Effect of Slash on Forwarder Soil Compaction

A study of the effect of slash on forwarder soil compaction was carried out. The level of soil compaction at two soil moisture contents, three slash densities (0, 10, and 20 kg/m2 ), and two levels of traffic (one and five passes) were measured. Results indicated that, on dry, loamy sand soils, the presence of slash did not decrease soil compaction after one forwarder pass, but did provide some protection from subsequent passes. The density of slash (over 10 kg/m2 ) did not affect compaction. On the same soils in a wetter condition, however, slash density at 20 kg/m2 was significantly less than on bare plots. At 10 kg/m2 , the increase in bulk density after five passes was smaller than on the bare plots, but not significantly so

DPpackage: Bayesian Semi- and Nonparametric Modeling in R

Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models for marginal and conditional density estimation, receiver operating characteristic curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison and for eliciting the precision parameter of the Dirichlet process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual sampling for each model is carried out using compiled C, C++ or Fortran code.

YURISDIKSI UNIVERSAL DALAM MENGADILI KEJAHATAN TERORISME MENURUT HUKUM PIDANA INTERNASIONAL

Kejahatan terorisme yang dilakukan oleh perorangan maupun organisasi dilihat dari sejarahnya sudah dikenal sangat lama, namun masalahnya dari aspek hukum adalah pendefinisian yang pasti tentang terorisme itu sendiri. Menurut Liga Bangsa-bangsa tahun 1937, “terrorism is all criminal acts directed against a state and intended and calculated to create a state of terror in the minds of particular persons a group of persons or the general public”. Namun konvensi ini tidak pernah berlaku sebagai hukum internasional positif, sebab tidak memenuhi syarat mengenai berlakunya karena tidak cukup jumlah minimum negara-negara yang meratifikasinya. Asal usul atau sejarah awal pemberlakuan yurisdiksi universal adalah bermula dari kasus pembajakan laut sejak tahun 1998. Pembajakan merupakan tindak pidana pertama yang tunduk dalam yurisdiksi universal. Bahkan dapat dikatakan bahwa prinsip yurisdiksi universal lahir pertama kali disebabkan karena adanya keinginan dari negara-negara untuk menindak kejahatan pembajakan yang dilakukan di laut lepas. Tujuan penelitian ini adalah untuk mengetahui Bagaimana eksistensi kejahatan terorisme dalam perspektif hukum pidana internasional dan Bagaimana penerapan prinsip yurisdiksi universal dalam mengadili kasus kejahatan terorisme, dengan menggunakan metode penelitian yuridis normatif dapat disimpulkan: 1. Dalam dinamika pencegahan dan pemberantasan kejahatan terorisme, masyarakat internasional terutama di kalangan negara-negara anggota PBB mengalami perbedaan pendapat berkenaan dengan pengertian terorisme itu sendiri. Al-Qaeda dan ISIS adalah dua kelompok besar terorisme internasional yang sangat populer bagi masyarakat internasional. Kejahatan terorisme digolongkan sebagai kejahatan transnasional karena penegakan hukumnya hanya bisa melalui yurisdiksi universal di setiap negara. 2. Yurisdiksi universal diterapkan negara tanpa batas atau tanpa keterkaitan dengan tindak pidana dan tidak mempedulikan kegiatan yang sedang dilakukan negara lain berkaitan dengan kejahatan tersebut. Kejahatan terorisme masuk ke dalam kompetensi yurisdiksi universal karena telah digolongkan sebagai kejahatan serius (serious crime). Penerapan yurisdiksi universal masih dianggap sebagai cara yang efektif untuk menghapuskan impunitas dalam tingkat nasional bagi pelaku terorisme, namun pada praktiknya teori ini masih mengalami banyak hambatan. Kata Kunci: Yurisdiksi Universal, Terorisme, Serious Crime, Transnasional

An Overview of STS-132 MRM1 Cargo Element Thermal Model Development and Analyses

STS-132 was launched in May 2010 and delivered the Russian Mini Research Module 1 (MRM1) cargo element to the International Space Station as part of the ULF-4 assembly flight. The cargo element consisted of the module outfitted with externally mounted Multi-purpose Laboratory Module (MLM) Airlock, MLM radiator, Portable Work Platform (PWP), and a European Robotic Arm (ERA) spare elbow. Prior to every Shuttle flight, hardware developers are required to determine compatibility of their hardware to thermal environments experienced during the Shuttle mission and once the element is integrated with the ISS. Thermal models are provided to the Shuttle program to determine the impact of the payload on the Orbiter hardware, as well as the ISS program to determine impacts on other ISS payloads in the Orbiter. Historically the Russian International Partner (IP) develops models in formats not compatible with software used by Space Shuttle or ISS programs. This prompted NASA and Lockheed Martin to develop a unique set of thermal models for the MRM1 cargo element. Subsequent ULF-4 mission analyses performed with the models assessed the launch to activation response, identified operational criteria documented in flight rules, and ensured compliance with the mission timeline and no hazards to the crew, orbiter, or ISS. This presentation provides an overview of the work performed, depicts unique approaches in model development, discusses lessons learned, and issue resolution approaches. Though development and analysis efforts spanned over four years and presented various integration challenges it provided an example of successful collaboration with our International Partners

The Reverse-Morals Clause: The Unique Way to Save Talent\u27s Reputation and Money in a New Era of Corporate Crimes and Scandals

This article sails into the largely unchartered waters of reverse-morals clauses because, to our knowledge, there are no law review or law journal articles that substantially address this still nascent area of law.25 Similarly, our research has not revealed any state or federal cases involving reverse-morals clauses.26 Nor has an actual talent contract containing such a clause been publicly revealed, either in terms of language or the identification of the parties to such a clause,27 although reportedly an increasingly larger number of talent are now asking for reverse-morals clauses in the wake of the Enron fallout and other high-profile corporate wrongdoings that have left talent in some cases receiving as much negative press as the companies themselves. 28 These issues merit our attention because the morals clause and the cutting-edge reverse-morals clause are fast becoming the most heavily negotiated aspect of any sports or entertainment contract. 29 Although, paradoxically, there is little information on reverse-morals clauses themselves, there is no single subject (i.e., the morals clause and reverse-morals clause) of greater import for the sports lawyer or agent in today\u27s brightly lit sports environment. 30 This article closes that gap in the legal literature

On the Social and Technical Challenges of Web Search Autosuggestion Moderation

Past research shows that users benefit from systems that support them in their writing and exploration tasks. The autosuggestion feature of Web search engines is an example of such a system: It helps users in formulating their queries by offering a list of suggestions as they type. Autosuggestions are typically generated by machine learning (ML) systems trained on a corpus of search logs and document representations. Such automated methods can become prone to issues that result in problematic suggestions that are biased, racist, sexist or in other ways inappropriate. While current search engines have become increasingly proficient at suppressing such problematic suggestions, there are still persistent issues that remain. In this paper, we reflect on past efforts and on why certain issues still linger by covering explored solutions along a prototypical pipeline for identifying, detecting, and addressing problematic autosuggestions. To showcase their complexity, we discuss several dimensions of problematic suggestions, difficult issues along the pipeline, and why our discussion applies to the increasing number of applications beyond web search that implement similar textual suggestion features. By outlining persistent social and technical challenges in moderating web search suggestions, we provide a renewed call for action.Comment: 17 Pages, 4 images displayed within 3 latex figure

DPpackage: Bayesian Semi- and Nonparametric Modeling in R

Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models for marginal and conditional density estimation, receiver operating characteristic curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison and for eliciting the precision parameter of the Dirichlet process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual sampling for each model is carried out using compiled C, C++ or Fortran code