Search results for: multidimensional process mining

Authors: Ezra Yehuwalashet, Alireza Malehmir

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Blötberget mining area in central Sweden, part of the Bergslagen mineral district, is well known for its various type of mineralization particularly iron-oxide deposits since the 1600. To shed lights on the knowledge of the host rock structures, depth extent and tonnage of the mineral deposits and support deep mineral exploration potential in the study area, new ground gravity and existing aeromagnetic data (from the Geological Survey of Sweden) were used for interpretations and modelling. A major boundary separating a gravity low from a gravity high in the southern part of the study area is noticeable and likely representing a fault boundary separating two different lithological units. Gravity data and modeling offers a possible new target area in the southeast of the known mineralization while suggesting an excess high-density region down to 800 m depth.

Keywords: gravity, magnetics, ore deposit, geophysics

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Authors: Ahmed Mohammed Alsalama

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Recommendation systems are widely used in e-commerce applications. The engine of a current recommendation system recommends items to a particular user based on user preferences and previous high ratings. Various recommendation schemes such as collaborative filtering and content-based approaches are used to build a recommendation system. Most of the current recommendation systems were developed to fit a certain domain such as books, articles, and movies. We propose a hybrid framework recommendation system to be applied on two-dimensional spaces (User x Item) with a large number of Users and a small number of Items. Moreover, our proposed framework makes use of both favorite and non-favorite items of a particular user. The proposed framework is built upon the integration of association rules mining and the content-based approach. The results of experiments show that our proposed framework can provide accurate recommendations to users.

Keywords: data mining, association rules, recommendation systems, hybrid systems

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Authors: Maria De Albuquerque Bercot, Caio Gustavo Mesquita Angelo, Daniela Maria Moreira Siqueira, Augusto Assucena De Vasconcellos, Rodrigo Studart Correa

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Mining is an important socioeconomic activity in Brazil although it negatively impacts the environment. Mineral operations cause irreversible changes in topography, removal of vegetation and topsoil, habitat destruction, displacement of fauna, loss of biodiversity, soil erosion, siltation of watercourses and have potential to enhance climate change. Due to the impacts and its pollution potential, mining activity in Brazil is legally subjected to environmental licensing. Unlicensed mining operations or operations that not abide to the terms of an obtained license are taken as environmental crimes in the country. This work reports a case analyzed in the Forensic Institute of the Brazilian Federal District Civil Police. The case consisted of detecting illegal aspects of sand exploitation from a licensed mine in Federal District, nearby Brasilia city. The fieldwork covered an area of roughly 6 ha, which was surveyed with an unmanned aerial vehicle (UAV) (PHANTOM 3 ADVANCED). The overflight with UAV took about 20 min, with maximum flight height of 100 m. 592 UAV georeferenced images were obtained and processed in a photogrammetric software (AGISOFT PHOTOSCAN 1.1.4), which generated a mosaic of geo-referenced images and a 3D model in less than six working hours. The 3D model was analyzed in a forensic software for accurate modeling and volumetric analysis. (MAPTEK I-SITE FORENSIC 2.2). To ensure the 3D model was a true representation of the mine site, coordinates of ten control points and reference measures were taken during fieldwork and compared to respective spatial data in the model. Finally, these spatial data were used for measuring mining area, excavation depth and volume of exploited sand. Results showed that mine holder had not complied with some terms and conditions stated in the granted license, such as sand exploration beyond authorized extension, depth and volume. Easiness, the accuracy and expedition of procedures used in this case highlight the employment of UAV imagery and computational photogrammetry as efficient tools for outdoor forensic exams, especially on environmental issues.

Keywords: computational photogrammetry, environmental monitoring, mining, UAV

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Authors: P. Ayawei, A. D. Spiff

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It is certain that most African diasporans have experienced several types of traumas, which have conjured unprecedented psychological disorders needing adequate psychomorphological psychometrics. However, slavery was a long-term, multidimensional experience involving black victimization as well as effective black coping. In assessing, collecting, and analyzing the needed data, we first identify a group that has experienced a jolting, unpredictable, and monstrous assault. Second, we assess the depth of the trauma and an unambiguous period that marks the termination of the trauma using the alliterational psychomorphological psychometrics deca perimeter.

