Search results for: modeling framework

Authors: Kumar Shashvat, Amol P. Bhondekar

Abstract:

In the midst of the five senses, odor is the most reminiscent and least understood. Odor testing has been mysterious and odor data fabled to most practitioners. The delinquent of recognition and classification of odor is important to achieve. The facility to smell and predict whether the artifact is of further use or it has become undesirable for consumption; the imitation of this problem hooked on a model is of consideration. The general industrial standard for this classification is color based anyhow; odor can be improved classifier than color based classification and if incorporated in machine will be awfully constructive. For cataloging of odor for peas, trees and cashews various discriminative approaches have been used Discriminative approaches offer good prognostic performance and have been widely used in many applications but are incapable to make effectual use of the unlabeled information. In such scenarios, generative approaches have better applicability, as they are able to knob glitches, such as in set-ups where variability in the series of possible input vectors is enormous. Generative models are integrated in machine learning for either modeling data directly or as a transitional step to form an indeterminate probability density function. The algorithms or models Linear Discriminant Analysis and Naive Bayes Classifier have been used for classification of the odor of cashews. Linear Discriminant Analysis is a method used in data classification, pattern recognition, and machine learning to discover a linear combination of features that typifies or divides two or more classes of objects or procedures. The Naive Bayes algorithm is a classification approach base on Bayes rule and a set of qualified independence theory. Naive Bayes classifiers are highly scalable, requiring a number of restraints linear in the number of variables (features/predictors) in a learning predicament. The main recompenses of using the generative models are generally a Generative Models make stronger assumptions about the data, specifically, about the distribution of predictors given the response variables. The Electronic instrument which is used for artificial odor sensing and classification is an electronic nose. This device is designed to imitate the anthropological sense of odor by providing an analysis of individual chemicals or chemical mixtures. The experimental results have been evaluated in the form of the performance measures i.e. are accuracy, precision and recall. The investigational results have proven that the overall performance of the Linear Discriminant Analysis was better in assessment to the Naive Bayes Classifier on cashew dataset.

Keywords: odor classification, generative models, naive bayes, linear discriminant analysis

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Authors: Huseyin Guler, Cigdem Kosar

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The aim of this study is to propose Bridge estimator for seasonal unit root tests. Seasonality is an important factor for many economic time series. Some variables may contain seasonal patterns and forecasts that ignore important seasonal patterns have a high variance. Therefore, it is very important to eliminate seasonality for seasonal macroeconomic data. There are some methods to eliminate the impacts of seasonality in time series. One of them is filtering the data. However, this method leads to undesired consequences in unit root tests, especially if the data is generated by a stochastic seasonal process. Another method to eliminate seasonality is using seasonal dummy variables. Some seasonal patterns may result from stationary seasonal processes, which are modelled using seasonal dummies but if there is a varying and changing seasonal pattern over time, so the seasonal process is non-stationary, deterministic seasonal dummies are inadequate to capture the seasonal process. It is not suitable to use seasonal dummies for modeling such seasonally nonstationary series. Instead of that, it is necessary to take seasonal difference if there are seasonal unit roots in the series. Different alternative methods are proposed in the literature to test seasonal unit roots, such as Dickey, Hazsa, Fuller (DHF) and Hylleberg, Engle, Granger, Yoo (HEGY) tests. HEGY test can be also used to test the seasonal unit root in different frequencies (monthly, quarterly, and semiannual). Another issue in unit root tests is the lag selection. Lagged dependent variables are added to the model in seasonal unit root tests as in the unit root tests to overcome the autocorrelation problem. In this case, it is necessary to choose the lag length and determine any deterministic components (i.e., a constant and trend) first, and then use the proper model to test for seasonal unit roots. However, this two-step procedure might lead size distortions and lack of power in seasonal unit root tests. Recent studies show that Bridge estimators are good in selecting optimal lag length while differentiating nonstationary versus stationary models for nonseasonal data. The advantage of this estimator is the elimination of the two-step nature of conventional unit root tests and this leads a gain in size and power. In this paper, the Bridge estimator is proposed to test seasonal unit roots in a HEGY model. A Monte-Carlo experiment is done to determine the efficiency of this approach and compare the size and power of this method with HEGY test. Since Bridge estimator performs well in model selection, our approach may lead to some gain in terms of size and power over HEGY test.

Keywords: bridge estimators, HEGY test, model selection, seasonal unit root

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Authors: Scott R. Butler

Abstract:

Ontario, Canada’s largest province, is in the midst of an interesting experiment in mandated asset management planning for local governments. At the beginning of 2021, Ontario’s 444 municipalities were responsible for the management of 302,864 lane kilometers of roads that have a replacement cost of $97.545 billion CDN. Roadways are by far the most complex, expensive, and extensive assets that a municipality is responsible for overseeing. Since adopting Ontario Regulation 588/47: Asset Management Planning for Municipal Infrastructure in 2017, the provincial government has established prescriptions for local road authorities regarding asset category and levels of service being provided. This provincial regulation further stipulates that asset data such as extent, condition, and life cycle costing are to be captured in manner compliant with qualitative descriptions and technical metrics. The Ontario Good Roads Association undertook an exercise to aggregate the road-related data contained within the 444 asset management plans that municipalities have filed with the provincial government. This analysis concluded that collectively Ontario municipal roadways have a $34.7 billion CDN in deferred maintenance. The ill-state of repair of Ontario municipal roads has lasting implications for province’s economic competitiveness and has garnered considerable political attention. Municipal efforts to address the maintenance backlog are stymied by the extremely limited fiscal parameters municipalities must operate within in Ontario. Further exacerbating the program are provincially designed programs that are ineffective, administratively burdensome, and not necessarily aligned with local priorities or strategies. This paper addresses how municipal asset management plans – and more specifically, the data contained in these plans – can be used to design innovative policy frameworks, flexible funding programs, and new levels of service that respond to these funding challenges, as well as emerging issues such as local economic development and climate change. To fully unlock the potential that Ontario Regulation 588/17 has imposed will require a resolute commitment to data standardization and horizontal collaboration between municipalities within regions.

Keywords: transportation, municipal asset management, subnational policy design, subnational funding program design

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Authors: Onyekachi Onwudike, Russell Lock, Iain Phillips

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The variability that exists across governments, her departments and the provisioning of services has been areas of concern in the E-Government domain. There is a need for reuse and integration across government departments which are accompanied by varying degrees of risks and threats. There is also the need for assessment, prevention, preparation, response and recovery when dealing with these risks or threats. The ability of a government to cope with the emerging changes that occur within it is known as resilience. In order to forge ahead with concerted efforts to manage reuse and integration induced risks or threats to governments, the ambiguities contained within resilience must be addressed. Enhancing resilience in the E-Government domain is synonymous with reducing risks governments face with provisioning of services as well as reuse of components across departments. Therefore, it can be said that resilience is responsible for the reduction in government’s vulnerability to changes. In this paper, we present the use of the ontology to measure the resilience of governments. This ontology is made up of a well-defined construct for the taxonomy of resilience. A specific class known as ‘Resilience Requirements’ is added to the ontology. This class embraces the concept of resilience into the E-Government domain ontology. Considering that the E-Government domain is a highly complex one made up of different departments offering different services, the reliability and resilience of the E-Government domain have become more complex and critical to understand. We present questions that can help a government access how prepared they are in the face of risks and what steps can be taken to recover from them. These questions can be asked with the use of queries. The ontology focuses on developing a case study section that is used to explore ways in which government departments can become resilient to the different kinds of risks and threats they may face. A collection of resilience tools and resources have been developed in our ontology to encourage governments to take steps to prepare for emergencies and risks that a government may face with the integration of departments and reuse of components across government departments. To achieve this, the ontology has been extended by rules. We present two tools for understanding resilience in the E-Government domain as a risk analysis target and the output of these tools when applied to resilience in the E-Government domain. We introduce the classification of resilience using the defined taxonomy and modelling of existent relationships based on the defined taxonomy. The ontology is constructed on formal theory and it provides a semantic reference framework for the concept of resilience. Key terms which fall under the purview of resilience with respect to E-Governments are defined. Terms are made explicit and the relationships that exist between risks and resilience are made explicit. The overall aim of the ontology is to use it within standards that would be followed by all governments for government-based resilience measures.

