Myspace, Yourspace, But Not Theirspace: The Constitutionality of Banning Sex Offenders From Social Networking Sites

In recent years there has been intense public pressure to enact increasingly restrictive and intrusive sex offender laws. The regulation of sex offenders has now moved online, where a growing amount of protected expression and activity occurs. The latest trend in sex offender policy has been the passage of state laws prohibiting sex offenders from visiting social networking sites, such as Myspace or Facebook. The use of these websites implicates the First Amendment right of expressive association. Broad social-networking-site bans threaten the First Amendment expressive association rights of sex offenders, who do not lose all of their constitutional rights by virtue of their conviction. Although social-networking-site bans are politically attractive on the surface, such prohibitions are fundamentally flawed because they are predicated on a number of widespread misconceptions about sex offenses and sex offender behavior. These misconceptions include the beliefs that all registered sex offenders are violent sexual predators who have extremely high recidivism rates and that Internet predators are increasing the incidence of sex crimes against minors. In fact, there is very little evidence to indicate that this type of legislation will help reduce sexual violence. This Note argues for empirically based and narrowly tailored sex offender policies that will strike the appropriate balance between protecting minors from sexual abuse and respecting sex offenders\u27 constitutional rights. Such an approach is more likely to help rehabilitate offenders and thus protect children and others from sexual predators

Advanced Local Binary Patterns for Remote Sensing Image Retrieval

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The standard Local Binary Pattern (LBP) is considered among the most computationally efficient remote sensing (RS) image descriptors in the framework of large-scale content based RS image retrieval (CBIR). However, it has limited discrimination capability for characterizing high dimensional RS images with complex semantic content. There are several LBP variants introduced in computer vision that can be extended to RS CBIR to efficiently overcome the above-mentioned problem. To this end, this paper presents a comparative study in order to analyze and compare advanced LBP variants in RS CBIR domain. We initially introduce a categorization of the LBP variants based on the specific CBIR problems in RS, and analyze the most recent methodological developments associated to each category. All the considered LBP variants are introduced for the first time in the framework of RS image retrieval problems, and have been experimentally compared in terms of their: 1) discrimination capability to model high-level semantic information present in RS images (and thus the retrieval performance); and 2) computational complexities associated to retrieval and feature extraction time.EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEart

Do bottom-up and independent agricultural cooperatives really perform better? : Insights from a technical efficiency analysis in Ethiopia

The cooperative landscape in Ethiopia is very heterogeneous with a mixture of remains of the pre-1991 government-controlled system and new post-1991 bottom-up collective action initiatives. This heterogeneity, coupled with a large growth in the number of cooperatives in the country, offers an interesting perspective to study the determinants of the (in)efficiency of cooperatives. In this paper, we analyse the performance of Ethiopian agricultural cooperatives, focusing on the degree of technical (in)efficiency and its determinants. We use the stochastic frontier approach in which we account for heteroskedasticity and the monotonicity of production functions, presenting a methodological improvement with respect to previous technical efficiency studies. The results show that NGO- and government-initiated cooperatives are less efficient than community-initiated ones, implying that governments and NGOs should not interfere too strongly in cooperative formation. Cooperatives with a high degree of heterogeneity in members' participation are found to be about 98% less efficient, while cooperatives that have paid employees are 33% more efficient. Besides, results show that cooperatives in Ethiopia function more efficiently if they incentivize committee members through monetary compensation

Climate Services for Resilient Development (CSRD) Technical Exchange in Eastern Africa Workshop Report

In 2005, the International Research Institute for Climate and Society published its assessment of key gaps in the use of climate information for health, agriculture, water and other sectors in countries across Africa. The results from the report were less than stellar. After an extensive review of use of climate information in the development sectors of Africa, the authors concluded that the continent suffered from “market atrophy” – the reinforcing effect of zero effective supply of climate information and zero effective demand. Twelve years later, organizations such as the IRI, CSRD, CCAFS, ICPAC, and UKMO have made enormous strides at increasing both climate information supply and demand through the implementation of climate data platforms and the organizing of capacity-building seminars. In order to capitalize on the presence of the many climate and sector experts from across the IGAD region, the organizations above held a joint event, the Climate Services for Resilient Development (CSRD) Technical Exchange workshop, in Zanzibar on August 23-25, 2017, immediately after the 47th Greater Horn of Africa Climate Outlook Forum (GHACOF47). The workshop was designed to offer potential and existing users a platform to voice their needs for the development and better use of historical, monitored and forecast information for the management of drought across climate-sensitive sectors

Discrete Element Modelling (DEM) For Earthmoving Equipment Design and Analysis: Opportunities and Challenges

Simulation of granular materials (soil, rocks) interaction with earthmoving machines provides opportunities to accelerate new equipment design and improve efficiency of earthmoving machine performances. Discrete Element Modelling (DEM) has a strong potential to model soil and rocks bulk behavior in response to forces applied through interaction with machinery. Numerical representation of granular materials and methodology to validate and verify constitutive micro-mechanical models in DEM will be presented. In addition, how DEM codes can be integrated to CAE tools such as multibody dynamics will also be discussed. A case study of tillage bar-soil interaction was modeled in EDEM to predict tillage draft force and soil failure zone in front of tool moving at 2.68-m/sec and depth of 102-mm. The draft force and soil failure zone was predicted at 10% and 20% error from laboratory measured data

Participative leadership: A study of faculty and administrators in a Lutheran liberal arts college