Keywords: slavery, Diaspora, Africa, psychomorphology, psychometrics, ancestry, disorder, phobias and trauma

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Authors: Lintang Putra Sadewa, Ilham Prasetya Budhi

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Slope monitoring program has now become a mandatory campaign for any open pit mines around the world to operate safely. Utilizing various slope monitoring instruments and strategies, miners are now able to deliver precise decisions in mitigating the risk of slope failures which can be catastrophic. Currently, the most sophisticated slope monitoring technology available is the Slope Stability Radar (SSR), whichcan measure wall deformation in submillimeter accuracy. One of its eminent features is that SSRcan provide a timely warning by automatically raise an alarm when a predetermined rate-of-movement threshold is reached. However, establishing proper alarm thresholds is arguably one of the onerous challenges faced in any slope monitoring program. The difficulty mainly lies in the number of considerations that must be taken when generating a threshold becausean alarm must be effectivethat it should limit the occurrences of false alarms while alsobeing able to capture any real wall deformations. In this sense, experience shows that a site-specific alarm thresholdtendsto produce more reliable results because it considers site distinctive variables. This study will attempt to determinealarming thresholds for safety-critical monitoring based on an empirical model of slope deformation behaviour that is defined statistically fromdeformation data captured by the Slope Stability Radar (SSR). The study area comprises of upright-dipping highwall setting in a coal mining area with intense mining activities, andthe deformation data used for the study were recorded by the SSR throughout the year 2022. The model is site-specific in nature thus, valuable information extracted from the model (e.g., time-to-failure, onset-of-acceleration, and velocity) will be applicable in setting up site-specific alarm thresholds and will give a clear understanding of how deformation trends evolve over the area.

Keywords: safety-critical monitoring, alarming strategy, slope deformation behaviour model, coal mining

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Authors: Sudipto Shanker Dasgupta

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One of the largest mining companies wanted to look at health analytics for their driverless trucks. These trucks were the key to their supply chain logistics. The automated trucks had multi-level sub-assemblies which would send out sensor information. The use case that was worked on was to capture the sensor signal from the truck subcomponents and analyze the health of the trucks from repair and replacement purview. Open source software was used to stream the data into a clustered Hadoop setup in Amazon Web Services cloud and Apache Spark SQL was used to analyze the data. All of this was achieved through a 10 node amazon 32 core, 64 GB RAM setup real-time analytics was achieved on ‘300 million records’. To check the scalability of the system, the cluster was increased to 100 node setup. This talk will highlight how Open Source software was used to achieve the above use case and the insights on the high data throughput on a cloud set up.

Keywords: streaming analytics, data science, big data, Hadoop, high throughput, sensor data

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Authors: Samaneh Rahimirshnani, Hossein Jafari

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In this paper, we consider fluid queueing processes fed by an isonormal Gaussian process. We study the correlation structure of the queueing process and the rate of convergence of the running supremum in the queueing process. The Malliavin calculus techniques are applied to obtain relations that show the workload process inherits the dependence properties of the input process. As examples, we consider two isonormal Gaussian processes, the sub-fractional Brownian motion (SFBM) and the fractional Brownian motion (FBM). For these examples, we obtain upper bounds for the covariance function of the queueing process and its rate of convergence to zero. We also discover that the rate of convergence of the queueing process is related to the structure of the covariance function of the input process.

Keywords: queue length process, Malliavin calculus, covariance function, fractional Brownian motion, sub-fractional Brownian motion

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Authors: Yiea Funk Te, Daniel Mueller, Irena Pletikosa Cvijikj

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Small and medium enterprises (SMEs) play an important role in the economy of many countries. When the overall world economy is considered, SMEs represent 95% of all businesses in the world, accounting for 66% of the total employment. Existing studies show that the current business environment is characterized as highly turbulent and strongly influenced by modern information and communication technologies, thus forcing SMEs to experience more severe challenges in maintaining their existence and expanding their business. To support SMEs at improving their competitiveness, researchers recently turned their focus on applying data mining techniques to build risk and growth prediction models. However, data used to assess risk and growth indicators is primarily obtained via questionnaires, which is very laborious and time-consuming, or is provided by financial institutes, thus highly sensitive to privacy issues. Recently, web mining (WM) has emerged as a new approach towards obtaining valuable insights in the business world. WM enables automatic and large scale collection and analysis of potentially valuable data from various online platforms, including companies’ websites. While WM methods have been frequently studied to anticipate growth of sales volume for e-commerce platforms, their application for assessment of SME risk and growth indicators is still scarce. Considering that a vast proportion of SMEs own a website, WM bears a great potential in revealing valuable information hidden in SME websites, which can further be used to understand SME risk and growth indicators, as well as to enhance current SME risk and growth prediction models. This study aims at developing an automated system to collect business-relevant data from the Web and predict future growth trends of SMEs by means of WM and data mining techniques. The envisioned system should serve as an 'early recognition system' for future growth opportunities. In an initial step, we examine how structured and semi-structured Web data in governmental or SME websites can be used to explain the success of SMEs. WM methods are applied to extract Web data in a form of additional input features for the growth prediction model. The data on SMEs provided by a large Swiss insurance company is used as ground truth data (i.e. growth-labeled data) to train the growth prediction model. Different machine learning classification algorithms such as the Support Vector Machine, Random Forest and Artificial Neural Network are applied and compared, with the goal to optimize the prediction performance. The results are compared to those from previous studies, in order to assess the contribution of growth indicators retrieved from the Web for increasing the predictive power of the model.