Keywords: E-Government, Ontology, Relationships, Resilience, Risks, Threats

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Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Nicole Scott, Emily Dwyer, Cara Patrissy, Samantha Bonventre, Lina Begdache

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Across North America, the use and abuse of Attention Deficit Hyperactivity Disorder (ADHD) medication, cannabis, nicotine, and other psychoactive substances across college campuses have become an increasingly prevalent problem. Students frequently use these substances to aid their studying or deal with their mental health issues. However, it is still unknown what psychoactive substances are likely to be abused when college students illicitly use ADHD medication. In addition, it is not clear which psychoactive substance is associated with mental distress. Thus, the purpose of this study is to fill these gaps by assessing the use of different psychoactive substances when illicit ADHD medication is used; and how this association relates to mental stress. A total of 702 undergraduate students from different college campuses in the U.S. completed an anonymous survey distributed online. Data were self-reported on demographics, the use of ADHD medications, cannabis, nicotine, other psychoactive drugs, and mental distress, and feelings and opinions on the use of illicit study drugs were all included in the survey. Mental distress was assessed using the Kessler Psychological Distress 6 Scale. Data were analyzed in SPSS, Version 25.0, using Pearson’s Correlation Coefficient. Our results show that use of ADHD medication, cannabis use (non-frequent and very frequent), and nicotine use (non-frequent and very frequent), there were both statistically significant positive and negative correlations to specific psychoactive substances and their corresponding frequencies. Along the same lines, ADHD medication, cannabis use (non-frequent and very frequent), and nicotine use (non-frequent and very frequent) had statistically significant positive and negative correlations to specific mental distress experiences. As these findings are combined, a vicious loop can initiate a cycle where individuals who abuse psychoactive substances may or may not be inclined to use other psychoactive substances. This may later inhibit brain functions in those main areas of the brain stem, amygdala, and prefrontal cortex where this vicious cycle may or may not impact their mental distress. Addressing the impact of study drug abuse and its potential to be associated with further substance abuse may provide an educational framework and support proactive approaches to promote awareness among college students.

Keywords: stimulant, depressant, nicotine, ADHD medication, psychoactive substances, mental health, illicit, ecstasy, adrenochrome

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Authors: Inda Annisa Fauzani, Rahayu Setyawati Arifin

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The development of Special Economic Zones in Indonesia cannot be separated from the development of the communities in them. In accordance with the SEZ's objectives as a driver of economic growth, the focus of SEZ development does not only prioritize investment receipts and infrastructure development. The community as one of the stakeholders must also be considered. This becomes a challenge when the development of an SEZ has the potential to have an impact on the community in it. These impacts occur due to changes in the development of the area in the form of changes in the main regional industries and changes in the main livelihoods of the community. As a result, people can feel threats and disturbances. The community as the object of development is required to be able to have resilience in order to achieve a synergy between regional development and community development. A lack of resilience in the community can eliminate the ability to recover from disturbances and difficulty to adapt to changes that occur in their area. Social resilience is the ability of the community to be able to recover from disturbances and changes that occur. The achievement of social resilience occurs when the community gradually has the capacity in the form of coping capacity, adaptive capacity, and transformative capacity. It is hoped that when social resilience is achieved, the community will be able to develop linearly with regional development so that the benefits of this development can have a positive impact on these communities. This study aims to identify and analyze social factors that influence the achievement of social resilience in the community in Special Economic Zones in Indonesia and develop a program framework for achieving social resilience capacity in the community so that it can be used as a strategy to support the successful development of Special Economic Zones in Indonesia that provide benefits to the local community. This study uses a quantitative research method approach. Questionnaires are used as research instruments which are distributed to predetermined respondents. Respondents in this study were determined by using purposive sampling of the people living in areas that were developed into Special Economic Zones. Respondents were given a questionnaire containing questions about the influence of social factors on the achievement of social resilience. As x variables, 42 social factors are provided, while social resilience is used as y variables. The data collected from the respondents is analyzed in SPSS using Spearman Correlation to determine the relation between x and y variables. The correlated factors are then used as the basis for the preparation of programs to increase social resilience capacity in the community.

Keywords: community development, program management, social factor, social resilience

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Authors: Samia Dahmani

Abstract:

The research investigated the process and outcomes of an ongoing project “Den Grønne Rute”: a network of recreational rooms to be implemented throughout a former vulnerable neighborhood: Trekanten in the city of Holstebro in Denmark. The aim of the project is to better integrate Trekanten in the city and which initiated reflections upon the (dis)connection and the integration in relation to urban planning and city management, as well as the extent to which the project considers diversity since Holstebro is a growing multicultural city. With this research, it was first verified if Trekanten is splintered from Holstebro city, and secondly if planning for diversity, by engaging people in the process via place-making approaches, can help redress the disconnection between the neighborhood and the rest of the city. More specifically, the paper aims at exploring the role of place-making “Den Grønne Rute” in Trekanten in promoting planning for diversity and reassembling the splintering in the city. The theoretical and conceptual framework served to analyze the relationship between the splintering urbanism concept and the community involvement’s role in an inclusive process. The field study examines the detachment between Trekanten and Holstebro and the extent to which the project can overcome the disconnection. Methodologically a mix-methods approach was adopted where two semi-structured interviews, a focus group, and an online survey were conducted. Contrary to prior assumptions, the results showed that not only is Trekanten splintered from the city, but also the city is greatly disconnected from Trekanten, and hence Holstebro is a splintered city. The surprise was that Trekanten is moreover inner-splintered. The splintering urbanism accordingly has different dimensions. Even though the project’s design seemed to incorporate diverse ages and groups of people, its process lacks an understanding of the diversity’s relevance in promoting inclusiveness. In fact, the analysis revealed socio-cultural and psychological splintering. Since place-making, as a collaborative approach in planning, is itself an expression of diversity (since it brings differences into play), reconsidering diversity within the process by engaging people at the early stages of planning was recommended. Another suggestion was not to limit the project to a destination but more as an experience to remember and a story to tell. Only by bringing people together in re-imagining the place can Trekanten reassemble with Holstebro and vice versa. The aim of the research was to add a new perspective to the splintering urbanism and planning for diversity so to advance place-making as an approach in promoting the latter and redressing the former.

