Strong Rules Research Articles

Climate Change Legislations and Environmental Degradation

This study investigates how the enactment of regulations and laws dealing with climate change influences CO2 emissions. Using a sample of 83 countries and covering the period from 2003 to 2014, our dynamic panel analysis yields two major results. First, the enactment of new climate change regulations (i.e., amendments, decrees, orders) associated with a strong rule of law significantly diminishes CO2 emissions. This result validates the hypothesis of Gunningham (J Environ Law 23:169–201, 2011. https://doi.org/10.1093/jel/eqr006 ), which argues that the efficiency of environmental legislation must be supported by regulatory institutions that can efficiently enforce the law. Second, the enactment of new climate change laws seems to not impact emissions levels independently of institutional quality. It may be possible that private agents anticipate the entry into force of the new law(s) and must consequently adapt to the requirements of such laws, leading to a nonsignificant impact on CO2 emissions.

PK-DB: pharmacokinetics database for individualized and stratified computational modeling.

A multitude of pharmacokinetics studies have been published. However, due to the lack of an open database, pharmacokinetics data, as well as the corresponding meta-information, have been difficult to access. We present PK-DB (https://pk-db.com), an open database for pharmacokinetics information from clinical trials. PK-DB provides curated information on (i) characteristics of studied patient cohorts and subjects (e.g. age, bodyweight, smoking status, genetic variants); (ii) applied interventions (e.g. dosing, substance, route of application); (iii) pharmacokinetic parameters (e.g. clearance, half-life, area under the curve) and (iv) measured pharmacokinetic time-courses. Key features are the representation of experimental errors, the normalization of measurement units, annotation of information to biological ontologies, calculation of pharmacokinetic parameters from concentration-time profiles, a workflow for collaborative data curation, strong validation rules on the data, computational access via a REST API as well as human access via a web interface. PK-DB enables meta-analysis based on data from multiple studies and data integration with computational models. A special focus lies on meta-data relevant for individualized and stratified computational modeling with methods like physiologically based pharmacokinetic (PBPK), pharmacokinetic/pharmacodynamic (PK/PD), or population pharmacokinetic (pop PK) modeling.

What is The Priority of Audit Problems at Zakat Institutions in Indonesia

This study aims to determine the opinions and recommendations from the experts regarding to the problem of the audit system in zakat institutions in Indonesia to determine the priority of the most important variables to be fixed. This research was conducted in August 2020 using primary data obtained from interviews with experts consisting of academics, practitioners and regulators with a background in sharia economics. The data were analyzed by the Delphi method to find the main priority and agreed upon convergence by experts. The results showed that of the 15 variables of audit problems at zakat institutions in Indonesia, there are 13 variables with convergent-convergent consensus based on expert opinion and only 2 variables with divergent consensus. The 3 main priority problems of auditing in zakat institutions in Indonesia are (1) Insufficient number of sharia auditors, (2) The absence of strong rules related to sharia auditing, (3) HR competence. This research can be used by readers to make improvements in the audit system of zakat institutions in Indonesia with priority according to the findings of this study. This research is the first research that comprehensively calculates the expert's assessment regarding audit problems in Indonesian zakat institutions using the Delphi method and its priority recommendations.

Predatory Rulers, Credible Commitment and Tax Compliance: How War Does Not Make the State Under Weak Rule of Law

This paper explores an underappreciated factor which can be necessary for warfare to strengthen fiscal capacity. It argues that in contexts with lower respect for the rule of law and worse private property rights, rulers' predatory behavior should incline wealth holders to shelter their wealth and therefore tax compliance should be lower. This will make it unlikely that during armed warfare, when rulers engage in more predatory behavior such as confiscation, there will be any property or income to be taxed and hence states will be unlikely to invest in fiscal capacity. Under stronger rule of law and better private property rights, the constraints on the rulers' predatory behavior should provide higher security for wealth holders and they should be less likely to shelter wealth, bringing higher tax compliance. Such compliance makes it more likely that states can increase fiscal revenues during war and therefore will be more likely to invest in fiscal capacity. These expectations are tested with an original dataset of Ottoman waqfs in modern-day Greece between 1600 and 1912 in addition to annual Ottoman fiscal revenue data. Results indicate that wars increase wealth sheltering under lower rule of law, while it does not under higher rule of law. Furthermore, while wars do not increase fiscal revenues under lower rule of law, they increase fiscal revenues under higher rule of law.

