Search results for: legal judgment prediction

Authors: Yuwaratu Syafira, Wahyu K. Y. Putra, Kusharisupeni Djokosujono

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Background/Objective: Body Mass Index (BMI) serves various purposes, including measuring the prevalence of obesity in a population, and also in formulating a patient’s diet at a hospital, and can be calculated with the equation = body weight (kg)/body height (m)². However, the BMI of an individual with difficulties in carrying their weight or standing up straight can not necessarily be measured. The aim of this study was to form a prediction model for the BMI of young adult students of Public Health Faculty of University of Indonesia. Subject/Method: This study used a cross sectional design, with a total sample of 132 respondents, consisted of 58 males and 74 females aged 21- 30. The dependent variable of this study was BMI, and the independent variables consisted of sex and anthropometric measurements, which included ulna length, arm length, tibia length, knee height, mid-upper arm circumference, and calf circumference. Anthropometric information was measured and recorded in a single sitting. Simple and multiple linear regression analysis were used to create the prediction equation for BMI. Results: The male respondents had an average BMI of 24.63 kg/m² and the female respondents had an average of 22.52 kg/m². A total of 17 variables were analysed for its correlation with BMI. Bivariate analysis showed the variable with the strongest correlation with BMI was Mid-Upper Arm Circumference/√Ulna Length (MUAC/√UL) (r = 0.926 for males and r = 0.886 for females). Furthermore, MUAC alone also has a very strong correlation with BMI (r = 0,913 for males and r = 0,877 for females). Prediction models formed from either MUAC/√UL or MUAC alone both produce highly accurate predictions of BMI. However, measuring MUAC/√UL is considered inconvenient, which may cause difficulties when applied on the field. Conclusion: The prediction model considered most ideal to estimate BMI is: Male BMI (kg/m²) = 1.109(MUAC (cm)) – 9.202 and Female BMI (kg/m²) = 0.236 + 0.825(MUAC (cm)), based on its high accuracy levels and the convenience of measuring MUAC on the field.

Keywords: body mass index, mid-upper arm circumference, prediction model, ulna length

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Authors: Ejaz Ahmed, Huang Yong

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The operation of aero engines has a critical importance in the vicinity of lean blowout (LBO) limits. Lefebvre’s model of LBO based on empirical correlation has been extended to flame volume concept by the authors. The flame volume takes into account the effects of geometric configuration, the complex spatial interaction of mixing, turbulence, heat transfer and combustion processes inside the gas turbine combustion chamber. For these reasons, flame volume based LBO predictions are more accurate. Although LBO prediction accuracy has improved, it poses a challenge associated with Vf estimation in real gas turbine combustors. This work extends the approach of flame volume prediction previously based on fuel iterative approximation with cold flow simulations to reactive flow simulations. Flame volume for 11 combustor configurations has been simulated and validated against experimental data. To make prediction methodology robust as required in the preliminary design stage, reactive flow simulations were carried out with the combination of probability density function (PDF) and discrete phase model (DPM) in FLUENT 15.0. The criterion for flame identification was defined. Two important parameters i.e. critical injection diameter (Dp,crit) and critical temperature (Tcrit) were identified, and their influence on reactive flow simulation was studied for Vf estimation. Obtained results exhibit ±15% error in Vf estimation with experimental data.

Keywords: CFD, combustion, gas turbine combustor, lean blowout

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Authors: G. T. Aigarinova

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This article considers the legal issues of food security as a major component of national security of the republic. The problem of food security is the top priority of the economic policy strategy of any state, the effectiveness of this solution influences social, political, and ethnic stability in society. Food security and nutrition is everyone’s business. Food security exists when all people, at all times, have physical, social and economic access to sufficient safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life. By analyzing the existing legislation in the area of food security, the author identifies weaknesses and gaps, suggesting ways to improve it.

