Search results for: uncertain demand

Authors: Gia Sirbiladze

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Route planning problems are among the activities that have the highest impact on logistical planning, transportation, and distribution because of their effects on efficiency in resource management, service levels, and client satisfaction. In extreme conditions, the difficulty of vehicle movement between different customers causes the imprecision of time of movement and the uncertainty of the feasibility of movement. A feasibility level of vehicle movement on the closed route of the disaster-streaking zone is defined for the construction of an objective function. Experts’ evaluations of the uncertain parameters in q-rung ortho-pair fuzzy numbers (q-ROFNs) are presented. A fuzzy bi-objective combinatorial optimization problem of fuzzy vehicle routine problem (FVRP) is constructed based on the technique of possibility theory. The FVRP is reduced to the bi-criteria partitioning problem for the so-called “promising” routes which were selected from the all-admissible closed routes. The convenient selection of the “promising” routes allows us to solve the reduced problem in real-time computing. For the numerical solution of the bi-criteria partitioning problem, the -constraint approach is used. The main results' support software is designed. The constructed model is illustrated with a numerical example.

Keywords: q-rung ortho-pair fuzzy sets, facility location selection problem, multi-objective combinatorial optimization problem, partitioning problem

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Authors: Ginés Hernández-Cánovas, Mª Camino Ramón-Llorens, Johanna Koëter-Kant

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This paper uses a survey data-set of 837 Spanish SMEs to analyze the association between borrower discouragement and prior firm´s strategic decisions, while controlling for firm and owner characteristics. While existing literature has neglected factors limiting the demand for resources by an overreliance on arguments which attempt to explain the existence of discouraged borrowers solely in terms of lack of access to supply of credit. The objective of this paper is to show that factors limiting the demand for resources and, therefore, reducing the availability of funds, can be traced back to the firm manager´s decision. Our hypothesis is that managers that undertake strategic decisions seeking growth or improvement in their business performance participate more in the banking market than those showing contentment with their current business situation. Our results shows that SMEs that undertake an active role in research and development activities and that achieve improvements in the operating performance of their business are less likely to be discouraged from applying for a loan. Who needs credit and who applies for credit is important for firms, prospective lenders and policymakers interested in the financial health of these firms. Credit constrained firms are less likely to invest in R&D and to introduce new products, possibly harming long-term economic growth. Knowing how important borrower discouragement is in Europe, is important for judging the priority which should be attached to government policies aimed at reducing its effects. For example, policy makers could encourage the transparency about credit eligibility and conditions in order to reduce discouragement.

Keywords: discouragement, financial constraints, SMEs financing

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Authors: Zhuoyuan Wang, Benson T. H. Lim, Imriyas Kamardeen

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Effective service quality management has long been touted as a means of improving project and organisational performance. Particularly, in construction projects, main contractors are often seen as a broker between clients and subcontractors, and their service quality is thus associated with the overall project affinity and outcomes. While a considerable amount of research has focused on the aspect of clients-main contractors, very little research has been done to explore the effect of contractors’ service quality on subcontractors’ behaviours and so project outcomes. In addressing this gap, this study surveyed 97 subcontractors in the Chinese Construction industry and data was analysed using the Partial Least Square (PLS) Structural Equation Modelling (SEM) technique. The overall findings reveal that subcontractors categorised main contractors’ service quality into three dimensions: assurance; responsiveness; reliability and empathy. Of these, it is found that main contractors’ ‘assurance’ and ‘responsiveness’ positively influence subcontractors’ intention to engage in contractual behaviours. The results further show that the subcontractors’ intention to engage in organizational citizenship behaviours is associated with how flexible and committed the main contractors are in reliability and empathy. Collectively, both subcontractors’ contractual and organizational citizenship behaviours positively influence the overall project outcomes. In conclusion, the findings inform contractors different strategies towards managing and gaining subcontractors’ behaviour commitment in a socially connected, yet complex and uncertain, business environment.

Keywords: construction firms, organisational citizenship behaviour, service quality, social exchange theory

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Keywords: DCOP, multi-agent reasoning, Bayesian reasoning, swarm intelligence

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Authors: Zviad Ghadua, Biswa Bhattacharya

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The Flash Flood Guidance (FFG) provides the rainfall amount of a given duration necessary to cause flooding. The approach is based on the development of rainfall-runoff curves, which helps us to find out the rainfall amount that would cause flooding. An alternative approach, mostly experimented with Italian Alpine catchments, is based on determining threshold discharges from past events and on finding whether or not an oncoming flood has its magnitude more than some critical discharge thresholds found beforehand. Both approaches suffer from large uncertainties in forecasting flash floods as, due to the simplistic approach followed, the same rainfall amount may or may not cause flooding. This uncertainty leads to the question whether a probabilistic model is preferable over a deterministic one in forecasting flash floods. We propose the use of a Bayesian probabilistic approach in flash flood forecasting. A prior probability of flooding is derived based on historical data. Additional information, such as antecedent moisture condition (AMC) and rainfall amount over any rainfall thresholds are used in computing the likelihood of observing these conditions given a flash flood has occurred. Finally, the posterior probability of flooding is computed using the prior probability and the likelihood. The variation of the computed posterior probability with rainfall amount and AMC presents the suitability of the approach in decision making in an uncertain environment. The methodology has been applied to the Posina basin in Italy. From the promising results obtained, we can conclude that the Bayesian approach in flash flood forecasting provides more realistic forecasting over the FFG.

