Search results for: recognition primed decision

Authors: Isabel Roiz, Alejandra Cossio

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This essay will talk about the problems of forced prostitution, human trafficking, and sexual exploitation in the Netherlands. This work conveys information from different sources stating the numbers and statistics of human trafficking throughout Europe and the different types of sexual exploitation as well as the means used for coercing victims into this illegal net. The research aims to inform and compare the way this business is handled and the ways used by criminals to lure and retain victims in spite of the law. It also tries to compare the laws in the Netherlands and Sweden regarding prostitution affects the illegal migration problems and how they change the ways those who work as prostitutes are treated. The aim of the paper is to take all of these aspects into consideration and reach a decision of what laws would most beneficiate the victims.

Keywords: human trafficking, prostitution, laws of migration, Amsterdam

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Authors: Abbas Hani, Maryam Jassasizadeh

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The concentrations of Cd, Pb, and Ni were determined from 40 soil samples collected in surface soils of Khark Island. Geostatistic methods and GIS were used to identify heavy metal sources and their spatial pattern. Principal component analysis coupled with correlation between heavy metals showed that level of mentioned heavy metal was lower than the standard level. Then the data obtained from the soil analyzing were studied for the purposes of normal distribution. The best way of interior finding for cadmium and nickel was ordinary kriging and the best way of interpolation of lead was inverse distance weighted. The result of this study help us to understand heavy metals distribution and make decision for remediation of soil pollution.

Keywords: geostatistics, ordinary kriging, heavy metals, GIS, Khark

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Authors: Riona Dighe

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This research attempts to identify an alternative approach to the measurement of Gross National Happiness (GNH). It uses artificial intelligence (AI), incorporating natural language processing (NLP) and sentiment analysis to measure GNH. We use ‘off the shelf’ NLP models responsible for the sentiment analysis of a sentence as a building block for this research. We constructed an algorithm using NLP models to derive a sentiment analysis score against sentences. This was then tested against a sample of 20 respondents to derive a sentiment analysis score. The scores generated resembled human responses. By utilising the MLP classifier, decision tree, linear model, and K-nearest neighbors, we were able to obtain a test accuracy of 89.97%, 54.63%, 52.13%, and 47.9%, respectively. This gave us the confidence to use the NLP models against sentences in websites to measure the GNH of a country.

Keywords: artificial intelligence, NLP, sentiment analysis, gross national happiness

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Authors: Bharatendra Rai

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Cutting tools are widely used in manufacturing processes and drilling is the most commonly used machining process. Although drill-bits used in drilling may not be expensive, their breakage can cause damage to expensive work piece being drilled and at the same time has major impact on productivity. Predicting drill-bit breakage, therefore, is important in reducing cost and improving productivity. This study uses twenty features extracted from two degradation signals viz., thrust force and torque. The methodology used involves developing and comparing decision tree, random forest, and multinomial logistic regression models for classifying and predicting drill-bit breakage using degradation signals.

Keywords: degradation signal, drill-bit breakage, random forest, multinomial logistic regression

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Authors: M. Inzunza

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In contemporary society, victims of crime has been given more recognition, which have contributed to advancing the knowledge on the effects of crime. There exists a complexity of who gets the status of victim and that the typology of good versus bad can interfere with the contact situation of the victim with the police. The aim of this study is to identify the most central areas affecting the contact situation between crime victims and the police to develop a conceptual model to be useful empirically. By considering previously documented problem areas and different theoretical domains, a conceptual model has been developed. Preliminary findings suggest that an area that should be given attention is to get a better understanding of the victim, not only in terms of demographics but also in terms of risk behavior and social network. This area has been considered to influence the status of the crime victim. Another domain of value is the type of crime and the context of the incident in more detail. The police officer approach style in the contact situation is also a pertinent area that is influenced by how the police based victim services are organized and how individual police officers are suited for the mission. Suitability includes constructs from empathy models adapted to the police context and especially focusing on sub-constructs such as perspective taking. Discussion will focus on how these findings can be operationalized in practice and how they are used in ongoing empirical studies.