Keywords: data mining, SME growth, success factors, web mining

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Authors: Rohana Musa, Yuzman Yusoff, Chia Chieu Yin, Hanif Che Lah

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This paper presents the design and characterization of a low power Complementary Metal Oxide Semiconductor (CMOS) process sensor. The design is targeted for implementation using Silterra’s 180 nm CMOS process technology. The proposed process sensor employs a voltage threshold (V_th) extractor architecture for detection of variations in the fabrication process. The process sensor generates output voltages in the range of 401 mV (fast-fast corner) to 443 mV (slow-slow corner) at nominal condition. The power dissipation for this process sensor is 6.3 µW with a supply voltage of 1.8V with a silicon area of 190 µm X 60 µm. The preliminary result of this process sensor that was fabricated indicates a close resemblance between test and simulated results.

Keywords: CMOS process sensor, PVT sensor, threshold extractor circuit, Vth extractor circuit

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Authors: Anyimc, C. J. Aneke, J. O. Orji, O. Nworie, U. C. C. Egbule

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The microbial examination and antimicrobial susceptibility profile of microorganism isolated from the salt mining site in Ebonyi state were evaluated in the present study using a standard microbiological technique. A total of 300 samples were randomly collected in three sample groups (A, B, and C) of 100 each. Isolation, Identification and characterization of organization present on the soil samples were determined by culturing, gram-staining and biochemical technique. The result showed the following organisms were isolated with their frequency as follow: Bacillus species (37.3%) and Staphylococcus species(23.5%) had the highest frequency in the whole Sample group A and B while Klebsiella specie (15.7%), Pseudomonas species(13.7%), and Erwinia species (9.8%) had the least. Rhizopus species (42.0%) and Aspergillus species (26.0%) were the highest fungi isolated, followed by Penicillum species (20.0%) while Mucor species (4.0%), and Fusarium species (8.0%) recorded the least. Sample group C showed high microbial population of all the microbial isolates when compared to sample group A and B. Disc diffusion method was used to determine the susceptibility of isolated bacteria to various antibiotics (oxfloxacin, pefloxacin, ciprorex, augumentin, gentamycin, ciproflox, septrin, ampicillin), while agar well diffusion method was used to determine the susceptibility of isolated fungi to some antifungal drugs (metronidazole, ketoconazole, itraconazole fluconazole). The antibacterial activity of the antibiotics used showed that ciproflux has the best inhibitory effect on all the test bacteria. Ketoconazole showed the highest inhibitory effect on the fungal isolates, followed by itraconazole, while metronidazole and fluconazole showed the least inhibitory effect on the entire test fungal isolates. Hence, the multiple drug resistance of most isolates to appropriate drugs of choice are of great public health concern and cells for periodic monitoring of antibiograms to detect possible changing patterns. Microbes isolated in the salt mining site can also be used as a source of gene(s) that can increase salt tolerance in different crop species through genetic engineering.

Keywords: microorganisms, antibacterial, antifungal, resistance, salt mining site, Ebonyi State

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Authors: Fethia Zenak, Salima Benbernou, Linda Zaoui

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Recently, many companies are based on process development from scratch to achieve their business goals. The process development is not trivial and the main objective of enterprise managing processes is to decrease the software development time. Several concepts have been proposed in the field of business process-based reused development, known as BP Mashup. This concept consists of reusing existing business processes which have been modeled in order to respond to a particular goal. To meet user process requirements, our contribution is to mix parts of processes as 'processes fragments' components to build a new process (i.e. process mashup). The main idea of our paper is to offer graphical framework tool for both creating and running processes mashup. Allow users to perform a mixture of fragments, using a simple interface with set of graphical mixture operators based on a proposed formal model. A process mashup and mixture behavior are described within a new specification of a high-level language, language for process mashup (BPML).

Keywords: business process, mashup, fragments, bp mashup

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Authors: Kolela J Nyembwe, Darlington C. Ashiegbu, Herman J. Potgieter

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Over the past few decades, the demand for metals has increased significantly. This has led to a decrease and decline of high-grade ore over time and an increase in mineral complexity and matrix heterogeneity. In addition to that, there are rising concerns about greener processes and a sustainable environment. Due to these challenges, the mining and metal industry has been forced to develop new technologies that are able to economically process and recover metallic values from low-grade ores, materials having a metal content locked up in industrially processed residues (tailings and slag), and complex matrix mineral deposits. Several methods to address these issues have been developed, among which are ionic liquids (IL), heap leaching, and bioleaching. Recently, the gas phase extraction technique has been gaining interest because it eliminates many of the problems encountered in conventional mineral processing methods. The technique relies on the formation of volatile metal complexes, which can be removed from the residual solids by a carrier gas. The complexes can then be reduced using the appropriate method to obtain the metal and regenerate-recover the organic extractant. Laboratory work on the gas phase have been conducted for the extraction and recovery of aluminium (Al), iron (Fe), copper (Cu), chrome (Cr), nickel (Ni), lead (Pb), and vanadium V. In all cases the extraction revealed to depend of temperature and mineral surface area. The process technology appears very promising, offers the feasibility of recirculation, organic reagent regeneration, and has the potential to deliver on all promises of a “greener” process.