Keywords: the splintering urbanism, place-making, planning for diversity, Den Grønne Rute, Trekanten, Holstebro

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Authors: Armana Huq, Wahidur Rahman, Sanwar Kader

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Inland waterways in Bangladesh play an important role in providing comfortable and low-cost transportation. However, a maritime accident takes away many lives and creates unwanted hazards every year. This article deals with a comprehensive review of inland waterway accidents in Bangladesh. Additionally, it includes a comparative study between international and local inland research studies based on maritime accidents. Articles from inland waterway areas are analyzed in-depth to make a comprehensive overview of the nature of the academic work, accident and risk management process and different statistical analyses. It is found that empirical analysis based on the available statistical data dominates the research domain. For this study, major maritime accident-related works in the last four decades in Bangladesh (1981-2020) are being analyzed for preparing a bibliometric analysis. A study of maritime accidents of passenger's vessels during (1995-2005) indicates that the predominant causes of accidents in the inland waterways of Bangladesh are collision and adverse weather (77%), out of which collision due to human error alone stands (56%) of all accidents. Another study refers that the major causes of waterway accidents are the collision (60.3%) during 2005-2015. About 92% of this collision occurs due to direct contact with another vessel during this period. Rest 8% of the collision occurs by contact with permanent obstruction on waterway roots. The overall analysis of another study from the last 25 years (1995-2019) shows that one of the main types of accidents is collisions, with about 50.3% of accidents being caused by collisions. The other accident types are cyclone or storm (17%), overload (11.3%), physical failure (10.3%), excessive waves (5.1%), and others (6%). Very few notable works are available in testing or comparing the methods, proposing new methods for risk management, modeling, uncertainty treatment. The purpose of this paper is to provide an overview of the evolution of marine accident-related research domain regarding inland waterway of Bangladesh and attempts to introduce new ideas and methods to abridge the gap between international and national inland maritime-related work domain which can be a catalyst for a safer and sustainable water transportation system in Bangladesh. Another fundamental objective of this paper is to navigate various national maritime authorities and international organizations to implement risk management processes for shipping accident prevention in waterway areas.

Keywords: inland waterways, safety, bibliometric analysis, risk management, accidents

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Authors: A. Schuchter, M. Promegger

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The outbreak of the COVID-19 pandemic has shown us that online education is so much more than just a cool feature for teachers – it is an essential part of modern teaching. In online math teaching, it is common to use tools to share screens, compute and calculate mathematical examples, while the students can watch the process. On the other hand, flipped classroom models are on the rise, with their focus on how students can gather knowledge by watching videos and on the teacher’s use of technological tools for information transfer. This paper proposes a co-educational teaching approach for coding and engineering subjects with the help of drone-building to spark interest in technology and create a platform for knowledge transfer. The project combines aspects from mathematics (matrices, vectors, shaders, trigonometry), physics (force, pressure and rotation) and coding (computational thinking, block-based programming, JavaScript and Python) and makes use of collaborative-shared 3D Modeling with clara.io, where students create mathematics knowhow. The instructor follows a problem-based learning approach and encourages their students to find solutions in their own time and in their own way, which will help them develop new skills intuitively and boost logically structured thinking. The collaborative aspect of working in groups will help the students develop communication skills as well as structural and computational thinking. Students are not just listeners as in traditional classroom settings, but play an active part in creating content together by compiling a Handbook of Knowledge (called “open book”) with examples and solutions. Before students start calculating, they have to write down all their ideas and working steps in full sentences so other students can easily follow their train of thought. Therefore, students will learn to formulate goals, solve problems, and create a ready-to use product with the help of “reverse engineering”, cross-referencing and creative thinking. The work on drones gives the students the opportunity to create a real-life application with a practical purpose, while going through all stages of product development.

Keywords: flipped classroom, co-creational education, coding, making, drones, co-education, ARCS-model, problem-based learning

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Authors: Veronica S. Moertini, Vinsensius Kevin, Gede Karya

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Customer reviews have been collected by many kinds of e-commerce websites selling products, services, hotel rooms, tickets and so on. Each website collects its own customer reviews. The reviews can be crawled, collected from those websites and stored as big data. Text analysis techniques can be used to analyze that data to produce summarized information, such as customer opinions. Then, these opinions can be published by independent service provider websites and used to help customers in choosing the most suitable products or services. As the opinions are analyzed from big data of reviews originated from many websites, it is expected that the results are more trusted and accurate. Indonesian customers write reviews in Indonesian language, which comes with its own structures and uniqueness. We found that most of the reviews are expressed with “daily language”, which is informal, do not follow the correct grammar, have many abbreviations and slangs or non-formal words. Hadoop is an emerging platform aimed for storing and analyzing big data in distributed systems. A Hadoop cluster consists of master and slave nodes/computers operated in a network. Hadoop comes with distributed file system (HDFS) and MapReduce framework for supporting parallel computation. However, MapReduce has weakness (i.e. inefficient) for iterative computations, specifically, the cost of reading/writing data (I/O cost) is high. Given this fact, we conclude that MapReduce function is best adapted for “one-pass” computation. In this research, we develop an efficient technique for extracting or mining opinions from big data of Indonesian reviews, which is based on MapReduce with one-pass computation. In designing the algorithm, we avoid iterative computation and instead adopt a “look up table” technique. The stages of the proposed technique are: (1) Crawling the data reviews from websites; (2) cleaning and finding root words from the raw reviews; (3) computing the frequency of the meaningful opinion words; (4) analyzing customers sentiments towards defined objects. The experiments for evaluating the performance of the technique were conducted on a Hadoop cluster with 14 slave nodes. The results show that the proposed technique (stage 2 to 4) discovers useful opinions, is capable of processing big data efficiently and scalable.

Keywords: big data analysis, Hadoop MapReduce, analyzing text data, mining Indonesian reviews

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Authors: Janneth Gonzalez, Andrés Pinzon Velasco, Maria Angarita

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Astrocytes play an important role in various processes in the brain, including pathological conditions such as neurodegenerative diseases. Recent studies have shown that the increase in saturated fatty acids such as palmitic acid (PA) triggers pro-inflammatorypathways in the brain. The use of synthetic neurosteroids such as tibolone has demonstrated neuro-protective mechanisms. However, broad studies with a systemic point of view on the neurodegenerative role of PA and the neuro-protective mechanisms of tibolone are lacking. In this study, we performed the integration of multi-omic data (transcriptome and proteome) into a human astrocyte genomic scale metabolic model to study the astrocytic response during palmitate treatment. We evaluated metabolic fluxes in three scenarios (healthy, induced inflammation by PA, and tibolone treatment under PA inflammation). We also applied a control theory approach to identify those reactions that exert more control in the astrocytic system. Our results suggest that PA generates a modulation of central and secondary metabolism, showing a switch in energy source use through inhibition of folate cycle and fatty acid β‐oxidation and upregulation of ketone bodies formation. We found 25 metabolic switches under PA‐mediated cellular regulation, 9 of which were critical only in the inflammatory scenario but not in the protective tibolone one. Within these reactions, inhibitory, total, and directional coupling profiles were key findings, playing a fundamental role in the (de)regulation of metabolic pathways that may increase neurotoxicity and represent potential treatment targets. Finally, the overall framework of our approach facilitates the understanding of complex metabolic regulation, and it can be used for in silico exploration of the mechanisms of astrocytic cell regulation, directing a more complex future experimental work in neurodegenerative diseases.