Keywords: food security, national security, agriculture, public resources, economic security

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Authors: Botirjon Kosimov

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This work sheds light on the reforms towards the independence of the judiciary in Uzbekistan, as well as issues of further ensuring judicial independence in the country based on international values, particularly the legal practice of the United States. In every democratic state infringed human rights are reinstated and violated laws are protected by the help of justice based on the strict principle of judicial independence. The realization of this principle in Uzbekistan has been paid much attention since the proclamation of its independence. In the country, a series of reforms have been implemented in the field of the judiciary in order to actualize the principle of judicial independence. Uzbekistan has been reforming the judiciary considering both international and national values and practice of foreign countries. While forming a democratic state based on civil society, Uzbekistan shares practice with the most developed countries in the world. The United States of America can be a clear example which is worth learning how to establish and ensure an independent judiciary. It seems that although Uzbekistan has reformed the judiciary efficiently, it should further reform considering the legal practice of the United States.

Keywords: dependent judges, independent judges, judicial independence, judicial reforms, judicial life tenure, obstacles to judicial independence

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Authors: Aasheesh Raturi, Vimal Kothiyal, P. D. Semalty

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We studied mechanical properties of Eucalyptus tereticornis using FT-NIR spectroscopy. Firstly, spectra were pre-processed to eliminate useless information. Then, prediction model was constructed by partial least squares regression. To study the influence of pre-processing on prediction of mechanical properties for NIR analysis of wood samples, we applied various pretreatment methods like straight line subtraction, constant offset elimination, vector-normalization, min-max normalization, multiple scattering. Correction, first derivative, second derivatives and their combination with other treatment such as First derivative + straight line subtraction, First derivative+ vector normalization and First derivative+ multiplicative scattering correction. The data processing methods in combination of preprocessing with different NIR regions, RMSECV, RMSEP and optimum factors/rank were obtained by optimization process of model development. More than 350 combinations were obtained during optimization process. More than one pre-processing method gave good calibration/cross-validation and prediction/test models, but only the best calibration/cross-validation and prediction/test models are reported here. The results show that one can safely use NIR region between 4000 to 7500 cm-1 with straight line subtraction, constant offset elimination, first derivative and second derivative preprocessing method which were found to be most appropriate for models development.

Keywords: FT-NIR, mechanical properties, pre-processing, PLS

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Authors: Tomas Vampola, Michael Valášek

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On the basis of simulation-generated failure states of structural elements of a turboprop engine suitable for the busy-jet class of aircraft, an algorithm for early prediction of damage or reduction in functionality of structural elements of the engine is designed and verified with real data obtained at dynamometric testing facilities of aircraft engines. Based on an expanding database of experimentally determined data from temperature and pressure sensors during the operation of turboprop engines, this strategy is constantly modified with the aim of using the minimum number of sensors to detect an inadmissible or deteriorated operating mode of specific structural elements of an aircraft engine. The assembled algorithm for the early prediction of reduced functionality of the aircraft engine significantly contributes to the safety of air traffic and to a large extent, contributes to the economy of operation with positive effects on the reduction of the energy demand of operation and the elimination of adverse effects on the environment.

Keywords: detectability of malfunction, dynamometric testing, prediction of damage, turboprop engine

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Authors: Kefaya Qaddoum

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Most of greenhouse growers desire a determined amount of yields in order to accurately meet market requirements. The purpose of this paper is to model a simple but often satisfactory supervised classification method. The original naive Bayes have a serious weakness, which is producing redundant predictors. In this paper, utilized regularization technique was used to obtain a computationally efficient classifier based on naive Bayes. The suggested construction, utilized L1-penalty, is capable of clearing redundant predictors, where a modification of the LARS algorithm is devised to solve this problem, making this method applicable to a wide range of data. In the experimental section, a study conducted to examine the effect of redundant and irrelevant predictors, and test the method on WSG data set for tomato yields, where there are many more predictors than data, and the urge need to predict weekly yield is the goal of this approach. Finally, the modified approach is compared with several naive Bayes variants and other classification algorithms (SVM and kNN), and is shown to be fairly good.