Keywords: flash flood, Bayesian, flash flood guidance, FFG, forecasting, Posina

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Authors: Alfonso Ippolito, Cristiana Bartolomei

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A growing demand is felt today for realistic 3D models enabling the cognition and popularization of historical-artistic heritage. Evaluation and preservation of Cultural Heritage is inextricably connected with the innovative processes of gaining, managing, and using knowledge. The development and perfecting of techniques for acquiring and elaborating photorealistic 3D models, made them pivotal elements for popularizing information of objects on the scale of architectonic structures.

Keywords: cultural heritage, databases, non-contact survey, 2D-3D models

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Authors: Usama Abdulwahab, Mohammed Wahab

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This paper presents the application of uncapacitated facility location problems (UFLP) and 1-median problems to support decision making in blood supply chain networks. A plethora of factors make blood supply-chain networks a complex, yet vital problem for the regional blood bank. These factors are rapidly increasing demand; criticality of the product; strict storage and handling requirements; and the vastness of the theater of operations. As in the UFLP, facilities can be opened at any of $m$ predefined locations with given fixed costs. Clients have to be allocated to the open facilities. In classical location models, the allocation cost is the distance between a client and an open facility. In this model, the costs are the allocation cost, transportation costs, and inventory costs. In order to address this problem the median algorithm is used to analyze inventory, evaluate supply chain status, monitor performance metrics at different levels of granularity, and detect potential problems and opportunities for improvement. The Euclidean distance data for some Ontario cities (demand nodes) are used to test the developed algorithm. Sitation software, lagrangian relaxation algorithm, and branch and bound heuristics are used to solve this model. Computational experiments confirm the efficiency of the proposed approach. Compared to the existing modeling and solution methods, the median algorithm approach not only provides a more general modeling framework but also leads to efficient solution times in general.

Keywords: approximate dynamic programming, facility location, perishable product, inventory model, blood platelet, P-median problem

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Authors: Misagh Ghobadi, Rich Crane, Karen Hudson-Edwards, Clemens Vinzenz Ullmann

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Lithium is recognized as the critical energy metal of the 21st century, comparable in importance to coal in the 19th century and oil in the 20th century, often termed 'white gold'. Current global demand for lithium, estimated at 0.95-0.98 million metric tons (Mt) of lithium carbonate equivalent (LCE) annually in 2024, is projected to rise to 1.87 Mt by 2027 and 3.06 Mt by 2030. Despite anticipated short-term stability in supply and demand, meeting the forecasted 2030 demand will require the lithium industry to develop an additional capacity of 1.42 Mt of LCE annually, exceeding current planned and ongoing efforts. Brine resources constitute nearly 65% of global lithium reserves, underscoring the importance of exploring lithium recovery from underutilized sources, especially geothermal brines. However, conventional lithium extraction from brine deposits faces challenges due to its time-intensive process, low efficiency (30-50% lithium recovery), unsuitability for low lithium concentrations (<300 mg/l), and notable environmental impacts. Addressing these challenges, direct lithium extraction (DLE) methods have emerged as promising technologies capable of economically extracting lithium even from low-concentration brines (>50 mg/l) with high recovery rates (75-98%). However, most studies (70%) have predominantly focused on synthetic brines instead of native (natural/real), with limited application of these approaches in real-world case studies or industrial settings. This study aims to bridge this gap by investigating a geothermal brine sample collected from a real case study site in the UK. A Mn-based lithium-ion sieve (LIS) adsorbent was synthesized and employed to selectively extract lithium from the sample brine. Adsorbents with a Li:Mn molar ratio of 1:1 demonstrated superior lithium selectivity and adsorption capacity. Furthermore, the pristine Mn-based adsorbent was modified through transition metals doping, resulting in enhanced lithium selectivity and adsorption capacity. The modified adsorbent exhibited a higher separation factor for lithium over major co-existing cations such as Ca, Mg, Na, and K, with separation factors exceeding 200. The adsorption behaviour was well-described by the Langmuir model, indicating monolayer adsorption, and the kinetics followed a pseudo-second-order mechanism, suggesting chemisorption at the solid surface. Thermodynamically, negative ΔG° values and positive ΔH° and ΔS° values were observed, indicating the spontaneity and endothermic nature of the adsorption process.

