Background: Smartphones allow for real-time monitoring of patients’ behavioral activities in a naturalistic setting. These data are suggested as markers for the mental state of patients with bipolar disorder (BD). Objective: We assessed the relations between data collected from smartphones and the clinically rated depressive and manic symptoms together with the corresponding affective states in patients with BD. Methods: BDmon, a dedicated mobile app, was developed and installed on patients’ smartphones to automatically collect the statistics about their phone calls and text messages as well as their self-assessments of sleep and mood. The final sample for the numerical analyses consisted of 51 eligible patients who participated in at least two psychiatric assessments and used the BDmon app (mean participation time, 208 [SD 132] days). In total, 196 psychiatric assessments were performed using the Hamilton Depression Rating Scale and the Young Mania Rating Scale. Generalized linear mixed-effects models were applied to quantify the strength of the relation between the daily statistics on the behavioral data collected automatically from smartphones and the affective symptoms and mood states in patients with BD. Results: Objective behavioral data collected from smartphones were found to be related with the BD states as follows: (1) depressed patients tended to make phone calls less frequently than euthymic patients (β=−.064, P=.01); (2) the number of incoming answered calls during depression was lower than that during euthymia (β=−.15, P=.01) and, concurrently, missed incoming calls were more frequent and increased as depressive symptoms intensified (β=4.431, P<.001; β=4.861, P<.001, respectively); (3) the fraction of outgoing calls was higher in manic states (β=2.73, P=.03); (4) the fraction of missed calls was higher in manic/mixed states as compared to that in the euthymic state (β=3.53, P=.01) and positively correlated to the severity of symptoms (β=2.991, P=.02); (5) the variability of the duration of the outgoing calls was higher in manic/mixed states (β=.0012, P=.045) and positively correlated to the severity of symptoms (β=.0017, P=.02); and (6) the number and length of the sent text messages was higher in manic/mixed states as compared to that in the euthymic state (β=.031, P=.01; β=.015, P=.01; respectively) and positively correlated to the severity of manic symptoms (β=.116, P<.001; β=.022, P<.001; respectively). We also observed that self-assessment of mood was lower in depressive (β=−1.452, P<.001) and higher in manic states (β=.509, P<.001). Conclusions: Smartphone-based behavioral parameters are valid markers for assessing the severity of affective symptoms and discriminating between mood states in patients with BD. This technology opens a way toward early detection of worsening of the mental state and thereby increases the patient’s chance of improving in the course of the illness.

The purpose of this study is to develop an effective control method for a hybrid energy storage system composed by a flow battery for daily energy balancing and a lithium-ion battery to provide peak power. It is assumed that the system operates behind the meter, the goal is to minimize the energy cost in the presence of a PV installation (as an example of a local renewable source) and energy prices are determined by 3-zone tariffs. The article presents the application of an optimization method to schedule the operation of each battery in the system. The authors have defined an optimization method aimed at minimizing the total cost of the system, taking into account energy costs and batteries depreciation. The techno-economical model of the system, including battery degradation, is constructed and the cost optimization methods are implemented in Python. The results are validated with real energy and price profiles and compared with conventional control strategies. The advantages of optimization in terms of energy cost are discussed. The experiment shows that not only is a hybrid energy system successful in lowering the total operation cost and in increasing self-consumption but also that the implemented methods have slightly different properties, benefits and issues.

Defuzzification of fuzzy sets is an important aspect of fuzzy processing, as it determines how the fuzziness is dealt with when performing the final step to obtain crisp solutions. In this contribution, we consider the possibility of defuzzifying multiple general type-1 fuzzy sets, given that their defuzzified values are bound by a single, known constraint. This stems from an application in spatial data processing, where we obtained a number of fuzzy sets whose defuzzified values should sum up to a crisp and known value. It is possible to defuzzify each fuzzy set individually and rescale the outcomes to meet the constraint. However, considering that the fuzzy set contains information on what constitutes good values, accounting for the constraint in the defuzzification process has the potential to yield better results. We considered this as an optimization problem and investigated appropriate goal functions. The approach and goal functions are discussed with respect to typical properties of defuzzifiers.

Nearly 60% of commercial customers are connected to a low-voltage network in Poland with a contractual capacity of more than 40 kW and are assigned a fixed tariff with flat prices for the whole year, no matter the usage volume. With smart meters, more data about how businesses use energy are becoming available to both energy providers and customers. This enables innovation in the structure and type of tariffs on offer in the energy market. Customers can explore their usage patterns to choose the most suitable tariff to benefit from lower prices and thus generate savings. In this paper, we analyzed whether customers’ electricity usage matched their optimal tariff and further investigated which of them could benefit or lose from switching the tariff based on the real dataset with the hourly energy readings of 1212 commercial entities in Poland recorded between 2016 and 2019. Three modelling approaches, i.e., the k-nearest neighbors, classification tree and random forest, were tested for optimal tariff classification, while for the benchmark, we used a simple approach, in which the tariff was proposed based on the customers’ previous electricity usage. The main findings from the research are threefold: (1) out of all the analyzed entities, on average, 76% of them could have benefited from the tariff switching, which suggests that customers may not be aware of the tariff change benefits, or they had chosen a tariff plan that was not tailored to them; (2) a random forest model offers a viable approach to accurate tariff classification; (3) the policy implication from the research is the need to increase the customers’ awareness about the tariffs and propose reliable tools for selecting the optimal tariff.