Keywords: tomato yield prediction, naive Bayes, redundancy, WSG

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Authors: Merita Dauti, Edita Alili-Idrizi, Sihana Ahmeti –Lika, Ledjan Malaj

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The extermination and proper disposal of pharmaceutical wastes from expired and unused medications remains a disputable issue due to their specific nature and characteristics. Even though the hazards from these wastes are already well known in terms of environment and human health, people still treat them as usual wastes. At a national level, in many countries the management of pharmaceutical and medical wastes has been one of the main objectives in order to protect people’s health and the environment. Even though many legal regulations exist in this respect, there has not been a single law that would clearly explain the procedures of returning medicines, ways of selection, treatment and extermination of pharmaceutical wastes. This paper aims at analyzing the practices of pharmaceutical waste management and treatment in some European countries as well as a review of the legislation and official guidelines in managing these kinds of wastes and protecting the environment and human health. A suitable treatment and management of expired medications and other similar wastes would be in the interest of public health in the first place, as well as in the interest of healthcare institutions and other bodies engaged in environment protection.

Keywords: pharmaceutical waste, legal regulation, proper disposal, environment pollution

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Authors: Riznaldi Akbar

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In this study, we compared the performance of the Artificial Neural Network (ANN) model with back-propagation algorithm in correctly predicting in-sample and out-of-sample external debt crisis in Indonesia. We found that exchange rate, foreign reserves, and exports are the major determinants to experiencing external debt crisis. The ANN in-sample performance provides relatively superior results. The ANN model is able to classify correctly crisis of 89.12 per cent with reasonably low false alarms of 7.01 per cent. In out-of-sample, the prediction performance fairly deteriorates compared to their in-sample performances. It could be explained as the ANN model tends to over-fit the data in the in-sample, but it could not fit the out-of-sample very well. The 10-fold cross-validation has been used to improve the out-of-sample prediction accuracy. The results also offer policy implications. The out-of-sample performance could be very sensitive to the size of the samples, as it could yield a higher total misclassification error and lower prediction accuracy. The ANN model could be used to identify past crisis episodes with some accuracy, but predicting crisis outside the estimation sample is much more challenging because of the presence of uncertainty.

Keywords: debt crisis, external debt, artificial neural network, ANN

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Authors: Phawichsak Prapassornpitaya, Wanida Jinsart

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Daily monitoring PM₁₀ and PM₂.₅ data from 2007 to 2017 were analyzed to provide baseline data for prediction of the air pollution in Bangkok in the period of 2018 -2020. Two statistical models, Autoregressive Integrated Moving Average model (ARIMA) were used to evaluate the trends of pollutions. The prediction concentrations were tested by root means square error (RMSE) and index of agreement (IOA). This evaluation of the traffic PM₂.₅ and PM₁₀ were studied in association with the regulatory control and emission standard changes. The emission factors of particulate matter from diesel vehicles were decreased when applied higher number of euro standard. The trends of ambient air pollutions were expected to decrease. However, the Bangkok smog episode in February 2018 with temperature inversion caused high concentration of PM₂.₅ in the air environment of Bangkok. The impact of traffic pollutants was depended upon the emission sources, temperature variations, and metrological conditions.

Keywords: fine particulate matter, ARIMA, RMSE, Bangkok

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Authors: Nirmalasanti Pramesi

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In 2004 the CLD KHI Team offered several new ideas in the field of Islamic family law, such as marriage, inheritance (waris), and waqf. The new idea is based on six main principles; pluralism, nationality, human rights, democracy, maslahah, and gender equality. However, the existence of this has actually caused various criticisms, appreciations, and controversies. For this reason, CLD-KHI, as the idea of reforming family law, especially in the field of marriage, really needs to be studied academically with a comprehensive method as an unfinished problem. The main issues examined in this study are what are the ideas for reforming the law of marriage that have been formulated by the CLD KHI team, as well as how to implement Maqasid Sharia in legal reform. The methodology used in this research is a qualitative method with a normative-empirical-sociological approach. The results of this research show every substance of the idea considers aspects of locality, nationality, and global ethics. The Maqasid approach used in most of the legal provisions is moderate (wasati). Meanwhile, in matters of wali niqah and inheritance, it is adjusted to the context of Indonesian society.

