Search results for: binary data

Authors: Julio Albuja, David Zaldumbide

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Data analysis is an important step before taking a decision about money. The aim of this work is to analyze the factors that influence the final price of the houses through data mining algorithms. To our best knowledge, previous work was researched just to compare results. Furthermore, before using the data of the data set, the Z-Transformation were used to standardize the data in the same range. Hence, the data was classified into two groups to visualize them in a readability format. A decision tree was built, and graphical data is displayed where clearly is easy to see the results and the factors' influence in these graphics. The definitions of these methods are described, as well as the descriptions of the results. Finally, conclusions and recommendations are presented related to the released results that our research showed making it easier to apply these algorithms using a customized data set.

Keywords: algorithms, data, decision tree, transformation

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Authors: Gete Umbrey, Saifur Rahman

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Graphs are found to be most capable in computing, and its abstract structures have been applied in some specific computations and algorithms like in phase encoding controller, processor microcontroller, and synthesis of a CMOS switching network, etc. Being motivated by these works, we develop an independent approach to study semiring structures and various properties by defining the binary operations which in fact, seems analogous to an existing definition in some sense but with a different approach. This work emphasizes specifically on the construction of semigroup and semiring structures on the set of undirected graphs, and their properties are investigated therein. It is expected that the investigation done here may have some interesting applications in theoretical computer science, networking and decision making, and also on joining of two network systems.

Keywords: graphs, join and union of graphs, semiring, weighted graphs

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Authors: Mengdi Zhang, Zhenji Gao, Ning Kang, Rongmei Liu

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Management and application of geological big data is an important part of China's national big data strategy. With the implementation of a national big data strategy, geological big data management becomes more and more critical. At present, there are still a lot of technology barriers as well as cognition chaos in many aspects of geological big data management and application, such as data sharing, intellectual property protection, and application technology. Therefore, it’s a key task to make better use of new technologies for deeper delving and wider application of geological big data. In this paper, we briefly introduce the basic principle of blockchain technology at the beginning and then make an analysis of the application dilemma of geological data. Based on the current analysis, we bring forward some feasible patterns and scenarios for the blockchain application in geological big data and put forward serval suggestions for future work in geological big data management.

Keywords: blockchain, intellectual property protection, geological data, big data management

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Authors: Thomas Suarez, Anne-Sophie Grenouilleau, Erwan Autin, Alexandre Biosse-Duplan, Emmanuelle Blondet, Laurence Chazalette, Marie Coniel, Agnes Dessaigne, Sylvie Lascols, Andrea Lasserre, Candice Legris, Pierre Liot, Aline Metais, Karine Petitprez, Christophe Varlet, Christian Saout

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Background: The role of biological sex and gender identity -whether assigned or chosen- as health determinants are far from a recent discovery: several reports have stressed out how being a woman or a man could affect health on various scales. However, taking it into consideration beyond stereotypes and rigid binary assumptions still seems to be a work in progress. Method: The report is a synthesis on a variety of specific topics, each of which was studied by a specialist from the French National Authority for Health (HAS), through an analysis of existing literature on both healthcare policy construction process and instruments (norms, data analysis, clinical trials, guidelines, and professional practices). This work also implied a policy analysis of French recent public health laws and a retrospective study of guidelines with a gender mainstreaming approach. Results: The analysis showed that though sex and gender were well-known determinants of health, their consideration by both public policy and health operators was often incomplete, as it does not incorporate how sex and gender interact, as well as how they interact with other factors. As a result, the health and social care systems and their professionals tend to reproduce some stereotypical and inadequate habits. Though the data available often allows to take sex and gender into consideration, such data is often underused in practice guidelines and policy formulation. Another consequence is a lack of inclusiveness towards transgender or intersex persons. Conclusions: This report first urges for raising awareness of all the actors of health, in its broadest definition, that sex and gender matter beyond first-look conclusions. It makes a series of recommendations in order to reshape policy construction in the health sector on the one hand and to design public health instruments to make them more inclusive regarding sex and gender on the other hand. The HAS finally committed to integrate sex and gender preoccupations in its workings methods, to be a driving force in the spread of these concerns.

