Industrial Robotics In The Lean Enterprise : A Case Study In Semi-Conductor Company

Industrial robotics replaced human workers in almost all field due to their abilities in multitasking, flexibility and configurability in any position they are involved in. However, implementing industrial robotics is challenging due to their high cost, expert handling and complexity. The case study determined the industrial robotics as a desirable tool in lean enterprise and through studying these areas availability, ease of use, standardization and visualization it shows the current mapping of the industrial robotics. Performance measurement of the industrial robotics is determined using the QCDAC method or (quality, cost, delivery, accountability and continual improvement). In terms of performance identification and ranking interpretive structural modelling (ISM) methodology is used to identify the most affected variable of the model. Cross tabulation showed the intersection result between the usage of industrial robotics and their performance to clarify the industrial robotics performance in these areas in which the industrial robotics was fit with and compatible with lean enterprise. The results showed that introducing the industrial robotics into lean enterprise will support it in terms of quality improvement, cost reduction and efficiency which lead the company to become a world class manufacturer

Performance Measure Of Industrial Robotics In Lean Enterprise: A Case Study In Semiconductor Industry

Industrial robotics replaced human workers in almost all fields due to their abilities to multitask, flexibility and configurability in any position they involved in. However, implementing industrial robotics is challenging due to their high cost, expert handling, and complexity. The object of this study is to determine the performance measurement using the QCDAC method or (quality, cost, delivery, accountability and continual improvement) then categorized according to lean principles and then identifying seven main areas that the industrial robotics contributes in the semi-conductor company. The performance identification and ranking is done by using Interpretive Structural Modelling (ISM) methodology to identify the most affected performance of the model and to clarify the industrial robotics performance in these areas in which the industrial robotics fit and compatible with the lean enterprise. Human- robot interaction considered to guarantee the workers' safety working alongside industrial robotics. The result of the ISM method shows the performance measure that affects the industrial robotics to support lean enterprise in terms of quality improvement, cost reduction and efficiency

Green Cost Effective Method for Nano zinc oxide Preparation as Marine Antifouling Additive via Wet Chemical Reduction Method

ABSTRACT Currently nano zinc oxide received great attention in many industrial applications especially in paint industry. Due to the high antimicrobial and antifungal actions of nanosized zinc oxide particles, it could be used as excellent antifouling additive in marine coating polymers. Nanosized of 20nm to 50nm especially 45nm are the optimum particle size of nano zinc oxide which could be used as marine antifouling additives in paint composition. Investigation of optimum operating conditions for the preparation of 45nm nano zinc oxide particles was the target of this study. Wet chemical reduction method via hydroxyethyl cellulose (HEC) as green reducing and stabilizing agent was used. The results show that optimum conditions were zinc ion concentration 1.3g/l, HEC concentration 2.9g/l, at reaction temperature of 80 O C for interval time 60min. at stirring rate of 150rpm and pH of 11, to reach maximum percentage obtained of the target size (45nm) 74%

METHOD DEVELOPMENT AND VALIDATION OF THE CHROMATOGRAPHIC ANALYSIS OF FLUTICASONE PROPIONATE AND SALMETEROL XINAFOATE COMBINATION IN SOLUTIONS AND HUMAN PLASMA USING HPLC WITH UV DETECTION

Objective: A simple, Rapid, and sensitive HPLC method utilizing UV detection was developed and validated for the simultaneous estimation of Fluticasone propionate (FP) and Salmeterol xinafoate (SX) in solutions and in vitro human plasma. Methods: Chromatographic analysis was done on SUPELCO® RP-C18 column (150 x 4.6 mm, 5 μm particle size) with an isocratic mobile phase composed of methanol, acetonitrile, and water (50:20:30, v/v) mixture while flow rate was set to 1 ml/min. Detection with UV at maximum absorbance wavelength (ʎmax) values of 236 and 252 for FP and SX, respectively. Spiked plasma samples were liquid-liquid extracted by diethyl ether and reconstituted using methanol. Results: Method was accurate and precise over a linear (R2>0.995) range of (0.067-100 µg/ml) and (0.0333-50 µg/ml) for FP and SX, respectively. LOD/lOQ values were 0.13/0.6 and 0.06/0.3 µg/ml for FP and SX, respectively. The developed method was successfully applied for the analysis of FP and SX in spiked human plasma samples. The method is considered to be accurate and precise over a linear (R2>0.9969) range of (6.67-66.67 µg/ml) and (3.33-33.3 µg/ml) for FP and SX, respectively. Extraction efficiency was approved by recovery values of (94.98–102.46 %) and (96.54–102.62 %) for FP and SX, respectively. Conclusion: This validated method revealed simple and cheap extraction procedures and detectors, non-buffered mobile phase, and short retention times with excellent resolution

