Chapters 11 and 13 of the Bankruptcy Code -- Observations on Using Case Authority from One of the Chapters in Proceedings Under the Other

This Article will focus on the relationship between Chapter 11 and Chapter 13 of the Bankruptcy Code.\u27 A number of issues are similar or identical in Chapter 11 and Chapter 13. Furthermore,much of the language of Chapter 13 mirrors that of Chapter 11. This Article explores whether courts should apply case law and concepts of one chapter when similar issues arise in proceedings under the other chapter. Parts II and III of this Article address basic similarities and differences between Chapters 11 and 13. Parts IV, V, and VI examine three issues governed by statutory language common to both chapters. Part IV discusses the discount factor applied in determining present value of deferred cash payments in a Chapter 11 or Chapter 13 plan. Part V analyzes the grounds for relief from an automatic stay. Part VI addresses the classification, under either chapter, of substantially similar claims

Remote lab experiments: opening possibilities for distance learning in engineering fields

Remote experimentation laboratories are systems based on real equipment, allowing students to perform practical work through a computer connected to the internet. In engineering fields lab activities play a fundamental role. Distance learning has not demonstrated good results in engineering fields because traditional lab activities cannot be covered by this paradigm. These activities can be set for one or for a group of students who work from different locations. All these configurations lead to considering a flexible model that covers all possibilities (for an individual or a group). An inter-continental network of remote laboratories supported by both European and Latin American institutions of higher education has been formed. In this network context, a learning collaborative model for students working from different locations has been defined. The first considerations are presented

Remote lab experiments: opening possibilities for distance learning in engineering fields

Remote experimentation laboratories are systems based on real equipment, allowing students to perform practical work through a computer connected to the internet. In engineering fields lab activities play a fundamental role. Distance learning has not demonstrated good results in engineering fields because traditional lab activities cannot be covered by this paradigm. These activities can be set for one or for a group of students who work from different locations. All these configurations lead to considering a flexible model that covers all possibilities (for an individual or a group). An inter-continental network of remote laboratories supported by both European and Latin American institutions of higher education has been formed. In this network context, a learning collaborative model for students working from different locations has been defined. The first considerations are presented.Education for the 21 st century - impact of ICT and Digital Resources ConferenceRed de Universidades con Carreras en Informática (RedUNCI

Roses Have Thorns: Understanding the Downside of Oncological Care Delivery Through Visual Analytics and Sequential Rule Mining

Personalized head and neck cancer therapeutics have greatly improved survival rates for patients, but are often leading to understudied long-lasting symptoms which affect quality of life. Sequential rule mining (SRM) is a promising unsupervised machine learning method for predicting longitudinal patterns in temporal data which, however, can output many repetitive patterns that are difficult to interpret without the assistance of visual analytics. We present a data-driven, human-machine analysis visual system developed in collaboration with SRM model builders in cancer symptom research, which facilitates mechanistic knowledge discovery in large scale, multivariate cohort symptom data. Our system supports multivariate predictive modeling of post-treatment symptoms based on during-treatment symptoms. It supports this goal through an SRM, clustering, and aggregation back end, and a custom front end to help develop and tune the predictive models. The system also explains the resulting predictions in the context of therapeutic decisions typical in personalized care delivery. We evaluate the resulting models and system with an interdisciplinary group of modelers and head and neck oncology researchers. The results demonstrate that our system effectively supports clinical and symptom research

Can an acoustic observatory contribute to the conservation of threatened species?

Observatories are designed to collect data for a range of uses. The Australian Acoustic Observatory (A2O) was established to collect environmental sound, including audible species calls, from 344 recorders at 86 sites around Australia. We examine the potential of the A2O to monitor near threatened, threatened, endangered and critically endangered species, based on their vocal behaviour, geographic distributions in relation to the sites of the A2O and on some knowledge of habitat use. Using IUCN and EPBC lists of threatened and endangered species, we extracted species that vocalized in the audible range, and using conservative estimates of their geographic ranges, determined whether there was a possibility of hearing them at these sites. We found that it may be possible to detect up to 171 threatened species at sites established for the A2O, and that individual sites have the potential to detect up to 40 threatened species. All 86 sites occurred in locations where threatened species could possibly be detected, and the list of detectable species included birds, amphibians, and mammals. We have incidentally detected one mammal and four bird species in the data during other work. Threatening processes to which potentially detectable species were exposed included all but two IUCN threat categories. We concluded that with applications of technology to search the audio data from the A2O, it could serve as an important tool for monitoring threatened species

Nucleon Spin Fluctuations and the Supernova Emission of Neutrinos and Axions

In the hot and dense medium of a supernova (SN) core, the nucleon spins fluctuate so fast that the axial-vector neutrino opacity and the axion emissivity are expected to be significantly modified. Axions with m_a\alt10^{-2}\,{\rm eV} are not excluded by SN~1987A. A substantial transfer of energy in neutrino-nucleon ($\nu N$) collisions is enabled which may alter the spectra of SN neutrinos relative to calculations where energy-conserving $\nu N$ collisions had been assumed near the neutrinosphere.Comment: 8 pages. REVTeX. 2 postscript figures, can be included with epsf. Small modifications of the text, a new "Note Added", and three new references. To be published in Phys. Rev. Let

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Cover title.Includes bibliographical references

Sterile neutrinos and supernova nucleosynthesis

A light sterile neutrino species has been introduced to explain simultaneously the solar and atmospheric neutrino puzzles and the results of the LSND experiment, while providing for a hot component of dark matter. Employing this scheme of neutrino masses and mixings, we show how matter-enhanced active-sterile neutrino transformation followed by active-active neutrino transformation can solve robustly the neutron deficit problem encountered by models of r-process nucleosynthesis associated with neutrino-heated supernova ejecta.Comment: 29 pages, 3 postscript figures, submitted to Phys. Rev.

DASS Good: Explainable Data Mining of Spatial Cohort Data

Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe the co-design of a modeling system, DASS, to support the hybrid human-machine development and validation of predictive models for estimating long-term toxicities related to radiotherapy doses in head and neck cancer patients. Developed in collaboration with domain experts in oncology and data mining, DASS incorporates human-in-the-loop visual steering, spatial data, and explainable AI to augment domain knowledge with automatic data mining. We demonstrate DASS with the development of two practical clinical stratification models and report feedback from domain experts. Finally, we describe the design lessons learned from this collaborative experience.Comment: 10 pages, 9 figure