Development of Cloud-Based Co-Parenting Strengthening System

Co-parenting emphasizes how couple manage their relationship in the parenting domains and carry out their role as parents. Based on research conducted by Feinberg and colleagues (2012), there are four overlapping domains of co-parenting: (1) child rearing agreement; (2) support/undermining partner’s parenting; (3) joint family management; and (4) division of labor. The interference of mobile-based technology is needed on the co-parenting relationship between the couple with reference to the four domains of co-parenting. The method used was the Systems Development Life Cycle (SDLC) model. It was divided into six stages of the process: requirement analysis, system design, implementation, system testing, system deployment, and system maintenance. In implementation, the system at an early stage has four main features: (1) child’s activity scheduling and reminder; (2) co-parenting’s education contents; (3) partner’s feedback; and (4) co-parenting’s assessment. The results that are proven by experts, agreed that educational content in the application is very necessary for more knowledge for parents, UI/UX design that is friendly and easy to understand by users, as well as the use of the latest reliable and flexible technology.

Benefits of Using Activity Recommender Technology for Self-management of Depressive Symptoms

Behavioral Activation (BA)therapy has shown to be effective in treating depression. Recommending healthy activities is a core principle in Behavioral Activation (BA), which is typically done by the therapist. However, most BA smartphone applications do not recommend specific activities. This article reports quantitative results from an 8-week feasibility study of a previously presented smartphone-based BA recommender system. The system supports the planning and enacting of pleasurable activities and promotes activation of diverse activity types. Enrollment included 43 clinically depressed patients who installed the system on their phone and initiated activity scheduling. Twenty-nine patients used the system daily for more than a week.These patients presented a significant reduction in depressive symptoms during the study period. They displayed a more personalized usage approach and created recurring health goals comprising of their own customized activities. Furthermore, they took inspiration within various types of activities, thereby displaying more activity diversity. This study suggests that enacting a diverse mixture of activities that promote good sleep, personal hygiene, exercise, social contact, and leisure time can be essential in managing depressive symptoms. A smartphone-based activity recommender system can help patients achieve this.

Portfolio-Wide Optimization of Pharmaceutical R&D Activities Using Mathematical Programming

The research and development (R&D) management in any major research pharmaceutical company is constantly faced with the need to make complicated activity scheduling and resource allocation decisions, as they carry out scientific work to develop new therapeutic products. This paper describes how we develop a decision support tool that allows practitioners to determine portfolio-wide optimal schedules in a systematic, quantitative, and largely automated fashion. Our tool is based on a novel mixed-integer linear optimization model that extends archetypal multimode resource-constrained project scheduling models in order to accommodate multiple rich features that are pertinent to the Chemistry, Manufacturing, and Controls (CMC) activities carried out within the pharmaceutical R&D setting. The tool addresses this problem at the operational level, determining schedules that are optimal in light of chosen business objectives under activity sequencing, resource availability, and deadline constraints. Applying the tool on current workload data demonstrates its tractability for practical adoption. We further illustrate how, by utilizing the tool under different input instances, one may conduct various tactical analyses to assess the system’s ability to cope with sudden changes or react to shifting management priorities.

Scheduling of Post-Flood Recovery Project with Flexible Project Structure

Flood events around the world result in the loss of human lives, disruption, damage to economic, infrastructural and ecological systems. Although, different frameworks to manage flood events exist; however, the complexity (i.e. adjustment and adaptation) associated with some of these approaches is often limited by constraints of time and resources. Therefore, this study attempts to apply a flexible project structure to schedule a post-flood recovery project (PFRP). Twenty-five (25) restorative activities in a PFRP were identified, categorised and scheduled as resource-constrained project scheduling problem with a flexible structure (RCPSP-FS). Monte Carlo simulation was used to reflect the uncertain characteristics of each restorative activity. PFRP completion time was 42 and 86 days under time and resource constraints assumptions, respectively. Thirty- four (34) network paths (sub-projects) were identified and grouped into 4 restorative measures as follows: (i) removal of hazardous materials (ii) evacuation of injured persons (iii) provision of flood technology warning system and technical facilities and (iv) construction of shelter, homes and bridges. Time and cost flexibility values for the network paths range from 6 to 63 days, and 14.79 to 288.77 thousand USD, respectively. Time and schedule sensitivity analysis revealed the impact of each restorative activity on simulated project completion time. Based on these results, it is concluded that a flexible project structure can respond to changing circumstances during post-flood restoration efforts which allow more degree of freedom in activity scheduling, flood events measures and cost alternatives.

