Vibration energy harvesting systems are using real ambient sources of vibration excitation. In our paper, we study the dynamical voltage response of the piezoelectric vibrational energy harvesting system (PVEHs) with a mechanical resonator possessing an amplitude limiter. The PVEHs consist of the cantilever beam with a piezoelectric patch. The proposed system was subjected to the inertial excitation from the engine suspension. Impacts of the beam resonator are useful to increase of system’s frequency transition band. The suitable simulations of the resonator and piezoelectric transducer are performed by using measured signal from the engine suspension. Voltage outputs of linear (without amplitude limiter) and nonlinear harvesters were compared indicating better efficiency of the nonlinear design.

Caban, J., Litak, G., Ambrożkiewicz, B., Gardyński, L., Stączek, P., & Wolszczak, P. (2020). Impact-based piezoelectric energy harvesting system excited from diesel engine suspension. Applied Computer Science, 16(3), 16-29. doi:10.23743/acs-2020-18

Caban, Jacek, Grzegorz Litak, Bartłomiej Ambrożkiewicz, Leszek Gardyński, Paweł Stączek, and Piotr Wolszczak. "Impact-Based Piezoelectric Energy Harvesting System Excited from Diesel Engine Suspension." Applied Computer Science 16, no. 3 (2020): 16-29.

J. Caban, G. Litak, B. Ambrożkiewicz, L. Gardyński, P. Stączek, and P. Wolszczak, "Impact-based piezoelectric energy harvesting system excited from diesel engine suspension," Applied Computer Science, vol. 16, no. 3, pp. 16-29, 2020, doi: 10.23743/acs-2020-18.

Caban J, Litak G, Ambrożkiewicz B, Gardyński L, Stączek P, Wolszczak P. Impact-based piezoelectric energy harvesting system excited from diesel engine suspension. Applied Computer Science. 2020;16(3):16-29.

The major challenge being faced by the financial related institutions, such as e-Commerce has been insecurity. Therefore, there is urgent need to develop a scheme to protect transmitted financial information or messages from getting to the third party, intruder and/or unauthorized person(s). Such scheme will be based on Advanced Encryption Standard (AES) and Neural Data Security (NDS) Model. Based on this background, an AES using Time-based Dynamic Key Generation coupled with NDS model will be used to develop security model for preventing e-commerce related crimes. While AES will secure users’ details in the database server and ensures login authentications, NDS model will fragment or partition sensitive data into High and Low levels of confidentiality. The sensitivity of the data will determine, which category of confidentiality the data will fall into. The fragmented data are saved into two different databases, on two different servers and on the same datacenter. In addition, an exploratory survey was carried out using different performance metrics with different classifications of algorithms. Out of the four algorithms considered, Naive Bayes performs better as it shows, out of a total of 105 instances that were observed, 85.71% were correctly classified while 14.29% were misclassified.

Akinyede, R. O., Adegbenro, S. O., & Omilodi, B. M. (2020). A security model for preventing e-Commerce related crimes. Applied Computer Science, 16(3), 30-41. doi:10.23743/acs-2020-19

Akinyede, Raphael Olufemi, Sulaiman Omolade Adegbenro, and Babatola Moses Omilodi. "A Security Model for Preventing E-Commerce Related Crimes." Applied Computer Science 16, no. 3 (2020): 30-41.

R. O. Akinyede, S. O. Adegbenro, and B. M. Omilodi, "A security model for preventing e-Commerce related crimes," Applied Computer Science, vol. 16, no. 3, pp. 30-41, 2020, doi: 10.23743/acs-2020-19.

Akinyede RO, Adegbenro SO, Omilodi BM. A security model for preventing e-Commerce related crimes. Applied Computer Science. 2020;16(3):30-41.

Effective decision-making in industry conditions requires access and proper presentation of manufacturing data on the realised manufacturing process. Although the frequently applied ERP systems allow for recording economic events, their potential for decision support is limited. The article presents an original system for reporting manufacturing data based on Business Intelligence technology as a support for junior and middle management. As an example a possibility of utilising data from ERP systems to support decision-making in the field of purchases and logistics in  small and medium enterprises.

Danilczuk, W., & Gola, A. (2020). Computer-Aided Material Demand Planning Using ERP Systems And Business Intelligence Technology. Applied Computer Science, 16(3), 42-55. doi:10.23743/acs-2020-20

Danilczuk, Wojciech, and Arkadiusz Gola. "Computer-Aided Material Demand Planning Using Erp Systems and Business Intelligence Technology." Applied Computer Science 16, no. 3 (2020): 42-55.

W. Danilczuk and A. Gola, "Computer-Aided Material Demand Planning Using ERP Systems And Business Intelligence Technology," Applied Computer Science, vol. 16, no. 3, pp. 42-55, 2020, doi: 10.23743/acs-2020-20.

Danilczuk W, Gola A. Computer-Aided Material Demand Planning Using ERP Systems And Business Intelligence Technology. Applied Computer Science. 2020;16(3):42-55.