Keywords: Maqasid syari'ah, CLD KHI, marriage law reform, moderate

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Authors: S. M. Gupta, Vanita Aggarwal, Som Nath Sachdeva

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The work reported through this paper is an experimental work conducted on High Performance Concrete (HPC) with super plasticizer with the aim to develop some models suitable for prediction of compressive strength of HPC mixes. In this study, the effect of varying proportions of fly ash (0% to 50% at 10% increment) on compressive strength of high performance concrete has been evaluated. The mix designs studied were M30, M40 and M50 to compare the effect of fly ash addition on the properties of these concrete mixes. In all eighteen concrete mixes have been designed, three as conventional concretes for three grades under discussion and fifteen as HPC with fly ash with varying percentages of fly ash. The concrete mix designing has been done in accordance with Indian standard recommended guidelines i.e. IS: 10262. All the concrete mixes have been studied in terms of compressive strength at 7 days, 28 days, 90 days and 365 days. All the materials used have been kept same throughout the study to get a perfect comparison of values of results. The models for compressive strength prediction have been developed using Linear Regression method (LR), Artificial Neural Network (ANN) and Leave One Out Validation (LOOV) methods.

Keywords: high performance concrete, fly ash, concrete mixes, compressive strength, strength prediction models, linear regression, ANN

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Authors: Francesca Nardi, Kashish Aneja, Katherine Ginsbach

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The COVID-19 pandemic has demonstrated both a need for evidence-based and rights-based public health policy and how challenging it can be to make effective decisions with limited information, evidence, and data. The O’Neill Institute, in conjunction with several partners, has been working since the beginning of the pandemic to collect, analyze, and distribute critical data on public health policies enacted in response to COVID-19 around the world in the COVID-19 Law Lab. Well-designed laws and policies can help build strong health systems, implement necessary measures to combat viral transmission, enforce actions that promote public health and safety for everyone, and on the individual level have a direct impact on health outcomes. Poorly designed laws and policies, on the other hand, can fail to achieve the intended results and/or obstruct the realization of fundamental human rights, further disease spread, or cause unintended collateral harms. When done properly, laws can provide the foundation that brings clarity to complexity, embrace nuance, and identifies gaps of uncertainty. However, laws can also shape the societal factors that make disease possible. Law is inseparable from the rest of society, and COVID-19 has exposed just how much laws and policies intersects all facets of society. In the COVID-19 context, evidence-based and well-informed law and policy decisions—made at the right time and in the right place—can and have meant the difference between life or death for many. Having a solid evidentiary base of legal information can promote the understanding of what works well and where, and it can drive resources and action to where they are needed most. We know that legal mechanisms can enable nations to reduce inequities and prepare for emerging threats, like novel pathogens that result in deadly disease outbreaks or antibiotic resistance. The collection and analysis of data on these legal mechanisms is a critical step towards ensuring that legal interventions and legal landscapes are effectively incorporated into more traditional kinds of health science data analyses. The COVID-19 Law Labs see a unique opportunity to collect and analyze this kind of non-traditional data to inform policy using laws and policies from across the globe and across diseases. This global view is critical to assessing the efficacy of policies in a wide range of cultural, economic, and demographic circumstances. The COVID-19 Law Lab is not just a collection of legal texts relating to COVID-19; it is a dataset of concise and actionable legal information that can be used by health researchers, social scientists, academics, human rights advocates, law and policymakers, government decision-makers, and others for cross-disciplinary quantitative and qualitative analysis to identify best practices from this outbreak, and previous ones, to be better prepared for potential future public health events.

Keywords: public health law, surveillance, policy, legal, data

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Authors: Suzan Anwar, Utkarsh Goyal

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Heart rate (HR) is a predictor of cardiovascular, cerebrovascular, and all-cause mortality in the general population, as well as in patients with cardio and cerebrovascular diseases. Machine learning (ML) significantly improves the accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment while avoiding unnecessary treatment of others. This research examines relationship between the individual's various heart health inputs like age, sex, cp, trestbps, thalach, oldpeaketc, and the likelihood of developing heart disease. Machine learning techniques like logistic regression and decision tree, and Python are used. The results of testing and evaluating the model using the Heart Failure Prediction Dataset show the chance of a person having a heart disease with variable accuracy. Logistic regression has yielded an accuracy of 80.48% without data handling. With data handling (normalization, standardscaler), the logistic regression resulted in improved accuracy of 87.80%, decision tree 100%, random forest 100%, and SVM 100%.