An Improved Approach to Analyze Accidents and Promote Road Safety using Association Rule Mining and Multi-Criteria Decision Analysis Methods

Background: Road accidents have become a major social and health problem for being dramatically increasing day after day worldwide. Scientists are conducting their studies to define the main attributes that share the severity of road accidents. Finding a new approach to analyze road accidents is of great urgency. Data mining techniques are best fitting to discover useful information out of enormous data which are used to make proactive decisions. Methods: This paper tempts a rule-based machine learning method known as association rule mining, which can identify strong rules discovered in databases using interesting measures. Given a data- set from the Lebanese territory for the years 2016-2017, the application of association rule mining, the Apriori method takes its place. However, its implementation leads to a very large number of rules. The task that is the most difficult is extracting meaningful and non-redundant rules. In order to find out the most interesting and relevant rules out of fatal rules such, ELECTRE TRI and PROMETHEE methods, the most significant methods of decision making, Multi-Criteria Decision Analysis (MCDA) are integrated to resolve the outranking problem. The integration is presented by the use of the same set of weights and the same constant values of Indifference and Preference thresholds used in ELECTRE-TRI method to define the linear preference function needed by PROMETHEE method. Realizing the sensitivity of the final output of alternatives ranking to the changes of the input parameters of the decision-making tool, this proposed integration helps the decision makers to overcome their ambivalence between preference and indifference thresholds and to cope adequately with the issue of the uncertainty of MCDA procedures; it comes up with the complete ranking of rules. Results: The obtained ranked rules declare the most significant attributes or combinations of attributes that influence the severity of road accidents. Four main factors of fatal road accidents are pinpointed: over-speeding mainly leading up to rollover crashes, pedestrians encountering in the context, distracted driving leading to fatal road vehicle collisions with Pedestrian victims; and wet roads particularly in the case of single car accidents. Meanwhile, the importance of ELECRE-TRI and PROMETHEE and their integration in dealing with such complex phenomena and corresponding database with a large number of involved attributes have been validated. Conclusion: This paper studies the phenomenon of road accidents. Association rule mining has been applied to discover all possible relations between the various attributes. The integration of ELECTRE- TRI and PROMETHEE MCDA techniques aims at extracting meaningful information from the big dataset. The obtained results have shown how influencing the behavior of the driver is on the occurrence of fatal road accidents. These findings contribute to supporting decision makers to draw new design conceptions for road infrastructure and develop preventive measures that improve road safety in Lebanon.

Strong interaction coupling-constant sum rules for heavy hadrons with broken SU(3) symmetry

Heavy baryons [Formula: see text] and heavy mesons [Formula: see text] for heavy quarks [Formula: see text], [Formula: see text], or [Formula: see text] and light quarks [Formula: see text], [Formula: see text], or [Formula: see text] are considered. Strong interaction coupling-constant sum rules with first-order breaking of SU(3) flavor symmetry are obtained for reaction [Formula: see text] where [Formula: see text] or [Formula: see text], [Formula: see text] or [Formula: see text], and [Formula: see text] is a light meson. Specific results for couplings of doubly charmed baryons and charm and bottom mesons are presented. Also, SU(3) Clebsch-Gordan coefficients for the product of representations [Formula: see text] are explicitly determined.

The relationship between a combinatorial processing rule and a continuous mate preference function in an insect.