Keywords: astrocytes, data integration, palmitic acid, computational model, multi-omics

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Authors: D. M. Cocârță, I. A. Istrate, C. Streche, D. M. Dumitru

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Soil contamination phenomena are a wide world issue that has received the important attention in the last decades. The main pollutants that have affected soils are especially those resulted from the oil extraction, transport and processing. This paper presents results obtained in the framework of a research project focused on the management of contaminated sites with petroleum products/ REMPET. One of the specific objectives of the REMPET project was to assess the electrochemical treatment (improved with polarity change respect to the typical approach) as a treatment option for the remediation of total petroleum hydrocarbons (TPHs) from contaminated soils. Petroleum hydrocarbon compounds attach to soil components and are difficult to remove and degrade. Electrochemical treatment is a physicochemical treatment that has gained acceptance as an alternative method, for the remediation of organic contaminated soils comparing with the traditional methods as bioremediation and chemical oxidation. This type of treatment need short time and have high removal efficiency, being usually applied in heterogeneous soils with low permeability. During the experimental tests, the following parameters were monitored: pH, redox potential, humidity, current intensity, energy consumption. The electrochemical method was applied in an experimental setup with the next dimensions: 450 mm x 150 mm x 150 mm (L x l x h). The setup length was devised in three electrochemical cells that were connected at two power supplies. The power supplies configuration was provided in such manner that each cell has a cathode and an anode without overlapping. The initial value of TPH concentration in soil was of 1420.28 mg/kg_dw. The remediation method has been applied for only 21 days, when it was already noticed an average removal efficiency of 31 %, with better results in the anode area respect to the cathode one (33% respect to 27%). The energy consumption registered after the development of the experiment was 10.6 kWh for exterior power supply and 16.1 kWh for the interior one. Taking into account that at national level, the most used methods for soil remediation are bioremediation (which needs too much time to be implemented and depends on many factors) and thermal desorption (which involves high costs in order to be implemented), the study of electrochemical treatment will give an alternative to these two methods (and their limitations).

Keywords: electrochemical remediation, pollution, total petroleum hydrocarbons, soil contamination

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Authors: Samia Dahmani

Abstract:

The research investigated the process and outcomes of an ongoing project, “Den Grønne Rute”: a network of recreational rooms to be implemented throughout a former vulnerable neighborhood: Trekanten, in the city of Holstebro in Denmark. The aim of the project is to better integrate Trekanten in the city and which initiated reflections upon the (dis)connection and the integration in relation to urban planning and city management, as well as the extent to which the project considers diversity since Holstebro is a growing multicultural city. With this research, it was first verified if Trekanten is splintered from Holstebro city, and secondly if planning for diversity, by engaging people in the process via place-making approaches, can help redress the disconnection between the neighborhood and the rest of the city. More specifically, the paper aims at exploring the role of place-making “Den Grønne Rute” in Trekanten in promoting planning for diversity and reassembling the splintering in the city. The theoretical and conceptual framework served to analyze the relationship between the splintering urbanism concept and the community involvement’s role for an inclusive process. The field study examines the detachment between Trekanten and Holstebro and the extent to which the project can overcome the disconnection. Methodologically a mix-methods approach was adopted where two semi-structured interviews, a focus group, and an online survey were conducted. Contrary to prior assumptions, the results showed that not only is Trekanten splintered from the city, but also the city is greatly disconnected from Trekanten, and hence Holstebro is a splintered city. The surprise was that Trekanten is moreover inner-splintered. The splintering urbanism accordingly has different dimensions. Even though the project’s design seemed to incorporate diverse ages and groups of people, its process lacks an understanding of the diversity’s relevance in promoting inclusiveness. In fact, the analysis revealed socio-cultural and psychological splintering. Since place-making, as a collaborative approach in planning, is itself an expression of diversity (since it brings differences into play), reconsidering diversity within the process by engaging people at the early sages of planning was recommended. Another suggestion was not to limit the project to a destination but more as an experience to remember and a story to tell. Only by bringing people together in re-imagining the place, can Trekanten reassemble with Holstebro and vice versa. The aim with the research was to add a new perspective to the splintering urbanism and planning for diversity so to advance place-making as an approach in promoting the latter and redressing the former.

Keywords: the splintering urbanism, placemaking, planning for diversity, den grønne rute, trekanten, holstebro

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Authors: Catherine Peyrols Wu

Abstract:

Intercultural interactions are becoming increasingly common in organizations and life. These interactions are often the stage of miscommunication and conflict. In management research, these problems are commonly attributed to cultural differences in values and interactional norms. As a result, the notion that intercultural competence can minimize these challenges is widely accepted. Cultural differences, however, are not the only source of a challenge during intercultural interactions. The need to rely on a lingua franca – or common language between people who have different mother tongues – is another important one. In theory, a lingua franca can improve communication and ease coordination. In practice however, disparities in people’s ability and confidence to communicate in the language can exacerbate tensions and generate inefficiencies. In this study, we draw on power theory to develop a model of disparities in language competence and conflict in a multicultural work context. Specifically, we hypothesized that differences in language competence between interaction partners would be positively related to conflict such that people would report greater conflict with partners who have more dissimilar levels of language competence and lesser conflict with partners with more similar levels of language competence. Furthermore, we proposed that cultural intelligence (CQ) an intercultural competence that denotes an individual’s capability to be effective in intercultural situations, would weaken the relationship between disparities in language competence and conflict such that people would report less conflict with partners who have more dissimilar levels of language competence when the interaction partner has high CQ and more conflict when the partner has low CQ. We tested this model with a sample of 135 undergraduate students working in multicultural teams for 13 weeks. We used a round-robin design to examine conflict in 646 dyads nested within 21 teams. Results of analyses using social relations modeling provided support for our hypotheses. Specifically, we found that in intercultural dyads with large disparities in language competence, partners with the lowest level of language competence would report higher levels of interpersonal conflict. However, this relationship disappeared when the partner with higher language competence was also high in CQ. These findings suggest that communication in a lingua franca can be a source of conflict in intercultural collaboration when partners differ in their level of language competence and that CQ can alleviate these effects during collaboration with partners who have relatively lower levels of language competence. Theoretically, this study underscores the benefits of CQ as a complement to language competence for intercultural effectiveness. Practically, these results further attest to the benefits of investing resources to develop language competence and CQ in employees engaged in multicultural work.