Keywords: empathy, perspective taking, police contact, victim of crime

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Authors: Rosario Rivera Rubite, Ching-I Peng, Che-Wei Lin, Mark Hughes, Yoshiko Kono, Kuo-Fang Chung

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The flora of Panay Island is under-collected compared with the other islands of the Philippines. In a joint expedition to the island, botanists from Taiwan and the Philippines found three unknown Begonia and compared them with potentially allied species. The three species are clearly assignable to Begonia section Baryandra which is largely endemic to the Philippines. Studies of literature, herbarium specimens, and living plants support the recognition of the three new species: Begonia culasiensis, Begonia merrilliana, and Begonia sykakiengii. Somatic chromosomes at metaphase were determined to be 2n=30 for B. culasiensis and 2n=28 for both B. merrilliana and B. sykakiengii, which are congruent with those of most species in sect. Baryandra. Molecular phylogenetic evidence is consistent with B. culasiensis being a relict from the late Miocene, and with B. merrilliana and B. sykakiengii being younger species of Pleistocene origin. The continuing discovery of endemic Philippine species means the remaining fragments of both primary and secondary native vegetation in the archipelago are of increasing value in terms of natural capital. A secure future for the species could be realized through ex-situ conservation collections and raising awareness with community groups.

Keywords: conservation, endemic , herbarium , limestone, phylogenetics, taxonomy

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Authors: Małgorzata Cichy

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Afghanistan is a source of conflicts that affect security on a global scale. The role of ethnic and national determinants in the peacebuilding process in this country remains an extremely important factor in this respect. Research methods include literature and data analysis (scientific literature, documents of governmental and non-governmental organizations, statistical data and media reports), institutional and legal analysis, as well as decision-making method. The main objective of the research is a comprehensive answer to the question of how ethnic and national factors affect the process of building peace in Afghanistan after 2021 and what impact it has on international security.

Keywords: Afghanistan, pashtuns, peace, taliban

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Authors: Chunying Fang, Haifeng Li, Lin Ma, Mancai Zhang

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Speech intelligibility assessment is an important measure to evaluate the functional outcomes of surgical and non-surgical treatment, speech therapy and rehabilitation. The assessment of pathological speech plays an important role in assisting the experts. Pathological speech usually is non-stationary and mutational, in this paper, we describe a multi-granularity combined feature schemes, and which is optimized by hierarchical visual method. First of all, the difference granularity level pathological features are extracted which are BAFS (Basic acoustics feature set), local spectral characteristics MSCC (Mel s-transform cepstrum coefficients) and nonlinear dynamic characteristics based on chaotic analysis. Latterly, radar chart and F-score are proposed to optimize the features by the hierarchical visual fusion. The feature set could be optimized from 526 to 96-dimensions.The experimental results denote that new features by support vector machine (SVM) has the best performance, with a recognition rate of 84.4% on NKI-CCRT corpus. The proposed method is thus approved to be effective and reliable for pathological speech intelligibility evaluation.

Keywords: pathological speech, multi-granularity feature, MSCC (Mel s-transform cepstrum coefficients), F-score, radar chart

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Authors: Alejandra Cardenas, Ona Flores, Fabiola Gretzinger

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Since the 1990s, legal strategies for the promotion and protection of maternal health rights have achieved significant gains. Successful litigation in courts around the world have shown that these rights can be judicially enforceable. Governments and international organizations have acknowledged the importance of a human rights-based approach to maternal mortality and morbidity, and obstetric violence has been recognized as a human rights issue. Despite the progress made, maternal mortality has worsened in some regions of the world, while progress has stagnated elsewhere, and mistreatment in maternal care is reported almost universally. In this context, issues of maternal autonomy and decision-making during pregnancy, labor, and delivery as a critical barrier to access quality maternal health have been largely overlooked. Indeed, despite the principles of autonomy and informed consent in medical interventions being well-established in international and regional norms, how they are applied particularly during childbirth and pregnancy remains underdeveloped. National and global legal standards and decisions related to maternal health were reviewed and analyzed to determine how maternal autonomy and decision-making during pregnancy, labor, and delivery have been protected (or not) by international and national courts. The results of this legal research and analysis lead to the conclusion that a few standards have been set by courts regarding pregnant people’s rights to make choices during pregnancy and birth; however, most undermine the agency of pregnant people. These decisions recognize obstetric violence and gender-based discrimination, but fail to protect pregnant people’s autonomy, privacy, and their right to informed consent. As current human rights standards stand today, maternal health is the only field in medicine and law in which informed consent can be overridden, and patients can be forced to submit to treatments against their will. Unconsented treatment and loss of agency during pregnancy and childbirth can have long-term physical and mental impacts, reduce satisfaction and trust in health systems, and may deter future health-seeking behaviors. This research proposes a path forward that focuses on the pregnant person as an independent agent, relying on the doctrine of self-determination during pregnancy and childbirth, which includes access to the necessary conditions to enable autonomy and choice throughout pregnancy and childbirth as a critical step towards our approaches to reduce maternal mortality, morbidity, and mistreatment, and realize the promise of access to quality maternal health as a human right.