Mate choice involves processing signals that can reach high levels of complexity and feature multiple components, even in small animals with tiny brains. This raises the question of whether and how such organisms deal with this complexity. One solution involves combinatorial processing, whereby different signal elements are processed as single units. Combinatorial processing has been described in several mammals and birds, and recently in a vibrationally signalling insect, Enchenopa treehoppers. Here, we ask about the relationship between combinatorial rules and mate preferences for continuously varying signal features. Enchenopa male advertisement signals are composed of two elements: a 'whine' followed by a set of pulses. The dominant frequency of the whine and element combination both matter to females. We presented synthetic signals varying in element order (natural [whine-pulses], reverse [pulses-whine]) and in frequency to Enchenopa females and recorded their responses. The reverse combination resulted in a decrease in attractiveness of the signals, and also slightly changed the shape of the preference for frequency. We found that females could be classified into three 'types': females with both a strong preference and a strong combinatorial rule, females with both a weak preference and weak rule, and females with a strong preference but a weak rule. Our results suggest that in Enchenopa signal processing, the mate preference for a continuous signal feature 'takes precedence' over, but also interacts with, the combinatorial rule. The relationship between the preference and the rule could evolve to take different forms according to selection on mate choice decisions. We suggest that exploring the relationship between such preferences and rules in species with more complex signals will bring insight into the evolution of the multi-component communication systems.

An empirical examination of the impact of country‐level corporate governance on profitability of Indian banks

AbstractThe main aim of the present study is to examine the impact of country‐level corporate governance on the profitability of Indian banks using a sample of 61 banks that is, 42 private and 19 public banks. The study employs descriptive statistics, correlation analysis, regression analysis of two‐way variable intercept random effect model, and robustness checks including robust regression, Generalized Methods of Moments, and Panel Correction Standard Error. The results reveal that country‐level corporate governance has a significant impact on the profitability of Indian banks. Private banks demonstrate better performance over public banks and the effect of country‐level governance on the profitability of private banks is positive and it is better than public banks. Further, the results show that demonetisation has a significant negative impact on the profitability of Indian banks as measured by Return on Assets and Return on Equity, but it also has a significant positive effect in the case of Net Interest Margin. The findings of the current study have considerable implications for regulators, policymakers, bankers, analysts, and academicians. Regulations and several policy measures could be introduced to improve the profitability of Indian banks. To promote greater transparency, better accountability, strong rule of law, and corporate governance, corruption must be eliminated to enhance higher profitability of Indian banks. This paper presents novel evidence linking country‐level corporate governance to the profitability of banks in the context of India, as an emerging economy. Therefore, it bridges an existing gap in the body of literature on the profitability of banks in India.

Hybrid safe–strong rules for efficient optimization in lasso-type problems

The lasso model has been widely used for model selection in data mining, machine learning, and high-dimensional statistical analysis. However, with the ultrahigh-dimensional, large-scale data sets now collected in many real-world applications, it is important to develop algorithms to solve the lasso that efficiently scale up to problems of this size. Discarding features from certain steps of the algorithm is a powerful technique for increasing efficiency and addressing the Big Data challenge. This paper proposes a family of hybrid safe–strong rules (HSSR) which incorporate safe screening rules into the sequential strong rule (SSR) to remove unnecessary computational burden. Two instances of HSSR are presented, SSR-Dome and SSR-BEDPP, for the standard lasso problem. SSR-BEDPP is further extended to the elastic net and group lasso problems to demonstrate the generalizability of the hybrid screening idea. Extensive numerical experiments with synthetic and real data sets are conducted for both the standard lasso and the group lasso problems. Results show that the proposed hybrid rules can substantially outperform existing state-of-the-art rules.