Keywords: heart rate, machine learning, SVM, decision tree, logistic regression, random forest

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Authors: Sagir M. Yusuf, Chris Baber

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

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

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Authors: Hayyan ul Haq, Zainal Asikin

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This work aims to explore the appropriate solution in solving legal problems stemmed from managing and exploiting strategic natural resources in Indonesia. This discussion will be focused on the exploitation of gold mining, i.e. divestation process in the New Mont Corporation, West Nusa Tenggara. These legal problems relate to the deviation of the national budget regulation, UU. No. 19/2012, and the implementation of the divestastion process, which infringes PP. No. 50/2007 concerning the Impelementation Procedure of Regional Cooperation, which is an implementation regulation of UU No. 1/2004 on State’s Treasury. The cooperation model, have been developed by the Provincial Government, failed to create a permanent legal solution through normative approach. It has merely used practical approach that tends (instant solution), by using some loopholes in the divestation process. The above blunders have accumulated by other secondary legal blunders, i.e. good governance principles, particularly justice, transparency, efficiency, effective principles and competitiveness principle. To solve the above problems, this work offers constitutionalisation of contract that aimed at reviewing and coherencing all deviated contracts, rules and policies that have deprived the national and societies’ interest to optimize the strategic natural resources towards the greatest benefit for the greatest number of people..

Keywords: constitutionalisation of contract, strategic national resources, divestation, the greatest benefit for the greatest number of people, Indonesian Pancasila values

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Authors: Mikhail Gritskevich, Sebastian Hohenstein

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The paper presents the results of a detailed assessment of several modern Reynolds Averaged Navier-Stokes (RANS) turbulence models for prediction of C3X vane film cooling at various injection regimes. Three models are considered, namely the Shear Stress Transport (SST) model, the modification of the SST model accounting for the streamlines curvature (SST-CC), and the Explicit Algebraic Reynolds Stress Model (EARSM). It is shown that all the considered models face with a problem in prediction of the adiabatic effectiveness in the vicinity of the cooling holes; however, accounting for the Reynolds stress anisotropy within the EARSM model noticeably increases the solution accuracy. On the other hand, further downstream all the models provide a reasonable agreement with the experimental data for the adiabatic effectiveness and among the considered models the most accurate results are obtained with the use EARMS.

Keywords: discrete holes film cooling, Reynolds Averaged Navier-Stokes (RANS), Reynolds stress tensor anisotropy, turbulent heat transfer

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Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

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In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

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Authors: A. Hakimiyan, H. Namazi

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Human behavior is defined as a range of behaviors exhibited by humans who are influenced by different internal or external sources. Human behavior is the subject of much research in different areas of psychology and neuroscience. Despite some advances in studies related to forecasting of human behavior, there are not many researches which consider the effect of the time delay between the presence of stimulus and the related human response. Analysis of EEG signal as a fractal time series is one of the major tools for studying the human behavior. In the other words, the human brain activity is reflected in his EEG signal. Artificial Neural Network has been proved useful in forecasting of different systems’ behavior especially in engineering areas. In this research, a time delay neural network is trained and tested in order to forecast the human EEG signal and subsequently human behavior. This neural network, by introducing a time delay, takes care of the lagging time between the occurrence of the stimulus and the rise of the subsequent action potential. The results of this study are useful not only for the fundamental understanding of human behavior forecasting, but shall be very useful in different areas of brain research such as seizure prediction.

Keywords: human behavior, EEG signal, time delay neural network, prediction, lagging time