Keywords: autonomy in childbirth and pregnancy, choice, informed consent, jurisprudential analysis

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Authors: Zouhair Gassim

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The reconfiguration of representations of women's bodies as a "chest of pregnancies", the growing commitment of their bodies to the decision of pregnancies previously monopolized by men, the emergence of new practices of the bodies of men and women suggest that the borders between the masculine and the feminine in Morocco are moving. However, the persistence of sexual violence against girls/women indicates that these changes did not contribute to the lifting of the men's control over the bodies of women. This paper aims to analyze the lessons learned about sex education as a discourse related to the fabric of bodies in relation to sexuality in school in order to understand to what extent this institution contributes to the (re) production of gender inequalities. As a result, the educational discourse on sexuality still remains one of the spaces of resistance against gender equality and thus contributes to the (re) production of gender inequalities.

Keywords: gender, sexual education, Morocco, educational system

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Authors: Tamara Trček Pečak, Andrej Mohar, Denis Trček

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This article investigates the latest technological means, which deploy smart structures that are based on (advanced) wireless sensors technologies and ubiquitous computing in general in order to support the above mentioned decision making. Based on two years of in-field research experiences it gives their analysis for these kinds of purposes and provides appropriate architectures and architectural solutions. Moreover, the directions for future research are stated, because these technologies are currently the most promising ones to enable cost-effective preservation of cultural heritage not only in uncontrolled places, but also in general.

Keywords: smart structures, wireless sensors, sensors networks, green computing, cultural heritage preservation, monitoring, cost effectiveness

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Authors: R. H. Fattepur, Sameer R. Fattepur, D. K. Sreekantha

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Dairy Farming is one of the key industries in India. India is the leading producer and also the consumer of milk, milk-based products in the world. In this paper, we have attempted to the replace the human expert system and to develop an artificial expert system prototype to increase the speed and accuracy of decision making dairy farming credit risk evaluation. Fuzzy logic is used for dealing with uncertainty, vague and acquired knowledge, fuzzy rule base method is used for representing this knowledge for building an effective expert system.

Keywords: expert system, fuzzy logic, knowledge base, dairy farming, credit risk

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Authors: Emmanuel Barrantes Chaves, Ernesto Rivera Alvarado

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This paper presents a method to enable agents for the Terraria game to evaluate algorithms commonly used in general video game artificial intelligence competitions. The usage of the ‘You Only Look Once’ model in the first layer of the process obtains information from the screen, translating this information into a video game description language known as “Video Game Description Language”; the agents take that as input to make decisions. For this, the state-of-the-art algorithms were tested and compared; Monte Carlo Tree Search and Rolling Horizon Evolutionary; in this case, Rolling Horizon Evolutionary shows a better performance. This approach’s main advantage is that a VGDL beforehand is unnecessary. It will be built on the fly and opens the road for using more games as a framework for AI.