Keywords: adsorption, critical minerals, DLE, geothermal brines, geochemistry, lithium, lithium-ion sieves

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Authors: Abeer A. Q. Ahmed, Tracey McKay

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Biomass can provide a sustainable source for the production of high valued chemicals. Under the uncertain availability of fossil resources biomass could be the only available source for chemicals in future. Cellulose and hemicellulose can be hydrolyzed into their building blocks (hexsoses and pentoses) which can be converted later to the desired high valued chemicals. A cellulase- and xylanase- producing bacterial strain identified as Paenibacillus illinoisensis CX11 by 16S rRNA gene sequencing and phylogenetic analysis was found to have the ability to saccharify different lignocellulosic materials. Cellulase and xylanase activities were evaluated by 3,5-dinitro-salicylic acid (DNS) method using CMC and xylan as substrates. Results showed that P. illinoisensis CX11 have cellulase (2.63± 0.09 mg/ml) and xylanase (3.25 ± 0.2 mg/ml) activities. The ability of P. illinoisensis CX11 to saccharify lignocellulosic materials was tested using wheat straw (WS), wheat bran (WB), saw dust (SD), and corn stover (CS). DNS method was used to determine the amount of reducing sugars that were released from lignocellulosic materials. P. illinoisensis CX11 showed to have the ability to saccharify lignocellulosic materials and producing total reducing sugars as 2.34 ± 0.12, 2.51 ± 0.37, 1.86 ± 0.16, and 3.29 ± 0.20 mg/l from WS, WB, SD, and CS respectively. According to the author's knowledge, current findings are the first to report P. illinoisensis CX11 as a cellulase and xylanase producing species and that it has the ability to saccharify different lignocellulosic materials. This study presents P. illinoisensis CX11 that can be good source for cellulase and xylanase enzymes which could be introduced into lignocellulose bioconversion processes to produce high valued chemicals.

Keywords: cellulase, high valued chemicals, lignocellulosic materials, Paenibacillus illinoisensis CX11, Xylanase

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Authors: Akilah A. Jardine

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The integration and expansion of the global economy have indeed brought about a number of positive changes such as access to new goods and services and the opportunity for individuals and businesses to migrate, communicate, and work globally. Nevertheless, the interconnectedness of world economies is not without its negative and shameful side effects. The subsequent overabundance of goods and services has resulted in heightened competition among firms and their supply chains, fuelling the exploitation of impoverished and vulnerable individuals who are unable to equally salvage from the benefits of the integrated economy. To maintain their position in a highly competitive arena, the operations of many businesses have adopted unethical and unscrupulous practices to maximise profit, often targeting the most marginalised members of society. Simultaneously, in a consumerist obsessed society preoccupied with the consumption and accumulation of material wealth, the demand for goods and services greatly contributes to the pressure on firms, thus bolstering the exploitation of labour. This paper aims to examine the impact of business operations on the practice of labour exploitation. It explores corrupt business practices that firms adopt and key labour exploitative conditions outlined by the International Labour Organization, particularly, paying workers low wages, forcing individuals to work in abusive and unsafe conditions, and considers the issue regarding individuals’ consent to exploitative environments. Further, it considers the role of consumers in creating the high demand for goods and services, which in turn fosters the exploitation of labour. This paper illustrates that the practice of labour exploitation in the economy is a by-product of both global competitive business operations and heightened consumer consumption.

Keywords: globalisation, labour exploitation, modern slavery, sweatshops, unethical business practices

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Authors: Ji-Seok Koo, Hee‑Yong Kwon, Hui-Young Yun, Kyung-Hui Wang, Youn-Seo Koo

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This paper presents a forecasting system for PM2.5 levels in Seoul, South Korea, leveraging a combination of chemical transport modeling and ConvLSTM-DNN machine learning technology. Exposure to PM2.5 has known detrimental impacts on public health, making its prediction crucial for establishing preventive measures. Existing forecasting models, like the Community Multiscale Air Quality (CMAQ) and Weather Research and Forecasting (WRF), are hindered by their reliance on uncertain input data, such as anthropogenic emissions and meteorological patterns, as well as certain intrinsic model limitations. The system we've developed specifically addresses these issues by integrating machine learning and using carefully selected input features that account for local and distant sources of PM2.5. In South Korea, the PM2.5 concentration is greatly influenced by both local emissions and long-range transport from China, and our model effectively captures these spatial and temporal dynamics. Our PM2.5 prediction system combines the strengths of advanced hybrid machine learning algorithms, convLSTM and DNN, to improve upon the limitations of the traditional CMAQ model. Data used in the system include forecasted information from CMAQ and WRF models, along with actual PM2.5 concentration and weather variable data from monitoring stations in China and South Korea. The system was implemented specifically for Seoul's PM2.5 forecasting.

Keywords: PM2.5 forecast, machine learning, convLSTM, DNN

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Authors: Vahid Nourani, Atefeh Ashrafi

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Prediction of treated wastewater quality is a matter of growing importance in water treatment procedure. In this way artificial neural network (ANN), as a robust data-driven approach, has been widely used for forecasting the effluent quality of wastewater treatment. However, developing ANN model based on appropriate input variables is a major concern due to the numerous parameters which are collected from treatment process and the number of them are increasing in the light of electronic sensors development. Various studies have been conducted, using different clustering methods, in order to classify most related and effective input variables. This issue has been overlooked in the selecting dominant input variables among wastewater treatment parameters which could effectively lead to more accurate prediction of water quality. In the presented study two ANN models were developed with the aim of forecasting effluent quality of Tabriz city’s wastewater treatment plant. Biochemical oxygen demand (BOD) was utilized to determine water quality as a target parameter. Model A used Principal Component Analysis (PCA) for input selection as a linear variance-based clustering method. Model B used those variables identified by the mutual information (MI) measure. Therefore, the optimal ANN structure when the result of model B compared with model A showed up to 15% percent increment in Determination Coefficient (DC). Thus, this study highlights the advantage of PCA method in selecting dominant input variables for ANN modeling of wastewater plant efficiency performance.