Keywords: gas-phase extraction, hydrometallurgy, low-grade ore, sustainable environment

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Authors: Mladen Dragičević, Mirjana Pejić Bach, Vanja Šimičević

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The purpose of this paper is to develop models that would enable predicting student success. These models could improve allocation of students among colleges and optimize the newly introduced model of government subsidies for higher education. For the purpose of collecting data, an anonymous survey was carried out in the last year of undergraduate degree student population using random sampling method. Decision trees were created of which two have been chosen that were most successful in predicting student success based on two criteria: Grade Point Average (GPA) and time that a student needs to finish the undergraduate program (time-to-degree). Decision trees have been shown as a good method of classification student success and they could be even more improved by increasing survey sample and developing specialized decision trees for each type of college. These types of methods have a big potential for use in decision support systems.

Keywords: data mining, knowledge discovery in databases, prediction models, student success

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Digitalisation in production technology is a driver for the application of machine learning methods. Through the application of predictive quality, the great potential for saving necessary quality control can be exploited through the data-based prediction of product quality and states. However, the serial use of machine learning applications is often prevented by various problems. Fluctuations occur in real production data sets, which are reflected in trends and systematic shifts over time. To counteract these problems, data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets to extract stable features. Successful process control of the target variables aims to centre the measured values around a mean and minimise variance. Competitive leaders claim to have mastered their processes. As a result, much of the real data has a relatively low variance. For the training of prediction models, the highest possible generalisability is required, which is at least made more difficult by this data availability. The implementation of a machine learning application can be interpreted as a production process. The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that describes the life cycle of data science. As in any process, the costs to eliminate errors increase significantly with each advancing process phase. For the quality prediction of hydraulic test steps of directional control valves, the question arises in the initial phase whether a regression or a classification is more suitable. In the context of this work, the initial phase of the CRISP-DM, the business understanding, is critically compared for the use case at Bosch Rexroth with regard to regression and classification. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. Suitable methods for leakage volume flow regression and classification for inspection decision are applied. Impressively, classification is clearly superior to regression and achieves promising accuracies.

Keywords: classification, CRISP-DM, machine learning, predictive quality, regression

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Authors: Obed Asamoah

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Forests are very vital for human survival and our well-being. During the past years, the world has taken an increasingly significant role in the modification of the global environment. The high rate of deforestation in Ghana is of primary national concern as the forests provide many ecosystem services and functions that support the country’s predominantly agrarian economy and foreign earnings. Ghana forest is currently major source of carbon sink that helps to mitigate climate change. Ghana forests, both the reserves and off-reserves, are under pressure of deforestation. The causes of deforestation are varied but can broadly be categorized into anthropogenic and natural factors. For the anthropogenic factors, increased wood fuel collection, clearing of forests for agriculture, illegal and poorly regulated timber extraction, social and environmental conflicts, increasing urbanization and industrialization are the primary known causes for the loss of forests and woodlands. Mineral exploitation in the forest areas is considered as one of the major causes of deforestation in Ghana. Mining activities especially mining of gold by both the licensed mining companies and illegal mining groups who are locally known as "gallantly mining" also cause damage to the nation's forest reserves. Several works have been conducted regarding the causes of the high rate of deforestation in Ghana, major attention has been placed on illegal logging and using forest lands for illegal farming and mining activities. Less emphasis has been placed on the timber production companies on their harvesting methods in the forests in Ghana and other activities that are carried out in the forest. The main objective of the work is to find out the harvesting methods and the activities of the timber production companies and their effects on the forests in Ghana. Both qualitative and quantitative research methods were engaged in the research work. The study population comprised of 20 Timber industries (Sawmills) forest areas of Ghana. These companies were selected randomly. The cluster sampling technique was engaged in selecting the respondents. Both primary and secondary data were employed. In the study, it was observed that most of the timber production companies do not know the age, the weight, the distance covered from the harvesting to the loading site in the forest. It was also observed that old and heavy machines are used by timber production companies in their operations in the forest, which makes the soil compact prevents regeneration and enhances soil erosion. It was observed that timber production companies do not abide by the rules and regulations governing their operations in the forest. The high rate of corruption on the side of the officials of the Ghana forestry commission makes the officials relax and do not embark on proper monitoring on the operations of the timber production companies which makes the timber companies to cause more harm to the forest. In other to curb this situation the Ghana forestry commission with the ministry of lands and natural resources should monitor the activities of the timber production companies and sanction all the companies that make foul play in their activities in the forest. The commission should also pay more attention to the policy “fell one plant 10” to enhance regeneration in both reserves and off-reserves forest.