Keywords: biological sex, determinants of health, gender, healthcare policy instruments, social accompaniment

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Authors: Tamanna Jethava, Rahul Joshi

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Frequent Item sets play an essential role in many data Mining tasks that try to find interesting patterns from the database. Typically it refers to a set of items that frequently appear together in transaction dataset. There are several mining algorithm being used for frequent item set mining, yet most do not scale to the type of data we presented with today, so called “BIG DATA”. Big Data is a collection of large data sets. Our approach is to work on the frequent item set mining over the large dataset with scalable and speedy way. Big Data basically works with Map Reduce along with HDFS is used to find out frequent item sets from Big Data on large cluster. This paper focuses on using pre-processing & mining algorithm as hybrid approach for big data over Hadoop platform.

Keywords: frequent item set mining, big data, Hadoop, MapReduce

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Authors: Hussaini Garba Mohammed

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Background/Significance: The lack of data gathering is affecting NGOs world-wide in general to have good data information about educational and health related issues among communities in any country and around the world. For example, HIV/AIDS smoking (Tuberculosis diseases) and COVID-19 virus carriers is becoming a serious public health problem, especially among old men and women. But there is no full details data survey assessment from communities, villages, and rural area in some countries to show the percentage of victims and patients, especial with this world COVID-19 virus among the people. These data are essential to inform programming targets, strategies, and priorities in getting good information about data gathering in any society.

Keywords: reliable information, data assessment, data mining, data communication

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Authors: Liang Zhao, Jili Zhang, Chongquan Zhong

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Energy consumption data, in particular those involving public buildings, are impacted by many factors: the building structure, climate/environmental parameters, construction, system operating condition, and user behavior patterns. Traditional methods for data analysis are insufficient. This paper delves into the data mining technology to determine its application in the analysis of building energy consumption data including energy consumption prediction, fault diagnosis, and optimal operation. Recent literature are reviewed and summarized, the problems faced by data mining technology in the area of energy consumption data analysis are enumerated, and research points for future studies are given.

Keywords: data mining, data analysis, prediction, optimization, building operational performance

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Authors: Shahnewaz Khan

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Machine learning (ML) techniques are often used in projects for creating data-driven applications. These tasks typically demand additional research and analysis. The proper technique and strategy must be chosen to ensure the success of data-driven projects. Otherwise, even exerting a lot of effort, the necessary development might not always be possible. In this post, an effort to examine the workflow of data-driven software development projects and its implementation process in order to describe how to manage a project successfully. Which will assist in minimizing the added workload.

Keywords: data, data-driven projects, data science, NLP, software project

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Authors: Desmond F. Bellamy

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Discourses about animal rights usually assume an ontological abyss between human and animal. This supposition of non-animality allows us to utilise and exploit non-humans, particularly those with commercial value, with little regard for their rights or interests. We can and do confine them, inflict painful treatments such as castration and branding, and slaughter them at an age determined only by financial considerations. This paper explores the way images and texts depicting human cannibalism reflect this deprivation of rights back onto our species and examines how this offers new perspectives on our granting or withholding of rights to farmed animals. The animals we eat – sheep, pigs, cows, chickens and a small handful of other species – are during processing de-animalised, turned into commodities, and made unrecognisable as formerly living beings. To do the same to a human requires the cannibal to enact another step – humans must first be considered as animals before they can be commodified or de-animalised. Different iterations of cannibalism in a selection of fiction and non-fiction texts will be considered: survivalism (necessitated by catastrophe or dystopian social collapse), the primitive savage of colonial discourses, and the inhuman psychopath. Each type of cannibalism shows alternative ways humans can be animalised and thereby dispossessed of both their human and animal rights. Human rights, summarised in the UN Universal Declaration of Human Rights as ‘life, liberty, and security of person’ are stubbornly denied to many humans, and are refused to virtually all farmed non-humans. How might this paradigm be transformed by seeing the animal victim replaced by an animalised human? People are fascinated as well as repulsed by cannibalism, as demonstrated by the upsurge of films on the subject in the last few decades. Cannibalism is, at its most basic, about envisaging and treating humans as objects: meat. It is on the dinner plate that the abyss between human and ‘animal’ is most challenged. We grasp at a conscious level that we are a species of animal and may become, if in the wrong place (e.g., shark-infested water), ‘just food’. Culturally, however, strong traditions insist that humans are much more than ‘just meat’ and deserve a better fate than torment and death. The billions of animals on death row awaiting human consumption would ask the same if they could. Depictions of cannibalism demonstrate in graphic ways that humans are animals, made of meat and that we can also be butchered and eaten. These depictions of us as having the same fleshiness as non-human animals reminds us that they have the same capacities for pain and pleasure as we do. Depictions of cannibalism, therefore, unconsciously aid in deconstructing the human/animal binary and give a unique glimpse into the often unnoticed repudiation of animal rights.

