Assessment of outcomes associated with a Moodle-based lesson design for a research course in pharmacy education: An experimental pilot process validation study

Background: This study aims to evaluate the effectiveness of an online Moodle-based lesson for pharmacy students developed and designed for a research course focused on different methodologies, study variables, and research process applications. Methods: An experimental research methodology was used to determine the effectiveness of the Moodle-based lesson. All fourth term Pharm.D. students were required to complete and provide self-reflection reports. The outcome variables were cluster-based completion time, earned score, subjective feedback on contents, gender differences, and performance. Mean values were used to conduct statistical analysis, and a logistic regression model was applied to identify the significance of independent variables. Results: A total of 35 students completed the Moodle-based lesson. The mean earned score for the module was 81.0%, with an average completion time of 28.5 (range 26.8-30.1) hours. Females showed completion in less time compared to males. The reported knowledge and understanding showed a significant (p<0.001) pre-post increase in mean percentages in all three clusters. The intragroup pool analysis also reported significant (p<0.001) differences among gender and performance. The general feedback was limited to technical difficulties and self-reflection questions. Conclusions: The findings of this study showed that the online Moodle-based lesson module for a research course is an effective and validated tool to improve the knowledge and understanding of pharmacy students.

Validation of the Zulfiqar Frailty Scale (ZFS): A New Tool for General Practitioners

Introduction: The early detection of frailty, a frequent transient state that can be reversible in the elderly and is responsible for significant morbidity and mortality, helps prevent complications from it. Objective: To evaluate the performance of the “ZFS” tool to screen for frailty as defined SEGA scale criteria in an ambulatory population of patients at least 65 years of age. Methods: A prospective non-interventional study conducted in Alsace for a duration of six months that included patients aged 65 and over, judged to be autonomous with an ADL > 4/6. Results: In this ambulatory population of 102 patients with an average age of 76 years, frailty, according to modified SEGA criteria grid A, had a prevalence of 19.6%. Frailty, according to the “ZFS” tool, had a prevalence of 35.0%, and all of its elements except weight loss were significantly associated with frailty. Its threshold for identifying frailty is three criteria out of six. It was rapid (average completion time: 87s), had a sensitivity of 100%, and a negative predictive value of 100%. Conclusions: The “ZFS” tool makes it possible to screen for frailty with a high level of sensitivity and a negative predictive value.

Online Makespan Scheduling with Job Migration on Uniform Machines

In the classic minimum makespan scheduling problem, we are given an input sequence of n jobs with sizes. A scheduling algorithm has to assign the jobs to m parallel machines. The objective is to minimize the makespan, which is the time it takes until all jobs are processed. In this paper, we consider online scheduling algorithms without preemption. However, we allow the online algorithm to change the assignment of up to k jobs at the end for some limited number k. For m identical machines, Albers and Hellwig (Algorithmica 79(2):598–623, 2017) give tight bounds on the competitive ratio in this model. The precise ratio depends on, and increases with, m. It lies between 4/3 and $$\approx 1.4659$$ ≈ 1.4659 . They show that $$k = O(m)$$ k = O ( m ) is sufficient to achieve this bound and no $$k = o(n)$$ k = o ( n ) can result in a better bound. We study m uniform machines, i.e., machines with different speeds, and show that this setting is strictly harder. For sufficiently large m, there is a $$\delta = \varTheta (1)$$ δ = Θ ( 1 ) such that, for m machines with only two different machine speeds, no online algorithm can achieve a competitive ratio of less than $$1.4659 + \delta $$ 1.4659 + δ with $$k = o(n)$$ k = o ( n ) . We present a new algorithm for the uniform machine setting. Depending on the speeds of the machines, our scheduling algorithm achieves a competitive ratio that lies between 4/3 and $$\approx 1.7992$$ ≈ 1.7992 with $$k = O(m)$$ k = O ( m ) . We also show that $$k = \varOmega (m)$$ k = Ω ( m ) is necessary to achieve a competitive ratio below 2. Our algorithm is based on maintaining a specific imbalance with respect to the completion times of the machines, complemented by a bicriteria approximation algorithm that minimizes the makespan and maximizes the average completion time for certain sets of machines.