Effective decision-making in industry conditions requires access and proper presentation of manufacturing data on the realised manufacturing process. Although the frequently applied ERP systems allow for recording economic events, their potential for decision support is limited. The article presents an original system for reporting manufacturing data based on Business Intelligence technology as a support for junior and middle management. As an example a possibility of utilising data from ERP systems to support decision-making in the field of purchases and logistics in  small and medium enterprises.

Woods, N., & Babatunde, G. (2020). A robust ensemble model for Spoken Language Recognition. Applied Computer Science, 16(3), 56-68. doi:10.23743/acs-2020-21

Woods, Nancy, and Gideon Babatunde. "A Robust Ensemble Model for Spoken Language Recognition." Applied Computer Science 16, no. 3 (2020): 56-68.

N. Woods and G. Babatunde, "A robust ensemble model for Spoken Language Recognition," Applied Computer Science, vol. 16, no. 3, pp. 56-68, 2020, doi: 10.23743/acs-2020-21.

Woods N, Babatunde G. A robust ensemble model for Spoken Language Recognition. Applied Computer Science. 2020;16(3):56-68.

The aim of the study was to evaluate the influence of lyophilized plant extract on color of canned meat with reduced amount of sodium (III) nitrite measured by spectrophotometric methods. The results were collected through the X-RiteColor® Master software. The results of the experiment show that reduction of nitrite salt is possible but additional fortification is required: the best results were obtained when the extract was added in the amount of 0.015%.

Ferysiuk, K., Wójciak, K. M., Kęska, P., & Stasiak, D. M. (2020). Instrumental color measurement of meat and meat products in X-RiteColor® Master. Applied Computer Science, 16(3), 69-79. doi:10.23743/acs-2020-22

Ferysiuk, Karolina, Karolina M. Wójciak, Paulina Kęska, and Dariusz M. Stasiak. "Instrumental Color Measurement of Meat and Meat Products in X-Ritecolor® Master." Applied Computer Science 16, no. 3 (2020): 69-79.

K. Ferysiuk, K. M. Wójciak, P. Kęska, and D. M. Stasiak, "Instrumental color measurement of meat and meat products in X-RiteColor® Master," Applied Computer Science, vol. 16, no. 3, pp. 69-79, 2020, doi: 10.23743/acs-2020-22.

Ferysiuk K, Wójciak KM, Kęska P, Stasiak DM. Instrumental color measurement of meat and meat products in X-RiteColor® Master. Applied Computer Science. 2020;16(3):69-79.

Wireless sensor network is a significant piece of wireless communication.It is a gathering of an enormous number of sensor nodes that are set in remote spots. The sensors have ability to do a typical undertaking. So energy exhaustion plays a significant job in keeping up a stable network. To build the system lifetime, a different energy effective algorithm is required which expands the network lifetime and makes the network more energy productive. For the augmenting, the lifetime of the network diverse routing technique has been utilized which help in expanding the lifetime of the network. This article portrays the diverse routing protocol which helps in energy efficient routing in a wireless sensor network.

Al-Rawi, M. F. (2020). Conventional energy efficient routing protocols in wireless sensor networks. Applied Computer Science, 16(3), 80-87. doi:10.23743/acs-2020-23

Al-Rawi, Muaayed F. "Conventional Energy Efficient Routing Protocols in Wireless Sensor Networks." Applied Computer Science 16, no. 3 (2020): 80-87.

M. F. Al-Rawi, "Conventional energy efficient routing protocols in wireless sensor networks," Applied Computer Science, vol. 16, no. 3, pp. 80-87, 2020, doi: 10.23743/acs-2020-23.

Al-Rawi MF. Conventional energy efficient routing protocols in wireless sensor networks. Applied Computer Science. 2020;16(3):80-7.

Changes in the compression strength of the PMMA bone cement with a variable powder/liquid component mix ratio were investigated. The strength test data served to develop basic mathematical models and an artificial neural network was employed for strength predictions. The empirical and numerical results were compared to determine modelling errors and assess the effectiveness of the proposed methods and models. The advantages and disadvantages of mathematical modelling are discussed.

Machrowska, A., Karpiński, R., Jonak, J., Szabelski, J., & Krakowski, P. (2020). Numerical prediction of the component-ratio-dependent compressive strength of bone cement. Applied Computer Science, 16(3), 88-101. doi:10.23743/acs-2020-24

Machrowska, Anna, Robert Karpiński, Józef Jonak, Jakub Szabelski, and Przemysław Krakowski. "Numerical Prediction of the Component-Ratio-Dependent Compressive Strength of Bone Cement." Applied Computer Science 16, no. 3 (2020): 88-101.

A. Machrowska, R. Karpiński, J. Jonak, J. Szabelski, and P. Krakowski, "Numerical prediction of the component-ratio-dependent compressive strength of bone cement," Applied Computer Science, vol. 16, no. 3, pp. 88-101, 2020, doi: 10.23743/acs-2020-24.

Machrowska A, Karpiński R, Jonak J, Szabelski J, Krakowski P. Numerical prediction of the component-ratio-dependent compressive strength of bone cement. Applied Computer Science. 2020;16(3):88-101.