Ender nailing of acute humeral shaft fractures in multiple injuries

From January 1987 to April 1993, 25 multiply injured patients were treated with closed intramedullary Ender nail fixation of the humeral shaft. Criteria for the procedure were humeral diaphyseal fractures with associated multiple injuries. Seventeen distal and mid-shaft fractures were treated via a modified anterograde approach in which the rotator cuff is not violated. Eight proximal third fractures were treated via the standard retrograde approach. Postoperative follow-up averaged 36 months. Full range of motion was attained 17 of the 25 patients. Of the eight remaining patients, three with a slightly limited range of motion achieved full range of motion following nail removal; three had preoperative radial nerve palsy which resolved within 1 year, and two patients required follow-on plating. In 92 per cent (23 of 25) there were no incidences of infection or non-union. Flexible nails avoid complications of reaming. The modified anterograde approach allows excellent shoulder motion since it does not violate the rotator cuff. Ender nails provide excellent fixation and clinical outcome in the multiply injured patient and are cost effective compared with interlocking nails.     

Coordinating a two-level supply chain with delay in payments and profit sharing

Achieving effective coordination among suppliers and retailers has become a pertinent research issue in supply chain management. Channel coordination is a joint decision policy achieved by a supplier(s) and a retailer(s) characterized by an agreement on the order quantity and the trade credit scenario (e.g., quantity discounts, delay in payments). This paper proposes a centralized model where players in a two-level (supplier–retailer) supply chain coordinate their orders to minimize their local costs and that of the chain. In the proposed supply chain model the permissible delay in payments is considered as a decision variable and it is adopted as a trade credit scenario to coordinate the order quantity between the two-levels. Computational results indicate that with coordination, the retailer orders in larger quantities than its economic order quantity, with savings to either both players, or to one in the supply chain. Moreover, a profit-sharing scenario for the distribution of generated net savings among the players in the supply chain is presented. Analytical and experimental results are presented and discussed to demonstrate the effectiveness of the proposed model.     

Evaluating the learning process of mechanical CAD students

There is little theoretical or experimental research on how beginner-level trainees learn CAD skills in formal training sessions. This work presents findings on how trainees develop their skills in utilizing a solid mechanical CAD tool (Pro/Engineer version 2000i² and later version Wildfire). Exercises at the beginner and intermediate levels were designed so that several variations of a solid object are built by non-experienced trainees as they accumulate training time. In this case, trainees are fourth year mechanical engineering seniors and as such, they were of a similar technical and gender make-up. This assessment was conducted over the duration of training (16-week long semester). The test exercises were used to assess the trainees’ speed and proficiency in the use of CAD by (1) measuring their performance time and (2) feature count (number of features-of-size used to build the test parts). Using performance time data, empirical learning curves are generated. Breaking these curves into declarative and procedural components provides insight into how fast the trainees develop cognitive and motor CAD skills. In order to confirm that this methodology can be extended to other CAD platforms, a follow-up study was performed on a different set of beginner-level trainees with similar make-up while using the same beginner-level parts but with a more recent version of Pro/Engineer: Wildfire. One significant result of this study is that the procedural and declarative components of CAD learning are largely cognitive.     

Comparison of optimum tapered cascade and optimal square cascade for separation of xenon isotopes using enhanced TLBO algorithm

The present study is conducted to propose a new code for optimizing a multicomponent isotope separation cascade. An efficient code is developed using an enhanced TLBO algorithm with a novel mutation for calculating the optimal parameters. The results indicate that by implementing a new objective function derived from the combination of D function with total interstage flow rates, the optimum tapered cascade is more efficient than the optimal square one. Also, the best feed locations for the separation of the middle components in the square cascade are the middle stages.     

Intumescent flame retardant polyurethane/starch composites: Thermal,mechanical, and rheological properties

Intumescent flame retardant polyurethane/starch (IFRPU/starch) composites were prepared by means of melt blending. Microencapsulated ammonium polyphosphate (MCAPP) was added to improve its compatibility with matrix, retardation of reaction between acid and carbon source, and its water resistancy. Fourier transform infrared spectroscopy (FTIR) confirmed the presence of hydrogen bonding and entangled network between IFR system and PU matrix. Further, scanning electron microscopy (SEM) illustrated homogeneity of starch in matrix. By addition of 10 wt % of starch and 20 wt % of IFR, limiting oxygen index (LOI) increased from 22.0 to 40.0 and UL94 V0 rating was achieved. Differential scanning calorimetry (DSC) detected three endothermic transitions and one glass transition (T_g). The temperature of transition III and T_g increased with starch due to crosslinking between PU and starch. The improved thermal stability in the presence of starch was confirmed by thermogravimetric analysis (TGA). Beside the fact that starch was used as a carbonization agent to improve flame retardancy, it also effectively led to enhanced mechanical and viscoelastic properties. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 41158.     

Synthesis of Organic Functionalized Silica from Rice Husk as an Antibacterial Agents

Silicon - In the present study, silica-aminothiazole (RHAC-ATH) and silica-aminophenol (RHAC-AMPH) have been prepared and characterized by different techniques. Fourier-Transform Infrared (FTIR)...     

Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images. Hyperspectral remote sensing contains acquisition of digital images from several narrow, contiguous spectral bands throughout the visible, Thermal Infrared (TIR), Near Infrared (NIR), and Mid-Infrared (MIR) regions of the electromagnetic spectrum. In order to the application of agricultural regions, remote sensing approaches are studied and executed to their benefit of continuous and quantitative monitoring. Particularly, hyperspectral images (HSI) are considered the precise for agriculture as they can offer chemical and physical data on vegetation. With this motivation, this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification (HOADTL-CC) model on Hyperspectral Remote Sensing Images. The presented HOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images. To accomplish this, the presented HOADTL-CC model involves the design of HOA with capsule network (CapsNet) model for generating a set of useful feature vectors. Besides, Elman neural network (ENN) model is applied to allot proper class labels into the input HSI. Finally, glowworm swarm optimization (GSO) algorithm is exploited to fine tune the ENN parameters involved in this article. The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects. Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.     

Exploring the Design and Spectroscopic Characteristics of PVA/Si3N4/SiBr4 New Structures for Electronics and Optics Devices

Silicon - This work aims to design of silicon nitride (Si3N4) and silicon bromide (SiBr4) doped polyvinyl alcohol (PVA) as promising semiconductors materials which can be used in various...     

Cat and Mouse Optimizer with Artificial Intelligence Enabled Biomedical Data Classification

Biomedical data classification has become a hot research topic in recent years, thanks to the latest technological advancements made in healthcare. Biomedical data is usually examined by physicians for decision making process in patient treatment. Since manual diagnosis is a tedious and time consuming task, numerous automated models, using Artificial Intelligence (AI) techniques, have been presented so far. With this motivation, the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI (BDC-CMBOAI) technique. The aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical data. Besides, the proposed BDC-CMBOAI technique involves the design of Cat and Mouse Optimizer-based Feature Selection (CMBO-FS) technique to derive a useful subset of features. In addition, Ridge Regression (RR) model is also utilized as a classifier to identify the existence of disease. The novelty of the current work is its designing of CMBO-FS model for data classification. Moreover, CMBO-FS technique is used to get rid of unwanted features and boosts the classification accuracy. The results of the experimental analysis accomplished by BDC-CMBOAI technique on benchmark medical dataset established the supremacy of the proposed technique under different evaluation measures.