Keywords: companies, deforestation, forest, Ghana, timber

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Authors: Chien-Hung Chen, Yan-Chun Hsing, Yung-Chun Chang

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The Internet has grown into a powerful medium for information dispersion and social interaction that leads to a rapid growth of social media which allows users to easily post their emotions and perspectives regarding certain topics online. Our research aims at using natural language processing and text mining techniques to explore the public emotions expressed on Twitter by analyzing the sentiment behind tweets. In this paper, we propose a composite kernel method that integrates tree kernel with the linear kernel to simultaneously exploit both the tree representation and the distributed emotion keyword representation to analyze the syntactic and content information in tweets. The experiment results demonstrate that our method can effectively detect public emotion of tweets while outperforming the other compared methods.

Keywords: emotion recognition, natural language processing, composite kernel, sentiment analysis, text mining

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Authors: Tassadit Issaadi Hamitouche, Nicolas Gillard, Jean Petit, Valerie Lavaste, Celine Mayousse

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Water is a fundamental resource for the industry. It is taken from the environment either from municipal distribution networks or from various natural water sources such as the sea, ocean, rivers, aquifers, etc. Once used, water is discharged into the environment, reprocessed at the plant or treatment plants. These withdrawals and discharges have a direct impact on natural water resources. These impacts can apply to the quantity of water available, the quality of the water used, or to impacts that are more complex to measure and less direct, such as the health of the population downstream from the watercourse, for example. Based on the analysis of data (meteorological, river characteristics, physicochemical substances), we wish to predict water stress episodes and anticipate prefectoral decrees, which can impact the performance of plants and propose improvement solutions, help industrialists in their choice of location for a new plant, visualize possible interactions between companies to optimize exchanges and encourage the pooling of water treatment solutions, and set up circular economies around the issue of water. The development of a system for the collection, processing, and use of data related to water resources requires the functional constraints specific to the latter to be made explicit. Thus the system will have to be able to store a large amount of data from sensors (which is the main type of data in plants and their environment). In addition, manufacturers need to have 'near-real-time' processing of information in order to be able to make the best decisions (to be rapidly notified of an event that would have a significant impact on water resources). Finally, the visualization of data must be adapted to its temporal and geographical dimensions. In this study, we set up an infrastructure centered on the TICK application stack (for Telegraf, InfluxDB, Chronograf, and Kapacitor), which is a set of loosely coupled but tightly integrated open source projects designed to manage huge amounts of time-stamped information. The software architecture is coupled with the cross-industry standard process for data mining (CRISP-DM) data mining methodology. The robust architecture and the methodology used have demonstrated their effectiveness on the study case of learning the level of a river with a 7-day horizon. The management of water and the activities within the plants -which depend on this resource- should be considerably improved thanks, on the one hand, to the learning that allows the anticipation of periods of water stress, and on the other hand, to the information system that is able to warn decision-makers with alerts created from the formalization of prefectoral decrees.

Keywords: data mining, industry, machine Learning, shortage, water resources

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Authors: Bin Wang, Yong Xu, Honggang Qu, Rongmei Liu, Zhenji Gao

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Today's mining industry is advancing gradually toward digital and visual direction. The three dimensional visualization geological modeling of mine is the digital characterization of mineral deposit, and is one of the key technology of digital mine. The three-dimensional geological modeling is a technology that combines the geological spatial information management, geological interpretation, geological spatial analysis and prediction, geostatistical analysis, entity content analysis and graphic visualization in three-dimensional environment with computer technology, and is used in geological analysis. In this paper, the three-dimensional geological modeling of an iron mine through the use of Surpac is constructed, and the weight difference of the estimation methods between distance power inverse ratio method and ordinary kriging is studied, and the ore body volume and reserves are simulated and calculated by using these two methods. Compared with the actual mine reserves, its result is relatively accurate, so it provided scientific bases for mine resource assessment, reserve calculation, mining design and so on.