Keywords: cultural intelligence, intercultural interactions, language competence, multicultural teamwork

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Authors: Fikret Yalcinkaya, Hamza Unsal

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To understand how neurons work, it is required to combine experimental studies on neural science with numerical simulations of neuron models in a computer environment. In this regard, the simplicity and applicability of spiking neuron modeling functions have been of great interest in computational neuron science and numerical neuroscience in recent years. Spiking neuron models can be classified by exhibiting various neuronal behaviors, such as spiking and bursting. These classifications are important for researchers working on theoretical neuroscience. In this paper, three different spiking neuron models; Izhikevich, Adaptive Exponential Integrate Fire (AEIF) and Hindmarsh Rose (HR), which are based on first order differential equations, are discussed and compared. First, the physical meanings, derivatives, and differential equations of each model are provided and simulated in the Matlab environment. Then, by selecting appropriate parameters, the models were visually examined in the Matlab environment and it was aimed to demonstrate which model can simulate well-known biological neuron behaviours such as Tonic Spiking, Tonic Bursting, Mixed Mode Firing, Spike Frequency Adaptation, Resonator and Integrator. As a result, the Izhikevich model has been shown to perform Regular Spiking, Continuous Explosion, Intrinsically Bursting, Thalmo Cortical, Low-Threshold Spiking and Resonator. The Adaptive Exponential Integrate Fire model has been able to produce firing patterns such as Regular Ignition, Adaptive Ignition, Initially Explosive Ignition, Regular Explosive Ignition, Delayed Ignition, Delayed Regular Explosive Ignition, Temporary Ignition and Irregular Ignition. The Hindmarsh Rose model showed three different dynamic neuron behaviours; Spike, Burst and Chaotic. From these results, the Izhikevich cell model may be preferred due to its ability to reflect the true behavior of the nerve cell, the ability to produce different types of spikes, and the suitability for use in larger scale brain models. The most important reason for choosing the Adaptive Exponential Integrate Fire model is that it can create rich ignition patterns with fewer parameters. The chaotic behaviours of the Hindmarsh Rose neuron model, like some chaotic systems, is thought to be used in many scientific and engineering applications such as physics, secure communication and signal processing.

Keywords: Izhikevich, adaptive exponential integrate fire, Hindmarsh Rose, biological neuron behaviours, spiking neuron models

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Authors: Najeh Lakhoua

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Introduction: Scientific developments and techniques for the systemic approach generate several names to the systemic approach: systems analysis, systems analysis, structural analysis. The main purpose of these reflections is to find a multi-disciplinary approach which organizes knowledge, creates universal language design and controls complex sets. In fact, system analysis is structured sequentially by steps: the observation of the system by various observers in various aspects, the analysis of interactions and regulatory chains, the modeling that takes into account the evolution of the system, the simulation and the real tests in order to obtain the consensus. Thus the system approach allows two types of analysis according to the structure and the function of the system. The purpose of this paper is to present an application of system analysis of Unmanned Aerial Vehicle (UAV) components in order to represent the architecture of this system. Method: There are various analysis methods which are proposed, in the literature, in to carry out actions of global analysis and different points of view as SADT method (Structured Analysis and Design Technique), Petri Network. The methodology adopted in order to contribute to the system analysis of an Unmanned Aerial Vehicle has been proposed in this paper and it is based on the use of SADT. In fact, we present a functional analysis based on the SADT method of UAV components Body, power supply and platform, computing, sensors, actuators, software, loop principles, flight controls and communications). Results: In this part, we present the application of SADT method for the functional analysis of the UAV components. This SADT model will be composed exclusively of actigrams. It starts with the main function ‘To analysis of the UAV components’. Then, this function is broken into sub-functions and this process is developed until the last decomposition level has been reached (levels A1, A2, A3 and A4). Recall that SADT techniques are semi-formal; however, for the same subject, different correct models can be built without having to know with certitude which model is the good or, at least, the best. In fact, this kind of model allows users a sufficient freedom in its construction and so the subjective factor introduces a supplementary dimension for its validation. That is why the validation step on the whole necessitates the confrontation of different points of views. Conclusion: In this paper, we presented an application of system analysis of Unmanned Aerial Vehicle components. In fact, this application of system analysis is based on SADT method (Structured Analysis Design Technique). This functional analysis proved the useful use of SADT method and its ability of describing complex dynamic systems.

Keywords: system analysis, unmanned aerial vehicle, functional analysis, architecture

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Authors: Yi-Tung Lin

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Researchers commonly assess implicit self-esteem with the Implicit Association Test (IAT). The IAT’s measure, often referred to as the IAT effect, indicates the strengths of automatic preferences for the self relative to others, which is often considered an index of implicit self-esteem. However, based on the Dual-process theory, the IAT does not rely entirely on the automatic process; it is also influenced by a controlled process. The present study, therefore, analyzed the IAT data with the Quad model, separating four processes on the IAT performance: the likelihood that automatic association is activated by the stimulus in the trial (AC); that a correct response is discriminated in the trial (D); that the automatic bias is overcome in favor of a deliberate response (OB); and that when the association is not activated, and the individual fails to discriminate a correct answer, there is a guessing or response bias drives the response (G). The AC and G processes are automatic, while the D and OB processes are controlled. The AC parameter is considered as the strength of the association activated by the stimulus, which reflects what implicit measures of social cognition aim to assess. The stronger the automatic association between self and positive valence, the more likely it will be activated by a relevant stimulus. Therefore, the AC parameter was used as the index of implicit self-esteem in the present study. Meanwhile, the relationship between implicit self-esteem and depression is not fully investigated. In the cognitive theory of depression, it is assumed that the negative self-schema is crucial in depression. Based on this point of view, implicit self-esteem would be negatively associated with depression. However, the results among empirical studies are inconsistent. The aims of the present study were to examine the psychometric properties of the AC (i.e., test-retest reliability and its correlations with explicit self-esteem and depression) and compare it with that of the IAT effect. The present study had 105 patients with depressive disorders completing the Rosenberg Self-Esteem Scale, Beck Depression Inventory-II and the IAT on the pretest. After at least 3 weeks, the participants completed the second IAT. The data were analyzed by the latent-trait multinomial processing tree model (latent-trait MPT) with the TreeBUGS package in R. The result showed that the latent-trait MPT had a satisfactory model fit. The effect size of test-retest reliability of the AC and the IAT effect were medium (r = .43, p < .0001) and small (r = .29, p < .01) respectively. Only the AC showed a significant correlation with explicit self-esteem (r = .19, p < .05). Neither of the two indexes was correlated with depression. Collectively, the AC parameter was a satisfactory index of implicit self-esteem compared with the IAT effect. Also, the present study supported the results that implicit self-esteem was not correlated with depression.

Keywords: cognitive modeling, implicit association test, implicit self-esteem, quad model

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Authors: Bilel Chalghaf, Mathieu Varin

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Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

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Authors: Paris Binos

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The present work investigates developmental aspects of the duration of isolated vowels in infants with normal hearing compared to those who received cochlear implants (CIs) before two years of age. Infants with normal hearing produced shorter vowel duration since this find related with more mature production abilities. First isolated vowels are transparent during the protophonic stage as evidence of an increased motor and linguistic control. Vowel duration is a crucial factor for the transition of prelexical speech to normal adult speech. Despite current knowledge of data for infants with normal hearing more research is needed to unravel productions skills in early implanted children. Thus, isolated vowel productions by two congenitally hearing-impaired Greek infants (implantation ages 1:4-1:11; post-implant ages 0:6-1:3) were recorded and sampled for six months after implantation with a Nucleus-24. The results compared with the productions of three normal hearing infants (chronological ages 0:8-1:1). Vegetative data and vocalizations masked by external noise or sounds were excluded. Participants had no other disabilities and had unknown deafness etiology. Prior to implantation the infants had an average unaided hearing loss of 95-110 dB HL while the post-implantation PTA decreased to 10-38 dB HL. The current research offers a methodology for the processing of the prelinguistic productions based on a combination of acoustical and auditory analyses. Based on the current methodological framework, duration measured through spectrograms based on wideband analysis, from the voicing onset to the end of the vowel. The end marked by two co-occurring events: 1) The onset of aperiodicity with a rapid change in amplitude in the waveform and 2) a loss in formant’s energy. Cut-off levels of significance were set at 0.05 for all tests. Bonferroni post hoc tests indicated that difference was significant between the mean duration of vowels of infants wearing CIs and their normal hearing peers. Thus, the mean vowel duration of CIs measured longer compared to the normal hearing peers (0.000). The current longitudinal findings contribute to the existing data for the performance of children wearing CIs at a very young age and enrich also the data of the Greek language. The above described weakness for CI’s performance is a challenge for future work in speech processing and CI’s processing strategies.

