A comparative analysis of business process modelling techniques

Business process modelling is an increasingly popular research area for both organisations and academia due to its usefulness in facilitating human understanding and communication. Several modelling techniques have been proposed and used to capture the characteristics of business processes. However, available techniques view business processes from different perspectives and have different features and capabilities. Furthermore, to date limited guidelines exist for selecting appropriate modelling techniques based on the characteristics of the problem and its requirements. This paper presents a comparative analysis of some popular business process modelling techniques. The comparative framework is based on five criteria: flexibility, ease of use, understandability, simulation support and scope. The study highlights some of the major paradigmatic differences between the techniques. The proposed framework can serve as the basis for evaluating further modelling techniques and generating selection procedures

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

A Semantic-based framework for discovering business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modeling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. This paper focuses on business process patterns and proposes an initial framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework synthesizes the idea from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Ransomware Detection Using Federated Learning with Imbalanced Datasets

Ransomware is a type of malware which encrypts user data and extorts payments in return for the decryption keys. This cyberthreat is one of the most serious challenges facing organizations today and has already caused immense financial damage. As a result, many researchers have been developing techniques to counter ransomware. Recently, the federated learning (FL) approach has also been applied for ransomware analysis, allowing corporations to achieve scalable, effective detection and attribution without having to share their private data. However, in reality there is much variation in the quantity and composition of ransomware data collected across multiple FL client sites/regions. This imbalance will inevitably degrade the effectiveness of any defense mechanisms. To address this concern, a modified FL scheme is proposed using a weighted cross-entropy loss function approach to mitigate dataset imbalance. A detailed performance evaluation study is then presented for the case of static analysis using the latest Windows-based ransomware families. The findings confirm improved ML classifier performance for a highly imbalanced dataset.Comment: 6 pages, 4 figures, 3 table

Measuring Impact of Air and Agricultural Soil Pollution on Social Development in Saudi Arabia

This research aimed to measure the impact of air and agricultural soil pollution on social development in Saudi Arabia from the period 1995–2019 by using social development indicators, concentrating on the percentages of expenditure on education and health, and the Human Development Index. In addition, this study uses multiple regressions in estimating the model to study the impact of air pollution and agricultural soil on social development. Results of the study showed that a 10% change in the number of chemical fertilizers and pesticides used in Saudi agriculture leads to a change in the total number of inpatients by 0.7% and 0.5%, respectively. It was also found that an increased percentage of health expenditure to total government spending by 10% leads to a decrease in the total number of patients in the hospital by 1.8%. An increase in air pollution, expressed as a 10% increase in CO2 emissions, increases the total number of hospitalized patients by 11.1%.  The increasing total number of patients by 10% leads to a decrease in the total productivity of the worker, as an indicator of 1.8%. Furthermore, a change of 10% in the ratio of education expenditure to total government expenditure leads to a change in the same direction of the Human Development Index by 9.6%. In light of these results, it can be recommended that the country need to reduce air pollution by expanding the use of natural gas in the industrial and transportation sectors, in addition to reducing the use of nitrogenous fertilizers and pesticides in Saudi agriculture through the expansion of clean farming and good agricultural practices

LOGISTICS IN CONTESTED ENVIRONMENTS

This report examines the transport and delivery of logistics in contested environments within the context of great-power competition (GPC). Across the Department of Defense (DOD), it is believed that GPC will strain our current supply lines beyond their capacity to maintain required warfighting capability. Current DOD efforts are underway to determine an appropriate range of platforms, platform quantities, and delivery tactics to meet the projected logistics demand in future conflicts. This report explores the effectiveness of various platforms and delivery methods through analysis in developed survivability, circulation, and network optimization models. Among other factors, platforms are discriminated by their radar cross-section (RCS), noise level, speed, cargo capacity, and self-defense capability. To maximize supply delivered and minimize the cost of losses, the results of this analysis indicate preference for utilization of well-defended convoys on supply routes where bulk supply is appropriate and smaller, and widely dispersed assets on shorter, more contested routes with less demand. Sensitivity analysis on these results indicates system survivability can be improved by applying RCS and noise-reduction measures to logistics assets.Director, Warfare Integration (OPNAV N9I)Major, Israel Defence ForcesCivilian, Singapore Technologies Engineering Ltd, SingaporeCommander, Republic of Singapore NavyCommander, United States NavyCaptain, Singapore ArmyLieutenant, United States NavyLieutenant, United States NavyMajor, Republic of Singapore Air ForceCaptain, United States Marine CorpsLieutenant, United States NavyLieutenant, United States NavyLieutenant, United States NavyLieutenant, United States NavyLieutenant, United States NavyCaptain, Singapore ArmyLieutenant Junior Grade, United States NavyCaptain, Singapore ArmyLieutenant Colonel, Republic of Singapore Air ForceApproved for public release. distribution is unlimite

Frequencies of BCR-ABL1 fusion transcripts among Sudanese chronic myeloid leukaemia patients

