confirmed HCABSIs, 125 uninfected patients) with a nested 1:1 matched case–control design (n = 125 in each group) was used based on data from one large referral hospital in Jordan over a period of 5 years. HCABSI cases were determined based on confirmed positive blood culture after 48 h of admission. The case–control analyses (n = 250 per group) matched on gender, age, same admission month and unit. Results: The overall incidence and mortality rates were 8.1 and 5.8 per 1000 admissions, respectively. Four-variable and three-variable multivariate models were proposed to explain the risk of HCABSIs in the matched analyses .The four-variable model consists of blood product (OR = 24.5), invasive procedures (OR = 4.3), renal failure (OR = 9.2), and presence of other infections (OR = 21.6). The three-variable model consists of recipient of blood product (OR = 19.7), invasive procedures (OR = 4.5), and renal failure (OR = 9.4). Conclusions: This study is a pioneer study that examined risk factors, the associated HCABSIs in Jordan. Results from this study can be used to influence infection control plans in Jordan. Keywords: Bloodstream infections, Risk factors, Healthcare acquired, Bloodstream infections, Healthcare costs, Charges, Length of stay

Underwater acoustic wireless sensor networks (UAWSNs) have many applications across various civilian and military domains. However, they suffer from the limited available bandwidth of acoustic signals and harsh underwater conditions. In this work, we present an Orthogonal Frequency Division Multiple Access (OFDMA)-based Media Access Control (MAC) protocol that is configurable to suit the operating requirements of the underwater sensor network. The protocol has three modes of operation, namely random, equal opportunity and energy-conscious modes of operation. Our MAC design approach exploits the multi-path characteristics of a fading acoustic channel to convert it into parallel independent acoustic sub-channels that undergo flat fading. Communication between node pairs within the network is done using subsets of these sub-channels, depending on the configurations of the active mode of operation. Thus, the available limited bandwidth gets fully utilized while completely avoiding interference. We derive the mathematical model for optimal power loading and subcarrier selection, which is used as basis for all modes of operation of the protocol. We also conduct many simulation experiments to evaluate and compare our protocol with other Code Division Multiple Access (CDMA)-based MAC protocols.