Keywords: three-dimensional geological modeling, geological database, geostatistics, block model

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Authors: Arturo Reyes Roman, Daniza Castillo Godoy, Francisca Balarezo Olivares, Francisco Arriagada Castro, Miguel Maulen Tapia

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Mining waste–based geopolymers are a sustainable alternative to conventional cement materials due to their contribution to the valorization of mining wastes as well as to the new construction materials with reduced fingerprints. The objective of this study was to determine the potential of leaching gravel (LG) from hydrometallurgical copper processing to be used as a raw material in the manufacture of geopolymer. NaOH, Na2SiO3 (modulus 1.5), and LG were mixed and then wetted with an appropriate amount of tap water, then stirred until a homogenous paste was obtained. A liquid/solid ratio of 0.3 was used for preparing mixtures. The paste was then cast in cubic moulds of 50 mm for the determination of compressive strengths. The samples were left to dry for 24h at room temperature, then unmoulded before analysis after 28 days of curing time. The compressive test was conducted in a compression machine (15/300 kN). According to the laser diffraction spectroscopy (LDS) analysis, 90% of LG particles were below 500 μm. The X-ray diffraction (XRD) analysis identified crystalline phases of albite (30 %), Quartz (16%), Anorthite (16 %), and Phillipsite (14%). The X-ray fluorescence (XRF) determinations showed mainly 55% of SiO2, 13 % of Al2O3, and 9% of CaO. ICP (OES) concentrations of Fe, Ca, Cu, Al, As, V, Zn, Mo, and Ni were 49.545; 24.735; 6.172; 14.152, 239,5; 129,6; 41,1;15,1, and 13,1 mg kg-1, respectively. The geopolymer samples showed resistance ranging between 2 and 10 MPa. In comparison with the raw material composition, the amorphous percentage of materials in the geopolymer was 35 %, whereas the crystalline percentage of main mineral phases decreased. Further studies are needed to find the optimal combinations of materials to produce a more resistant and environmentally safe geopolymer. Particularly are necessary compressive resistance higher than 15 MPa are necessary to be used as construction unit such as bricks.

Keywords: mining waste, geopolymer, construction material, alkaline activation

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Authors: Ahmed Elwaleed, Huiho Jeong, Ali H. Abdelbagi, Nguyen Thi Quynh, Koji Arizono, Yasuhiro Ishibashi

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Artisanal and small-scale gold mining (ASGM) poses substantial risks to both human health and the environment, particularly through contamination of soil, water, and air. Prolonged exposure to ASGM-contaminated soils can lead to acute or chronic mercury toxicity. This study assesses the human health risks associated with mercury-contaminated soils and tailings in the Alebediah mining community in Sudan. Soil samples were collected from various locations within Alebediah, including ASGM areas, farmlands, and residential areas, along with tailings samples commonly found within ASGM sites. The evaluation of potential health risks to humans included the computation of the estimated daily intake (AvDI), the hazard quotient (HQ), and the hazard index (HI) for both adults and children. The primary exposure route identified as potentially posing a significant health risk was the volatilization of mercury from tailings samples, where mercury concentrations reached up to 25.5 mg/kg. In contrast, other samples within the ASGM area showed elevated mercury levels but did not present significant health risks, with HI values below 1. However, all areas indicated HI values above 1 for the remaining exposure routes. The study observed a decrease in mercury concentration with increasing distance from the ASGM community. Additionally, soil samples revealed elevated mercury levels exceeding background values, prompting an assessment of contamination levels using the enrichment factor (EF). The findings indicated that farmlands and residential areas exhibited depleted EF, while areas surrounding the ASGM community showed none to moderate pollution. In contrast, ASGM areas exhibited significant to extreme pollution. A GIS map was generated to visually depict the extent of mercury pollution, facilitating communication with stakeholders and decision-makers.

Keywords: mercury pollution, artisanal and small-scale gold mining, health risk assessment, hazard index, soil and tailings, enrichment factor

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Authors: Shigeaki Mishima

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Pharmacist labor shortage has become a long-term problem in Japan. This paper discusses the relationship between organizational commitment and pharmacists' organizational behavior in the context of labor shortage. Based on a multidimensional view of organizational commitment, effective commitment and continuous commitment are measured. It is suggested that the continuous commitment has a unique impact on withholding information behavior. We also discuss the impact of labor supply and demand on continuous commitment of retail pharmacist.

Keywords: organizational commitment, pharmacist, labor shortage, professional

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Authors: Princes Ann E. Prieto, Denise F. Alpuerto, John Rafael C. Unlayao, Neil Concibido, Monet Concepcion Maguyon-Detras

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Failure Mode and Effects Analysis (FMEA) has been used in the virtual Process Hazard Analysis (PHA) of the Acetone production process through the dehydrogenation of isopropyl alcohol, for which very limited process risk assessment has been published. In this study, the potential failure modes, effects, and possible causes of selected major equipment in the process were identified. During the virtual FMEA mock sessions, the risks in the process were evaluated and recommendations to reduce and/or mitigate the process risks were formulated. The risk was estimated using the calculated risk priority number (RPN) and was classified into four (4) levels according to their effects on acetone production. Results of this study were also used to rank the criticality of equipment in the process based on the calculated criticality rating (CR). Bow tie diagrams were also created for the critical hazard scenarios identified in the study.

