Abstract
Background: The short-term benefits of aerobic and resistance exercise in subjects affected by Peripheral Arterial Disease (PAD) are scarcely examined in interaction. This study aimed to identify the effects of combined aerobic and resistance exercise programs on walking performance compared with isolated aerobic exercise or with the usual care in patients with intermittent claudication. Methods: A systematic review was conducted following the PRISMA statement. A total of five electronic databases were searched (until October 2019) for randomized and non-randomized controlled trials. The focus comprised PAD patients with intermittent claudication who performed a combined aerobic and resistance exercise program that assessed the walking performance. Results: Seven studies include combined aerobic and resistance exercise vs. isolated aerobic or vs. usual care. The studies represented a sample size of 337 participants. The follow-up ranged from 4 to 12 weeks, 2 to 5 times-per-week. The risk of bias in the trials was a deemed moderate-to-high risk. After the interventions, the percent change in walking performance outcomes had a large variation. In the combined and isolated aerobic programs, the walking performance always improved, while in the usual care group oscillates between the deterioration and the improvement in all outcomes. Combined exercise and isolated aerobic exercise improved the claudication onset distance from 11 to 396%, and 30 to 422%, the absolute claudication distance from 81 to 197%, and 53 to 121%, and the maximal walking distance around 23 and 10%, respectively. Conclusions: Currently, there is insufficient evidence about the effects of combined aerobic and resistance exercise compared to isolated aerobic exercise or usual care on walking performance. However, despite the low quality of evidence, the combined aerobic and resistance exercise seems to be an effective strategy to improve walking performance in patients with intermittent claudication. These combined exercise modes or isolated aerobic exercise produce positive and significant results on walking performance. The usual care approach has a trend to deteriorate the walking performance. Thus, given the scarcity of data, new randomized controlled trial studies that include assessments of cardiovascular risk factors are urgently required to better determine the effect of this exercise combination.

Abstract
This work addresses a stochastic framework for optimal operation and long-term expansion planning of combined heat and power based microgrid as a part of an active distributing system. The microgrid utilizes renewable energy sources, electricity and heat generation units, energy storage systems, and demand response programs. The proposed model determines the optimal location and capacity of the electrical and thermal facilities, and it considers the impact of renewable energy sources and demand response on the expansion-planning problem. A stochastic mixed-integer linear programming formulation is utilized to minimize the investment and operation costs of system for five years. To evaluate the effectiveness of the proposed model, the algorithm is assessed for the 9-bus system and the 33-bus IEEE test systems. The results demonstrate that the utilization of the proposed algorithm reduces the operational cost and increases system revenues. © 2020 IEEE.

Abstract

Abstract
Fraud in telephony incurs huge revenue losses and causes a menace to both the service providers and legitimate users. This problem is growing alongside augmenting technologies. Yet, the works in this area are hindered by the availability of data and confidentiality of approaches. In this work, we deal with the problem of detecting different types of unsolicited users from spammers to fraudsters in a massive phone call network. Most of the malicious users in telecommunications have some of the characteristics in common. These characteristics can be defined by a set of features whose values are uncommon for normal users. We made use of graph-based metrics to detect profiles that are significantly far from the common user profiles in a real data log with millions of users. To achieve this, we looked for the high leverage points in the 99.99th percentile, which identified a substantial number of users as extreme anomalous points. Furthermore, clustering these points helped distinguish malicious users efficiently and minimized the problem space significantly. Convincingly, the learned profiles of these detected users coincided with fraudulent behaviors.

Abstract
Today, the fact that consumers are becoming more active in electrical power systems, along with the development in electronic and control devices, makes the design of Home Energy Management Systems (HEMSs) an expedient approach to mitigate their costs. The added costs incurred by consumers are mainly paying for the peak-load demand and the system's operation and maintenance. Thus, developing and utilizing an efficient HEMS would provide an opportunity both to the end-users and system operators to reduce their costs. Accordingly, this paper proposes an effective HEMS design for the self-scheduling of assets of a residential end-user. The suggested model considers the existence of a dynamic pricing scheme such as Real-Time Pricing (RTP), Time-of-Use (TOU), and Inclining Block Rate (IBR), which are effective Demand Response Programs (DRPs) put in place to alleviate the energy bill of consumers and incentivize demand-side participation in power systems. In this respect, the self-scheduling problem is modeled using a stochastic Mixed-Integer Linear Programming (MILP) framework, which allows optimal determination of the status of the home appliances throughout the day, obtaining the global optimal solution with a fast convergence rate. It is noted that the consumer is equipped with self-generation assets through a Photovoltaic (PV) panel and a battery. This system would make the consumers have energy arbitrage and transact energy with the utility grid. Consequently, the proposed model is demonstrated by determining the best operation schedule for different case studies, highlighting the impact each different DRP has on designing and utilizing the HEMS system for best results.

Abstract
Facial expression analysis is a widespread technology applied in various research areas, including sports science. In the last few decades, facial expression analysis has become a key technology for monitoring physical exercise. In this paper, a deep neural network is proposed to recognize seven basic emotions and their corresponding probability values (scores). The score of the neutral emotion was tracked throughout the exercise and related with heart rate and power generation by a stationary bicycle. It was found that in a certain power range, a participant changes his/her expression drastically. Twelve university students participated in the sub-maximal physical exercise in stationary bicycles. A facial video, heart rate,and power generation were recorded throughout the exercise. All the experiments, including the facial expression analysis, were carried out offline. The score of the neutral emotion and its derivative was plotted against maxHR% and maxPower%. The threshold point was determined by calculating the local minima, with the threshold power for all the participants being within 80% to 90% of its maximum value. From the results, it is concluded that the facial expression was different from one individual to another, but it was more consistant with power generation. The threshold point can be a useful cue for various purposes, such as: physiological parameter prediction and automatic load control in the exercise equipment, such as treadmill and stationary bicycle. © 2020, Springer Nature Switzerland AG.