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Authors: Dini Dewi Heniarti

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Military Court which is a judicial institution within the military institution has a heavy duty. Military court has to ensuring a fair legal process for military personnel (due process of law) and enforces military discipline. Military justice must also ensure protects the rights of military personnel. In Indonesia tren of military court changes in vision. The debate is happened on the jurisdiction of military court that allegedly has the potential existence of impunity. The Decree of People’s Consultative Assembly Number VII/MPR/2000 which states that the army general who committed the crime should not be tried in military court is one that underlies the proposed amendment limits the jurisdiction of military court. For the identify of the background in a specific format that is limited to juridical review. The goals this research is to gain knowledge, deep understanding and the concept of jurisdiction of military courts for military members who committed general crimes in adjudication procedure from the perspective of legal reform as alternative to establish independency of military judiciary. This research using Rule of Law as Grand Theory, Development Legal Theory as a Middle Theory and Criminal Justice System and concept of jurisdiction as supporting as Applied Theory. This study using a normative juridical approach, and equipped by primary data juridical approach of historical and comparative approach. The author uses descriptive analytical specifications. The main data used in this research is secondary data, which includes primary legal materials, secondary legal material and legal materials tertiary. Analysis primary data and qualitative data is done legally. Technique checking the validity of the data in this study used multiple methods with the research triangulation. This paper will demonstrate the problems concerning the jurisdiction of military courts for military personnel who committed general crimes in perspective of military justice reform Indonesia and adjudication procedures for military member who committed general crimes in the military justice system in Indonesia, as alternative to establish independency of judiciary in military justice in Indonesia. Comparative approached the military justice system from another countries is aimed to development military justice in Indonesia.

Keywords: jurisdiction, military courts, military justice, independency of judiciary

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Authors: Renata Hrecska

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This research aims to present today's corporate law reforms in the micro, small and medium-sized enterprise sector. The UN Commission on International Trade Law (UNCITRAL) currently deals with emerging issues in the sector in its Working Group I that has specifically focused on possible company law simplifications, including the creation of a fully unique company, the UNCITRAL Limited Liability Organization. However, beyond the work at the UN, the different states has also been focusing on simplification efforts and demands in the sphere of commercial law. We can observe that e.g. Slovakia, Serbia, Poland, Croatia, Hungary, Romania and France are undergoing legal reforms aimed at restructuring the sector through simplification of registration or operation. An important objective of the research is to examine where the boundary is for the legal entity to be more transparent and accountable, while the legislator wants to bring the possibility of establishing a company closer to the citizen. The research material presents the advantages and disadvantages of different initiatives with comparative legal instruments and draws conclusions on the possible future vision. The researcher herself attended some of the meetings of the relevant UNCITRAL working group as a national delegated expert, giving her a personal insight into the UNLLO discourse.

Keywords: commercial law, company formation, MSME, UNCITRAL

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Authors: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Harry G. Segal

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The idea of the “self” has been essential ever since the early modern period in western culture, especially since the development of psychotherapy, but advances in neuroscience and cognitive theory challenge traditional notions of the self. More specifically, neuroanatomists have found no location of “the self” in the brain; instead, consciousness has been posited to be a rapid combination of perception, memory, anticipation of future events, and judgment. In this paper, a theoretical model is presented to address these neuroanatomical findings and to revise the historical understanding of “selfhood” in the practice of psychotherapy.

Keywords: the self, psychotherapy, the self and the brain

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Authors: Bao Thach Nguyen, Abbas Mohajerani

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The California bearing ratio (CBR) has been acknowledged as an important parameter to characterize the bearing capacity of earth structures, such as earth dams, road embankments, airport runways, bridge abutments, and pavements. Technically, the CBR test can be carried out in the laboratory or in the field. The CBR test is time-consuming and is infrequently performed due to the equipment needed and the fact that the field moisture content keeps changing over time. Over the years, many correlations have been developed for the prediction of CBR by various researchers, including the dynamic cone penetrometer, undrained shear strength, and Clegg impact hammer. This paper reports and discusses some of the results from a study on the prediction of CBR. In the current study, the CBR test was performed in the laboratory on some fine-grained subgrade soils collected from various locations in Victoria. Based on the test results, a satisfactory empirical correlation was found between the CBR and the physical properties of the experimental soils.