Keywords: Artificial Neural Networks, biochemical oxygen demand, principal component analysis, mutual information, Tabriz wastewater treatment plant, wastewater treatment plant

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Authors: Ali Jahanfar, Brajesh Dubey, Bahram Gharabaghi, Saber Bayat Movahed

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Ever increasing population growth of major urban centers and environmental challenges in siting new landfills have resulted in a growing trend in design of mega-landfills some with extraordinary heights and dangerously steep slopes. Landfill failure mobility risk analysis is one of the most uncertain types of dynamic rheology models due to very large inherent variabilities in the heterogeneous solid waste material shear strength properties. The waste flow of three historic dumpsite and two landfill failures were back-analyzed using run-out modeling with DAN-W model. The travel distances of the waste flow during landfill failures were calculated approach by taking into account variability in material shear strength properties. The probability distribution function for shear strength properties of the waste material were grouped into four major classed based on waste material compaction (landfills versus dumpsites) and composition (high versus low quantity) of high shear strength waste materials such as wood, metal, plastic, paper and cardboard in the waste. This paper presents a probabilistic method for estimation of the spatial extent of waste avalanches, after a potential landfill failure, to create maps of vulnerability scores to inform property owners and residents of the level of the risk.

Keywords: landfill failure, waste flow, Voellmy rheology, friction coefficient, waste compaction and type

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Authors: Berit Johannessen

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Introduction: Depression and dementia are the two most common psychiatric disorders of older people. The use of antidepressants does not always have the desired effect and serious side effects are common. Aroma therapists claim that the essential oil of Bergamot has an antidepressant effect. Aromatherapy is defined as holistic or complementary medicine and is rarely used in the Norwegian public health service, but in recent years, there has been an increasing interest in, and use of complementary and alternative therapies (CAM) in the Norwegian population, and aromatherapy is one of the most widely used. Focus for this study: How do nurses experience the use of Bergamot essential oil for patients suffering from dementia and depression? Method: Action research study including 12 patients and 8 nurses. The patients were strategically selected by the nurses and were given Bergamot in a fan diffuser every day for 12 weeks. The patients' mood and behavior patterns were reported daily and the nurse`s experiences were reported weekly. Individual interviews with the nurses were conducted at the end of the project. Results: The nurses reported that bergamot had positive impact on patients mood and wellbeing, and was considered as an effective method for six patients, four had uncertain effect and two had no effect. They also reported less use of medication and that the fan diffusers were easy and pleasant to administer. They found the use of natural remedies as Bergamot inspiring and wanted to learn more about aromatherapy and its use in nursing. Some were disturbed by the smell and some had to deal with critical and negative colleagues. Conclusion: Nurses experienced aromatherapy using bergamot oil in fan diffusers as a simple and useful procedure for patients suffering from dementia and depression. The effects were varying. Further research is needed.

Keywords: aromatherapy, bergamot, dementia, depression

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Authors: Samira Rostom, Robert Symonds, Robin W. Hughes

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Hydrogen is considered as one of the most important clean and renewable energy carriers for a sustainable energy future. However, its efficient and cost-effective purification remains challenging. This paper presents the potential of using metal–organic frameworks (MOFs) in combination with pressure swing adsorption (PSA) technology for syngas based H2 purification. PSA process analysis is done considering high pressure and elevated temperature process conditions, it reduces the demand for off-gas recycle to the fuel reactor and simultaneously permits higher desorption pressure, thereby reducing the parasitic load on the hydrogen compressor. The elevated pressure and temperature adsorption we present here is beneficial to minimizing overall process heating and cooling demand compared to existing processes. Here, we report the comparative performance of zeolite-5A, Cu-BTC, and the mix of zeolite-5A/Cu-BTC for H2 purification from syngas typical of those exiting water-gas-shift reactors. The MOFs were synthesized hydrothermally and then mixed systematically at different weight ratios to find the optimum composition based on the adsorption performance. The formation of different compounds were characterized by XRD, N2 adsorption and desorption, SEM, FT-IR, TG, and water vapor adsorption technologies. Single-component adsorption isotherms of CO2, CO, CH4, N2, and H2 over single materials and composites were measured at elevated pressures and different temperatures to determine their equilibrium adsorption capacity. The examination of the stability and regeneration performance of metal–organic frameworks was carried out using a gravimetric system at temperature ranges of 25-150℃ for a pressure range of 0-30 bar. The studies of adsorption/desorption on the MOFs showed selective adsorption of CO2, CH4, CO, and N2 over H2. Overall, the findings of this study suggest that the Ni-MOF-74/Cu-BTC composites are promising candidates for industrial H2 purification processes.

