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The approach of this chapter is polemical in nature, reflecting the very fluid situation that lies ahead for orchestras post COVID-19. The chapter has three main academic research objectives. First, to put the current debate in context, it looks at the key challenges that orchestras have faced since the turn of the last century and in what way COVID-19 posed new problems that impacted orchestral music. The second objective is to outline some special short-term measures introduced to mitigate the impact of COVID-19, namely: (i) the income-support measures needed to sustain orchestras; and (ii) the extent to which orchestras could come together and practice, and in fact perform, even if only in front of no or very limited live audiences. The third objective is to discuss what possibly lies ahead for live orchestral music, post-COVID-19, and in a rapidly changing world regarding technological advances in the production and consumption of orchestral music. To inform this discussion, some broad trends in the ‘consumption’ of orchestral music over time, particularly in terms of numbers attending live concerts and revenues from streamed concerts, are examined.

We read with interest a consensus statement of the ScotCap clinical leads collaboration reported by Macleod et al. on the use of Computer Tomography Colonography (CTC) and Colon Capsule Endoscopy (CCE) as promoted diagnostic modalities in the Covid-19 era and how to deal with diminutive and small polyps with a 'realistic medicine' approach (1). The issues discussed are important, and the suggestions made are essential to improve healthcare provision for patients, doctors, hospitals, and society. The number needed to colonoscope to find one significant neoplastic lesion has increased dramatically in recent years - in Denmark more than 4-fold in 15 years, whereas the number of cancers diagnosed has increased very modestly (20-30% in 15 years). In Europe alone, more than 12m colonoscopies are performed each year. Complications are primarily associated with therapeutic optical colonoscopies (OC); significant complications (bleeding, perforation and polypectomy syndrome) are seen in 0.7% and adding up to over 400.000 cases yearly (2).

Purpose: Exercise therapy in combination with education is recommended as first-line treatment for painful knee osteoarthritis (KOA). In clinical practice, supervised exercise therapy and education demonstrate approx. 25% pain relief following an 8-week treatment program. Studies indicate that some patients with KOA experience larger pain relief compared to others. Assessments of peripheral and central pain mechanisms has been used to predict responders and non-responders to treatment. Studies on surgery and treatment with non-steroid anti-inflammatory drugs in patients with KOA have indicated that patients with higher levels of pain sensitivity might respond less positive. The primary aim of this observational study was to investigative if measures of pre-treatment pain sensitivity was associated with clinical outcomes after supervised exercise therapy and education. Methods: Patients with painful KOA (numeric rating scale [NRS, 0-10] ≥ 3) were included, and examined before and 1-2 weeks after 6-8 weeks of supervised exercise therapy (2 sessions of 1 hour per week) and 2 sessions of patient education. Handheld pressure pain threshold (PPT) was assessed locally at the most painful knee at 4 peripatellar sites (knee) and at two remote sites at the m. tibialis anterior (TA) and the contralateral m. extensor carpi radialis longus (ECRL). Further, computer-controlled cuff algometry at the lower leg with the most intense knee pain was used to assess pain detection threshold (cPDT), pain tolerance threshold (cPTT) and conditioned pain modulation (cCPM). Peak pain intensity within the last 24 hours (NRS, 0-10), PainDetect questionnaire (PDQ, 0-38) and Knee injury and Osteoarthritis Outcome Score (KOOS) were assessed as clinical measures. PDQ assesses the pain phenotype with a score ≤ 12 indicating probably nociceptive pain, 13-18 uncertain pain phenotype and ≥ 19 probably neuropathic pain. KOOS4 was defined as the average score of the subscale scores for Pain, Symptoms, Activity of Daily Living and Quality of Life (0-100 with 0 indicating extreme problems and 100 indicating no problems). Physical performance was assessed using the 40-meter walk test (40MWT). A treatment attendance score (%) was calculated for each patient by dividing the number of sessions attended by the number of sessions scheduled (twice per week). This study was approved by the local ethical committee (N-20190045) and pre-registered at (NCT04123756). All participants gave oral and written informed consent prior to enrollment. Results: This interim analysis reports on the first patients recruited for this observational study. Eleven KOA patients (6 women) with mean peak pain intensity of 6.0 ± 1.5, median pain duration 17.0 months (range: 5-120) and body mass index of 30.3 ± 5.7 were included in this interim analysis. In one subject, follow-up was made by telephone due to the COVID-19 situation, leaving 10 subject for the analysis on changes in pain sensitivity measures. Attendance score was 98.1 ± 18.1% during 7.1 ± 0.7 weeks. Following treatment, improvements were observed in KOOS4 (57.1 ± 10.0 at baseline vs. 65.3 ± 13.1 at follow-up, P 0.15). Pre-treatment cPDT (rs 0.05). Conclusions: These results indicate that patients with higher pre-treatment pressure pain sensitivity have worse KOOS4 scores following supervised exercise therapy and education.

In the past few months more than a billion people have faced restrictions unlike any seen before. Shops are closed; the death toll is rising; people across the globe have been forced to rise to an extraordinary challenge. But it is important to remember that humans have experienced pandemics before. In this documentary Dan Snow explores some of these previous pandemics and what they can teach us about Covid-19. He talks to Dr James Rogers about what lessons we can learn from WW2.

Dr James Rogers explains how we can draw parallels between the current COVID-19 pandemic and the Second World War, particularly in how humans have responded to an extraordinary challenge.

Electricity theft caused a major loss for electricity power provider. The anomaly detection helps to predict the abnormal load usage of a consumer. Usually, the classification method used in anomaly detection. This research paper proposed to identify the potential anomaly points by using threshold and outliers. The prediction in time-series applied Long Short-Term Memory (LSTM) algorithm. The historical electricity load dataset of a single industrial consumer was used to generate the prediction of electricity load. There were five optimizers used to produce the model: Adam, Adadelta, Adagrad, RMSProp, and Stochastic gradient descent (SGD). The prediction model was evaluated using mean squared error (MSE) and mean absolute error (MAE). The best model among all five models was generated by Adadelta optimizer with the error rate value of 0.091982 for MSE and 0.018433 for MAE. The prediction values were generated by this model. The anomaly point was detected by using threshold and outliers. The threshold value was 0.218983. One week in August 2019 was chosen to detect any anomaly load occurrences. There were 24 outliers were found within the selected week. The study shall expand on the electricity usage trend during COVID-19 pandemic period.