Two nostratic etxmologies

1. *qamA "dry, warm" In my earlier paper I reconstructed the Nostratic word *qamA "dry, warm"; this etymological nest can be enlarged with some new examples: AA: Sem. *hmm "to be warm": Hebr. hām "warm", Ugar. hm, Syr. hammīmā, Akkad. emmu, Arab. hāmm; Eg. bmm "to be warm, to be hot" , Kopt. (Saĭd.) hmom "to become warm”; U članku je prosirena vee ranije predložena etimologija nostr. *qamA "suh, topao i pred1ožena je mogučnost rekonstrukcije nostr. *bagE i1i *PakA "h1adan".1. *qamA "dry, warm" In my earlier paper I reconstructed the Nostratic word *qamA "dry, warm"; this etymological nest can be enlarged with some new examples: AA: Sem. *hmm "to be warm": Hebr. hām "warm", Ugar. hm, Syr. hammīmā, Akkad. emmu, Arab. hāmm; Eg. bmm "to be warm to be hot" , Kopt. (Saĭd.) hmom "to become warm”;

Equipment Reliability Process in Krško NPP

To ensure long-term safe and reliable plant operation, equipment operability and availability must also be ensured by setting a group of processes to be established within the nuclear power plant. Equipment reliability process represents the integration and coordination of important equipment reliability activities into one process, which enables equipment performance and condition monitoring, preventive maintenance activities development, implementation and optimization, continuous improvement of the processes and long term planning. The initiative for introducing systematic approach for equipment reliability assuring came from US nuclear industry guided by INPO (Institute of Nuclear Power Operations) and by participation of several US nuclear utilities. As a result of the initiative, first edition of INPO document AP-913, ‘Equipment Reliability Process Description’ was issued and it became a basic document for implementation of equipment reliability process for the whole nuclear industry. The scope of equipment reliability process in Krško NPP consists of following programs: equipment criticality classification, preventive maintenance program, corrective action program, system health reports and long-term investment plan. By implementation, supervision and continuous improvement of those programs, guided by more than thirty years of operating experience, Krško NPP will continue to be on a track of safe and reliable operation until the end of prolonged life time

A comparison of generative and discriminative appliance recognition models for load monitoring

Appliance-level Load Monitoring (ALM) is essential, not only to optimize energy utilization, but also to promote energy awareness amongst consumers through real-time feedback mechanisms. Non-intrusive load monitoring is an attractive method to perform ALM that allows tracking of appliance states within the aggregated power measurements. It makes use of generative and discriminative machine learning models to perform load identification. However, particularly for low-power appliances, these algorithms achieve sub-optimal performance in a real world environment due to ambiguous overlapping of appliance power features. In our work, we report a performance comparison of generative and discriminative Appliance Recognition (AR) models for binary and multi-state appliance operations. Furthermore, it has been shown through experimental evaluations that a significant performance improvement in AR can be achieved if we make use of acoustic information generated as a by-product of appliance activity. We demonstrate that our a discriminative model FF-AR trained using a hybrid feature set which is a catenation of audio and power features improves the multi-state AR accuracy up to 10 %, in comparison to a generative FHMM-AR model

Low-Power Appliance Monitoring Using Factorial Hidden Markov Models

To optimize the energy utilization, intelligent energy management solutions require appliance-specific consumption statistics. One can obtain such information by deploying smart power outlets on every device of interest, however it incurs extra hardware cost and installation complexity. Alternatively, a single sensor can be used to measure total electricity consumption and thereafter disaggregation algorithms can be applied to obtain appliance specific usage information. In such a case, it is quite challenging to discern low-power appliances in the presence of high-power loads. To improve the recognition of low-power appliance states, we propose a solution that makes use of circuit-level power measurements. We examine the use of a specialized variant of Hidden Markov Model (HMM) known as Factorial HMM (FHMM) to recognize appliance specific load patterns from the aggregated power measurements. Further, we demonstrate that feature concatenation can improve the disaggregation performance of the model allowing it to identify device states with an accuracy of 90% for binary and 80% for multi-state appliances. Through experimental evaluations, we show that our solution performs better than the traditional event based approach. In addition, we develop a prototype system that allows real-time monitoring of appliance states

Etrurski malena "zrcalo"

Etruscan malena "mirror" is an adjective in -(e)na derived from *mal "to look, to see" as Slavic *zьrkadlo "mirror" (SCr. zȑcalo, Russ. zérkalo etc.) came from *zьrkati "to look, to see" (Russ. dial zérkat' compare OSL zьrěti etc.).Hipotetička etrurska osnova *mal- sadržana u riječi malena "zrcalo" refleks je nostratičke osnove *mi\Lf'/\ "gledati, vidjeti" rekonstruirane na osnovi kuš. *mAll!\'- "vidjeti", ie. *mel- "pokazati se", ? alt. *muli "misliti", nivh. *mal- "što se vidi, što je blizu"

Acoustic and Device Feature Fusion for Load Recognition

Appliance-specific Load Monitoring (LM) provides a possible solution to the problem of energy conservation which is becoming increasingly challenging, due to growing energy demands within offices and residential spaces. It is essential to perform automatic appliance recognition and monitoring for optimal resource utilization. In this paper, we study the use of non-intrusive LM methods that rely on steady-state appliance signatures for classifying most commonly used office appliances, while demonstrating their limitation in terms of accurately discerning the low-power devices due to overlapping load signatures. We propose a multilayer decision architecture that makes use of audio features derived from device sounds and fuse it with load signatures acquired from energy meter. For the recognition of device sounds, we perform feature set selection by evaluating the combination of time-domain and FFT-based audio features on the state of the art machine learning algorithms. The highest recognition performance however is shown by support vector machines, for the device and audio recognition experiments. Further, we demonstrate that our proposed feature set which is a concatenation of device audio feature and load signature significantly improves the device recognition accuracy in comparison to the use of steady-state load signatures only

Introduction. Leave no stone unturned: Perspectives on ground stone artefact research

Ground stone tools served in many physical and social contexts through millennia, reflecting a wide variety of functions. Although ground stone tool studies were neglected for much of early archaeology, the last few decades witnessed a notable international uptick in the way archaeologists confront this multifaceted topic. Today, with the advance of archaeology as a discipline, research into ground stone artefacts is moving into a new phase that integrates high resolution documentation with new methodological, analytical techniques, and technological approaches. These open new vistas for an array of studies and wide-ranging interpretive endeavours related to understanding ground stone tool production and use. Inspired by these diverse analytical approaches and interpretive challenges, we founded the international Association for Ground Stone Tools Research (AGSTR) in order to promote dialogue and create an optimal, inclusive arena for scholars studying various aspects of ground stone artefacts. Scholars from around the globe met for a five day conference at the University of Haifa, for the first meeting of the newly founded AGSTR. This included the presentation of 47 papers and 17 posters. The current paper serves as an introduction to this special issue of JLS, devoted to the proceedings of the founding conference of the Association for Ground Stone Tool Research, held at the University of Haifa during July 2015