ANALISIS TPM MESIN PEMOTONG TEKSTIL OTOMATIS CNC-CUTTER KURIS DENGAN METODE OEE DI PT. YIFAN JAYA

Mesin CNC-Cutter C 3030 menjadi salah satu mesin pemotong otomatis tekstil yang cukup diandalkan dalam meningkatkan efisiensi waktu produksi. Mesin ini berfungsi sebagai mesin pemotong otomatis kain atau tekstil dengan menggunakan program sehingga desain yang sudah dibentuk bisa langsung input ke mesin dan mesin akan memotong kain sesuai desain. Tujuan penelitian ini dimaksudkan untuk mengetahui nilai efektivitas produksi pada mesin CNC-Cutter C 3030, nilai efektivitas meliputi availability, performance rate dan quality rate, meode penelitian yang dilakukan dengan metode total productive maintenance, setelah dilakukan penelitian didapatkan Nilai availability sebesar 86,26% pada setiap minggu, Nilai performance rate, quality Rate mesin menunjukan hasil sebesar 96,8%, dan hasil perhitungan Overall Equipment Effectiveness adalah 80,83%.

Reducing infantile anemia: insight on patterns of process and outcome indicators by ethnicity and socioeconomic class during a 10-year intervention program and 5 years after

Background In 2005, Clalit Health Services (CHS), the largest health maintenance organization in Israel, initiated an intervention program aimed at reducing the prevalence rate of infantile anemia (IA). This study evaluated the progress made during the intervention (2005–2014) and its yield 5 years after it ended (2019). Methods The CHS database was retrospectively reviewed twice yearly from 2005 to 2014 for repetitive samples of children aged 9 to 18 months regarding the previous half-year interval, and a single sample in 2019. Data were collected on gender, ethnicity (Jewish/non-Jewish), socioeconomic class (SEC; low/intermediate/high), hemoglobin testing (yes/no), and hemoglobin level (if tested). Excluded were infants with documented or suspected hemoglobinopathy. Results At study initiation, the rate of performance of hemoglobin testing was 54.7%, and the IA prevalence rate was 7.8%. The performance rate was lower in the Jewish than the non-Jewish subpopulation. The low-SEC subpopulation had a similar hemoglobin testing rate to the high-SEC subpopulation but double the IA prevalence rate. Overall, by the end of the intervention (2014), the performance rate increased to 87.5%, and the AI prevalence rate decreased to 3.4%. In 2019, there was little change in the performance rate from the end of the intervention (88%) and the IA prevalence was further reduced to 2.7%. The non-Jewish and low-SEC subpopulations showed the most improvement which was maintained and even bettered 5 years after the intervention ended. Conclusions The 10-year IA intervention program introduced by CHS in 2005 led to a reduction in IA prevalence rate to about 3.5% in all sub-populations evaluated. By program end, the results in the weaker subpopulations, which had the highest prevalence of IA at baseline, were not inferior to those in the stronger subpopulations. We recommended to the Israel Ministry of Health to adopt the intervention countrywide, and we challenge other countries to consider similar interventions.

Interactive assistant tool for the evaluation of kinematic patterns and EMG signals in patients with a forearm injury

Subjective feelings feedbacks are commonly employed by a patient during forearm rehabilitation therapy without real-time data, leading to suboptimal recovery results in some patients. Technological innovations in the field of assisted rehabilitation have enabled the evolution of real-time monitoring systems. In this paper, interactive assistant development is presented as the interface to define the relationship between the kinematics patterns and the electromyographic signals during the forearm rehabilitation routine. Leap Motion (LM) and Shimmer3 EMG sensors read the routine behavior by following the movements that appear on the software. Real-time targets are programmed to lead the necessary forearm movements that the therapist sets to determine the recovery progress. The integration of software and hardware shows a dataset basis on interaction variables such as arm velocity, arm position, performance rate, and electrical muscle pulse. The results obtained from tests show that the system works effectively within a range of movement of 9 to 88 degrees in rotation about the axes, and velocities under 190 mm/s show stable movement representation on software. Finally, the outcomes ranges show an alternative tool to evaluate patients with a forearm injury.

Artificial intelligence in constructing personalized and accurate feedback systems for students

Artificial Intelligence (AI) systems have evolved with digital learning developments to provide thriving soft groups with digital opportunities in response to feedback. When it comes to learning environments, educators’ training feedback is often used as a response recourse. Through the use of final evaluations, students receive feedback that improves their education abilities. To improve academic achievement and explore knowledge in the learning process, this section provides an AI-assisted personalized feedback system (AI-PFS). An individualized feedback system is implemented to learn more about the student’s lack of academic experience interactivity and different collaboration behaviors. According to their benchmark, PFS aims to establish a personalized and reliable feedback process for each class based on their collaborative process and learn analytics modules. It has been proposed to use multi-objective implementations to evaluate students regarding the learning results and teaching methods. With different series of questions sessions for students, AI-PFS has been designed, and the findings showed that it greatly enhances the performance rate of 95.32% with personalized and reasonable predictive.