Optimising brain age estimation through transfer learning:A suite of pre-trained foundation models for improved performance and generalisability in a clinical setting

Estimated age from brain MRI data has emerged as a promising biomarker of neurological health. However, the absence of large, diverse, and clinically representative training datasets, along with the complexity of managing heterogeneous MRI data, presents significant barriers to the development of accurate and generalisable models appropriate for clinical use. Here, we present a deep learning framework trained on routine clinical data (N up to 18,890, age range 18–96 years). We trained five separate models for accurate brain age prediction (all with mean absolute error ≤4.0 years, R2 ≥.86) across five different MRI sequences (T2-weighted, T2-FLAIR, T1-weighted, diffusion-weighted, and gradient-recalled echo T2*-weighted). Our trained models offer dual functionality. First, they have the potential to be directly employed on clinical data. Second, they can be used as foundation models for further refinement to accommodate a range of other MRI sequences (and therefore a range of clinical scenarios which employ such sequences). This adaptation process, enabled by transfer learning, proved effective in our study across a range of MRI sequences and scan orientations, including those which differed considerably from the original training datasets. Crucially, our findings suggest that this approach remains viable even with limited data availability (as low as N = 25 for fine-tuning), thus broadening the application of brain age estimation to more diverse clinical contexts and patient populations. By making these models publicly available, we aim to provide the scientific community with a versatile toolkit, promoting further research in brain age prediction and related areas.</p

Burden of visceral leishmaniasis in villages of eastern gedaref state, Sudan: an exhaustive cross-sectional survey.

Since December 2009, Médecins Sans Frontières has diagnosed and treated patients with visceral leishmaniasis (VL) in Tabarak Allah Hospital, eastern Gedaref State, one of the main endemic foci of VL in Sudan. A survey was conducted to estimate the VL incidence in villages around Tabarak Allah

The relationships between West Nile and Kunjin viruses.

Until recently, West Nile (WN) and Kunjin (KUN) viruses were classified as distinct types in the Flavivirus genus. However, genetic and antigenic studies on isolates of these two viruses indicate that the relationship between them is more complex. To better define this relationship, we performed sequence analyses on 32 isolates of KUN virus and 28 isolates of WN virus from different geographic areas, including a WN isolate from the recent outbreak in New York. Sequence comparisons showed that the KUN virus isolates from Australia were tightly grouped but that the WN virus isolates exhibited substantial divergence and could be differentiated into four distinct groups. KUN virus isolates from Australia were antigenically homologous and distinct from the WN isolates and a Malaysian KUN virus. Our results suggest that KUN and WN viruses comprise a group of closely related viruses that can be differentiated into subgroups on the basis of genetic and antigenic analyses

Letrozole before TESE in Non-Obstructive Azoospermia, Does It Affect Sperm Retrieval Rate, A Retrospective Study

Objective: This study was designed to evaluate the effect of letrozole 2.5 mg, an aromatase inhibitor, on the sperm retrieval rate (SRR) by the testicular sperm extraction (TESE) procedures that was done for the treatment of males with non-obstructive azoospermia (NOA).Materials and methods: Data was collected retrospectively from males diagnosed with non-obstructive azoospermia who underwent TESE procedure in the duration between May 2010 until June, 2018. The collected data includes the age of the patient, body mass index (BMI), testicular volume, hormonal profile (FSH LH, prolactin, testosterone), and the use of letrozole preoperatively. Logistic regression was done to address the association of these parameters to the sperm’s retrieval rate.Results: The study screaned 145 patients. Eighty patients fit the inclusion criteria and thus they were statistically analyzed. The use of letrozole was associated with negative TESE outcome (p=0.006), odd (0.154) CI 0.04-0.579. The other factors had no significant correlation to the TESE results.Conclusion: The evidence in this study showed an adverse effect of letrozole use on TESE results of those with high FSH