3,579 research outputs found
Price-setting for Residential Water: Estimation of Water Demand in Lahore
The Water and Sewerage Agency (WASA) of Lahore is facing soaring demand and rising costs. But while massive investments are made to augment supply, tariffs remain low and are not adjusted in line with growing expenses. This has resulted not only in heavy and increasingly unsustainable reliance on loans and subsidies, but also in consumers undervaluing the resource, resulting in its inefficient utilisation. In this scenario, water tariffs badly need to be reformed. This study explores the potential of a pricing policy to regulate residential water demand in order to achieve the objectives of cost recovery, efficient water use, and equitable allocation of water resources. To this end, a demand function is estimated using household level data about water consumption and socio-economic characteristics of 156 households supplied by WASA, Lahore, for the period 2004-2006. Under block-rate tariffs the price variable is endogenously determined and a system of simultaneous equations emerges, solved here using two-stage least squares method. The estimated model explains 57 percent variation in water demand. The study finds water demand to be inelastic to price and, considering WASA’s exceedingly low tariffs, recommends up to 50 percent increase in the current tariff structure. Further computations show that a 50 percent increase will not endanger lifeline water supply. However, tariff increases may not be felt uniformly across all income groups, and absence of income data remains a limitation of this study. The study also recommends linking the non-volumetric part of tariffs to wealth-determined variables, such as property value and income.Water Demand; Price-setting
Improving wafer-scale Josephson junction resistance variation in superconducting quantum coherent circuits
Quantum bits, or qubits, are an example of coherent circuits envisioned for
next-generation computers and detectors. A robust superconducting qubit with a
coherent lifetime of (100 s) is the transmon: a Josephson junction
functioning as a non-linear inductor shunted with a capacitor to form an
anharmonic oscillator. In a complex device with many such transmons, precise
control over each qubit frequency is often required, and thus variations of the
junction area and tunnel barrier thickness must be sufficiently minimized to
achieve optimal performance while avoiding spectral overlap between neighboring
circuits. Simply transplanting our recipe optimized for single, stand-alone
devices to wafer-scale (producing 64, 1x1 cm dies from a 150 mm wafer)
initially resulted in global drifts in room-temperature tunneling resistance of
30%. Inferring a critical current variation from this
resistance distribution, we present an optimized process developed from a
systematic 38 wafer study that results in 3.5% relative standard deviation
(RSD) in critical current () for 3000 Josephson junctions (both single-junctions and
asymmetric SQUIDs) across an area of 49 cm. Looking within a 1x1 cm moving
window across the substrate gives an estimate of the variation characteristic
of a given qubit chip. Our best process, utilizing ultrasonically assisted
development, uniform ashing, and dynamic oxidation has shown = 1.8% within 1x1 cm, on average,
with a few 1x1 cm areas having 1.0% (equivalent to 0.5%). Such stability would drastically improve the yield of
multi-junction chips with strict critical current requirements.Comment: 10 pages, 4 figures. Revision includes supplementary materia
4-(4-Nitrophenoxy)biphenyl
The two phenyl rings of the biphenyl unit of the title compound, C18H13NO3, are almost coplanar [dihedral angle 6.70 (9)°]. The nitrophenyl ring, on the other hand, is significantly twisted out of the plane of the these two rings, making dihedral angles of 68.83 (4)° with the middle ring and 62.86 (4)° with the end ring. The nitro group is twisted by 12.1 (2)° out of the plane of the phenyl ring to which it is attached. Key indicators: single-crystal X-ray study; T = 173 K; mean σ(C–C) = 0.002 A° ; R factor = 0.040; wR factor = 0.118; data-to-parameter ratio = 12.8
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