Despite the numerous reference to and importance of the term participative leadership in various leadership and organizational theories and practices, the term itself remains ambiguous. While it is often used synonymously with terms such as collaborative, autonomy, influence, participative decision making, collegiality, and team, many people questioned whether these terms are truly synonymous. Moreover, because those who advocate this approach to leadership have many purposes in mind, the practice of participative leadership manifests itself in different forms. Hence, a need exists to clarify as to what practices are actually participative. This study examines the meaning of the concept in theory and practice. The focus is on clarifying the concept in higher education by eliciting faculty and administrators\u27 understandings of the concept, their rationales for accepting it, and the conditions and ways they desire to see this approach practiced in their organization. This examination involves an intensive review of the literature, an analysis of institutional documents, and a series of in-depth interviews with six faculty and seven administrators at a Lutheran liberal arts college. The literature review indicated that the complexities of the terms leadership and participation contributed to the different understandings of the concepts. The work of different scholars, based on different paradigms, and different leadership and organizational theories, along with an emphasis of different issues revealed that in certain cases certain characteristics of participation are concealed, while in other instances other characteristics are emphasized. By studying participative leadership from the different participants\u27 perspectives a more holistic understanding emerged of the concept and its implications for administrators, faculty, and the college. Although gender, status, position, and the type of issues raised determine how participants understand and intend to apply the concept, every participant gave different labels, rationales, metaphors, and ways of interpreting and evaluating the concept. The findings, in general, confirm that many individuals and groups can have many labels, definitions, rationales, and ideals of participative leadership. The factors such as institutional history, mission, and structure and individual differences with respect to gender, position, status, background, interest, beliefs, and values determine the interpretation and implementation of participative leadership. Theorists and practitioners must consider these factors when they study and attempt to implement participative leadership

Discrete Element Modeling (DEM) of Cone Penetration Testing on Soil With Varying Relative Soil Density

Modeling soil-tool interaction is essential for equipment design and performance evaluation on soil behavior responses under loading. Computational tools based on particle-based mechanics such as Discrete Element Modeling (DEM) and Smoothed Particle Hydrodynamics (SPH) have potential in modeling large strain soil dynamic behaviors from soil-tool interaction. The objective of this study is to validate the accuracy and robustness of DEM calibration methodology as it relates to soil deformation during cone penetration on varying initial soil relative density. The influence of factors such as DEM material properties and cone to particle size ratio on DEM cone penetration simulation will be investigated. The paper presents a comparison of DEM predicted cone penetration resistance and laboratory measured penetration data on Norfolk sandy loam. Soil mechanical behavior was modeled with Hertz-Mindlin (HM) contact stiffness model and a new coupled frictional law for static and rolling resistance coefficients. The DEM material properties were calibrated using residual strength from direct shear test. DEM simulations were performed using LIGGGHTS, open source DEM code. Cone penetrometer experiments using anÂASABE standard cone with 12.53 mm cone base diameter and 30-degree cone tip were used to validate the calibrated DEM model. DEM prediction of cone penetration resistance trend and steady state values were in close agreement with the laboratory measured data for relative density range from 5 to 30%. At higher dense states (relative density of 90%), DEM calibration requires further improvement

Evaluation of Low Inflation Tire Technologies on Soil Compaction

Evaluation of recent advances in tire technologies such as advanced deflection agricultural tires (Firestone IF and VF) and precision tire inflation technologies on soil compaction, traction, fuel economy and crop yield responses are important. The purpose of the study was to investigate the effects of field and transport (road) tire inflation pressure settings of row-crop agricultural tractor and planter tires on soil compaction. A randomized complete block design experiment was conducted at the Iowa State University farm at Boone, Iowa for two tire inflation pressure levels on Dual Front (Firestone IF 420/85R34) and Dual Rear (Firestone IF 480/80R50) tires on a John Deere 8310R MFWD tractor, and transport tires (Super single 445/50R22.5) on a John Deere DB60 planter. Soil compaction was measured using Stress State Transducers (SSTs) buried at 15-cm and 30-cm depths beneath the untrafficked soil surface. The soil cone index depth profile was measured at tire-centerline, tire-edge and 20 cm laterally outboard of the tire edge before and after tractor-planter tire passes. Peak Octahedral Normal Stress (ONS) and the corresponding Octahedral Shear Stress (OSS) values in soil were calculated from the SST data. The peak ONS and corresponding OSS values from the road tire inflation pressure settings were statistically higher (p-value \u3c 0.05) than the field tire inflation pressure settings. The maximum ONS was observed at 15 cm soil depth from the road tire inflation pressure setting of the rear tractor tires (179 kPa tire inflation pressure and 33 kN load per tire). The ONS from the front tractor tires (138 kPa tire inflation pressure and 17 kN load per tire) and planter transportation tires (620 kPa tire inflation pressure and 16.5 kN load per tire) were similar. Cone index data also showed significant differences, comparing before and after tires passes, at the tire-centerline. The peak cone index values for the 0 to 100 mm soil depth range were 1.3 MPa and 1.2 MPa from the road and field tire inflation pressure settings, respectively

Deep Learning with EEG Data

Electroencephalogram (EEG) data has shown great promise but requires sophisticated methods due to the complex spatial and temporal patterns found in such data, so this research was conducted with the objective to investigate the efficiency of different types of deep learning models that includes Convolutional Neural Networks (CNNs), Long Short-Term Memory(LSTMs), Hybrid CNNs and Siamese LSTMs in classifying EEG data associated with schizophrenia. What was demonstrated was that these models were able to capture the intricate patterns within EEG data exceptionally well leading to accurate predictions about the patient’s condition, and results from evaluating different models indicate that the Hybrid (CNN+LSTM) architecture offers optimal suitability for this specific application because of improved outcomes. Important implications regarding the improvement of diagnosis and treatment for schizophrenia and other neurological disorders can be achieved through deep learning models as shown by this research.publishedVersio