Infections Musculosquelettiques De L’adulte AU Centre Hospitalier Universitaire De Brazzaville: Difficultés Diagnostiques Et De Prise En Charge

Objectif: Rapporter les difficultés diagnostiques et de prise en charge des infections musculosquelettiques. Patients et méthodes: Etude rétrospective menée dans les services de Rhumatologie et de Traumatologie/Orthopédie du CHU de Brazzaville du 1er Janvier 2017 au 30 Septembre 2020. Nous avons colligé et analysé les dossiers des patients hospitalisés pour infections musculosquelettiques. Le diagnostic d’infection musculosquelettique était retenu sur critères de Wald Vogel et al. Résultats: La fréquence hospitalière était de 1.9%. Les hommes prédominaient (54.8%) avec un sex-ratio de 1.2. L’âge moyen était de 41.7ans (extrêmes 17 et 77 ans). Treize de nos patients étaient les travailleurs indépendants , 12 sans-emploi, les salariés et les étudiants respectivement dans 9 et 8 cas. La majorité de nos patients provenait du milieu rural dans 29 cas (69.1%). Il s’agissait de 13 cas (30.9%) d’ostéomyélites/Ostéites, 12 cas (28.5%) d’arthrites infectieuses, 9 cas (21.4%) de spondylodiscites tuberculoses, 4 cas (9.6%) d’infections sur matériel d’ostéosynthèse et les myosites suppurées. Seuls 6 patients (14%) avaient réalisé une imagerie en coupes (l’IRM dans 4 cas ,la TDM dans 2 cas), avec un délai moyen de réalisation de 6,1 jours (extrêmes 4 et 10 jours) et l’échographie ostéoarticulaire dans 6 cas (14%). L’analyse bactériologique était effectuée dans 25 cas (59.5%), parmi eux, une Pyo culture dans 11 cas (44%), l’analyse du liquide articulaire dans 5 cas (20%), les hémocultures 2 cas et la biopsie osseuse dans 1 cas(4%), avec un délai moyen de réalisation de 5,8 jours (extrêmes 2 et 11 jours). La culture était positive dans 13 cas (52%). Les principaux germes isolés étaient : le streptocoque dans 5 cas (38.4%), le staphylocoque 3 cas (23.1%), l’entérobactérie et le Pseudomonas dans 2cas (15.4%), le bacteroides dans 1 cas (7.7%). Conclusion: Les infections musculosquelettiques sont une urgence diagnostique et thérapeutique et de diagnostic souvent tardif. Les principales difficultés sont le bas niveau socioéconomique et la faible accessibilité du plateau technique. Objective: This paper focuses on reporting the difficulties in diagnosing and managing musculoskeletal infections. Patients and Methods: Retrospective study was conducted in the Rheumatology and Traumatology/Orthopedics departments of the Brazzaville University Hospital from January 1st, 2017 to September 30th, 2020. The files of hospitalized patients were collected and analyzed for musculoskeletal infections. The diagnosis of musculoskeletal infection was made according to the criteria of Wald Vogel et al. Results: The hospital frequency was 1.9%. Men predominated (54.8%) with a sex ratio of 1.2. The average age was 41.7 years (range 17 and 77). Thirteen of the patients were self-employed, while 12 were unemployed. There were also 9 employees and 8 students. The majority of patients came from rural areas in 29 cases (69.1%). These were 13 cases (30.9%) of osteomyelitis/Osteitis, 12 cases (28.5%) of infectious arthritis, 9 cases (21.4%) of tuberculosis spondylodiscitis, 4 cases (9.6% ) of infections on osteosynthesis material and suppurative myositis. Only 6 patients (14%) had performed sectional imaging (MRI in 4 cases, CT in 2 cases), with an average completion time of 6.1 days (range 4 and 10 days). There were also 6 cases of ultrasound osteoarticular (14%). Bacteriological analysis was performed in 25 cases (59.5%): Pyo culture in 11 cases (44%), joint fluid analysis in 5 cases (20%), blood cultures in 2 cases and bone biopsy in 1 case (4%), with an average completion time of 5.8 days (range 2 and 11 days). Culture was positive in 13 cases (52%). The main germs isolated were streptococcus in 5 cases (38.4%), staphylococcus in 3 cases (23.1%), enterobacteria and Pseudomonas in 2 cases (15.4%), and bacteroides in 1 case ( 7.7%). Conclusion: Musculoskeletal infections are a diagnostic and therapeutic emergency which is often late in diagnosis. The main difficulties are the low socioeconomic level and the poor accessibility of the technical platform.