ІСТОРИЧНІ ТА ФІЛОСОФСЬКІ ЗАСАДИ СТАНОВЛЕННЯ ТА РОЗВИТКУ ПРИНЦИПУ ЗАКОННОСТІ В КРИМІНАЛЬНОМУ ПРАВІ

The system of strong rules of imposing penalties is the main characteristic of the field of criminal law in modern democratic states. Incorrect implication of the criminal law provisions will directly affect human rights and freedoms. The issues of criminal law correspondence with the human rights concept and the rules of interpreting criminal law provisions are still actual even for the states with sustainable democratic development. All these problems are covered by legality in the rule of law concept. Historical method is quite useful in legal research, especially in untangling legal problems, which have roots in the past. Raising and development of legality through centuries will help us to fully interpret values, which are the grounds of this principle. In the XXI century B.C., the law on criminal liability contented one beautiful rule – “if you act illegally – we can kill you”. Fortunately, criminal law provisions are changing through time. Arbitrariness of the sovereign, vague in the implication of penalty, uncertainty in the criminal law provisions – all these problems were the main engines for the liberal revolutions in the XVII century. As a result, modern nations have similar meaning of the legality in criminal law, as in the French Declaration of the Rights of Man and of the Citizen of 1789 or the Constitution of the United States of 1787. Legality in criminal law consists of such main values as freedom and individual autonomy, democracy, separation of power as well as the rule of law. The works of philosophers in the Enlightenment era (such as Montesquieu, Hobbs, Feuerbach, Beccaria) have had a huge impact on the development of these values and can help us to find modern interpretation of the meaning of legality. This paper provides us with the reasoning, why the above values are important for understanding legality as the principle of criminal law. Legality not only brings us protection from the unlawful legal provisions. There are examples in history, when the law allowed to commit atrocities. The decisions of International Military Tribunal at Nuremberg show us the way to resolving this dilemma and import us the general principles of law recognised by civilised nations as a ground for criminal liability. Our results illustrate that the legality in criminal law is the scope of provisions, which establish special requirements to the lawmaking process (including the elements of substantive and procedural democracy), the process of interpreting the criminal law provisions as well as the process of applying the law in every individual case.

IMPLEMENTASI DATA MINING UNTUK MENGETAHUI POLA PEMBELIAN OBAT MENGGUNAKAN ALGORITMA APRIORI

Competition in the business world requires entrepreneurs to think of finding a way or method to increase the transaction of goods sold. The purpose of this research is to provide drug stock data that is widely purchased by pharmacy customers at Kimia Farma, Green Lake branch in Jakarta. The algorithm used in this study is a priori to determine the relationship between the frequency of sales of drug brands most frequently purchased by customers. The association pattern formed with a minimum support of 40% and a minimum value of 70% confidence produces 17 association rules. The strong rules obtained are that if you buy a 500Mg Ponstan KPL @ 100, you will buy an Incidal OD 10Mg Cap with a support value of 59% and a confidence value of 84%. A priori algorithm can be used by companies to develop marketing strategies in marketing products by examining consumer purchasing patterns.

The Global Waste Trafficking and Its Correlates

Our understanding of illicit waste trafficking (IWT) is in its embryonic stages; most notably, the transnational nature of this phenomenon has hitherto been neglected in extant empirical research. This study provides the first analysis of the possible coorrelates of transnational IWT at a global level. Through recourse to information extracted from the official Basel Convention National Reports, we constructed a network of the most relevant IWT connections between 148 countries. Next, we quantitatively investigated the role of specific potential factors that influence the structure of this transnational network. Our results indicate that illicit waste is trafficked toward poorer and more insecure countries, primarily via former colonial connections. As such, IWT poses a direct threat to the sustainable development of these countries. Mere adherence to international treaties and promulgation of environmental laws does not in and of themselves explain whether a country is part of the global IWT network, although the establishment of dedicated courts and tribunals does reduce the risk of being a recipient of trafficked waste. Solid anticorruption measures and a strong rule of law increased the likelihood of being a source country in the IWT network, which, in turn, calls for a more global approach to the management of environmental issues.

IMPLEMENTASI DATA MINING UNTUK MENGETAHUI POLA PEMBELIAN OBAT MENGGUNAKAN ALGORITMA APRIORI

Competition in the business world requires entrepreneurs to think of finding a way or method to increase the transaction of goods sold. The purpose of this research is to provide drug stock data that is widely purchased by pharmacy customers at Kimia Farma, Green Lake branch in Jakarta. The algorithm used in this study is a priori to determine the relationship between the frequency of sales of drug brands most frequently purchased by customers. The association pattern formed with a minimum support of 40% and a minimum value of 70% confidence produces 17 association rules. The strong rules obtained are that if you buy a 500Mg Ponstan KPL @ 100, you will buy an Incidal OD 10Mg Cap with a support value of 59% and a confidence value of 84%. A priori algorithm can be used by companies to develop marketing strategies in marketing products by examining consumer purchasing patterns.