Keywords: MOF, H2 purification, high T, PSA

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Authors: Dilsad Akal

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Aim: Companies communicate with each other and with their costumers via call centers. Call centers are defined as stressful because of their uncertain working hours, inadequate relief time, performance based system and heavy workload. In literature, this sector is defined as risky as mining sector by means of health and safety. The aim of this research is to enlight the relatively dark area. Subject and Methods: The collection of data for this study completed during April-May 2015 for the two selected call centers in different parts of Turkey. The applied question mostly investigated the health conditions of call center workers. Electromagnetic field measurements were completed at the same time with applying the question poll. The ratio of employee accessibility noted as 73% for the first call center and 87% for the second. Results: The results of electromagnetic field measurements were as between 371 V/m-32 V/m for the first location and between 370 V/m-61 V/m for the second. The general complaints of the employees for both workplaces can be counted as; inadequate relief time, inadequate air conditioning, disturbance, poor thermal conditions, inadequate or extreme lighting. Furthermore, musculoskeletal discomfort, stress, ear and eye discomfort are main health problems of employees. Conclusion: The measured values and the responses to the question poll were found parallel with the other similar research results in literature. At the end of this survey, a risk map of workplace was prepared in terms of safety and health at work in general and some suggestions for resolution were provided.

Keywords: call center, health and safety, electromagnetic field, risk map

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Authors: Wiem Daoud Ben Amor, Luis Martínez López, Hela Moalla Frikha

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Most of the multi-criteria group decision making (MCGDM) problems dealing with qualitative criteria require consideration of the large background of expert information. It is common that experts have different degrees of knowledge for giving their alternative assessments according to criteria. So, it seems logical that they use different evaluation scales to express their judgment, i.e., multi granular linguistic scales. In this context, we propose the extension of the classical additive ratio assessment (ARAS) method to the case of a hierarchical linguistics term for managing multi granular linguistic scales in uncertain contexts where uncertainty is modeled by means in linguistic information. The proposed approach is called the extended hierarchical linguistics-ARAS method (ARAS-ELH). Within the ARAS-ELH approach, the DM can diagnose the results (the ranking of the alternatives) in a decomposed style, i.e., not only at one level of the hierarchy but also at the intermediate ones. Also, the developed approach allows a feedback transformation i.e the collective final results of all experts able to be transformed at any level of the extended linguistic hierarchy that each expert has previously used. Therefore, the ARAS-ELH technique makes it easier for decision-makers to understand the results. Finally, An MCGDM case study is given to illustrate the proposed approach.

Keywords: additive ratio assessment, extended hierarchical linguistic, multi-criteria group decision making problems, multi granular linguistic contexts

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Authors: C. B. Le, V. N. Pham

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In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

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Authors: Asibor Godwin, Adeniyi Funsho

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This study was carried out to investigate the impact of biological treated effluent on the physico-chemical properties of receiving waterbodies and also to establish its suitability for other purposes. It focused on the changes of some physic-chemical variables as one move away from the point of discharge downstream of the waterbodies. Water samples were collected from 14 sampling stations made up of the untreated effluent, treated effluent and receiving streams (before and after treated effluent discharge) over a period of 6 months spanning the dry and rainy seasons. Analyses were carried out on the following: temperature, turbidity, pH, conductivity, major anions and cation, dissolved oxygen, percentage oxygen Saturation, biological oxygen demand (BOD), solids (total solids, suspended solids and dissolved solids), nitrates, phosphates, organic matter and flow discharge using standard analytical methods. The relationships between investigated sites with regards to their physico-chemical properties were analyzed using student-t statistics. Also changes in the treated effluent receiving streams after treated effluent outfall was discussed fully. The physico-chemical water quality of the receiving water bodies meets most of the general water requirements for both domestic and industrial uses. The untreated effluent quality was shown to be of biological origin based on the biological oxygen demand, chloride, dissolved oxygen, total solids, pH and organic matter. The treated effluent showed significant improvement over the raw untreated effluent based on most parameters assessed. There was a significant difference (p<0.05) between the physico-chemical quality of untreated effluent and the treated effluent for the most of the investigated physico-chemical quality. The difference between the discharged treated effluent and the unimpacted section of the receiving waterbodies was also significant (p<0.05) for the most of the physico-chemical parameters.

