Enhancing the Stability of the Improved-LEACH Routing Protocol for WSNs

Recently, increasing battery lifetime in wireless sensor networks has turned out to be one of the major challenges faced by researchers. The sensor nodes in wireless sensor networks use a battery as their power source, which is hard to replace during deployment. Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the most prominent wireless sensor network routing protocols that have been proposed to improve network lifetime by utilizing energy-efficient clustering. However, LEACH has some issues related to cluster-head selection, where the selection is done randomly. This leads to rapid loss of energy in the network. Improved LEACH is a LEACH alternative that has the ability to increase network lifetime by using the nodes' residual energy and their distance to the base station to select cluster-head nodes. However, Improved LEACH causes reduced stability, where the stability period is the duration before the death of the first node. The network stability period is important for applications that require reliable feedback from the network. Thus, we were motivated to investigate the Improved LEACH algorithm and to try to solve the stability problem. A new protocol is proposed in this paper: Stable Improved Low Energy Adaptive Clustering Hierarchy (SILEACH), which was developed to overcome the flaws of the Improved LEACH protocol. SILEACH balances the load between the nodes by utilizing an optimized method that considers the nodes' distance to the base station and their residual energy to select the cluster-head nodes and considers the nodes' distance to the cluster head and the base station to form clusters. The simulation results revealed that SILEACH is significantly more efficient than Improved LEACH in terms of stability period and network lifetime

Automating Production Process Data Acquisition Towards Spaghetti Chart 4.0

Automation of logistics and production processes is one of the significant goals of the Industry 4.0 movement. Thus, this study uses the automatic data acquisition approach to automatically create Spaghetti Charts (SC), decrease the required time spent on collecting data, and provide a fast analysis (feedback). Smartphone technology used for drawing the automated spaghetti diagram has potential drawbacks related to safety, security, the privacy of organizations, and independency. So, Method Time Measurement 4.0 and RStudio software are used to collect the real data and draw the spaghetti chart, respectively, as an alternative solution. The experiment results for four scenarios (different Facility layouts and processes) prove that the approach is competitive, with cycle time reduction exceeding 40% less than the conventional facility layout and process plan. The proposed solution of the wearable device is promising for replacing smartphone technology with smart spaghetti chart systems

Atomistic investigation on the effect of temperature on mechanical properties of diffusion-welded Aluminium-Nickel

Atomistic investigation of diffusion welding between Aluminium and Nickel has been investigated, by means of Molecular Dynamics (MD) simulation. This study focuses on examining the effect of temperature on diffusion welding between Al-Ni for which it is still lacking. Employing several different temperatures, this study aims to examine the influence of temperature on the mechanical properties of diffusion-welded Al-Ni. The results have shown that the structural evolution significantly affected by the temperature. Better bonding structure is achieved as the temperature is increased which indicated by the wider interfacial region thickness on concentration profiles. However, as the temperature is increased lower ultimate tensile strength is obtained. Therefore, precisely estimates the temperature for particular materials in diffusion welding is a critical point. In this study, the optimum condition that fitsA on the diffusion welding process is when the temperature set on 500 K

Acute phase proteins in calves naturally infected with cryptosporidium

Infectious diarrhea remains one of the most important health challenges in dairy industries during the first four weeks of life, with Cryptosporidium infection as one of the main causes of this diarrhea. This study aimed to evaluate the blood concentration of some acute phase proteins in calves naturally infected with Cryptosporidium. Ninety-six, 1 day to 4 week-old Holstein calves were allotted into a control group (G1 n=48 healthy calves) and calves infected with Cryptosporidium (G2 n=48). Blood and fecal samples were collected from each calf on the same day. Enzyme-Linked Immuno-Sorbent Assay (ELISA) was used to estimate serum levels of haptoglobin (Hp), serum amyloid A (SAA), and tumor necrosis factor alpha (TNFα), while gel electrophoresis was used to determine serum level of fibrinogen. Serum SAA, Hp, and fibrinogen significantly increased in infected calves, whereas there was no significant difference in serum level of TNFα between the two groups