Keywords: AI, MCTS, RHEA, Terraria, VGDL, YOLOv5

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Authors: Lubna Alhinti, Heidi Christensen, Stuart Cunningham

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People with speech disorders may rely on augmentative and alternative communication (AAC) technologies to help them communicate. However, the limitations of the current AAC technologies act as barriers to the optimal use of these technologies in daily communication settings. The ability to communicate effectively relies on a number of factors that are not limited to the intelligibility of the spoken words. In fact, non-verbal cues play a critical role in the correct comprehension of messages and having to rely on verbal communication only, as is the case with current AAC technology, may contribute to problems in communication. This is especially true for people’s ability to express their feelings and emotions, which are communicated to a large part through non-verbal cues. This paper focuses on understanding more about the non-verbal communication ability of people with dysarthria, with the overarching aim of this research being to improve AAC technology by allowing people with dysarthria to better communicate emotions. Preliminary survey results are presented that gives an understanding of how people with dysarthria convey emotions, what emotions that are important for them to get across, what emotions that are difficult for them to convey, and whether there is a difference in communicating emotions when speaking to familiar versus unfamiliar people.

Keywords: alternative and augmentative communication technology, dysarthria, speech emotion recognition, VIVOCA

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Authors: Lya Porto, Giulia Giacchè, Mario Aquino Alves

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The paper aims to describe the linkage between government and civil society proposing a study on agro-ecological agriculture policy and urban action in São Paulo city underling the main achievements obtained. The negotiation processes between social movements and the government (inputs) and its results on political regulation and public action for Urban Agriculture (UA) in São Paulo city (outputs) have been investigated. The method adopted is qualitative, with techniques of semi-structured interviews, participant observation, and documental analysis. The authors conducted 30 semi-structured interviews with organic farmers, activists, governmental and non-governmental managers. Participant observation was conducted in public gardens, urban farms, public audiences, democratic councils, and social movements meetings. Finally, public plans and laws were also analyzed. São Paulo city with around 12 million inhabitants spread out in a 1522 km2 is the economic capital of Brazil, marked by spatial and socioeconomic segregation, currently aggravated by environmental crisis, characterized by water scarcity, pollution, and climate changes. In recent years, Urban Agriculture (UA) social movements gained strength and struggle for a different city with more green areas, organic food production, and public occupation. As the dynamics of UA occurs by the action of multiple actresses and institutions that struggle to build multiple senses on UA, the analysis will be based on literature about solidarity economy, governance, public action and networks. Those theories will mark out the analysis that will emphasize the approach of inter-subjectivity built between subjects, as well as the hybrid dynamics of multiple actors and spaces in the construction of policies for UA. Concerning UA we identified four main typologies based on land ownership, main function (economic or activist), form of organization of the space, and type of production (organic or not). The City Hall registers 500 productive unities of agriculture, with around 1500 producers, but researcher estimated a larger number of unities. Concerning the social movements we identified three categories that differ in goals and types of organization, but all of them work by networks of activists and/or organizations. The first category does not consider themselves as a movement, but a network. They occupy public spaces to grow organic food and to propose another type of social relations in the city. This action is similar to what became known as the green guerrillas. The second is configured as a movement that is structured to raise awareness about agro-ecological activities. The third one is a network of social movements, farmers, organizations and politicians that work focused on pressure and negotiation with executive and legislative government to approve regulations and policies on organic and agro-ecological Urban Agriculture. We conclude by highlighting how the interaction among institutions and civil society produced important achievements for recognition and implementation of UA within the city. Some results of this process are awareness for local production, legal and institutional recognition of the rural zone around the city into the planning tool, the investment on organic school public procurements, the establishment of participatory management of public squares, the inclusion of UA on Municipal Strategic Plan and Master Plan.

Keywords: public action, policies, agroecology, urban and peri-urban agriculture, Sao Paulo

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Authors: Riffat Sultana, Ahmed Ali Samejo

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Desert Locust is a critical pest of crop and non-crop plants throughout the old world including Pakistan. Geographically, this pest invades 31 million km2 in about 60 countries during the gregarious phase which may bring calamity. The present study is carried out in order to conduct field observations on oviposition behavior from Thar Desert, Pakistan. Females preferred loose soil for oviposition rather than packed or hard soil. The depth of egg pods inside the soil was measured up to 8.996±1.40 cm, and duration of egg laying was measured up to 105.9±26.4 min. Besides this, an insightful recognition has been made that the solitary females oviposited predominantly in the vicinity of pearl millet (Pennisetum glaucum) and guar or cluster bean (Cyamopsis tetragonoloba) crops in cultivated fields while in uncultivated land preferred the surroundings of bekar grass (Indigofera caerulea) and snow bush (Aerva javanica). It was also observed that nymphs preferred to feed on these host plants. Furthermore, experimental outcomes indicated that gravid females oviposited on the bottom of perforated plastic cages while, they did not find suitable soil for oviposition.