Reduction in greenhouse gas emissions from national climate legislation

The international response to climate change has been inadequate, but not zero. There are 1,800 climate change laws worldwide. We use panel data on legislative activity in 133 countries over the period 1999–2016 to identify statistically the short-term and long-term impact of climate legislation. Each new law reduces annual carbon dioxide (CO2) emissions per unit of gross domestic product by 0.78% nationally in the short term (during the first three years) and by 1.79% in the long term (beyond three years). The results are driven by parliamentary acts and by countries with a strong rule of law. In 2016, current climate laws were associated with an annual reduction in global CO2 emissions of 5.9 GtCO2, more than the US CO2 output that year. Cumulative CO2 emissions savings from 1999 to 2016 amount to 38 GtCO2, or one year’s worth of global CO2 output. The impact on other greenhouse gases is much lower. Climate change laws are shown to reduce national CO2 emissions by 0.78% in their first three years and 1.79% in the longer term. These reductions add up to 38 GtCO2 of avoided emissions for 1999–2016—equal to a year of CO2 emissions.

The impact of financial development, IFRS, and rule of LAW on foreign investments: A cross-country analysis

The purpose of this study is to examine the effects of financial development, International Financial Reporting Standards (IFRS), and rule of law on foreign investments in 51 advanced and developing countries over the period from 1997 to 2017. The study is motivated by the argument that the lack of portfolio investments results mainly from asymmetric information that can be mitigated by providing reliable and relevant information based on IFRS and a strong rule of law. Using country-level data obtained from the IMF, World Development Indicators, and World Governance Indicators websites, it was found that financial development, IFRS, and rule of law are important for foreign investments. Although empirical analyses indicate that financial development, IFRS and rule of law have individually a significant positive effect on foreign direct investments relative to total investments, this positive effect is mitigated for interactions of these variables, suggesting that international investors would prefer portfolio rather than direct investments in financially developed countries adopting IFRS and an effective rule of law. The findings are important for stakeholders who are interested in financial development, IFRS, rule of law and their impacts on foreign investments.

Expropriations of Private Property for Economic ‘Development’ in the United States: Re-Thinking the Titling and Rule of Law Solutions to Land Grabs in the Global South

Mainstream discourses tend to treat land dispossession as a ‘developing’ country problem that arises due to weak/corrupt legal systems and inadequate property institutions. This article unsettles such discourses by examining expropriations for economic ‘development’ in the United States —a country typically deemed to have strong property institutions and a strong rule of law. Drawing on various examples, I propose that expropriation in the us is neither rigorously conditional nor particularly exceptional. While most ‘takings’ laws are supposed to restrict the State’s power, this restriction hinges on the definition of public use, purpose, necessity, or interest. And in many countries, including the us, these concepts are now defined broadly and vaguely so as to include private for-profit projects. Ultimately, the contents, interpretation, and application of the law are subject to social and political struggles; this point is habitually overlooked in the rule of law ‘solutions’ to land grabbing—. For these reasons, titling/registration programs and policies aimed at strengthening the rule of law, even if successful, are likely to transform rather than ‘solve’ dispossession in the global South.

Reliability Analysis of Distribution Network Operation Based On Short-Term Future Big Data Technology

This paper firstly establishes a four-dimensional index system for distribution network operation reliability. Using principal component analysis method, it extracts the main evaluation indicators from a large amount of data, and analyzes the influencing factors of the indicators according to the main evaluation indicators. According to the parallel association rules The method establishes the relevant model, and extracts the main indicators of operational reliability and the strong correlation rules between each influencing factors, so as to obtain the main influencing factors. The artificial neural network is proposed to predict, based on historical data and real-time data. The main influencing factors are used as the input and output of the forecast, and the output is expressed by the main evaluation index, and the operational reliability index for a period of time is judged. The calculation of the proposed strategy can quickly and effectively predict the operational reliability of the distribution network.