The development of Djelli cattle breeding in Niger remains a challenge due to prejudices about its productivity. The particularities of its production systems have not been sufficiently developed. The aim of this study is to develop a typology of local Djelli cattle production systems in six localities in the communes of Gotheye, Sinder and Say in Niger. A survey was carried out among 150 breeders. Discriminant factor analysis (DFA) and hierarchical ascending classification (HAC) were used to identify three types of production system: The first type is made up of transhumant herders whose main activity is livestock rearing and who belong to the Peulh ethnic group. This type is characterized by large herds (27 head). They have 3 breeding cows and 4 suckling cows for 16 breeding females. The second type is made up of sedentary breeders. Their main activities are fishing and trading, and they belong to the Sonrai socio-cultural ethnic group. Their herds are modest (17 head), comprising 2 breeding and 5 suckling cows for 11 breeding females. The third type is made up of semi-transhumant herders. Their main activity is farming, and they belong to the Sonrai socio-cultural ethnic group, with an equally modest herd size (18 head). The number of breeding cows is similar to that of the sedentary herders (2 head). However, the number of sucklers is reduced to 3 head for 10 breeding females. The daily intake of bran concentrate per suckling female is the highest (5 kg of bran concentrate per day). The ratio of lactating females to females of breeding age was r =0.45 in the sedentary group, compared with r =0.30 in the semi-transhumant group and r = 0.25 in the transhumant group. The results also showed that the rearing environment is dominated by a diet of rice straw (39%) and rice bran (49%). The most frequently vaccinated disease on the farms was pasteurellosis (87.3%), and the highest deworming rate was 40%. The environment also revealed a variation in lactating females for the riparian localities of Say and Yelwani (5.4[+ or -]2.6b and 6.0[+ or -]2.8b respectively), which are very significantly different (P Key words : Production systems, herd size, sedentary breeders, lactating female Le développement de l'élevage du bovin de race Djelli du Niger reste un défi à cause des préjugés sur sa productivité. Les particularités de ses systèmes de productions n'ont pas été suffisamment développées. L'objectif de cette étude est d'élaborer une typologie des systèmes de productions du bovin local de race Djelli dans six localités des communes de Gothèye, Sinder et Say au Niger. Une enquête a été menée auprès de 150 éleveurs. Une analyse factorielle discriminante (AFD) et une classification ascendante hiérarchique (CAH) ont permis d'identifier trois types de systèmes de productions : Le premier type est constitué d'éleveurs transhumants ayant comme activité principale l'élevage et appartenant à l'ethnie Peulh. Ce type est caractérisé par des grands effectifs de troupeau (27 têtes). Ils ont 3 géniteurs et 4 allaitantes pour 16 femelles reproductrices. Le deuxième type est constitué d'éleveurs sédentaires. Les éleveurs de ce type ont comme activité principale la pêche et le commerce et appartiennent à l'ethnie socioculturelle Sonrai. Ils ont des modestes effectifs de troupeau (17 têtes) constitués de 2 géniteurs et 5 allaitantes pour 11 femelles reproductrices. Le troisième type est constitué des éleveurs semi-transhumants. Ils ont comme activité principale agriculture et sont de l'ethnie socioculturelle Sonrai avec des effectifs de troupeau également modestes (18 têtes). L'effectif des géniteurs est similaire à celui des Sédentaires (2 têtes). Cependant, l'effectif des allaitants est réduit à 3 têtes pour 10 femelles reproductrices. L'Apport journalier en concentré de son par allaitante est le plus élevé (5 kg de concentré de son par jour). Le ratio femelle allaitantes sur femelles en âge de reproduction est r =0,45 chez les sédentaires contre r =0,30 chez les semi-transhumants et r = 0,25 chez les transhumants. Les résultats ont aussi montré que l'environnement d'élevage est dominé par une alimentation en paille de riz (39%) et de Son de riz (49%). Dans les élevages, la maladie la plus vaccinée est la pasteurellose (87,3%) et le taux de vermification le plus élevé est de 40%. L'environnement a aussi révélé une variation des femelles allaitantes pour les localités riveraines de Say et Yelwani (respectivement 5,4[+ or -]2,6b et 6,0[+ or -]2,8b) qui sont très significativement différente (P Mots-clés : Systèmes de productions, effectifs de troupeau, éleveurs sédentaires, femelles allaitantes, INTRODUCTION L'élevage est un secteur producteur de richesses dans le Sahel et la Corne de l'Afrique. Les résultats des données collectées dans seize (16) pays africains indiquent que la contribution [...]

Travertine deposits in Deir Alla, Suwayma, and Az Zara areas were investigated. Mineralogy, geochemistry, stable isotopes and age dating indicate the presence of low-Mg calcite, with minor quartz components. The variable isotope (δ13C and δ18O) signatures indicate dependence on water temperature and water/rock isotopic exchange. In contrast, the high δ13C values in some travertine samples reflect 12CO2 degassing processes, increased input of 13C-enriched groundwater, and the presence of surface and groundwater hydrological systems. The high δ18O values may be attributed to evaporation effects and low water temperature during the formation of localized travertine. The age of travertine is the Late Pleistocene.

ABSTRACT Background Objective Design Methods Results Conclusions Sleep is a fundamental prerequisite for physical and mental health. Poor quality of sleep is common among post‐cardiac surgery patients and leads to serious health conditions.To conduct a systematic review that investigates the effectiveness of eye masks, earplugs and deep‐breathing exercise on sleep quality among post‐cardiac surgery patients.A systematic review of interventional studies was established to meet the PRISMA guidelines.PRISMA guidelines were used to assess the findings of 11 selected studies that met the inclusion criteria, published between 2007 and 2023 across four databases: CINAHL, JDNR, MEDLINE and PubMed. The search was conducted on 23 November 2023.The 11 most eligible studies were analysed. All of them were interventional, encompassing a total of 787 participants. Randomised controlled trials were the most common design. Interventions included eye masks, earplugs and deep‐breathing exercises. The Richards‐Campbell Sleep Questionnaire was the most used assessment scale. Most of the reviewed studies found that the use of non‐pharmacological interventions (eye masks, earplugs and deep‐breathing exercise) significantly improves the quality of sleep. These interventions were also found to have potentially positive effects on reducing pain and delirium experienced by patients after undergoing cardiac surgery.Non‐pharmacological interventions (eye masks, earplugs and deep‐breathing exercise) were found to be cost‐effective interventions that could be easily applied in the clinical setting and are effective in improving the quality of sleep among patients after cardiac surgery. [ABSTRACT FROM AUTHOR]