Keywords: calamity, cultivated fields, desert locust, host plants, oviposition behavior

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Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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Authors: Weiwei Cui, Dejian Lin, Leigang Zhang, Yao Wang, Zheng Sun, Lianfeng Li

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Built-in test (BIT) is an important technology in testability field, and it is widely used in state monitoring and fault diagnosis. With the improvement of modern equipment performance and complexity, the scope of BIT becomes larger, and it leads to the emergence of false alarm problem. The false alarm makes the health assessment unstable, and it reduces the effectiveness of BIT. The conventional false alarm suppression methods such as repeated test and majority voting cannot meet the requirement for a complicated system, and the intelligence algorithms such as artificial neural networks (ANN) are widely studied and used. However, false alarm has a very low frequency and small sample, yet a method based on ANN requires a large size of training sample. To recognize the false alarm, we propose a method based on multi-classification support vector machine (SVM) in this paper. Firstly, we divide the state of a system into three states: healthy, false-alarm, and faulty. Then we use multi-classification with '1 vs 1' policy to train and recognize the state of a system. Finally, an example of fault injection system is taken to verify the effectiveness of the proposed method by comparing ANN. The result shows that the method is reasonable and effective.

Keywords: false alarm, fault diagnosis, SVM, k-means, BIT

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Authors: Razieh Khalafi

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The brain is the information processing centre of the human body. Stimuli in the form of information are transferred to the brain and then brain makes the decision on how to respond to them. In this research, we propose a new partial differential equation which analyses the EEG signals and make a relationship between the incoming stimuli and the brain response to them. In order to test the proposed model, a set of external stimuli applied to the model and the model’s outputs were checked versus the real EEG data. The results show that this model can model the EEG signal well. The proposed model is useful not only for modelling of EEG signal in case external stimuli but it can be used for modelling of brain response in case of internal stimuli.

Keywords: brain, stimuli, partial differential equation, response, EEG signal

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Authors: Emily Gould, Walter Wehremeyer, David Greaves, Rodney Turtle

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This paper considers current thinking on the pathway for electric vehicles, identifying the development blocks of alternative innovation within the market and analyse technological lock-in. The relationship between transition pathways and technological lock-in is largely under-researched particularly in the field of e-mobility. This paper is based on a study with three commercial-urban fleets that examines strategic decisions in new technology adaption alongside vehicle procurement and driver perspective. The paper will analyse the fleet’s decision matrix upon electric vehicles and seek to understand the influence of company culture, strategy and technology applicability, within the context of transition pathways.

Keywords: electric vehicles, fleets, path dependencies, transition pathways

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Authors: Soheila Sadeghi

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In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.

Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes

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Authors: Luciano Oliveira, Elsa Vásquez-Alvarez

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The increase in rail freight transport in Brazil in recent years requires new railway lines and the maintenance of existing ones, which generates high costs for concessionaires. It is in this context that this work is inserted, whose objective is to propose a method that uses Binary Linear Programming for the choice of sleeper and rail fastening, from various options, including the way to apply these materials, with focus to minimize costs. Unit value information, the life cycle each of material type, and service expenses are considered. The model was implemented in commercial software using real data for its validation. The formulated model can be replicated to support decision-making for other railway projects in the choice of sleepers and rail fastening with lowest cost.