Keywords: animal rights, cannibalism, human/animal binary, objectification

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Authors: Kaili Wang, Botao Zhang, Enming Zhang

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Background: Low back pain (LBP) is a common problem affecting athletes' training and competition. Although the association between a limited hip range of motion and prevalence of low back pain has been studied extensively, it has not been studied in table tennis. Aim: The main purposes of this study in table tennis players were (1) to investigate if there is a difference in hip internal rotation (HIR) and external rotation (HER) range of motion (ROM) between players with LBP and players without LBP and (2) to analyze the association between HIR and HER ROM and LBP. Methods: Forty-six table tennis players from the Chinese table tennis team were evaluated for passive maximum HIR and HER ROM. LBP was retrospectively recorded for the last 12 months before the date of ROM assessment by a physical therapist. The data were analyzed the difference in HIR and HER ROM between players with LBP and players without LBP by Mann-Whitney U test, and the association between the difference in HIR and HER ROM and LBP was analyzed via a binary logistic regression. Results: The 54% of players had developed LBP during the retrospective study period. Significant difference between LBP group and the asymptomatic group for HIR ROM (z=4.007, p<0.001) was observed. Difference between LBP group and asymptomatic group for HER ROM (z=1.117, p=0.264) was not significant. Players who had HIR ROM deficit had an increased risk of LBP compared with players without HIR ROM deficit (OR=5.344, 95%CI: 1.006-28.395, P=0.049). Conclusion: HIR ROM of a table tennis player with LBP was less than a table tennis player without LBP. Compared with player whose HIR ROM was normal, player who had HIR ROM deficit appeared to have a higher risk for LBP.

Keywords: assessment, injury prevention, low back pain, table tennis players

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Authors: Md. Khayrul Bashar, Md. Saiful Islam, Kimiko Yamashita, Yano Midori

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Despite having some progress in human authentication, conventional biometrics (e.g., facial features, fingerprints, retinal scans, gait, voice patterns) are not robust against falsification because they are neither confidential nor secret to an individual. As a non-invasive tool, electrocardiogram (ECG) has recently shown a great potential in human recognition due to its unique rhythms characterizing the variability of human heart structures (chest geometry, sizes, and positions). Moreover, ECG has a real-time vitality characteristic that signifies the live signs, which ensure legitimate individual to be identified. However, the detection accuracy of the current ECG-based methods is not sufficient due to a high variability of the individual’s heartbeats at a different instance of time. These variations may occur due to muscle flexure, the change of mental or emotional states, and the change of sensor positions or long-term baseline shift during the recording of ECG signal. In this study, a new method is proposed for human identification, which is based on the extraction of the local roughness of ECG heartbeat signals. First ECG signal is preprocessed using a second order band-pass Butterworth filter having cut-off frequencies of 0.00025 and 0.04. A number of local binary patterns are then extracted by applying a moving neighborhood window along the ECG signal. At each instant of the ECG signal, the pattern is formed by comparing the ECG intensities at neighboring time points with the central intensity in the moving window. Then, binary weights are multiplied with the pattern to come up with the local roughness description of the signal. Finally, histograms are constructed that describe the heartbeat signals of individual subjects in the database. One advantage of the proposed feature is that it does not depend on the accuracy of detecting QRS complex, unlike the conventional methods. Supervised recognition methods are then designed using minimum distance to mean and Bayesian classifiers to identify authentic human subjects. An experiment with sixty (60) ECG signals from sixty adult subjects from National Metrology Institute of Germany (NMIG) - PTB database, showed that the proposed new method is promising compared to a conventional interval and amplitude feature-based method.

