Use of Parallel Explanatory Models to Enhance Transparency of Neural Network Configurations for Cell Degradation Detection.

In a previous paper, we have shown that a recurrent neural network (RNN) can be used to detect cellular network radio signal degradations accurately. We unexpectedly found, though, that accuracy gains diminished as we added layers to the RNN. To investigate this, in this article, we build a parallel model to illuminate and understand the internal operation of neural networks (NNs), such as the RNN, which store their internal state to process sequential inputs. This model is widely applicable in that it can be used with any input domain where the inputs can be represented by a Gaussian mixture. By looking at RNN processing from a probability density function (pdf) perspective, we are able to show how each layer of the RNN transforms the input distributions to increase detection accuracy. At the same time we also discover a side effect acting to limit the improvement in accuracy. To demonstrate the fidelity of the model, we validate it against each stage of RNN processing and output predictions. As a result, we have been able to explain the reasons for RNN performance limits with useful insights for future designs for RNNs and similar types of NN.

Recent Advances in Machine Learning for Network Automation in the O-RAN.

The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.

Use of Processed Electroencephalography in the Clinical Setting.

PURPOSE OF REVIEW: Processed electroencephalography (pEEG) is widely used in clinical practice. Few clinicians utilize the full potential of these devices. This brief review will address the improvements in patient management available from the utilization of all pEEG data. RECENT FINDINGS: Anesthesiologists easily learn to recognize raw pEEG patterns that are consistent with an appropriate level of hypnotic effect. Power distribution within the waveform can be displayed in a visual format that identifies signatures of the principal anesthetic hypnotics. Opinion on the benefit of pEEG data in the mitigation of postoperative neurological impairment remains divided. SUMMARY: Looking beyond the index number can aid clinical decision making and improve confidence in the benefits of this monitoring modality.

Is harmonic scalpel an effective tool for oesophagectomy?

INTRODUCTION: Use of electrocautery in oesophagectomy is standard; however, the introduction of the harmonic scalpel (HS) and its use has changed the methodology of oesophagectomy in recent years. We have assessed the efficiency of HS in oesophageal cancer surgery. The parameters studied were blood loss, transfusion rates, and postoperative complications. METHODS: Our cohort included 142 patients who underwent elective oesophagectomy from January 1999 to December 2004. The control group was the patients undergoing electrocautery oesophagectomy (n=98) between 1999 and 2002. Furthermore, 44 patients who were operated with the HS were included in the study group. RESULTS: The numbers of units transfused were significantly less in HS group (median 0) in comparison with controls (median 2), p=0.003. Median blood loss in HS and the controls was 500 and 700 ml respectively (p=0.123). Mortality in HS group was 2.27%compared to 3.06% in controls (p=0.14). The complication (principally respiratory) rate was only 13.6% of patients in HS group compared to 17.3% in the controls. CONCLUSION: Our study shows that HS reduces transfusion rates and postoperative complications, highlighting it as a safe and effective alternative to traditional electrocautery.