Keywords: eflluent, Opa River, physico-chemical, waterbody

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Authors: Abolghasem Yousefi-Babadi, Ali Bozorgi-Amiri, Aida Kazempour, Reza Tavakkoli-Moghaddam, Maryam Irani

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All over the world, natural disasters (e.g., earthquakes, floods, volcanoes and hurricanes) causes a lot of deaths. Earthquakes are introduced as catastrophic events, which is accident by unusual phenomena leading to much loss around the world. Such could be replaced by disasters or any other synonyms strongly demand great long-term help and relief, which can be hard to be managed. Supplies and facilities are very important challenges after any earthquake which should be prepared for the disaster regions to satisfy the people's demands who are suffering from earthquake. This paper proposed disaster response facility allocation problem for disaster relief operations as a mathematical programming model. Not only damaged people in the earthquake victims, need the consumable commodities (e.g., food and water), but also they need non-consumable commodities (e.g., clothes) to protect themselves. Therefore, it is concluded that paying attention to disaster points and people's demands are very necessary. To deal with this objective, both commodities including consumable and need non-consumable commodities are considered in the presented model. This paper presented the multi-objective multi-period mathematical programming model regarding the minimizing the average of the weighted response times and minimizing the total operational cost and penalty costs of unmet demand and unused commodities simultaneously. Furthermore, a Chebycheff multi-objective solution procedure as a powerful solution algorithm is applied to solve the proposed model. Finally, to illustrate the model applicability, a case study of the Tehran earthquake is studied, also to show model validation a sensitivity analysis is carried out.

Keywords: facility location, multi-objective model, disaster response, commodity

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Authors: Young Yoon, Jinsoo Kim

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The adoption of information and communication technologies (ICT) in all industry is growing under industry 4.0. Many oil companies also are increasingly adopting ICT to improve the efficiency of existing operations, take more accurate and quicker decision making and reduce entire cost by optimization. It is true that ICT is playing an important role in the process of unconventional oil and gas development and companies must take advantage of ICT to gain competitive advantage. In this study, real option approach has been applied to Unconventional gas R&D project to evaluate ICT of them. Many unconventional gas reserves such as shale gas and coal-bed methane(CBM) has developed due to technological improvement and high energy price. There are many uncertainties in unconventional development on the three stage(Exploration, Development, Production). The traditional quantitative benefits-cost method, such as net present value(NPV) is not sufficient for capturing ICT value. We attempted to evaluate the ICT valuation by applying the compound option model; the model is applied to real CBM project case, showing how it consider uncertainties. Variables are treated as uncertain and a Monte Carlo simulation is performed to consider variables effect. Acknowledgement—This work was supported by the Energy Efficiency & Resources Core Technology Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20152510101880) and by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-205S1A3A2046684).

Keywords: information and communication technologies, R&D, real option, unconventional gas

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Authors: Yifu Ding, Vijay Avinash, Malcolm McCulloch

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As the proliferation of decentralized energy systems, the UK power system allows small-scale entities such as microgrids (MGs) to tender multiple energy services including energy arbitrage and frequency responses (FRs). However, its operation requires the balance between the uncertain renewable generations and loads in real-time and has to fulfill their provision requirements of contract services continuously during the time window agreed, otherwise it will be penalized for the under-delivered provision. To hedge against risks due to uncertainties and maximize the economic benefits, we propose a stochastic model predictive control (SMPC) framework to optimize its operation for the multi-service provision. Distinguished from previous works, we include a detailed economic-degradation model of the lithium-ion battery to quantify the costs of different service provisions, as well as accurately describe the changing dynamics of the battery. Considering a branch of load and generation scenarios and the battery aging, we formulate a risk-averse cost function using conditional value at risk (CVaR). It aims to achieve the maximum expected net revenue and avoids severe losses. The framework will be performed on a case study of a PV-battery grid-tied microgrid in the UK with real-life data. To highlight its performance, the framework will be compared with the case without the degradation model and the deterministic formulation.

Keywords: model predictive control (MPC), battery degradation, frequency response, microgrids

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Authors: Erik Eklund

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Mary Kathleen was a purpose-built company town located in northwest Queensland in Australia. It was created to work on a rich uranium deposit discovered in the area in July 1954. The town was complete by 1958, possessing curved streets, modern materials, and a progressive urban planning scheme. Formed in the minds of corporate executives and architects and made manifest in arid zone country between Cloncurry and Mount Isa, Mary Kathleen was a modern marvel in the outback, a town that tamed the wild country of northwest Queensland, or so it seemed. The town was also a product of the Cold War. In the context of a nuclear arms race between the Soviet Union and her allies, and the United States of America (USA) and her Allies, a rapid rush to locate, mine, and process uranium after 1944 led to the creation of uranium towns in Czechoslovakia, Canada, the Soviet Union, USA and Australia of which Mary Kathleen was one such example. Mary Kathleen closed in 1981, and most of the town’s infrastructure was removed. Since then, the town’s ghostly remains have attracted travellers and tourists. Never an officially-sanctioned tourist site, the area has nevertheless become a regular stop for campers and day trippers who have engaged with the site often without formal interpretation. This paper explores the status of this vernacular heritage and asks why it has not gained any official status and what visitors might see in the place despite its uncertain status.