Activity Scheduling Behavior of the Visitors to an Outdoor Recreational Facility Using GPS Data

Understanding the decision-making behavior of pedestrians is essential for urban designers and developers in enhancing the commercial and aesthetic value of streets and other urban spaces. However, limited research has been conducted on the activity scheduling behavior of pedestrians. The majority of the studies conducted on outdoor facilities utilize spatial representations by links and are unable to sufficiently represent the highly flexible behavior of pedestrians. This study proposes a new method to discretize data from the global positioning system (GPS) into a two-dimensional grid-based spatial representation with a high spatial resolution. The information regarding the stay at the point of interests (POIs) is extracted from the discretized data, and the activity scheduling model is estimated. The estimation results indicate that the visitors’ attributes, such as the age of the representative and number of children, affect the probability of the activity choice and the time spent at the POI. The probability of choosing the main gate increases in the latter half of the stay, confirming the existence of time pressure. The information on the decision-making behavior of the visitors to a facility, obtained from the GPS data, can be applied to the data-oriented spatial design process to create attractive and lively spaces.

A New Decision Support System for Optimal Integrated Project Scheduling and Resource Planning

Two scheduling practices are commonly used depending on the availability of resources. When resources are not expensive, activities are scheduled and then resources are allocated until the available resources are exhausted. Then, iterative adjustments are applied to the resource allocation plan and the activities sequence to reach a feasible solution. Conversely, when expensive resources are involved, a resource allocation plan based on the economics of the resource is established and then activities are scheduled accordingly. However, Resource Constrained Scheduling Problems (RCSP) are not solved efficiently with either of these approaches. To find the optimal solution, activity scheduling and resource allocation should be formulated as an integrated optimization problem. Such models become numerically cumbersome for practical size problems and difficult to solve. In this article, a novel mathematical formulation and an efficient solution algorithm are proposed for solving RCSPs. Then, this framework is used for solving a practical problem in the context of the construction industry.

Technique for accounting for the decrease in labor productivity due to workspace overcrowding

In civil engineering, delays are ubiquitous. They cause significant economic damage. One way to prevent deadline disruption is to lower the duration of tasks by increasing the number of workers on critical path activities. However, when the number of workers increases above a certain level, the duration stops decreasing due to the interference between workers that occurs in a limited work space. This situation is called “work space overcrowding”. To determine the number of workers at which work space overcrowding occurs, as well as to improve activity scheduling accuracy, the author has developed a technique for accounting for labor productivity decline due to work space overcrowding. During the development of this technique, an analysis of existing approaches was carried out. Their shortcomings in practice are revealed. Then, the factors of productivity decline associated with overcrowding are identified. These factors are divided into two categories: “spatial” and “socio-psychological”. Two models are developed based on a mathematical formalization of these categories. The criterion for choosing between the two models is the intensity of workers' movements. The technique was successfully verified using peer review by specialists in nuclear power plant construction. It was found that the technique accurately reflects the general nature of labor productivity dependence on the number of workers: constant levels of productivity up to a certain number, followed by decreasing levels of productivity, at accelerating rates, above that number. However, application of the technique provides only a rough approximation of this dependence in quantitative terms. This is compensated for by the technique’s simplicity, requiring a limited number of inputs to produce a meaningful result. Finally, applying the technique to two specific cases, a recommended limit of the increase in the number of workers is determined for both. In the future, the technique can be applied to a wider range of construction activities.

Travel Time Reliability of Bus Operation in Heterogeneous Traffic Conditions of Dar es Salaam City, Tanzania

The assessment of travel time reliability enables precise prediction of travel times, better activity scheduling and decisions for all users of the road network. Furthermore, it helps to monitor traffic flow as a crucial strategy for reducing traffic congestion and ensuring high-quality service in urban roads. Travel time reliability is a useful reference tool for evaluating transport service quality, operating costs and system efficiency. However, many analyses of travel time reliability do not provide true travel variation under heterogeneous traffic flow conditions where traffic flow is a mixture of motorized and non-motorized transport. This study analysed travel time reliability under heterogeneous traffic conditions. The travel reliabilities focused on passenger waiting time at bus stops, in-vehicle travel time, and delay time at intersections which were analysed using buffer time, standard deviation, coefficient of variation, and planning time. The data used were obtained from five main bus routes in Dar es Salaam. The results indicate low service reliability in the outbound directions compared to inbound directions. They also intend to raise awareness of policy-makers about the situation and to make them shift from expanding road networks towards optimising road operations.