Keywords: chemical process safety, failure mode and effects analysis (FMEA), process hazard analysis (PHA), process safety management (PSM)

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Authors: Heejung Lee

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In a highly competitive environment, it becomes more important to shorten the whole business process while delivering or even enhancing the business value to the customers and suppliers. Although the workflow management systems receive much attention for its capacity to practically support the business process enactment, the effective workflow modeling method remain still challenging and the high degree of process complexity makes it more difficult to gain the short lead time. This paper presents a workflow structuring method in a holistic way that can reduce the process complexity using activity-needs and formal concept analysis, which eventually enhances the key performance such as quality, delivery, and cost in business process.

Keywords: workflow management, re-engineering, formal concept analysis, business process

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Authors: Carine Cridelich, Pablo Juesas Cano, Emmanuel Ramasso, Noureddine Zerhouni, Bernd Weiler

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Safety is one of the most important considerations when buying a new car. While active safety aims at avoiding accidents, passive safety systems such as airbags and seat belts protect the occupant in case of an accident. In addition to legal regulations, organizations like Euro NCAP provide consumers with an independent assessment of the safety performance of cars and drive the development of safety systems in automobile industry. Those ratings are mainly based on injury assessment reference values derived from physical parameters measured in dummies during a car crash test. The components and sub-systems of a safety system are designed to achieve the required restraint performance. Sled tests and other types of tests are then carried out by car makers and their suppliers to confirm the protection level of the safety system. A Knowledge Discovery in Databases (KDD) process is proposed in order to minimize the number of tests. The KDD process is based on the data emerging from sled tests according to Euro NCAP specifications. About 30 parameters of the passive safety systems from different data sources (crash data, dummy protocol) are first analysed together with experts opinions. A procedure is proposed to manage missing data and validated on real data sets. Finally, a procedure is developed to estimate a set of rough initial parameters of the passive system before testing aiming at reducing the number of tests.

Keywords: KDD process, passive safety systems, sled test, dummy injury assessment reference values, frontal impact

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Authors: Thomas Lauer

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The paper aims to give an overview of change management research by using bibliometric methodology. Based on research papers of the last decade, which are listed on Research Gate, a multidimensional categorization is done. Considering categories like topic (e.g., success factors), industry, or research methodology, the development of the discipline is traced and, in a second step, confronted with external developments of the business environment, such as climate change, gen Z or COVID, to name a few. Based on these findings, a final evaluation concerning the thematical fit of previous research topics is also made, as well as a preview of likely future trends in change management sciences.

Keywords: change management, bibliometrics, scientific trends, research topics

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Authors: Guettal Djaouida, Ziadi Abdelkader

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In this paper, we study a coupling of the Alienor method with the algorithm of Piyavskii-Shubert. The classical multidimensional global optimization methods involves great difficulties for their implementation to high dimensions. The Alienor method allows to transform a multivariable function into a function of a single variable for which it is possible to use efficient and rapid method for calculating the the global optimum. This simplification is based on the using of a reducing transformation called Alienor.

Keywords: global optimization, reducing transformation, α-dense curves, Alienor method, Piyavskii-Shubert algorithm

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Authors: Ljubica Milanović Glavan, Vesna Bosilj Vukšić, Dalia Suša

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In order to gain a competitive advantage, many companies are focusing on reorganization of their business processes and implementing process-based management. In this context, assessing process performance is essential because it enables individuals and groups to assess where they stand in comparison to their competitors. In this paper, it is argued that process performance measurement is a necessity for a modern process-oriented company and it should be supported by a holistic process performance measurement system. It seems very unlikely that a universal set of performance indicators can be applied successfully to all business processes. Thus, performance indicators must be process-specific and have to be derived from both the strategic enterprise-wide goals and the process goals. Based on the extensive literature review and interviews conducted in Croatian company a conceptual framework for process performance measurement system was developed. The main objective of such system is to help process managers by providing comprehensive and timely information on the performance of business processes. This information can be used to communicate goals and current performance of a business process directly to the process team, to improve resource allocation and process output regarding quantity and quality, to give early warning signals, to make a diagnosis of the weaknesses of a business process, to decide whether corrective actions are needed and to assess the impact of actions taken.

