Feasibility Pilot: Problem Adaptation Therapy for Emotion Regulation in Community-Dwelling Older Adults

Emotion regulation (ER) difficulties in older adults are associated with increased depression and decreased resiliency to stressful life events. In general, maladaptive ER is a transdiagnostic risk factor for a range of psychological and physical problems across the lifespan. Thus, interventions targeting ER may be valuable in reducing risk for a range of late-life pathologies. The present study evaluated and adapted an existing ER-focused treatment (i.e., Problem Adaptation Therapy (PATH)) for community older adults. We completed a small clinical pilot study to assess the feasibility of the adapted protocol and initial signals of effect of the intervention on ER, depression, and resiliency. Participants were recruited using an online survey, which was used to then identify participants scoring in the highest and lowest quartiles for ER. Individuals in the lowest ER quartile (N=27) were randomly assigned to the PATH condition or a physical health education (PHET) control condition. Of the 27 participants in the low ER group, four participants (3 PATH, 1 PHET) dropped out of the intervention. A paired samples t-tests revealed significant decreases in depressive symptoms, significant increases in self-reported ER skill, and improvements in resiliency (all ps<.05) for the PATH condition. For the PHET condition, only significant increases in self-reported ER skill (t(12) = -2.68, p = .020) were observed. In sum, the intervention protocol proved feasibility and demonstrated initial signals of effect in the expected directions. Future studies will examine mechanisms of action and the efficacy of the adapted PATH protocol.

Forest path condition monitoring based on crowd-based trajectory data analysis

The development of Road Information Acquisition Systems (RIASs) based on the Mobile Crowdsensing (MCS) paradigm has been widely studied for the last years. In that sense, most of the existing MCS-based RIASs focus on urban road networks and assume a car-based scenario. However, there exist a scarcity of approaches that pay attention to rural and country road networks. In that sense, forest paths are used for a wide range of recreational and sport activities by many different people and they can be also affected by different problems or obstacles blocking them. As a result, this work introduces SAMARITAN, a framework for rural-road network monitoring based on MCS. SAMARITAN analyzes the spatio-temporal trajectories from cyclists extracted from the fitness application Strava so as to uncover potential obstacles in a target road network. The framework has been evaluated in a real-world network of forest paths in the city of Cieza (Spain) showing quite promising results.

Multiclass obstacles detection and classification using stereovision and Bayesian network for intelligent vehicles

Intelligent vehicles should be able to detect various obstacles and also identify their types so that the vehicles can take an appropriate level of protection and intervention. This article presents a method of detecting and classifying multiclass obstacles for intelligent vehicles. A stereovision-based method is used to segment obstacles from traffic background and measure three-dimensional geometrical features. A Bayesian network (BN) model has been established to further classify them into five classes, including pedestrian, cyclist, car, van, and truck. The BN model is trained using substantial data samples. The optimized structure of the model is determined from the necessary path condition method with a presupposition constraint (NPC+PC). The conditional probability table of the discrete nodes and the conditional probability distribution of the continuous nodes are determined from expectation maximization (EM) training algorithm with consideration of prior domain knowledge. Experiments were conducted using the object detection data set on the public KITTI benchmark, and the results show that the proposed BN model exhibits an excellent performance for obstacle classification while the full pipeline of the method including detection and classification is in the upper middle level compared with other existing methods.