Prediction the Performance Rate of Chain Type Trenching Machine

The mathematical analysis for estimating the performance rate "RP" of chain-type trenching machine was studied. The mathematical analysis ended with an equation for this type. This mathematical equation was checked under different operating conditions. The practical study of the performance rate showed that the deviation of the theoretical performance rate from the actual performance rate ranged from -3.4 to +2% only for the 150.7 cm and 120.7 cm trench depth respectively. The machine field efficiency ranged from 46.7 to 57% for the 150.7 cm and 120.7 cm depth respectively. It also showed an increase in machine field efficiency by decreasing the trench depth.

Impacts of various shaped Cu-nanomaterial on the heat transfer over a bilateral stretchable surface: Numerical investigation

The heat transport in the nanofluids attained much interest of the researchers and engineers due to broad uses in medical sciences, paint industries, aerodynamics, wheel alignment and manufacturing of aircraft parts. Therefore, keeping in mind the paramount significance of the heat transfer, the study of Cu-nanomaterials based nanofluid is conducted. The governing nanofluid model transformed in dimensionless version via similarity transformations. For numerical simulation of the dynamics of Cu-H2O, RK technique with shooting algorithm is employed and presented behavior of the fluid motion, temperature, wall shear stresses and local thermal performance rate via graphical aid. It is noted that the heat transfer augmented promptly by increasing [Formula: see text] and volumetric fraction of Cu nanomaterial. Further, graphical and tabular comparison is also provided under certain assumptions which authenticate the study.

Performance Improvement on Some Closed Loop Plastic Recycling Plants

This research focuses on pertinent parameters to improve the performance of four plastic recycling plants. The method used in this work is the overall equipment effectiveness (OEE) analysis. After assessing data collected and comparing calculated values with global accepted standards, there was need to optimize the OEE value of 74.40% for plant P48mold which was below the universally accepted value of 85% and for plant P72mold which is exactly 85%. Optimization toolbox is used to select values for cycle time ranging between 0.75min and 0.73min to improve the calculated value of 74.40% for plant P48mold and 85% for plant P72mold. Optimizing the OEE values significantly increases production by making the operation cost-effective. The OEE value was obtained by multiplying the three factors of availability rate, performance rate and quality rate and optimized values of 87% for P48 mould and 115% for P72 mould were obtained respectively. The cycle time was used to optimize the performance of both plants, which improved the OEE values. These values proved that change in cycle time can improve OEE. A Pareto principle 80/20 rule was also used to proactively check the effects of the planned and unplanned downtime. Keywords: Throughput, Efficiency, Overall Equipment Effectiveness, Cycle time. Cite the Article: Davids S. O., Sodiki J. I., and Isaac O. E. (2021) Performance Improvement on Some Closed Loop Plastic Recycling Plants. Journal of Newviews in Engineering and Technology (JNET),

Trajectories of performance change indicate multiple dissociable links between working memory and fluid intelligence

Many areas of psychology assume that performance on tasks of interest is stable through time. Here, using time-sensitive modeling of working memory task performance, we show not only was this assumption incorrect, but that certain components of the performance trajectory (e.g., final task performance; rate of change) were independently predictive of fluid intelligence. This fact has clear implications for theoretical frameworks linking working memory and fluid intelligence, and beyond.

Are Semantic Annotators Able to Extract Relevant Complexity-Related Concepts from Clinical Notes?

Clinical decision support systems (CDSSs) implementing cancer clinical practice guidelines (CPGs) have the potential to improve the compliance of decisions made by multidisciplinary tumor boards (MTB) with CPGs. However, guideline-based CDSSs do not cover complex cases and need time for discussion. We propose to learn how to predict complex cancer cases prior to MTBs from breast cancer patient summaries (BCPSs) resuming clinical notes. BCPSs being unstructured natural language textual documents, we implemented four semantic annotators (ECMT, SIFR, cTAKES, and MetaMap) to assess whether complexity-related concepts could be extracted from clinical notes. On a sample of 24 BCPSs covering 35 complexity reasons, ECMT and MetaMap were the most efficient systems with a performance rate of 60% (21/35) and 49% (17/35), respectively. When using the four annotators in sequence, 69% of complexity reasons were extracted (24/35 reasons).