A learning automata-based algorithm for solving the target [formula omitted]-coverage problem in directional sensor networks with adjustable sensing ranges

In order to find a solution for the target coverage problem in directional sensor networks (DSNs), some researchers have recently introduced several efficient algorithms. These sensors are conventionally supposed to have a single power level and a single coverage is just needed for targets. In other words, there are various sensing ranges and power consumptions for these sensors under real conditions and at least k times monitoring is required for each target. The present paper addresses this issue as the target k-coverage with adjustable sensing range, which has not been already studied in DSNs. To solve this problem, two learning automata-based algorithms (Algorithms 1 and 2) are proposed and equipped with a strong pruning rule that facilitates the selection of appropriate sensor directions capable of providing the targets with k-coverage. After evaluating the efficiency of the algorithms’ performance by conducting several experiments, the results were compared to those ones obtained by a greedy-based algorithm, which is discussed in the literature. Finding indicated that algorithms have superiority over their rivals regarding the prolonged lifetime of their network.

LEGAL PROTECTION TO MANAGE FOREST RESOURCES BASED ON LOCAL WISDOM

Abstract Legal protection of forest resources is not only important for communities around the forest, but also the benefits of sustainable forests felt by wider public such as air, land and water quality, and climate. Management of forest resources based on local wisdom is one way to ensure the benefits and functions of forests maintained. Jambi Province and Thailand have various forest management initiated by the community still experience problems, especially in terms of legal protection. The certainty of management area from conflict, the threat of diversion, becomes a business area for forestry companies, plantations and even mining. Appropriate policies to address legal issues are faced both in Jambi Province and Thailand. The results of the study revealed; first, the management of local wisdom-based forest resources by people in Jambi has received recognition, meanwhile there are still problems with the variety of stipulation forms, such the minister decree, regent, and regional regulation. While in Thailand, there are constraints on the relationship with the government. Local wisdom-based forest management patterns in Thailand have not been supported by strong rules for the community to use forest resources for life. In addition, the benefits of environmental services in Thailand are good enough. Second, sustainability in local marinebased forest management is a common problem faced both in Jambi and Thailand. For this reason, improvements in policies appropriate for sustainability are needed. In Jambi, it requires legal products in the form of stipulations in a regional head (Bupati) decree on community forest management areas. While changes of regulations in Thailand can guarantee legal certainty for the community. Keywords: forests, legal protection, local wisdom

Hyperfine excitation of SH+ by H

Context. SH+ is a surprisingly widespread molecular ion in diffuse interstellar clouds. There, it plays an important role by triggering the sulfur chemistry. In addition, SH+ emission lines have been detected at the UV-illuminated edges of dense molecular clouds, so-called photo-dissociation regions (PDRs), and toward high-mass protostars. An accurate determination of the SH+ abundance and of the physical conditions prevailing in these energetic environments relies on knowing the rate coefficients of inelastic collisions between SH+ molecules and hydrogen atoms, hydrogen molecules, and electrons. Aims. We derive SH+–H fine and hyperfine-resolved rate coefficients from recent quantum calculations for the SH+–H collisions, including inelastic, exchange, and reactive processes. Methods. The method we used is based on the infinite-order sudden approach. Results. State-to-state rate coefficients between the first 31 fine levels and 61 hyperfine levels of SH+ were obtained for temperatures ranging from 10 to 1000 K. Fine-structure resolved rate coefficients present a strong propensity rule in favor of Δj = ΔN transitions. The Δj = ΔF propensity rule is observed for the hyperfine transitions. Conclusions. The new rate coefficients will help significantly in the interpretation of SH+ spectra from PDRs and UV-irradiated shocks where the abundance of hydrogen atoms with respect to hydrogen molecules can be significant.