Keywords: human identification, ECG biometrics, local roughness patterns, supervised classification

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Authors: Amin Saeidi

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Using a representation-theoretic approach and considering G to be a finite primitive permutation group of degree n, our aim is to determine linear codes of length n that admit G as a permutation automorphism group. We can show that in some cases, every binary linear code admitting G as a permutation automorphism group is a submodule of a permutation module defined by a primitive action of G. As an illustration of the method, we consider the sporadic simple group M₁₁ and the unitary group U(3,3). We also construct some point- and block-primitive 1-designs from the supports of some codewords of the codes in the discussion.

Keywords: linear code, permutation representation, support design, simple group

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Authors: M. Naidoo

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Artificial intelligence often requires large amounts of good quality data. Within important fields, such as healthcare, the training of AI systems predominately relies on health and personal data; however, the usage of this data is complicated by various layers of law and ethics that seek to protect individuals’ privacy rights. This research seeks to establish the challenges AI and data sciences pose to (i) informational rights, (ii) privacy rights, and (iii) data protection. To solve some of the issues presented, various methods are suggested, such as embedding values in technological development, proper balancing of rights and interests, and others.

Keywords: artificial intelligence, data science, law, policy

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Authors: Carlos F. Sanz-Navarro, Sonia Fereres

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Binary and ternary mixtures of molten salts are excellent thermal energy storage systems and have been widely used in commercial tanks both in nuclear and solar thermal applications. However, the energy density of the commercially used mixtures is still insufficient, and therefore, new systems based on latent heat storage (or phase change materials, PCM) are currently being investigated. In order to shed some light on the macroscopic physical properties of the molten salt phases, knowledge of the microscopic structure and dynamics is required. Several molecular dynamics (MD) simulations have been performed to model the thermal behavior of (Li,K)2CO3 mixtures. Up to this date, this particular molten salt mixture has not been extensively studied but it is of fundamental interest for understanding the behavior of other commercial salts. Molten salt diffusivities, the internal electrical ion mobility, and the physical properties of the solid-liquid phase transition have been calculated and compared to available data from literature. The effect of anion polarization and the application of a strong external electric field have also been investigated. The influence of electrical ion mobility on local composition is explained through the Chemla effect, well known in electrochemistry. These results open a new way to design optimal high temperature energy storage materials.

Keywords: atomistic simulations, thermal storage, latent heat, molten salt, ion mobility

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Authors: Handan Ertaş

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The aim of the study is to determine the relationship between organizational trust level and organizational justice of Municipality officials. Correlational method has been used via descriptive survey model and Organizational Justice Perception Scale, Organizational Trust Inventory and Interpersonal Trust Scale have been applied to 353 participants who work in Konya Metropolitan Municipality and central district municipalities in the study. Frequency as statistical method, Independent Samples t test for binary groups, One Way-ANOVA analyses for multi-groups and Pearson Correlation analysis have been used to determine the relation in the data analysis process. It has been determined in the outcomes of the study that participants have high level of organizational trust, “Interpersonal Trust” is in the first place and there is a significant difference in the favor of male officials in terms of Trust on the Organization Itself and Interpersonal Trust. It has also been understood that officials in district municipalities have higher perception level in all dimensions, there is a significant difference in Trust on the Organization sub-dimension and work status is an important factor on organizational trust perception. Moreover, the study has shown that organizational justice implementations are important in raising trust of official on the organization, administrator and colleagues, and there is a parallel relation between Organizational Trust components and Organizational Trust dimensions.