Keywords: cochlear implant, duration, spectrogram, vowel

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Authors: Imran Aziz Tunio

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Islamkot is an underdeveloped sub-district (Taluka) in the Tharparkar district Sindh province of Pakistan located between latitude 24°25'19.79"N to 24°47'59.92"N and longitude 70° 1'13.95"E to 70°32'15.11"E. The Islamkot has an arid desert climate and the region is generally devoid of perennial rivers, canals, and streams. It is highly dependent on rainfall which is not considered a reliable surface water source and groundwater is the only key source of water for many centuries. To assess groundwater’s potential, an electrical resistivity survey (ERS) was conducted in Islamkot Taluka. Groundwater investigations for 128 Vertical Electrical Sounding (VES) were collected to determine the groundwater potential and obtain qualitatively and quantitatively layered resistivity parameters. The PASI Model 16 GL-N Resistivity Meter was used by employing a Schlumberger electrode configuration, with half current electrode spacing (AB/2) ranging from 1.5 to 100 m and the potential electrode spacing (MN/2) from 0.5 to 10 m. The data was acquired with a maximum current electrode spacing of 200 m. The data processing for the delineation of dune sand aquifers involved the technique of data inversion, and the interpretation of the inversion results was aided by the use of forward modeling. The measured geo-electrical parameters were examined by Interpex IX1D software, and apparent resistivity curves and synthetic model layered parameters were mapped in the ArcGIS environment using the inverse Distance Weighting (IDW) interpolation technique. Qualitative interpretation of vertical electrical sounding (VES) data shows the number of geo-electrical layers in the area varies from three to four with different resistivity values detected. Out of 128 VES model curves, 42 nos. are 3 layered, and 86 nos. are 4 layered. The resistivity of the first subsurface layers (Loose surface sand) varied from 16.13 Ωm to 3353.3 Ωm and thickness varied from 0.046 m to 17.52m. The resistivity of the second subsurface layer (Semi-consolidated sand) varied from 1.10 Ωm to 7442.8 Ωm and thickness varied from 0.30 m to 56.27 m. The resistivity of the third subsurface layer (Consolidated sand) varied from 0.00001 Ωm to 3190.8 Ωm and thickness varied from 3.26 m to 86.66 m. The resistivity of the fourth subsurface layer (Silt and Clay) varied from 0.0013 Ωm to 16264 Ωm and thickness varied from 13.50 m to 87.68 m. The Dar Zarrouk parameters, i.e. longitudinal unit conductance S is from 0.00024 to 19.91 mho; transverse unit resistance T from 7.34 to 40080.63 Ωm2; longitudinal resistance RS is from 1.22 to 3137.10 Ωm and transverse resistivity RT from 5.84 to 3138.54 Ωm. ERS data and Dar Zarrouk parameters were mapped which revealed that the study area has groundwater potential in the subsurface.

Keywords: electrical resistivity survey, GIS & RS, groundwater potential, environmental assessment, VES

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Authors: Mohd Nizam Osman

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This study explores issues and challenges of information and communication technology (ICT) adoption and application for business-related performance of Agro-based small and medium-scale enterprises (SMEs) in the state of Selangor, Malaysia. Globally, SMEs have championed the socio-economic development of nations across the globe, including Malaysia. Thus, the objectives of this study explore issues and challenges of agro-based SMEs' adoption and usage of ICT, the business-related performance of SMEs via the adoption of ICT, and the impact of incentives on SMEs' adoption and use of ICT. The study was conducted in Selangor, Malaysia. A qualitative research approach was deployed for the study. Data for the study emanated from semi-structured interviews and field note observation of 14 informants who are registered as small-scale business owners and operators. Based on thematic analysis, data were triangulated to ensure consistency and validation of findings for the study. Findings revealed that SMEs are faced with a lack of funding, low expertise, and lack of storage, leading to an unsustainable supply of goods and services. Although effective communication, ease of business activities/transactions, and information search by way of research were among the business performance experienced by SMEs' adoption of ICT. Further findings showed that loan conditions and personal and business interests hindered SMEs' reception and access to programs, schemes, and incentives geared at aiding the continuous growth and development of agro-based SMEs. The study suggests the need for policy change in terms of diversification of channels of funding and access to funds to enable credit guarantee schemes and peer or community-based financing. Consequently, the study recommends the engagement of SMEs in policy decision-making to ascertain the type of incentives relevant to their business operations. Likewise, from a technological standpoint, the study suggests the expansion of the framework of technology acceptance with focuses on affordability, type of users, and level of usage.

Keywords: ICT adoption, business related performance, agro-based SMEs, ICT application for SMEs

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Authors: Sabyasachi Mukhopadhyay, Joseph Ogutu, Gundula Bartzke, Hans-Peter Piepho

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Rainfall is a critical component of climate governing vegetation growth and production, forage availability and quality for herbivores. However, reliable rainfall measurements are not always available, making it necessary to predict rainfall values for particular locations through time. Predicting rainfall in space and time can be a complex and challenging task, especially where the rain gauge network is sparse and measurements are not recorded consistently for all rain gauges, leading to many missing values. Here, we develop a flexible Bayesian model for predicting rainfall in space and time and apply it to Narok County, situated in southwestern Kenya, using data collected at 23 rain gauges from 1965 to 2015. Narok County encompasses the Maasai Mara ecosystem, the northern-most section of the Mara-Serengeti ecosystem, famous for its diverse and abundant large mammal populations and spectacular migration of enormous herds of wildebeest, zebra and Thomson's gazelle. The model incorporates geographical and meteorological predictor variables, including elevation, distance to Lake Victoria and minimum temperature. We assess the efficiency of the model by comparing it empirically with the established Gaussian process, Kriging, simple linear and Bayesian linear models. We use the model to predict total monthly rainfall and its standard error for all 5 * 5 km grid cells in Narok County. Using the Monte Carlo integration method, we estimate seasonal and annual rainfall and their standard errors for 29 sub-regions in Narok. Finally, we use the predicted rainfall to predict large herbivore biomass in the Maasai Mara ecosystem on a 5 * 5 km grid for both the wet and dry seasons. We show that herbivore biomass increases with rainfall in both seasons. The model can handle data from a sparse network of observations with many missing values and performs at least as well as or better than four established and widely used models, on the Narok data set. The model produces rainfall predictions consistent with expectation and in good agreement with the blended station and satellite rainfall values. The predictions are precise enough for most practical purposes. The model is very general and applicable to other variables besides rainfall.