Keywords: California bearing ratio, fine-grained soils, soil physical properties, pavement, soil test

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Authors: J. Jayaletchmi

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In seeking to manage a multiracial and multi-religious society, Singapore provides a unique solution – a dual court system whereby a common law system co-exists with a Syariah law system that administers Syariah law for the Muslim population. In this respect, Singapore seems to provide a feasible example of legal pluralism to countries grappling with a burgeoning Muslim population. However, problems have arisen regarding this peaceful coexistence of secular and religious laws that seek to balance the rights of women and religious freedom. Singapore’s interpretation of Syariah law in the context of marriage and divorce has resulted in certain inequalities for Muslim women, which are exemplified in light of the Women’s Charter, a landmark piece of legislation which provides the legal basis for equity between husband and wife, but excludes Muslims from its ambit. The success of Singapore’s dual court system has largely been at the expense of Muslim women’s rights, and, as a result, the Muslim community as a whole has begun trailing behind the progressive society it forms a part of. This paper explores the prevailing unfairness of rules governing Muslim marriage and divorce in Singapore, and puts forth bold reforms.

Keywords: legal pluralism, Singapore, Syariah law, women’s rights

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Authors: Jacky Liu

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This paper develops a team sports outcome prediction system with potential for wide-ranging applications across various disciplines. Despite significant advancements in predictive analytics, existing studies in sports outcome predictions possess considerable limitations, including insufficient feature engineering and underutilization of advanced machine learning techniques, among others. To address these issues, we extend the Sports Cross Industry Standard Process for Data Mining (SRP-CRISP-DM) framework and propose a unique, comprehensive predictive system, using National Basketball Association (NBA) data as an example to test this extended framework. Our approach follows a holistic methodology in feature engineering, employing both Time Series and Non-Time Series Data, as well as conducting Explanatory Data Analysis and Feature Selection. Furthermore, we contribute to the discourse on target variable choice in team sports outcome prediction, asserting that point spread prediction yields higher profits as opposed to game-winner predictions. Using machine learning algorithms, particularly XGBoost, results in a significant improvement in predictive accuracy of team sports outcomes. Applied to point spread betting strategies, it offers an astounding annual return of approximately 900% on an initial investment of $100. Our findings not only contribute to academic literature, but have critical practical implications for sports betting. Our study advances the understanding of team sports outcome prediction a burgeoning are in complex system predictions and pave the way for potential profitability and more informed decision making in sports betting markets.

Keywords: machine learning, team sports, game outcome prediction, sports betting, profits simulation

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Authors: Deba Kumar Sarma, Sanjib Kr. Rajbongshi

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Surface finish is an important product quality in machining. At first, experiments were carried out to investigate the effect of the cutting tool nose radius (considering 1mm and 0.65mm) in prediction of surface finish with process parameters of cutting speed, feed and depth of cut. For all possible cutting conditions, full factorial design was considered as two levels four parameters. Commercial Mild Steel bar and High Speed Steel (HSS) material were considered as work-piece and cutting tool material respectively. In order to obtain functional relationship between process parameters and surface roughness, neural network was used which was found to be capable for the prediction of surface roughness within a reasonable degree of accuracy. It was observed that tool nose radius of 1mm provides better surface finish in comparison to 0.65 mm. Also, it was observed that feed rate has a significant influence on surface finish.

Keywords: full factorial design, neural network, nose radius, surface finish

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Authors: Fatemeh Noori

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The fashion industry is a dynamic and ever-evolving realm that continuously shapes societal perceptions of beauty and style. Within this industry, fashion modeling plays a crucial role, acting as the visual representation of brands and designers. However, behind the glamorous façade lies a complex web of legal considerations that govern the rights, responsibilities, and ethical practices within the field. This paper aims to explore the legal landscape surrounding fashion modeling, shedding light on key issues such as contract law, intellectual property, labor rights, and the increasing importance of ethical considerations in the industry. Fashion modeling involves the collaboration of various stakeholders, including models, designers, agencies, and photographers. To ensure a fair and transparent working environment, it is imperative to establish a comprehensive legal framework that addresses the rights and obligations of each party involved. One of the primary legal considerations in fashion modeling is the contractual relationship between models and agencies. Contracts define the terms of engagement, including payment, working conditions, and the scope of services. This section will delve into the essential elements of modeling contracts, the negotiation process, and the importance of clarity to avoid disputes. Models are not just individuals showcasing clothing; they are integral to the creation and dissemination of artistic and commercial content. Intellectual property rights, including image rights and the use of a model's likeness, are critical aspects of the legal landscape. This section will explore the protection of models' image rights, the use of their likeness in advertising, and the potential for unauthorized use. Models, like any other professionals, are entitled to fair and ethical treatment. This section will address issues such as working conditions, hours, and the responsibility of agencies and designers to prioritize the well-being of models. Additionally, it will explore the global movement toward inclusivity, diversity, and the promotion of positive body image within the industry. The fashion industry has faced scrutiny for perpetuating harmful standards of beauty and fostering a culture of exploitation. This section will discuss the ethical responsibilities of all stakeholders, including the promotion of diversity, the prevention of exploitation, and the role of models as influencers for positive change. In conclusion, the legal considerations in fashion modeling are multifaceted, requiring a comprehensive approach to protect the rights of models and ensure ethical practices within the industry. By understanding and addressing these legal aspects, the fashion industry can create a more transparent, fair, and inclusive environment for all stakeholders involved in the art of modeling.