Keywords: organizational trust level, organizational justice perceptions, staff, Konya

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Authors: Javier E. García-Gallo, Nelson J. Fonseca-Ruiz, John F. Duitama-Munoz

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Sepsis is a syndrome that occurs with physiological and biochemical abnormalities induced by severe infection and carries a high mortality and morbidity, therefore the severity of its condition must be interpreted quickly. After patient admission in an intensive care unit (ICU), it is necessary to synthesize the large volume of information that is collected from patients in a value that represents the severity of their condition. Traditional severity of illness scores seeks to be applicable to all patient populations, and usually assess in-hospital mortality. However, the use of machine learning techniques and the data of a population that shares a common characteristic could lead to the development of customized mortality prediction scores with better performance. This study presents the development of a score for the one-year mortality prediction of the patients that are admitted to an ICU with a sepsis diagnosis. 5650 ICU admissions extracted from the MIMICIII database were evaluated, divided into two groups: 70% to develop the score and 30% to validate it. Comorbidities, demographics and clinical information of the first 24 hours after the ICU admission were used to develop a mortality prediction score. LASSO (least absolute shrinkage and selection operator) and SGB (Stochastic Gradient Boosting) variable importance methodologies were used to select the set of variables that make up the developed score; each of this variables was dichotomized and a cut-off point that divides the population into two groups with different mean mortalities was found; if the patient is in the group that presents a higher mortality a one is assigned to the particular variable, otherwise a zero is assigned. These binary variables are used in a logistic regression (LR) model, and its coefficients were rounded to the nearest integer. The resulting integers are the point values that make up the score when multiplied with each binary variables and summed. The one-year mortality probability was estimated using the score as the only variable in a LR model. Predictive power of the score, was evaluated using the 1695 admissions of the validation subset obtaining an area under the receiver operating characteristic curve of 0.7528, which outperforms the results obtained with Sequential Organ Failure Assessment (SOFA), Oxford Acute Severity of Illness Score (OASIS) and Simplified Acute Physiology Score II (SAPSII) scores on the same validation subset. Observed and predicted mortality rates within estimated probabilities deciles were compared graphically and found to be similar, indicating that the risk estimate obtained with the score is close to the observed mortality, it is also observed that the number of events (deaths) is indeed increasing as the outcome go from the decile with the lowest probabilities to the decile with the highest probabilities. Sepsis is a syndrome that carries a high mortality, 43.3% for the patients included in this study; therefore, tools that help clinicians to quickly and accurately predict a worse prognosis are needed. This work demonstrates the importance of customization of mortality prediction scores since the developed score provides better performance than traditional scoring systems.

Keywords: intensive care, logistic regression model, mortality prediction, sepsis, severity of illness, stochastic gradient boosting

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Authors: Vahid Bairami Rad

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Extracting knowledge from spatial data like GIS data is important to reduce the data and extract information. Therefore, the development of new techniques and tools that support the human in transforming data into useful knowledge has been the focus of the relatively new and interdisciplinary research area ‘knowledge discovery in databases’. Thus, we introduce a set of database primitives or basic operations for spatial data mining which are sufficient to express most of the spatial data mining algorithms from the literature. This approach has several advantages. Similar to the relational standard language SQL, the use of standard primitives will speed-up the development of new data mining algorithms and will also make them more portable. We introduced a database-oriented framework for spatial data mining which is based on the concepts of neighborhood graphs and paths. A small set of basic operations on these graphs and paths were defined as database primitives for spatial data mining. Furthermore, techniques to efficiently support the database primitives by a commercial DBMS were presented.

Keywords: spatial data base, knowledge discovery database, data mining, spatial relationship, predictive data mining

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Authors: B. Suraj Aravind, M. H. M. Krishna Prasad

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There exist emerging applications of data streams that require association rule mining, such as network traffic monitoring, web click streams analysis, sensor data, data from satellites etc. Data streams typically arrive continuously in high speed with huge amount and changing data distribution. This raises new issues that need to be considered when developing association rule mining techniques for stream data. This paper proposes to introduce an improved data stream association rule mining algorithm by eliminating the limitation of resources. For this, the concept of cloud computing is used. Inclusion of this may lead to additional unknown problems which needs further research.