Keywords: linear programming, rail fastening, rail sleeper, railway

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Authors: Chhavi Rana

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The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, Healthcare, and business intelligence contain voluminous and incremental data, which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organization's decision-making strategy can be enhanced using big data analytics and applying different machine learning techniques and statistical tools on such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates on various frameworks in the process of Analysis using different machine-learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

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Authors: Chhavi Rana

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The aim of the paper is to highlight the existing development in the field of big data analytics. Applications like bioinformatics, smart infrastructure projects, healthcare, and business intelligence contain voluminous and incremental data which is hard to organise and analyse and can be dealt with using the framework and model in this field of study. An organisation decision-making strategy can be enhanced by using big data analytics and applying different machine learning techniques and statistical tools to such complex data sets that will consequently make better things for society. This paper reviews the current state of the art in this field of study as well as different application domains of big data analytics. It also elaborates various frameworks in the process of analysis using different machine learning techniques. Finally, the paper concludes by stating different challenges and issues raised in existing research.

Keywords: big data, big data analytics, machine learning, review

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Authors: Hargunjeet Shergill, Palwinder Singh

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The present paper analyzes the lack of teachers' awareness and knowledge regarding the Attention Deficit Hyperactivity Disorder in the college students. Attention deficit hyperactivity disorder causes individuals to consistently display extreme inattention, impulsivity and in many cases hyperactivity as a result of the physiological differences of the brain. Teachers have a formative influence on their students and can play a key role in identifying and supporting students with Attention Deficit/Hyperactivity Disorder (ADHD). Despite the pervasiveness and salience of this disorder, educators at college continue to labor under a number of misconceptions about the nature of ADHD. In order to fulfill this important role, it is imperative for teachers to have explicit knowledge about this disorder. ADHD in college students remains the most under-recognized and undertreated mental health condition. The overall aim of this study is to investigate teachers’ knowledge and misconceptions of ADHD with a particular focus on recognition, assessment and management of ADHD in adult college students. It designed to assess the college teachers' knowledge, opinions, and experience related to the diagnosis of attention-deficit/hyperactivity disorder (ADHD) and by maintaining open lines of communication with the students and understanding some key elements that can affect students’ overall growth and ability. The discussion focuses on the value of the role of teachers and their relationship with each college student dealing with ADHD.

Keywords: attention deficit hyperactivity disorder, development of ADHD, diagnostic criteria, role of teachers

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Authors: Hira Jabbar, Tanzeel-Ur Rehman

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Urban parks and open spaces play a significant role in improving physical and mental health of the citizens, strengthen the societies and make the cities more attractive places to live and work. As the world’s cities continue to grow, continuing to value green space in cities is vital but is also a challenge, particularly in developing countries where there is pressure for space, resources, and development. Offering equal opportunity of accessibility to parks is one of the important issues of park distribution. The distribution of parks should allow all inhabitants to have close proximity to their residence. Remote sensing and Geographic information systems (GIS) can provide decision makers with enormous opportunities to improve the planning and management of Park facilities. This study exhibits the capability of GIS and RS techniques to provide baseline knowledge about the distribution of parks, level of accessibility and to help in identification of potential areas for such facilities. For this purpose Landsat OLI imagery for year 2016 was acquired from USGS Earth Explorer. Preprocessing models were applied using Erdas Imagine 2014v for the atmospheric correction and NDVI model was developed and applied to quantify the land use/land cover classes including built up, barren land, water, and vegetation. The parks amongst total public green spaces were selected based on their signature in remote sensing image and distribution. Percentages of total green and parks green were calculated for each town of Lahore City and results were then synchronized with the recommended standards. ANGSt model was applied to calculate the accessibility from parks. Service area analysis was performed using Network Analyst tool. Serviceability of these parks has been evaluated by employing statistical indices like service area, service population and park area per capita. Findings of the study may contribute in helping the town planners for understanding the distribution of parks, demands for new parks and potential areas which are deprived of parks. The purpose of present study is to provide necessary information to planners, policy makers and scientific researchers in the process of decision making for the management and improvement of urban parks.

Keywords: accessible natural green space standards (ANGSt), geographic information systems (GIS), remote sensing (RS), United States geological survey (USGS)

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Authors: Tizian Matschak, Ilja Nastjuk, Stephan Kühnel, Simon Trang

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We argue that besides well-known primary effects of information security controls (ISCs), namely confidentiality, integrity, and availability, ISCs can also have secondary effects. For example, while IT can add business value through impacts on business processes, ISCs can be a barrier and distort the relationship between IT and organizational value through the impact on business processes. By applying the Delphi method with 28 experts, we derived 27 business process influence dimensions of ISCs. Defining and understanding these mechanisms can change the common understanding of the cost-benefit valuation of IT security investments and support managers' effective and efficient decision-making.