Optimal Workload Allocation for Edge Computing Network Using Application Prediction

By deploying edge servers on the network edge, mobile edge computing network strengthens the real-time processing ability near the end devices and releases the huge load pressure of the core network. Considering the limited computing or storage resources on the edge server side, the workload allocation among edge servers for each Internet of Things (IoT) application affects the response time of the application’s requests. Hence, when the access devices of the edge server are deployed intensively, the workload allocation becomes a key factor affecting the quality of user experience (QoE). To solve this problem, this paper proposes an edge workload allocation scheme, which uses application prediction (AP) algorithm to minimize response delay. This problem has been proved to be a NP hard problem. First, in the application prediction model, long short-term memory (LSTM) method is proposed to predict the tasks of future access devices. Second, based on the prediction results, the edge workload allocation is divided into two subproblems to solve, which are the task assignment subproblem and the resource allocation subproblem. Using historical execution data, we can solve the problem in linear time. The simulation results show that the proposed AP algorithm can effectively reduce the response delay of the device and the average completion time of the task sequence and approach the theoretical optimal allocation results.

Optimization of Pipe Production Scheduling in Line 18 Using First Come First Serve (Fcfs), Earlier Due Date (Edd), Short Process Time (Spt) Methods (Case Study: Pt Wtur)

For machine line 18, is intended to produce PVC pipe with a 5 ", 6" and 8 "with each contained item type AW and type D with a turn of the cycle a certain schedule. From the turn of this cycle, there are a couple of the impact is too long good finish duration of vacant stock in the Warehouse Production (GHP) pipe. The impact of the long duration of the good stock of empty finish unachieved impact on consumer demand (in this case the agent). By not reaching the consumer demand impact on service level SCM & Delivery. The purpose of this study to determine the optimal level of machine line 18 so that the products PVC size 5 ", 6" and 8 "are produced to meet the buffer stock storage. Thus, this research is expected to make a production schedule to meet the stock warehouse to the fullest, can meet the needs of consumers (agent) to the maximum but not over. From the data and data analysis using the First Come First Serve (FCFS), Earlier Due Date (EDD), Short Process Time (SPT), the SPT method is the most optimal method by having an average completion time of 4,10 days with 36,75% utility, then the average number of jobs 2,77 work, and delays the average occupation is 0,42 days.

A Qualitative Examination Detailing Medical Student Experiences of a Novel Competency-Based Neuroanatomy eLearning Intervention Designed to Bridge a Gap Within an Integrated Medical Curriculum

Background: A 1-year time-gap between first- and second-year neuroanatomy courses was created at our institution as a result of restructuring the curriculum from systems-based to an integrated format. Additionally, neuroanatomy hours decreased significantly (48.8%) when transitioning to an integrated curriculum, similar to other medical schools. Competency-based eLearning in medical education has shown promising results with decreasing overall learning time and improving accuracy. To date, competency-based eLearning has not yet been explored in neuroanatomy education. Objective: The purpose of this study is to develop and assess a novel competency-based neuroanatomy eLearning intervention for second-year medical students designed to bridge a 1-year time-gap, without adding significant instructional hours, in an integrated curriculum. Methods: A competency-based eLearning intervention encompassing the major tracts, brainstem anatomy, and an interactive case featuring a simulated patient experience was developed in the Articulate Storyline® platform. Student usage data, single-session course evaluations, and a focus group were used to evaluate the module’s effectiveness. Results: Student usage data showed an average completion time of M = 2:59:25 hours which fit within the scheduled 3-hour timeframe. Students rated the module’s overall effectiveness as M = 3.65 (out of 4) on a single-session evaluation. A focus group provided qualitative feedback suggesting improvements to the eLearning module in the domains of content, mechanics, and timing. Conclusion: A competency-based neuroanatomy eLearning intervention shows promising initial results to bridge a 1-year educational gap within an integrated curriculum. Overall, students described this educational tool as helpful and outlined ways in which to improve this resource.