Keywords: data stream, association rule mining, cloud computing, frequent itemsets

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Authors: Aleksandra Zysk, Pawel Badura

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Recognizing and controlling vocal registers during singing is a difficult task for beginner vocalist. It requires among others identifying which part of natural resonators is being used when a sound propagates through the body. Thus, an application has been designed allowing for sound recording, automatic vocal register recognition (VRR), and a graphical user interface providing real-time visualization of the signal and recognition results. Six spectral features are determined for each time frame and passed to the support vector machine classifier yielding a binary decision on the head or chest register assignment of the segment. The classification training and testing data have been recorded by ten professional female singers (soprano, aged 19-29) performing sounds for both chest and head register. The classification accuracy exceeded 93% in each of various validation schemes. Apart from a hard two-class clustering, the support vector classifier returns also information on the distance between particular feature vector and the discrimination hyperplane in a feature space. Such an information reflects the level of certainty of the vocal register classification in a fuzzy way. Thus, the designed recognition and training application is able to assess and visualize the continuous trend in singing in a user-friendly graphical mode providing an easy way to control the vocal emission.

Keywords: classification, singing, spectral analysis, vocal emission, vocal register

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Authors: Tosin Ige

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Ethics must be a condition of the world, like logic. (Ludwig Wittgenstein, 1889-1951). As important as data mining is, it possess a significant threat to ethics, privacy, and legality, since data mining makes it difficult for an individual or consumer (in the case of a company) to control the accessibility and usage of his data. This research focuses on Current issues and the latest research and development on Privacy preserving data mining methods as at year 2022. It also discusses some advances in those techniques while at the same time highlighting and providing a new technique as a solution to an existing technique of privacy preserving data mining methods. This paper also bridges the wide gap between Data mining and the Web Application Programing Interface (web API), where research is urgently needed for an added layer of security in data mining while at the same time introducing a seamless and more efficient way of data mining.

Keywords: data, privacy, data mining, association rule, privacy preserving, mining technique

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Authors: A. Alexandru, C. A. Alexandru, D. Coardos, E. Tudora

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Big Data (BD) is associated with a new generation of technologies and architectures which can harness the value of extremely large volumes of very varied data through real time processing and analysis. It involves changes in (1) data types, (2) accumulation speed, and (3) data volume. This paper presents the main concepts related to the BD paradigm, and introduces architectures and technologies for BD and BD sets. The integration of BD with the Hadoop Framework is also underlined. BD has attracted a lot of attention in the public sector due to the newly emerging technologies that allow the availability of network access. The volume of different types of data has exponentially increased. Some applications of BD in the public sector in Romania are briefly presented.

Keywords: big data, big data analytics, Hadoop, cloud

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Authors: Aïcha Ben Salem, Faouzi Boufares, Sebastiao Correia

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The subject covered in this paper aims at assisting the user in its quality approach. The goal is to better extract, mix, interpret and reuse data. It deals with the semantic schema recognition of a data source. This enables the extraction of data semantics from all the available information, inculding the data and the metadata. Firstly, it consists of categorizing the data by assigning it to a category and possibly a sub-category, and secondly, of establishing relations between columns and possibly discovering the semantics of the manipulated data source. These links detected between columns offer a better understanding of the source and the alternatives for correcting data. This approach allows automatic detection of a large number of syntactic and semantic anomalies.

Keywords: schema recognition, semantic data profiling, meta-categorisation, semantic dependencies inter columns

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Authors: Winfred Okoe Addy, Jean Jacques Dominique Beraud

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Access control systems (ACs) are some of the most important components in safety areas. Inaccuracies of regulatory frameworks make personal policies and remedies more appropriate than standard models or protocols. This problem is exacerbated by the increasing complexity of software, such as integrated Big Data (BD) software for controlling large volumes of encrypted data and resources embedded in a dedicated BD production system. This paper proposes a general access control strategy system for the diffusion of Big Data domains since it is crucial to secure the data provided to data consumers (DC). We presented a general access control circulation strategy for the Big Data domain by describing the benefit of using designated access control for BD units and performance and taking into consideration the need for BD and AC system. We then presented a generic of Big Data access control system to improve the dissemination of Big Data.

