Abstract: An indoor-outdoor garden unit including a basin having an overhead canopy, connected to a water source via a main water tube and holding a plurality of opaque growing trays with removable opaque lids for optimal sprout growth in the dark. The main water supply tube operationally communicates with an on-off timer controlling water cycles. Bottom holes in each growing tray allow water, infused into the basin by any of three selectable mechanisms, to flow upwardly to water the sprouts from underneath, and serve to drain water from the growing trays and to aerate the plants between watering cycles. Sprayers on the underside of each growing tray lid allow watering from the top side. Water can be infused into the basin by the direct basin fill inlet or by a water fountain from which water impacts a water catcher member undermounted on the canopy to shed droplets into the basin.

Abstract: Systems and methods for forecasting device return rates are described. Some implementations include initializing a model representing a device's readiness for market based on one or more key performance indicators (KPIs) of the device, the model being represented by a curve, computing differences between measured value of KPIs of the device and values of KPIs fitted to the curve, identifying an inflection point of the curve based on the computed differences, interpreting the shape of the curve based on the identified inflection point, a readiness index and the curvature of the curve and determining a state of readiness of the device based on the interpreted shape of the curve.

Abstract: Systems and methods for forecasting device return rates are disclosed. Some implementations include identifying correlations between device return cause codes and corresponding device return rates for one or more devices, computing, based on the identified correlations, correlation indexes for each of the device return cause codes, ranking the computed correlation indexes associated with each of the device return cause codes, selecting, a subset of device return cause codes, determining a relationship between the selected subset of the return cause codes and corresponding device return rates, mapping one or more measured key performance indicators (KPIs) of a particular device to return cause codes of the device, and estimating a device return rate for the particular device based on the mapped KPIs and the determined relationship.

Abstract: Systems and methods for evaluating cellular voice call quality are disclosed. In some implementations, training data points are received. Each data point includes a call quality value and values of key performance indicators (KPIs) related to call quality. For each KPI, a linear relationship, having a goodness-of-fit value, between the KPI and the call quality value is determined using the multiple data points. Compulsory KPIs are selected based on the goodness-of-fit values. The training data points are separated into clusters based on the compulsory KPI values and the quality values. For each cluster, a mathematical relationship for calculating the call quality within the cluster is determined based on the one or more compulsory KPIs. A module is generated for predicting the call quality value by combining the determined mathematical relationships.