Experimental Study of LoRa Transmission over Seawater

Low Power Wide Area Networks (LPWANs) are gaining attention in both academia and industry by offering the possibility of connecting a large number of nodes over extended distances. LoRa is one of the technologies used as a physical layer in such networks. This paper investigates the LoRa links over seawater in two typical scenarios: clear Line-of-Sight (LOS) and obstructed path in two different Industrial, Scientific and Medical (ISM) radio bands: 868 MHz and 434 MHz. We used three different LoRa devices in the experiments: the Own Developed LoRa Transceiver (ODT) and two commercial transceivers. Firstly we investigated transceivers’ Receive Signal Strength Indicator (RSSI) and Signal-to-Noise (SNR) measurement chain linearity and provided correction factors for RSSI to correlate it with actual signal levels received at transceivers’ inputs. Next, we carried out field experiments for three different LoRa Spreading Factors, S F ∈ [ 7 , 10 , 12 ] , within a bandwidth of B W = 125 kHz and Coding Rate C R = 4 / 6 . The experiments showed that LoRa links are fully feasible over seawater at distances at least 22 km long, using only low-cost off-the-shelf rubber duck antennas in LOS path condition in both ISM bands. In addition, we showed that LoRa links can be established over 28 km obstructed LOS oversea path in ISM 434 MHz band, but using costly, higher gain antennas. Furthermore, the laboratory experiments revealed that RSSI is linear in a wide range, up to - 50 dBm, whereas the SNR measurement chain goes into saturation for Received Signal Strength (RSS) values higher than - 100 dBm. These findings enabled accurate interpretation of the results obtained in field experiments.

QoS Analysis on OSPFv3 And RIPng Using GRE Tunneling on IPv6 Integrated Ipv4 Network

Every year, the availability of public IPv4 addresses is running low. However, the IETF (Internet Engineering Task Force) has set a new addressing standard called IPv6 (Internet Protocol version 6). IPv6 implementations can not be immediately performed on all end-to-end lines, a transition phase is required, one of which is the GRE tunneling method. IPv6 has some differences with IPv4, then in line with the presence of IPv6 will required the protocol that compatible with IPv6, among which is the routing protocol. Some dynamic routing protocols are created to support and can run on IPv6 such as RIPng and OSPFv3. Aim of this study is examines OSPFv3 routing protocols and RIPng routing protocols in terms of Quality of Service (QoS). The test is done by using seven routers with three scenarios in each routing protocol, ie scenario with best path active condition, when the network changes in other words best path down, and the scenario with best path condition has changed and the network has returned to normal. Testing is done by testing QoS parameters (delay, packet loss, and throughput) when the client computer downloads files with the .iso extension from the server. Downloaded files have different sizes, from 100 Mega Byte to 1 Giga Byte. From the results obtained that OSPFv3 provides better QoS (delay, packet loss, and throughput) than RIPng on integrated IPv6 network using GRE tunnel and can be a reference when going to transition from IPv4 to IPv6 using GRE Tunnel.

Isolating Graphical Failure-Inducing Input for Privacy Protection in Error Reporting Systems

This work proposes a new privacy-enhancing system that minimizes the disclosure of information in error reports. Error reporting mechanisms are of the utmost importance to correct software bugs but, unfortunately, the transmission of an error report may reveal users’ private information. Some privacy-enhancing systems for error reporting have been presented in the past years, yet they rely on path condition analysis, which we show in this paper to be ineffective when it comes to graphical-based input. Knowing that numerous applications have graphical user interfaces (GUI), it is very important to overcome such limitation. This work describes a new privacy-enhancing error reporting system, based on a new input minimization algorithm called GUIᴍɪɴ that is geared towards GUI, to remove input that is unnecessary to reproduce the observed failure. Before deciding whether to submit the error report, the user is provided with a step-by-step graphical replay of the minimized input, to evaluate whether it still yields sensitive information. We also provide an open source implementation of the proposed system and evaluate it with well-known applications.

An Open Path to the Future: Perceived Financial Resources and School Motivation

One contributing factor to gaps in academic achievement may be that some students perceive long-term educational goals, such as college, as financially out of reach, which can make schoolwork feel meaningless even several years before college. However, information that leads students to perceive that the financial path to college is open for them (i.e., need-based financial aid) can increase school motivation. Two classroom-based field experiments expand this area of theory and research. Early adolescent students who were randomly assigned to receive information about need-based financial aid (open path condition) showed greater school motivation than those who were randomly assigned to a control condition, specifically if they came from low-asset households. In a second exploratory experiment, the open path effect was mediated by an increased likelihood that students envision a future career that includes college (education-dependent identity). Implications for the study of identity and disparities in academic achievement are discussed.