Keywords: uranium mining, planned communities, official heritage, vernacular heritage, Australian history

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Authors: Jang-Hwan Jo

Abstract:

Implementing sustainable island forest management policies requires categorizing islands into groups based on key indicators and establishing a consistent management system. Building on the results of previous studies, a typology of forested islands was established: Type 1 – connected islands with high natural vegetation cover; Type 2 – connected islands with moderate natural vegetation cover; Type 3 – connected islands with low natural vegetation cover; Type 4 – unconnected islands with high natural vegetation cover; Type 5 – unconnected islands with moderate natural vegetation cover; and Type 6 – unconnected islands with low natural vegetation cover. An AHP analysis was conducted with island forest experts to identify priority ecosystem services (ESs) for the sustainable management of each island type. In connected islands, provisioning services (natural resources, natural medicines, etc.) assumed greater importance than regulating (erosion control) and supporting services (genetic diversity). In unconnected islands, particularly those with a small proportion of natural vegetation, regulating services (erosion control) requires greater emphasis in management. Considering that Type 3 islands require urgent management as connectivity to the mainland makes natural vegetation-sparse island forest ecosystems vulnerable to anthropogenic activities, the land-use scoring method was carried out on Jin-do, a Type 3 forested island. Comparisons between AHP-derived expert demand for key island ESs and the spatial distribution of ES supply potential revealed mismatches between the supply and demand of erosion control, freshwater supply, and habitat provision. The framework developed in this study can help guide decisions and indicate where interventions should be focused to achieve sustainable island management.

Keywords: ecosystem service, sustainable management, forested islands, Analytic hierarchy process

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Authors: Alhaji M. H. Conteh, Xiangbin Yan, Issa Fofana, Brima Gegbe, Tamba I. Isaac

Abstract:

Rice grain is Sierra Leone’s staple food and the nation imports over 120,000 metric tons annually due to a shortfall in its cultivation. Thus, the insufficient level of the crop's cultivation in Sierra Leone is caused by many problems and this led to the endlessly widening supply and demand for the crop within the country. Consequently, this has instigated the government to spend huge money on the importation of this grain that would have been otherwise cultivated domestically at a cheaper cost. Hence, this research attempts to explore the response of rice supply with respect to its demand in Sierra Leone within the period 1980-2010. The Nerlovian adjustment model to the Sierra Leone rice data set within the period 1980-2010 was used. The estimated trend equations revealed that time had significant effect on output, productivity (yield) and area (acreage) of rice grain within the period 1980-2010 and this occurred generally at the 1% level of significance. The results showed that, almost the entire growth in output had the tendency to increase in the area cultivated to the crop. The time trend variable that was included for government policy intervention showed an insignificant effect on all the variables considered in this research. Therefore, both the short-run and long-run price response was inelastic since all their values were less than one. From the findings above, immediate actions that will lead to productivity growth in rice cultivation are required. To achieve the above, the responsible agencies should provide extension service schemes to farmers as well as motivating them on the adoption of modern rice varieties and technology in their rice cultivation ventures.

Keywords: Nerlovian adjustment model, price elasticities, Sierra Leone, trend equations

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Authors: Rama Shanker, Anil Kumar Sachan

Abstract:

Basic ingredients of concrete are cement, fine aggregate, coarse aggregate and water. To produce a concrete of certain specific properties, optimum proportion of these ingredients are mixed. The important factors which govern the mix design are grade of concrete, type of cement and size, shape and grading of aggregates. Concrete mix design method is based on experimentally evolved empirical relationship between the factors in the choice of mix design. Basic draw backs of this method are that it does not produce desired strength, calculations are cumbersome and a number of tables are to be referred for arriving at trial mix proportion moreover, the variation in attainment of desired strength is uncertain below the target strength and may even fail. To solve this problem, a lot of cubes of standard grades were prepared and attained 28 days strength determined for different combination of cement, fine aggregate, coarse aggregate and water. An artificial neural network (ANN) was prepared using these data. The input of ANN were grade of concrete, type of cement, size, shape and grading of aggregates and output were proportions of various ingredients. With the help of these inputs and outputs, ANN was trained using feed forward back proportion model. Finally trained ANN was validated, it was seen that it gave the result with/ error of maximum 4 to 5%. Hence, specific type of concrete can be prepared from given material properties and proportions of these materials can be quickly evaluated using the proposed ANN.

Keywords: aggregate proportions, artificial neural network, concrete grade, concrete mix design

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Authors: Montserrat Crespi-Vallbona; Oscar Mascarilla-Miró

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Mature destinations have different challenges trying to attract tourism and please its citizens. Hence, they have to maintain their touristic interest to standard demand and also not to undeceive those tourists with more advanced experiences. Second, they have to be concerned for the daily life of citizens and avoid the negative effects of touristification. This balance is quite delicate and often has to do with the sensitivity and commitment of the party in the local government. However, what is a general consensus is the need for destinations to differentiate from the homogeneous rest of regions and create new content, consumable resources or marketing events to guarantee their positioning. In this sense, the main responsibility of destinations is to satisfy users, tourists and citizens. Hence, its aim has to do with holistic experiences, which collect these wide approaches. Specifically, this research aims to analyze the volume and growth of tourist houses in the central touristic neighborhoods of Barcelona (this is Ciutat Vella) as the starting point to identify the behavior of tourists regarding their interests in searching for local heritage attractiveness and community atmosphere. Then, different cases are analyzed in order to show how Barcelona struggles to keep its attractive brand for the visitors, as well as for its inhabitants. Methodologically, secondary data used in this research comes from official registered tourist houses (Catalunya Government), Open Data (Barcelona municipality), the Airbnb tourist platform, from the Incasol Data and Municipal Register of Inhabitants. Primary data are collected through in-depth interviews with neighbors, social movement managers and political representatives from Turisme de Barcelona (local DMO, Destination Management Organization). Results show what the opportunities and priorities are for key actors to design policies to find a balance between all different interests.