Keywords: non-stationary covariance function, gaussian process, ungulate biomass, MCMC, maasai mara ecosystem

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Authors: Mohammad Saber Eslamlou, Manouchehr Tabibian, Mahta Mirmoghtadaei

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The history of urban development and the increasing complexities of urban life have long been intertwined with different natural and man-made disasters. Sometimes, these unpleasant events have destroyed the cities forever. The growth of the urban population and the increase of social and economic resources in the cities increased the importance of developing a holistic approach to dealing with unknown urban disasters. As a result, the interest in resilience has increased in most of the scientific fields, and the urban planning literature has been enriched with the studies of the social, economic, infrastructural, and physical abilities of the cities. In this regard, different conceptual frameworks and patterns have been developed focusing on dimensions of resilience and different kinds of disasters. As the most frequent and likely natural disaster in Iran is flooding, the present study aims to develop a cause-effect model of urban resilience against flood in Karaj City. In this theoretical study, desk research and documentary studies were used to find the elements and dimensions of urban resilience. In this regard, 6 dimensions and 32 elements were found for urban resilience and a questionnaire was made by considering the requirements of TOPSIS techniques (pairwise comparison). The sample of the research consisted of 10 participants who were faculty members, academicians, board members of research centers, managers of the Ministry of Road and Urban Development, board members of New Towns Development Company, experts, and practitioners of consulting companies who had scientific and research backgrounds. The gathered data in this survey were analyzed using TOPSIS and Shannon Entropy techniques. The results show that Infrastructure/Physical, Social, Organizational/ Institutional, Structural/Physical, Economic, and Environmental dimensions are the most effective factors in urban resilience against floods in Karaj, respectively. Finally, a comprehensive model and a systematic framework of factors that affect the urban resilience of Karaj against floods was developed. This cause – effect model shows how different factors are related and influence each other, based on their connected structure and preferences.

Keywords: urban resilience, TOPSIS, Shannon entropy, cause-effect model of resilience, flood

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Authors: Khiddir Iddris, Martin Oteng – Ababio, Andreas Bürkert, Christoph Scherrer, Katharina Hemmler

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Agbogbloshie, as an informal settlement and economy where the e-waste sector thrives, has become a global hub of complex urban contestations involving sociocultural, economic, and environmental dimensions due to the implication that e-waste and informal economic patterns have on livelihoods, urbanisation, development and sustainability. Multi-author collaborations have produced an ever-growing body of literature on Agbogbloshie and the informal e-waste economy. There is, however, a dearth of an assessment of Agbogbloshie as an urban informal settlement's intricate nexus of socioecological contestations. We address this gap by systematising, from literature, the context knowledge, navigating the complex terrain of Agbogbloshie's challenges, and employing a multidimensional lens to unravel the sociocultural intricacies, economic dynamics, and environmental complexities shaping its identity. A systematic critical review approach was espoused, with a pragmatic consolidation of content analysis and controversy mapping, grounded on the concept of ‘sustainable rurbanism,’ highlighted core themes and identified contrasting viewpoints. An analytical framework is presented. Five categories – geohistorical, sociocultural, economic, environmental and future trends - are proposed as an approach to systematising the literature. The review finds that the sociocultural dimension unveils a mosaic of cultural amalgamation, communal identity, and tensions impacting community cohesion. The analysis of economic intricacies reveals the prevalence of informal economies sustaining livelihoods yet entrenching economic disparities and marginalisation. Environmental scrutiny exposes the grim realities of e-waste disposal, pollution, and land use conflicts. The findings suggest that there is a high resilience within the community and the potential for sustainable trajectories. Theoretical and conceptual synergy is limited. This review provides a comprehensive exploration, offering insights and directions for future research, policy formulation, and community-driven interventions aimed at fostering sustainable transformations in Agbogbloshie and analogous urban contexts.

Keywords: Agbogbloshie, economic complexities, environmental challenges, resilience, sociocultural dynamics, sustainability, urban informal settlement

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Authors: Tatsuya Yamazaki, Kazuya Miyakawa, Tomohiko Sugiyama, Toshitaka Iwatani

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The introduction of sensor technologies into agriculture is a necessary step to realize Precision Agriculture. Although sensing methodologies themselves have been prevailing owing to miniaturization and reduction in costs of sensors, there are some difficulties to analyze and understand the sensing data. Targeting at pears ’Le Lectier’, which is particular to Niigata in Japan, cultivation environmental data have been collected at pear fields by eight sorts of sensors: field temperature, field humidity, rain gauge, soil water potential, soil temperature, soil moisture, inner-bag temperature, and inner-bag humidity sensors. With regard to the inner-bag temperature and humidity sensors, they are used to measure the environment inside the fruit bag used for pre-harvest bagging of pears. In this experiment, three kinds of fruit bags were used for the pre-harvest bagging. After over 100 days continuous measurement, volumes of sensing data have been collected. Firstly, correlation analysis among sensing data measured by respective sensors reveals that one sensor can replace another sensor so that more efficient and cost-saving sensing systems can be proposed to pear farmers. Secondly, differences in characteristic and performance of the three kinds of fruit bags are clarified by the measurement results by the inner-bag environmental sensing. It is found that characteristic and performance of the inner-bags significantly differ from each other by statistical analysis. Lastly, a relational model between the sensing data and the pear outlook quality is established by use of Structural Equation Model (SEM). Here, the pear outlook quality is related with existence of stain, blob, scratch, and so on caused by physiological impair or diseases. Conceptually SEM is a combination of exploratory factor analysis and multiple regression. By using SEM, a model is constructed to connect independent and dependent variables. The proposed SEM model relates the measured sensing data and the pear outlook quality determined on the basis of farmer judgement. In particularly, it is found that the inner-bag humidity variable relatively affects the pear outlook quality. Therefore, inner-bag humidity sensing might help the farmers to control the pear outlook quality. These results are supported by a large quantity of inner-bag humidity data measured over the years 2014, 2015, and 2016. The experimental and analytical results in this research contribute to spreading Precision Agriculture technologies among the farmers growing ’Le Lectier’.