The incidence of one or other rearrangement in chronic myeloid leukemia (CML) patients varies in different reported series. In this study we report the frequencies of BCR-ABL1 fusion transcript variants studied in 43 CML patients from Sudan. The study includes 46 Sudanese patients, three of which negative for the BCR-ABL1 fusion transcript. More than half of 43 positive patients showed b2a2 fusion transcript (53.5%), while (41.9%) showed b3a2 transcript and the remaining (4.6%) coexpression of b3a2/ b2a2 and b3a2/b2a2/e19a2. We detected neither coexpression of p210/p190 nor e1a2 alone. Male patients showed a tendency to express b2a2, while female tende to express b3a2 (p = 0.017). Moreover, a single nucleotide polymorphism was detected in BCR exon 13 in one out of four patients and this patient showed only b2a2 expression. In conclusion, we observed a significant correlation between sex and type of BCR-ABL1 transcript, an observation that deserves further investigation

Slaughterhouse Wastewater Treatment by Combined Chemical Coagulation and Electrocoagulation Process

Slaughterhouse wastewater contains various and high amounts of organic matter (e.g., proteins, blood, fat and lard). In order to produce an effluent suitable for stream discharge, chemical coagulation and electrocoagulation techniques have been particularly explored at the laboratory pilot scale for organic compounds removal from slaughterhouse effluent. The purpose of this work was to investigate the feasibility of treating cattle-slaughterhouse wastewater by combined chemical coagulation and electrocoagulation process to achieve the required standards. The influence of the operating variables such as coagulant dose, electrical potential and reaction time on the removal efficiencies of major pollutants was determined. The rate of removal of pollutants linearly increased with increasing doses of PACl and applied voltage. COD and BOD5 removal of more than 99% was obtained by adding 100 mg/L PACl and applied voltage 40 V. The experiments demonstrated the effectiveness of chemical and electrochemical techniques for the treatment of slaughterhouse wastewaters. Consequently, combined processes are inferred to be superior to electrocoagulation alone for the removal of both organic and inorganic compounds from cattle-slaughterhouse wastewater

Automatsko brojanje putnika u javnom željezničkom prijevozu uporabom waveleta

Previously, we introduced a passengers’ counting algorithm in public rail transport. The main disadvantage of that algorithm is it lacks automatic event detection. In this article, we implement two automatic wavelet-based passengers counting algorithms. The new algorithms employ the spatial-domain Laplacian-of-Gaussian-based wavelet, and the frequency-domain applied Non-Linear Difference of Gaussians-based wavelet bandpass video scene filters to extract illumination invariant scene features and to combine them efficiently into the background reference frame. Manual segmentation of the scene into rectangles and tiles for detecting an object as seated is no longer needed as we now apply a boundary box tracker on the segmented moving objects’ blobs. A scene map is combined with the wavelet-based methods and the boundary box for multi-camera object registration. We have developed a novel holistic geometrical approach for exploiting the scene map and the recorded video sequences from both cameras installed in each train coach to separate the detected objects and locate their positions on the scene map. We test all the algorithms with several video sequences recorded from the both cameras installed in each train coach. We compare the previously developed non-automatic passengers’ counting algorithm with the two new automatic wavelet-based passengers’ counting algorithms, and an additional spatial-domain automatic non-wavelet based Simple Mixture of Gaussian Models algorithm.U prethodnim radovima uveli smo algoritam za brojanje putnika u javnom željezničkom prijevozu. Glavna manjkavost dosadašnjeg algoritma odsustvo je sustava za automatsko otkrivanje događaja. U ovom radu implementirali smo dva algoritma za automatsko brojanje putnika temeljena na waveletima. Novi algoritmi koriste LoG (Laplacian-of-Gaussian-based) wavelete u prostornoj domeni i pojasne filtre temeljene na waveletima nastalim na nelinearnim razlikama Gaussovih funkcija u frekvencijskoj domeni, pomoću kojih se izdvajaju značajke neosjetljive na razlike u osvjetljenju iz pojedine scene. Te značajke kombiniraju se u referentnu sliku koja prikazuje pozadinu scene. Ručna segmentacija scene u pravokutnike korištena u prethodnom algoritmu više nije potrebna jer se sada koristi automatsko praćenje rubova na segmentiranim objektima. Mapa scene kombinirana je s wavelet metodama i okvirom granica slike u svrhu registracije objekata pomoću više kamera. Razvili smo i novi cjeloviti geometrijski pristup koji koristi mapu scene i snimljeni videozapis iz dvije kamere postavljene u svakom vagonu vlaka pomoću kojeg možemo odvojiti detektirane objekte i locirati njihove položaje na mapi scene. Algoritmi su ispitani na nekoliko videosekvenci snimljenih s dvije kamere u vagonima. Usporedili smo ranije razvijene neautomatske algoritme za brojanje putnika s dva nova algoritma i s jednostavnim MoG algoritmom u prostornoj domeni