Keywords: touristification, tourist houses, governance, tourism demand, airbnbfication

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Authors: Salah Hosseini, Hadi Ramezani, Bagher Shahbazi, Hossein Rabiei, Jafar Hooshmand, Hiwa Khaldi

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Diversity of energy portfolio and fluctuation of urban energy demand establish the need for more operational flexibility of combined Cooling, Heat, and Power Plants. Currently, the most common way to achieve these specifications is the use of heat storage devices or wet operation of gas turbines. The current work addresses using variable extraction steam turbine in conjugation with a gas turbine inlet cooling system as an alternative way for enhancement of a CCHP cycle operating range. A thermodynamic model is developed and typical apartments building in PARDIS Technology Park (located at Tehran Province) is chosen as a case study. Due to the variable Heat demand and using excess chiller capacity for turbine inlet cooling purpose, the mentioned steam turbine and TIAC system provided an opportunity for flexible operation of the cycle and boosted the independence of the power and heat generation in the CCHP plant. It was found that the ratio of power to the heat of CCHP cycle varies from 12.6 to 2.4 depending on the City heating and cooling demands and ambient condition, which means a good independence between power and heat generation. Furthermore, selection of the TIAC design temperature is done based on the amount of ratio of power gain to TIAC coil surface area, it was found that for current cycle arrangement the TIAC design temperature of 15 C is most economical. All analysis is done based on the real data, gathered from the local weather station of the PARDIS site.

Keywords: CCHP plant, GTG, HRSG, STG, TIAC, operational flexibility, power to heat ratio

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Authors: Shravani Itkelwar, Deepak Tewari, Bhaskar Natarajan

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The energy consumption data plays a vital role in energy efficiency policy design, implementation, and impact assessment. Any demand-side energy management intervention's success relies on the availability of accurate, comprehensive, granular, and up-to-date data on energy consumption. The Building sector, including residential and commercial, is one of the largest consumers of energy in India after the Industrial sector. With economic growth and increasing urbanization, the building sector is projected to grow at an unprecedented rate, resulting in a 5.6 times escalation in energy consumption till 2047 compared to 2017. Therefore, energy efficiency interventions will play a vital role in decoupling the floor area growth and associated energy demand, thereby increasing the need for robust data. In India, multiple institutions are involved in the collection and dissemination of data. This paper focuses on energy consumption data management in the building sector in India for both residential and commercial segments. It evaluates the robustness of data available through administrative and survey routes to estimate the key performance indicators and identify critical data gaps for making informed decisions. The paper explores several issues in the data, such as lack of comprehensiveness, non-availability of disaggregated data, the discrepancy in different data sources, inconsistent building categorization, and others. The identified data gaps are justified with appropriate examples. Moreover, the paper prioritizes required data in order of relevance to policymaking and groups it into "available," "easy to get," and "hard to get" categories. The paper concludes with recommendations to address the data gaps by leveraging digital initiatives, strengthening institutional capacity, institutionalizing exclusive building energy surveys, and standardization of building categorization, among others, to strengthen the management of building sector energy consumption data.

Keywords: energy data, energy policy, energy efficiency, buildings

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Authors: Nadya Patel, Jawn Lim

Abstract:

Foresight 3.0 is a synergistic methodology that encompasses systems analysis, future studies, capacity building, and forward planning. These components are interconnected, fostering a collective anticipatory intelligence that promotes societal resilience (Ravetz, 2020). However, traditional applications of these strands can often fall short, leading to missed opportunities and narrow perspectives. Therefore, Foresight 3.0 champions a holistic approach to tackling complex issues, focusing on systemic transformations and power dynamics. Businesses are pivotal in preparing the workforce for an increasingly uncertain and complex world. This necessitates the adoption of innovative tools and methodologies, such as Foresight 3.0, that can better equip young employees to anticipate and navigate future challenges. Firstly, the incorporation of its methodology into workplace training can foster a holistic perspective among employees. This approach encourages employees to think beyond the present and consider wider social, economic, and environmental contexts, thereby enhancing their problem-solving skills and resilience. This paper discusses our research on integrating Foresight 3.0's transformative principles with a newly developed Critical Design Futures (CDF) framework to equip organisations with the ability to innovate for the world's most complex social problems. This approach is grounded in 'collective forward intelligence,' enabling mutual learning, co-innovation, and co-production among a diverse stakeholder community, where business transformation and innovation are achieved.

Keywords: business transformation, innovation, foresight, critical design

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