Keywords: business process dimensions, dark side of information security, Delphi study, IT security controls

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Authors: Susana Marques

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Research on how store environment cues influence consumers’ store choice decision criteria, such as store operations, product quality, monetary price, store image and sales promotion, is sparse. Especially absent research on the simultaneous impact of multiple store environment cues. The authors propose a comprehensive store choice model that includes: three types of store environment cues as exogenous constructs; various store choice criteria as possible mediating constructs, and store patronage intentions as an endogenous construct. On the basis of testing with a sample of 561 customers of hypermarkets, the model is partially supported. This study used structural equation modelling to test the proposed model.

Keywords: store choice, store patronage, structural equation modelling, retailing

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Authors: Xulu Yao, Moi Hoon Yap, Yanlong Zhang

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As a branch of artificial neural network, deep learning is widely used in the field of image recognition, but the lack of its dataset leads to imperfect model learning. By analysing the data scale requirements of deep learning and aiming at the application in GUI generation, it is found that the collection of GUI dataset is a time-consuming and labor-consuming project, which is difficult to meet the needs of current deep learning network. To solve this problem, this paper proposes a semi-supervised deep learning model that relies on the original small-scale datasets to produce a large number of reliable data sets. By combining the cyclic neural network with the generated countermeasure network, the cyclic neural network can learn the sequence relationship and characteristics of data, make the generated countermeasure network generate reasonable data, and then expand the Rico dataset. Relying on the network structure, the characteristics of collected data can be well analysed, and a large number of reasonable data can be generated according to these characteristics. After data processing, a reliable dataset for model training can be formed, which alleviates the problem of dataset shortage in deep learning.

Keywords: GUI, deep learning, GAN, data augmentation

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Authors: Erika Yamaguchi, Sirawadee Arunyanrt, Shunichi Ohmori, Kazuho Yoshimoto

Abstract:

In this paper, we suggest a simulation tool to make a decision of monthly production planning for maximizing a profit of Specialty store retailer of Private label Apparel (SPA) firms. Most of SPA firms are fabless and make outsourcing deals for productions with factories of their subcontractors. Every month, SPA firms make a booking for production lines and manpower in the factories. The booking is conducted a few months in advance based on a demand prediction and a monthly production planning at that time. However, the demand prediction is updated month by month, and the monthly production planning would change to meet the latest demand prediction. Then, SPA firms have to change the capacities initially booked within a certain range to suit to the monthly production planning. The booking system is called “capacity-booking”. These days, though it is an issue for SPA firms to make precise monthly production planning, many firms are still conducting the production planning by empirical rules. In addition, it is also a challenge for SPA firms to match their products and factories with considering their demand predictabilities and regulation abilities. In this paper, we suggest a model for considering these two issues. An objective is to maximize a total profit of certain periods, which is sales minus costs of production, inventory, and capacity-booking penalty. To make a better monthly production planning at SPA firms, these points should be considered: demand predictabilities by random trends, previous and next month’s production planning of the target month, and regulation abilities of the capacity-booking. To decide matching products and factories for outsourcing, it is important to consider seasonality, volume, and predictability of each product, production possibility, size, and regulation ability of each factory. SPA firms have to consider these constructions and decide orders with several factories per one product. We modeled these issues as a linear programming. To validate the model, an example of several computational experiments with a SPA firm is presented. We suppose four typical product groups: basic, seasonal (Spring / Summer), seasonal (Fall / Winter), and spot product. As a result of the experiments, a monthly production planning was provided. In the planning, demand predictabilities from random trend are reduced by producing products which are different product types. Moreover, priorities to produce are given to high-margin products. In conclusion, we developed a simulation tool to make a decision of monthly production planning which is useful when the production planning is set every month. We considered the features of capacity-booking, and matching of products and factories which have different features and conditions.

Keywords: capacity-booking, SPA, monthly production planning, linear programming

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