Automated Fault Detection in the Arabian Basin

In recent years, there has been a rapid development of the computer-aided interpretation of seismic data to reduce the otherwise intensive manual labor. A variety of seed detection algorithms for horizon and fault identification are integrated into popular seismic software packages. Recently, there has been an increasing focus on using neural networks for fully automatic faults detection without manually seeding each fault. These networks are usually trained with synthetic fault data sets. These data sets can be used across multiple seismic data sets; however, they are not as accurate as real seismic data, particularly in structurally complex regions associated with several generations of faults. The approach taken here is to combine the accuracy of manual fault identification in certain parts of the data set with a convolutional neural network that can then sweep through the entire data set to identify faults. We have implemented our method using 3D seismic data acquired from the Arabian Basin in Saudi Arabia covering an area of 1051 km2. The network is trained, validated, and tested with samples that included a seismic cube and fault images that are labeled manually corresponding to the seismic cube. The model successfully identified faults with an accuracy of 96% and an error rate of 0.12 on the training data set. To achieve a robust model, we further enhanced the prediction results using postprocessing by linking discontinued segments of the same fault line, thus reducing the number of detected faults. The postprocessing improved the prediction results from the test data set by 77.5%. In addition, we introduced an efficient framework to correlate the predictions and the ground truth by measuring their average distance value. Furthermore, tests using this approach also were conducted on the F3 Netherlands survey with complex fault geometries and find promising results. As a result, fault detection and diagnosis were achieved efficiently with structures similar to the trained data set

Detection of AFM1 in Milk and Some Dairy Products in Iraq using different techniques

The 130 samples of milk and some dairy products were randomly collected from Baghdad markets from September 2014 to June 2015 and distributes into imported and local samples include: liquid and powder milk, white and soft cheese in addition to yoghurt. The samples were analyzed to qualitative and quantitative detection of aflatoxin M1 (AFM1) using different techniques {Thin layer chromatography- TLC (qualitative), High performance liquid chromatography- HPLC and Enzyme Linked Immune Sorbent Assay- ELISA (quantitative}. The positive results (contaminated with AFM1),  showed  as 50 (38.5%), 65 (50 %) and 70 (53.8%) respectively,  furthermore,  yogurt and cheese showed more contamination with AFM1 than other products and the highest concentration of AFM1 in the local cheese reached 300.7ng/L and 939.67ng/L when detected with HPLC and ELISA techniques respectively. We concluded that ELISA technique was found to be most advisable for detection of low-level AFM1 contamination in milk and dairy products . On other side the local products were contaminated with AFM1 than imported products , in addition to  yogurt and cheese were more contaminated with AFM1 than other samples. Key words: Detection, AFM1, TLC, HPLC , ELISA, Milk, Dairy Products, Ira

A novel methodology to predict monthly municipal water demand based on weather variables scenario

This study provides a novel methodology to predict monthly water demand based on several weather variables scenarios by using combined techniques including discrete wavelet transform, principal component analysis, and particle swarm optimisation. To our knowledge, the adopted approach is the first technique to be proposed and applied in the water demand prediction. Compared to traditional methods, the developed methodology is superior in terms of predictive accuracy and runtime. Water consumption coupled with weather variables of the Melbourne City, from 2006 to 2015, were obtained from the South East Water retail company. The results showed that using data pre-processing techniques can significantly improve the quality of data and to select the best model input scenario. Additionally, it was noticed that the particle swarm optimisation algorithm accurately predicts the constants of the suggested model. Furthermore, the results confirmed that the proposed methodology accurately estimated the monthly data of municipal water demand based on a range of statistical criteria

Measuring the coupling of procedural programs

Coupling is one of two attributes of software that have great impact on software quality. Quite a few methods have been established to quantify the measurement of coupling. This paper presents a new method that provides coupling measurement of procedural programs. The first step of this method populates a description matrix that describes the software system that is being evaluated capturing all system attributes that affect coupling. Factors that affect coupling were studied and a scheme to reflect them in the description matrix was developed. A method has also been developed to calculate coupling between each two components of the system. The second step uses this method to populate a coupling matrix that indicates the coupling measurement between each two components of the system. Other metrics such as calculating the overall coupling of the system can be evaluated from the generated matrix. One of the strengths of this approach is that it can be used to measure coupling of software of the procedural languages as well as object-oriented languages. The procedures that populate the description matrix are different for the two different paradigms but everything else is the same. A comparison with three other software metrics is illustrated with the result of two experiment