Keywords: fashion modeling contracts, image rights in modeling, labor rights for models, ethical practices in fashion, diversity and inclusivity in modeling

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Authors: Nazli Ustunes Demirhan

Abstract:

Norms of any nature are subject to pressures of change throughout their lifespans, as they are interpreted and re-interpreted every time they are used rhetorically or practically by international actors. The inevitable contestation of different interpretations may lead to an erosion of the norm, as well as to its strengthening. This paper aims to question the role of formal legality on the change of norm strength, using a norm contestation framework and a multidimensional norm strength conceptualization. It argues that the role of legality is not necessarily linked to the formal legal characteristics of a norm, but is about the legality of the contestation processes. In order to demonstrate this argument, the paper examines the evolutionary path of the humanitarian intervention norm as a case study. Humanitarian intervention, as a norm of very low formal legal characteristics, has been subject to numerous cycles of contestation, demonstrating a fluctuating pattern of norm strength. With the purpose of examining the existence and role of legality in the selected contestation periods from 1999 to 2017, this paper uses process tracing method with a detailed document analysis on the Security Council documents; including decisions, resolutions, meeting minutes, press releases as well as individual country statements. Through the empirical analysis, it is demonstrated that the legality of the contestation processes has a positive effect at least on the authoritativeness dimension of norm strength. This study tries to contribute to the developing dialogue between international relations (IR) and internal law (IL) disciplines with its better-tuned understanding of legality. It connects to further questions in IR/IL nexus, relating to the value added of norm legality, and politics of legalization as well as better international policies for norm reinforcement.

Keywords: humanitarian intervention, legality, norm contestation, norm dynamics, responsibility to protect

Procedia PDF Downloads 129

Authors: Dorota Habrat

Abstract:

In Polish law, the idea of the introduction of corporate responsibility for crimes is becoming more popular and creates a lot of questions. The need to introduce into the Polish legal system liability of corporate (collective entities) has resulted, among others, from the Polish Republic's international commitments, in particular related to membership in the European Union. The Act of 28 October 2002 on the liability of collective entities for acts prohibited under penalty is one of the example of adaptation of Polish law to Community law. Introduction to Polish law a criminal nature liability of corporations (legal persons) has resulted in a lot of controversy and lack of acceptance from both the scientific community as well as the judiciary. The responsibility of collective entities under the Act has a criminal nature. The main question concerns the ability of the collective entity to be brought to guilt under criminal law sense. Polish criminal law knows only the responsibility of individual persons. So far, guilt as a personal feature of action, based on the ability of the offender to feel in his psyche, could be considered only in relation to the individual person, while the said Act destroyed this conviction. Guilt of collective entity must be proven under at least one of the three possible forms: the guilt in the selection or supervision and so called organizational guilt. The next question is how the principle of proportionality in relation to criminal measures in response of collective entities should be considered. It should be remembered that the legal subjectivity of collective entities, including their rights and freedoms, is an emanation of the rights and freedoms of individual persons which create collective entities and through these entities implement their rights and freedoms. The adopted Act largely reflects the international legal regulations but also contains the unknown and original legislative solutions.

Keywords: criminal corporate responsibility, Polish criminal law, legislative solutions, Act of 28 October 2002

Procedia PDF Downloads 484