Keywords: precision agriculture, pre-harvest bagging, sensor fusion, structural equation model

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Authors: P. R. Chauhan, S. K. Tyagi

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Adsorption refrigeration technology offers a sustainable and energy-efficient cooling alternative over traditional refrigeration technologies for meeting the fast-growing cooling demands. With its ability to utilize natural refrigerants, low-grade heat sources, and modular configurations, it has the potential to revolutionize the cooling industry. Despite these benefits, the commercial viability of this technology is hampered by several fundamental limiting constraints, including its large size, low uptake capacity, and poor performance as a result of deficient heat and mass transfer characteristics. The primary cause of adequate heat and mass transfer characteristics and magnitude of exergy loss in various real processes of adsorption cooling system can be assessed by the entropy generation rate analysis, i. e. Second law of Thermodynamics. Therefore, this article presents the second law of thermodynamic-based investigation in terms of entropy generation rate (EGR) to identify the energy losses in various processes of the HPCC-based adsorption system using MATLAB R2021b software. The adsorption technology-based cooling system consists of two beds made up of silica gel and arranged in a single stage, while the water is employed as a refrigerant, coolant, and hot fluid. The variation in process-wise EGR is examined corresponding to cycle time, and a comparative analysis is also presented. Moreover, the EGR is also evaluated in the external units, such as the heat source and heat sink unit used for regeneration and heat dump, respectively. The research findings revealed that the combination of adsorber and desorber, which operates across heat reservoirs with a higher temperature gradient, shares more than half of the total amount of EGR. Moreover, the EGR caused by the heat transfer process is determined to be the highest, followed by a heat sink, heat source, and mass transfer, respectively. in case of heat transfer process, the operation of the valve is determined to be responsible for more than half (54.9%) of the overall EGR during the heat transfer. However, the combined contribution of the external units, such as the source (18.03%) and sink (21.55%), to the total EGR, is 35.59%. The analysis and findings of the present research are expected to pinpoint the source of the energy waste in HPCC based adsorption cooling systems.

Keywords: adsorption cooling cycle, heat transfer, mass transfer, entropy generation, silica gel-water

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Authors: Maryam Mousavi Nik, Sara Pasandian

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The aim of the current research was to determine the effectiveness of Cognitive-Behavioral Group Therapy (G-CBT) on stress, illness anxiety and obsessions-compulsion caused by the coronavirus crisis in adolescents (14-18-Year-olds) in Tehran, Iran. This research was carried out in the form of a semi-experimental study with a control group and in the framework of a pre-test and post-test design for both experimental and control groups. The statistical population of this research consisted of all high schools in Tehran in 2022. The sample size includes 32 Adolescents (14-18-Year-olds) who were selected using a cluster sampling method, and then they were randomly replaced in two experimental (n=16) and control (n=16) groups. In this research, an adolescent stress questionnaire (ASQ-N) with an emphasis on the impact of Coronavirus, Coronavirus disease anxiety (CDAS) and The Children's Yale-Brown Obsessive Compulsive Symptom Scale (CY-BOCS) emphasis on the Coronavirus were used, and group therapy intervention with The cognitive-behavioral approach was conducted for 8 sessions of 90 minutes in the experimental group. The research data were analyzed by Multivariate analysis of covariance (MANCOVA) and covariance (ANCVA) tests. The results of multivariate covariance analysis showed that group therapy intervention with a cognitive-behavioral approach had a significant effect on at least one of the variables of stress, illness anxiety and obsession-compulsion at the level (P<0.01, F=94.772) in the post-test stage. Also, the results of covariance analysis of one variable showed that group therapy intervention with a cognitive-behavioral approach in the level of (P<0.01, F=106.377) stress, in the level of (P<0.01, F=48.147) disease anxiety and in the level (P>0.01, F=17.033) of obsession-compulsion had a significant effect in the post-test stage. The results showed that The treatment with GCBT can be effective in decreasing stress, illness anxiety and obsessions and compulsion caused by the coronavirus crisis in Adolescents (15-20-Year-olds) and may be considered as an alternative to either individual cognitive-behavioral therapy or medication.

Keywords: stress, disease anxiety, obsession-compulsion, coronavirus (Covid-19) crisis, and cognitive-behavioral therapy

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Authors: Ronli Sifris

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The last Australian Parliamentary inquiry into surrogacy took place in 2016. Since then, a number of countries have reviewed their surrogacy laws, including countries such as New Zealand and the United Kingdom, which traditionally have invoked similar legal approaches to Australia on a broad range of issues. The time is ripe to reform Australia’s surrogacy laws with a view to putting in place a system that best protects the rights of all parties to a surrogacy arrangement, and especially the rights of the child. There are two specific, linked issues which tend to be particularly contentious in the surrogacy context. The first relates to legal parentage. There are questions around whether the surrogate or the intended parents should be deemed the legal parents of a child born through surrogacy and what should be the process for any transfer of parentage. The second key issue relates to compensation and whether a surrogate should be compensated for the reproductive labour inherent in conceiving, gestating, and birthing a child. This paper will invoke a comparative analysis with a view to considering how different countries are regulating surrogacy and which approach best protects the rights all parties involved in the surrogacy arrangement, especially the rights of the children born through surrogacy. The specific countries to be considered are Australia, Canada, and California (United States). I have selected these countries for the following reasons: Australia is the jurisdiction where the author is based, it is, therefore, the jurisdiction with which she has the most familiarity. It allows altruistic surrogacy only and post-birth parentage orders in favour of the intended parents of children born through altruistic surrogacy California, as a jurisdiction allowing for compensated surrogacy and pre-birth parentage orders in favour of the intended parents, sits at the other end of the spectrum to Australia thereby providing an interesting point of comparison. Canada sits somewhere in the middle; it ostensibly allows only altruistic surrogacy, but in practice, many aspects of the Canadian process resemble compensated surrogacy. In addition to conducting a comparative analysis with other countries, the paper will also consider international human rights law as its overarching framework for determining the approach that best protects the rights of a child born through surrogacy. Particular attention will be paid to the United Nations Convention on the Rights of the Child as the key children’s rights treaty. The European Court of Human Rights will also be extensively considered as it has decided a number of cases relating to the rights of children born through surrogacy.

Keywords: surrogacy, children’s rights, australia, compensation, parentage

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Authors: Samira Mehrabi

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Geriatric depression is a common late-life mental health disorder that increases morbidity and mortality. It has been shown that exercise is effective in alleviating symptoms of geriatric depression. However, inconsistencies across studies and lack of optimal dose-response of exercise for improving geriatric depression have made it challenging to draw solid conclusions on the effectiveness of exercise in late-life depression. Purpose: To further investigate the moderators of the effectiveness of exercise on geriatric depression across the current body of evidence. Methods: Based on the Arksey and O’Malley framework, an extensive search strategy was performed by exploring PubMed, Scopus, Sport Discus, PsycInfo, ERIC, and IBSS without limitations in the time frame. Eight systematic reviews with empirical results that evaluated the effect of exercise on depression among people aged ≥ 60 years were identified and their individual studies were screened for inclusion. One additional study was found through the hand searching of reference lists. After full-text screening and applying inclusion and exclusion criteria, 21 studies were retained for inclusion. Results: The review revealed high variability in characteristics of the exercise interventions and outcome measures. Sample characteristics, nature of comparators, main outcome assessment, and baseline severity of depression also varied notably. Mind-body and aerobic exercises were found to significantly reduce geriatric depression. However, results on the relationship between resistance training and improvements in geriatric depression were inconsistent, and results of the intensity-related antidepressant effects of exercise interventions were mixed. Extensive use of self-reported questionnaires for the main outcome assessment and lack of evidence on the relationship between depression severity and observed effects were of the other important highlights of the review. Conclusion: Several literature gaps were found regarding the potential effect modifiers of exercise and geriatric depression. While acknowledging the complexity of establishing recommendations on the exercise variables and geriatric depression, future studies are required to understand the interplay and threshold effect of exercise for treating geriatric depression.

Keywords: exercise, geriatric depression, healthy aging, older adults, physical activity intervention, scoping review

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