Application and Storage-Aware Data Placement and Job Scheduling for Hadoop Clusters

As one of the most popular frameworks for large-scale analytics processing, Hadoop is facing two challenges: both applications and storage devices become heterogeneous. However, existing data placement and job scheduling schemes pay little attention to such heterogeneity of either application I/O requirements or I/O device capability, thus can greatly degrade system efficiencies. In this paper, we propose ASPS, an Application and Storage-aware data Placement and job Scheduling approach for Hadoop clusters. The idea is to place application data and schedule application tasks considering both application I/O requirements and storage device characteristics. Specifically, ASPS first introduces novel metrics to quantify I/O requirements of applications. Then, based on the quantification, ASPS places data of different applications to the preferred storage devices. Finally, ASPS tries to launch jobs with high I/O requirements on the nodes with the same type of faster devices to improve system efficiency. We have implemented ASPS in Hadoop framework. Experimental results show that ASPS can reduce the completion time of a single application by up to 36% and the average completion time of six concurrent applications by 27%, compared to existing data placement policies and job scheduling approaches.

Single-machine Integrated Production Preventive Maintenance Scheduling: A Simheuristic Approach

Preventive maintenance (PM) planning becomes a crucial issue in the real world of the manufacturing process. It is important in the manufacturing industry to maintain the optimum level of production and minimize its investments. Thus, this paper focuses on multiple jobs with a single production line by considering stochastic machine breakdown time. The aim of this paper is to propose a good integration of production and PM schedule that will minimize total completion time. In this study, a hybrid method, which is a genetic algorithm (GA), is used with the Monte Carlo simulation (MCS) technique to deal with the uncertain behavior of machine breakdown time. A deterministic model is adopted and tested under different levels of complexity. Its performance is evaluated based on the value of average completion time. The result clearly shows that the proposed integrated production with PM schedule can reduce the average completion time by 11.68% compared to the production scheduling with machine breakdown time.

Studi Perbandingan Performa Algoritma Penjadwalan untuk Real Time Data Twitter pada Hadoop

Hadoop merupakan sebuah framework software yang bersifat open source dan berbasis java. Hadoop terdiri atas dua komponen utama, yaitu MapReduce dan Hadoop Distributed File System (HDFS). MapReduce terdiri atas Map dan Reduce yang digunakan untuk pemrosesan data, sementara HDFS adalah tempat atau direktori dimana data hadoop dapat disimpan. Dalam menjalankan job yang tidak jarang terdapat keragaman karakteristik eksekusinya, diperlukan job scheduler yang tepat. Terdapat banyak job scheduler yang dapat di pilih supaya sesuai dengan karakteristik job. Fair Scheduler menggunakan salah satu scheduler dimana prisnsipnya memastikan suatu jobs akan mendapatkan resource yang sama dengan jobs yang lain, dengan tujuan meningkatkan performa dari segi Average Completion Time. Hadoop Fair Sojourn Protocol Scheduler adalah sebuah algoritma scheduling dalam Hadoop yang dapat melakukan scheduling berdasarkan ukuran jobs yang diberikan. Penelitian ini bertujuan untuk melihat perbandingan performa kedua scheduler tersebut untuk karakteristik data twitter. Hasil pengujian menunjukan Hadoop Fair Sojourn Protocol Scheduler memiliki performansi lebih baik dibandingkan Fair Scheduler baik dari penanganan average completion time sebesar 9,31% dan job throughput sebesar 23,46%. Kemudian untuk Fair Scheduler unggul dalam parameter task fail rate sebesar 23,98%.