Network slicing will allow 5G network operators to oer a diverse set of services over a shared physical infrastructure. We focus on supporting the operation of the Radio Access Network (RAN) slice broker, which maps slice requirements into allocation of Physical Resource Blocks (PRBs). We rst develop a new metric, REVA, based on the number of PRBs available to a single Very Active bearer. REVA is independent of channel conditions and allows easy derivation of an individual wireless link's throughput. In order for the slice broker to eciently utilize the RAN, there is a need for reliable and short term prediction of resource usage by a slice. To support such prediction, we construct an LTE testbed and develop custom additions to the scheduler. Using data collected from the testbed, we compute REVA and develop a realistic time series prediction model for REVA. Specically, we present the X-LSTM prediction model, based upon Long Short-Term Memory (LSTM) neural networks. Evaluated with data collected in the testbed, X-LSTM outperforms Autoregressive Integrated Moving Average Model (ARIMA) and LSTM neural networks by up to 31%. X-LSTM also achieves over 91% accuracy in predicting REVA. By using X-LSTM to predict future usage, a slice broker is more adept to provision a slice and reduce over-provisioning and SLA violation costs by more than 10% in comparison to LSTM and ARIMA. CCS CONCEPTS• Networks → Wireless access points, base stations and infrastructure; • Computing methodologies → Neural networks;

Background India's flagship National Health insurance programme (AB-PMJAY) requires accurate cost information for evidence-based decision-making, strategic purchasing of health services and setting reimbursement rates. To address the challenge of limited health service cost data, this study used econometric methods to identify determinants of cost and estimate unit costs for each Indian state. Methods Using data from 81 facilities in six states, models were developed for inpatient and outpatient services at primary and secondary level public health facilities. A best-fit unit cost function was identified using guided stepwise regression and combined with data on health service infrastructure and utilisation to predict state-level unit costs. Results Health service utilisation had the greatest influence on unit cost, while number of beds, facility level and the state were also good predictors. For district hospitals, predicted cost per inpatient admission ranged from 1028 (313-3429) Indian Rupees (INR) to 4499 (1451-14,159) INR and cost per outpatient visit ranged from 91 (44-196) INR to 657 (339-1337) INR, across the states. For community healthcare centres and primary healthcare centres, cost per admission ranged from 412 (148-1151) INR to 3677 (1359-10,055) INR and cost per outpatient visit ranged from 96 (50-187) INR to 429 (217-844) INR. Conclusion This is the first time cost estimates for inpatient admissions and outpatient visits for all states have been estimated using standardised data. The model demonstrates the usefulness of such an approach in the Indian context to help inform health technology assessment, budgeting and forecasting, as well as differential pricing, and could be applied to similar country contexts where cost data are limited.

The systematic evaluation of the link budget calculation for the satellite and terrestrial communication is presented in this article. Communication link between the satellite and earth station is dependent on various propagation and associated losses which are either constant or vary with weather conditions. Role of receiver noise, antenna pointing mechanism, atmospheric effects, slant height, interferences, bit error rate on the link margin are detailed in this article. Various equations for link budget calculation and a comparative table at various frequency bands are shown in this article which is useful for predicting link margin of LEO, GEO and Deep space missions. Tele-command, telemetry and ranging link margin at various frequencies are presented and budget analysis at Ka-band frequency performed.

India has announced the ambitious program to transform the current primary healthcare facilities to health and wellness centres (HWCs) for provision of comprehensive primary health care (CPHC). We undertook this study to assess the cost of this scale-up to inform decisions on budgetary allocation, as well as to set the norms for capitation-based payments. The scale-up cost was assessed from both a financial and an economic perspective. Primary data on resources used to provide services in 93 sub-health centres (SHCs) and 38 primary health care centres (PHCs) were obtained from the National Health System Cost Database. The cost of additional infrastructure and human resources was assessed against the normative guidelines of Indian Public Health Standards and the HWC. The cost of other inputs (drugs, consumables, etc.) was determined by undertaking the need estimation based on disease burden or programme guidelines, standard treatment guidelines and extent and pattern of care utilization from nationally representative sample surveys. The financial cost is reported in terms of the annual incremental cost at health facility level, as well as its implications at national level, given the planned scale-up path. Secondly, economic cost is assessed as the total annual as well as annual per capita cost of services at HWC level. Bootstrapping technique was undertaken to estimate 95% confidence intervals for cost estimations. Scaling to CPHC through HWC would require an additional ₹ 721 509 (US$10 178) million allocation of funds for primary healthcare >5 years from 2019 to 2023. The scale-up would imply an addition to Government of India’s health budget of 2.5% in 2019 to 12.1% in 2023. Our findings suggest a scale-up cost of 0.15% of gross domestic product (GDP) for full provision of CPHC which compares with current public health spending of 1.28% of GDP and a commitment of 2.5% of GDP by 2025 in the National Health Policy. If a capitation-based payment system was used to pay providers, provision of CPHC would need to be paid at between ₹ 333 (US$4.70) and ₹ 253 (US$3.57) per person covered for SHC and PHC, respectively.

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