Keywords: Croatia, key performance indicators, performance measurement, process performance

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Authors: Maria M. Calheiros, Carla Silva, Eunice Magalhães

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Professionals working in child protection services (CPS) need to have common and clear criteria to identify cases of maltreatment and to differentiate levels of severity in order to determine when CPS intervention is required, its nature and urgency, and, in most countries, the service that will be in charge of the case (community or specialized CPS). Actually, decision-making process is complex in CPS, and, for that reason, such criteria are particularly important for who significantly contribute to that decision-making in child maltreatment cases. The main objective of this presentation is to describe the Maltreatment Severity Assessment Questionnaire (MSQ), specifically designed to be used by professionals in the CPS, which adopts a multidimensional approach and uses a scale of severity within subtypes. Specifically, we aim to provide evidence of validity and reliability of this tool, in order to improve the quality and validity of assessment processes and, consequently, the decision making in CPS. The total sample was composed of 1000 children and/or adolescents (51.1% boys), aged between 0 and 18 years old (M = 9.47; DP = 4.51). All the participants were referred to official institutions of the children and youth protective system. Children and adolescents maltreatment (abuse, neglect experiences and sexual abuse) were assessed with 21 items of the Maltreatment Severity Questionnaire (MSQ), by professionals of CPS. Each item (sub-type) was composed of four descriptors of increasing severity. Professionals rated the level of severity, using a 4-point scale (1= minimally severe; 2= moderately severe; 3= highly severe; 4= extremely severe). The construct validity of the Maltreatment Severity Questionnaire was assessed with a holdout method, performing an Exploratory Factor Analysis (EFA) followed by a Confirmatory Factor Analysis (CFA). The final solution comprised 18 items organized in three factors 47.3% of variance explained. ‘Physical neglect’ (eight items) was defined by parental omissions concerning the insurance and monitoring of the child’s physical well-being and health, namely in terms of clothing, hygiene, housing conditions and contextual environmental security. ‘Physical and Psychological Abuse’ (four items) described abusive physical and psychological actions, namely, coercive/punitive disciplinary methods, physically violent methods or verbal interactions that offend and denigrate the child, with the potential to disrupt psychological attributes (e.g., self-esteem). ‘Psychological neglect’ (six items) involved omissions related to children emotional development, mental health monitoring, school attendance, development needs, as well as inappropriate relationship patterns with attachment figures. Results indicated a good reliability of all the factors. The assessment of child maltreatment cases with MSQ could have a set of practical and research implications: a) It is a valid and reliable multidimensional instrument to measure child maltreatment, b) It is an instrument integrating the co-occurrence of various types of maltreatment and a within-subtypes scale of severity; c) Specifically designed for professionals, it may assist them in decision-making processes; d) More than using case file reports to evaluate maltreatment experiences, researchers could guide more appropriately their research about determinants and consequences of maltreatment.

Keywords: assessment, maltreatment, children and youth, decision-making

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Authors: Francesca Maria Melucci, Marco Panettieri, Rocco Roma

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Cities are at the forefront of achieving the sustainable development goals set out in the Sustainable Development Goals of Agenda 2030. For these reasons, increasing attention has been given to the creation of resilient, sustainable, inclusive and green cities and finding solutions to these problems is one of the greatest challenges faced by researchers today. In particular urban green infrastructures, including green roofs, play a key role in tackling environmental, social and economic problems. The starting point was an extensive literature review on 1. research developments on the benefits (environmental, economic and social) and implications of green roofs; 2. sustainability assessment and applied methodologies; 3. specific indicators to measure impacts on urban sustainability. Through this review, the appropriate qualitative and quantitative characteristics that are part of the complex 'green roof' system were identified, as studies that holistically capture its multifunctional nature are still lacking. So, this paper aims to find a method to improve community participation in green roof initiatives and support local governance processes in developing efficient proposals to achieve better sustainability and resilience of cities. To this aim, the multidimensional indicator-based framework, presented by Tapia in 2021, has been tested for the first time in the case of a green roof in the city of Madrid. The framework's set of indicators was implemented with other indicators such as those of waste management and circularity (OECD Inventory of Circular Economy indicators) and sustainability performance. The specific indicators to be used in the case study were decided after a consultation phase with relevant stakeholders. Data on the community's willingness to participate in green roof implementation initiatives were collected through interviews and online surveys with a heterogeneous sample of citizens. The results of the application of the framework suggest how the different aspects of sustainability influence the choice of a green roof and provide input on the main mechanisms involved in citizens' willingness to participate in such initiatives.

Keywords: urban agriculture, green roof, urban sustainability, indicators, multi-criteria analysis

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Authors: Safeer Hussain Khan

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In this paper, we prove a strong convergence result using a recently introduced iterative process with contractive-like operators. This improves and generalizes corresponding results in the literature in two ways: the iterative process is faster, operators are more general. In the end, we indicate that the results can also be proved with the iterative process with error terms.

Keywords: contractive-like operator, iterative process, fixed point, strong convergence

Procedia PDF Downloads 432