Keywords: access control, security, Big Data, domain

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Authors: S. Yousefi Oderji, B. Chen, M. A. Yazdi, J. Yang

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This study investigates the influence of water-binder ratio, mineral admixtures (silica fume and ground granulated blast furnace slag), and copper coated steel fiber on fluidity diameter, compressive and flexural strengths of reactive powder concrete (RPC). The test results show that the binary combination of silica fume and blast-furnace slag provided a positive influence on the mechanical properties of RPC. Although the addition of fibers reduced the workability, results indicated a higher mechanical strength in the inclusion of fibers.

Keywords: RPC, steel fiber, fluidity, mechanical properties

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Authors: Molla Tadesse Lakew

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This study was intended to identify the factors influencing the use of mobile phones in vegetable marketing in Fogera district. The use of mobile phones in vegetable marketing and factors influencing mobile phone use were specific objectives of the study. Three kebeles from the Fogera district were selected purposively based on their vegetable production potential. A simple random sampling technique (lottery method) was used to select 153 vegetable producer farmers. Interview schedule and key informants interviews were used to collect primary data. For analyzing the data, descriptive statistics like frequency and percentage, two independent t-tests, and chi-square were used. Furthermore, econometric analysis (binary logistic model) was used to assess the factors influencing mobile phone use for vegetable market information. Contingency coefficient and variance inflation factor were used to check multicollinearity problems between the independent variables. Of 153 respondents, 82 (61.72%) were mobile phone users, while 71 (38.28 %) were mobile phone nonusers. Moreover, the main use of mobile phones in vegetable marketing includes communicating at a distance to save time and minimizing transport costs, getting vegetable marketing price information, identifying markets and buyers to sell the vegetable, deciding when to sell the vegetable, negotiating with buyers for better vegetable prices and for searching of the fast market to avoid from losing of product through perishing. The model result indicated that the level of education, size of land, income, access to credit, and age were significant variables affecting the use of mobile phones in vegetable marketing. It could be recommended to encourage adult education or give training for farmers on how to operate mobile phones and create awareness for the elderly rural farmers as they are able to use the mobile phone for their vegetable marketing. Moreover, farmers should be aware that mobile phones are very important for those who own very small land to get maximum returns from their production. Lastly, providing access to credit and improving and diversifying income sources for the farmers to have mobile phones were recommended to improve the livelihood of farmers.

Keywords: mobile phone, farmers, vegetable marketing, Fogera District

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Authors: Michael Gidey Gebru

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Most of Data Envelopment Analysis models operate in a static environment with input and output parameters that are chosen by deterministic data. However, due to ambiguity brought on shifting market conditions, input and output data are not always precisely gathered in real-world scenarios. Fuzzy numbers can be used to address this kind of ambiguity in input and output data. Therefore, this work aims to expand crisp Data Envelopment Analysis into Data Envelopment Analysis with fuzzy environment. In this study, the input and output data are regarded as fuzzy triangular numbers. Then, the Data Envelopment Analysis model with fuzzy environment is solved using a multi-objective method to gauge the Decision Making Units' efficiency. Finally, the developed Data Envelopment Analysis model is illustrated with an application on real data 50 educational institutions.

Keywords: efficiency, Data Envelopment Analysis, fuzzy, higher education, input, output

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Authors: Umar Shehu Umar

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Contract farming is a viable strategy agribusinesses rely on to strengthen vertical coordination. However, low contract compliance remains a significant setback to agribusinesses' contract performance. The present study aims to understand what drives smallholder farmers’ contract compliance behaviour. Qualitative information was collected through Focus Group Discussions to enrich the design of the survey questionnaire administered on a sample of 300 randomly selected farmers contracted by the Dangote Tomato Processing Plant (DTPP) in four regions of northern Nigeria. Novel transaction level data of tomato sales covering one season were collected in addition to socio-economic information of the sampled farmers. Binary logistic model results revealed that open fresh market tomato prices and payment delays negatively affect farmers' compliance behaviour while quantity harvested, education level and input provision correlated positively with compliance. The study suggests that contract compliance will increase if contracting firms devise a reliable and timely payment plan (e.g., digital payment), continue input and service provisions (e.g., improved seeds, extension services) and incentives (e.g., loyalty rewards, bonuses) in the contract.

