A South African review of harmonic emission level assessment as per IEC61000-3-6

Large-scale renewable power producing plants are being integrated into South African networks. Network operators need to ensure that Renewable Power Plants (RPP) do not negatively affect the power quality levels of their networks, as harmonics amongst others could become a concern. IEC 61000-3-6 details a method for allocating voltage harmonic emission limits for distorting loads. This method works well for the allocation of emission limits; however it does not address the management of harmonic emissions once a plant is connected to the network. The management of harmonic emissions requires that network operators measure or quantify the emissions from loads and generators to determine compliance. Post-connection quantification of harmonic levels and compliance is a challenge for network operators. The question asked is “How should a network operator measure/quantify the harmonic emissions of a load/generator to establish compliance with the calculated limits as per IEC 61000-3-6”. This paper reviews within a South African context methods of assessing harmonic emission levels and then evaluates these methods by means of field data. Opportunities for improvement are identified and operational requirements discussed

Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires

Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deployment of such models in real-world healthcare applications faces challenges including poor out-of-domain generalization and lack of trust in black box models. In this work, we propose approaches for depression detection that are constrained to different degrees by the presence of symptoms described in PHQ9, a questionnaire used by clinicians in the depression screening process. In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9's symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach. Furthermore, this approach can still perform competitively on in-domain data. These results and our qualitative analyses suggest that grounding model predictions in clinically-relevant symptoms can improve generalizability while producing a model that is easier to inspect

Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires

Automated methods have been widely used to identify and analyze mental healthconditions (e.g., depression) from various sources of information, includingsocial media. Yet, deployment of such models in real-world healthcareapplications faces challenges including poor out-of-domain generalization andlack of trust in black box models. In this work, we propose approaches fordepression detection that are constrained to different degrees by the presenceof symptoms described in PHQ9, a questionnaire used by clinicians in thedepression screening process. In dataset-transfer experiments on three socialmedia datasets, we find that grounding the model in PHQ9's symptomssubstantially improves its ability to generalize to out-of-distribution datacompared to a standard BERT-based approach. Furthermore, this approach canstill perform competitively on in-domain data. These results and ourqualitative analyses suggest that grounding model predictions inclinically-relevant symptoms can improve generalizability while producing amodel that is easier to inspect.<br

An Exact Algorithm for Side-Chain Placement in Protein Design

Computational protein design aims at constructing novel or improved functions on the structure of a given protein backbone and has important applications in the pharmaceutical and biotechnical industry. The underlying combinatorial side-chain placement problem consists of choosing a side-chain placement for each residue position such that the resulting overall energy is minimum. The choice of the side-chain then also determines the amino acid for this position. Many algorithms for this NP-hard problem have been proposed in the context of homology modeling, which, however, reach their limits when faced with large protein design instances. In this paper, we propose a new exact method for the side-chain placement problem that works well even for large instance sizes as they appear in protein design. Our main contribution is a dedicated branch-and-bound algorithm that combines tight upper and lower bounds resulting from a novel Lagrangian relaxation approach for side-chain placement. Our experimental results show that our method outperforms alternative state-of-the art exact approaches and makes it possible to optimally solve large protein design instances routinely

Experimental determination of the local heat transfer coefficient on a thermally thick wall downstream of a backward-facing step

Abstract A heat transfer measurement technique based on the combination of infrared thermography and numerical computation is presented in the case of a turbulent reattachment downstream of a backwardfacing step. The presence of a CaF2 window and low surface temperatures has led to develop a specific infrared system calibration. Heat transfer measurement technique and infrared system calibration are both presented in this paper