Keywords: contract farming, compliance, farmers and processors., smallholder

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Authors: Vineet Barwal, Sajid Husain, Nanhe Kumar Gupta, Soumyarup Hait, Sujeet Chaudhary

Abstract:

High perpendicular magnetic anisotropy (PMA) and low damping constant (α) in ferromagnets are one of the few necessary requirements for their potential applications in the field of spintronics. In this regards, ferromagnetic τ-phase of MnAl possesses the highest PMA (Ku > 107 erg/cc) at room temperature, high saturation magnetization (Ms~800 emu/cc) and a Curie temperature of ~395K. In this work, we have investigated the magnetotransport behaviour of this potentially useful binary system MnₓAl₁₋ₓ films were synthesized by co-sputtering (pulsed DC magnetron sputtering) on Si/SiO₂ (where SiO₂ is native oxide layer) substrate using 99.99% pure Mn and Al sputtering targets. Films of constant thickness (~25 nm) were deposited at the different growth temperature (Tₛ) viz. 30, 300, 400, 500, and 600 ºC with a deposition rate of ~5 nm/min. Prior to deposition, the chamber was pumped down to a base pressure of 2×10⁻⁷ Torr. During sputtering, the chamber was maintained at a pressure of 3.5×10⁻³ Torr with the 55 sccm Ar flow rate. Films were not capped for the purpose of electronic transport measurement, which leaves a possibility of metal oxide formation on the surface of MnAl (both Mn and Al have an affinity towards oxide formation). In-plane and out-of-plane transverse magnetoresistance (MR) measurements on films sputtered under optimized growth conditions revealed non-saturating behavior with MR values ~6% and 40% at 9T, respectively at 275 K. Resistivity shows a parabolic dependence on the field H, when the H is weak. At higher H, non-saturating positive MR that increases exponentially with the strength of magnetic field is observed, a typical character of hopping type conduction mechanism. An anomalous decrease in MR is observed on lowering the temperature. From the temperature dependence of reistivity, it is inferred that the two competing states are metallic and semiconducting, respectively and the energy scale of the phenomenon produces the most interesting effects, i.e., the metal-insulator transition and hence the maximum sensitivity to external fields, at room temperature. Theory of disordered 3D systems effectively explains the crossover temperature coefficient of resistivity from positive to negative with lowering of temperature. These preliminary findings on the MR behavior of MnAl thin films will be presented in detail. The anomalous large MR in mixed phase MnAl system is evidently useful for future spintronic applications.

Keywords: magnetoresistance, perpendicular magnetic anisotropy, spintronics, thin films

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Authors: Benjawan Rangsikamol, Chutimet Srinilta

Abstract:

This research paper deals with extraction of Thai character features using the proposed histogram projection so as to improve the recognition performance. The process starts with transformation of image files into binary files before thinning. After character thinning, the skeletons are entered into the proposed extraction using histogram projection (horizontal and vertical) to extract unique features which are inputs of the subsequent recognition step. The recognition rate with the proposed extraction technique is as high as 97 percent since the technique works very well with the idiosyncrasies of Thai characters.

Keywords: character recognition, histogram projection, multilayer perceptron, Thai character features extraction

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Authors: Kacper Tomasz Kröber-Mulawa

Abstract:

This paper will address the topic of data ownership from an economic perspective, and examples of economic limitations of data property rights will be provided, which have been identified using methods and approaches of economic analysis of law. To properly build a background for the economic focus, in the beginning a short perspective of data and data ownership in the EU’s legal system will be provided. It will include a short introduction to its political and social importance and highlight relevant viewpoints. This will stress the importance of a Single Market for data but also far-reaching regulations of data governance and privacy (including the distinction of personal and non-personal data, data held by public bodies and private businesses). The main discussion of this paper will build upon the briefly referred to legal basis as well as methods and approaches of economic analysis of law.

Keywords: antitrust, data, data ownership, digital economy, property rights

Procedia PDF Downloads 76