Jomon Aliyas Paul

Effect of online social networking on student academic performance

Online social networks (OSNs) have permeated all generations of Internet users, becoming a prominent communications tool, particularly in the student community. Thus, academic institutions and faculty are increasingly using social networking sites, such as Facebook and LinkedIn, to connect with current and potential students and to deliver instructional content. This has led to a rise in questions about the impact of OSN on academic performance and the possibility of using it as an effective teaching tool. To learn more about the impact on academic performance, we conducted a survey of business students at a large state university. Survey results were analyzed using structural equation modeling (SEM). The results revealed a statistically significant negative relationship between time spent by students on OSN and their academic performance. The time spent on OSN was found to be heavily influenced by the attention span of the students. Specifically, we determined that the higher the attention span, the lower is the time spent on OSN. Further, attention span was found to be highly correlated with characteristics that predict or influence student behavior, such as their perceptions about societys view of social networking, their likes and dislikes of OSN, ease of use of OSN, etc.

Models for Improving Patient Throughput and Waiting at Hospital Emergency Departments

BACKGROUND Overcrowding diminishes Emergency Department (ED) care delivery capabilities. OBJECTIVES We developed a generic methodology to investigate the causes of overcrowding and to identify strategies to resolve them, and applied it in the ED of a hospital participating in the study. METHODS We utilized Discrete Event Simulation (DES) to capture the complex ED operations. Using DES results, we developed parametric models for checking the effectiveness and quantifying the potential gains from various improvement alternatives. We performed a follow-up study to compare the outcomes before and after the model recommendations were put into effect at the hospital participating in the study. RESULTS Insufficient physicians during peak hours, the slow process of admitting patients to inpatient floors, and laboratory and radiology test turnaround times were identified as the causes of reduced ED throughput. Addition of a physician resulted in an almost 18% reduction in the ED Main discharged patient length of stay. CONCLUSION The case study results demonstrated the effectiveness of the generic methodology. The research contributions were validated through statistically significant improvements seen in patient throughput and waiting time at the hospital participating in the study.

Improving bid pricing for humanitarian logistics

Purpose – Humanitarian logistics plays a critical role in the aid response to hunger and disasters worldwide. The US Department of Agriculture (USDA) uses a competitive bidding process to procure P.L. 480 Title II food aid, a

Location-allocation planning of stockpiles for effective disaster mitigation

2 billion business annually. This paper describes a mixed‐integer program (MIP) decision tool that mimics the USDA bid approach in order to improve ocean carrier and food supplier bid pricing strategy.Design/methodology/approach – First, the USDA bid process is detailed and the MIP decision tool is described. Then how the tool is run against historical data to approximate future USDA bid awards is explained, allowing the authors to subsequently advise food supplier and ocean carrier clients of expected price competition and pricing flexibility before they submit bids.Findings – The MIP decision tool has demonstrated its effectiveness in supporting

Location and Capacity Allocations Decisions to Mitigate the Impacts of Unexpected Disasters

8 million in food aid bids. Bidding implications for food aid carriers and suppliers are provided as well as suggestions for additional ...

Hospital capacity planning for efficient disaster mitigation during a bioterrorist attack

In the existing framework for receiving and allocating Strategic National Stockpile (SNS) assistance, there are three noticeable delays: the delay by the state in requesting federal assets, the delay in the federal process which releases assets only upon the declaration of a disaster and lastly the time it takes to reach supplies rapidly from the SNS stockpile to where it is needed. The most efficient disaster preparedness plan is one that addresses all three delays taking into account the unique nature of each disaster. In this paper, we propose appropriate changes to the existing framework to address the first two delays and a generic model to address the third which determines the locations and capacities of stockpile sites that are optimal for a specific disaster. Specifically, our model takes into account the impact of disaster specific casualty characteristics, such as the severity and type of medical condition and the unique nature of each type of disaster, particularly with regard to advance warning and factors affecting damage. For disasters involving uncertainty (magnitude/severity) with regard to future occurrences, such as an earthquake, development of appropriate solution strategies involves an additional step using scenario planning and robust optimization. We illustrate the application of our model via case studies for hurricanes and earthquakes and are able to outline an appropriate response framework for each.

Optimal Allocation of Resources in Airport Security: Profiling vs. Screening

This paper develops a stochastic modeling framework to determine the location and capacities of distribution centers for emergency stockpiles to improve preparedness in the event of a disaster for which there is little to no forewarning. The proposed framework is applicable to emergency planning that must incorporate multiple sources of uncertainty, including the timing and severity of a potential event, as well as the resulting impact, while taking into consideration both disaster and region specific characteristics. To demonstrate the modeling approach, we apply it to a region prone to earthquakes. The model incorporates various uncertainties such as facility damage and casualty losses, based upon their severity and remaining survivability time, as a function of the magnitude of the earthquake. Given the computational complexity of the problem of interest, we develop an evolutionary optimization heuristic aided by an innovative mixed integer programming model that generates time efficient high quality solutions. We demonstrate the effectiveness of the heuristic via a case study featuring the HAZUS-MH software from the Federal Emergency Management Agency (FEMA). Finally, given the uncertainty associated with the magnitude of the earthquake, we use a decision analysis approach to develop robust solutions while taking into account the geological characteristics of the region.

Determination of number of dedicated OR's and supporting pricing mechanisms for emergent surgeries

Effective hospital capacity planning can not only significantly enhance the capability and effectiveness of the treatment provided to patients during a bioterrorist attack but can also provide critical information. While a lot of work has been done to model hospital capacity estimates for natural disasters the same cannot be said for manmade biological disasters like anthrax or smallpox. In this paper, we develop a generic simulation model of hospital capacity planning during a bioterrorist attack. We model both cases in which the occurrence of the attack and the type of agent used are known as well as when they are not known. The model is also unique in developing a feedback loop to alert emergency management officials about the occurrence and type of an attack. Our results are able to pinpoint the characteristics of the hospitals that are most relevant at various stages of exposure and provide policy recommendations.

Improving hurricane disaster preparedness: models for optimal reallocation of hospital capacity

This model examines the role of intelligence gathering and screening in providing airport security. We analyze this problem using a game between the government and a terrorist. By investing in intelligence gathering, the government can improve the precision of its information. In contrast, screening can be used to search a passenger and thereby deter terrorist attacks. We determine the optimal allocation of resources between these two strategies wherein we model the role of intelligence using the concept of supermodular precision. One striking result is that under certain circumstances, an increase in the investment in intelligence can induce a more devious terrorist to attack with a higher probability. We also find that when there is a cost-reducing innovation in the screening technology, then the optimal investment in intelligence gathering can go either way. However, such an innovation unambiguously improves social welfare. Another interesting implication is that a developed economy would value intelligence inputs more than a developing economy. We also examine the efficacy of a program such as PreCheck that allows some select passengers expedited screening in exchange for voluntarily revealing information about themselves. Our analysis shows that such a program can be used to cushion the adverse effect of budgetary shortages. Finally, we also examine the role of enhanced punishment on the optimal level of intelligence. We find that the result can go both ways. If the initial level of punishment is high, then any further enhancement reduces the optimal level of intelligence gathering. However, this result is reversed if the initial level of punishment is low.

Does Terrorism Increase after a Natural Disaster? An Analysis Based Upon Property Damage

Inefficient management of emergent surgeries in hospitals can, in part, be attributed to a lack of rigorous analysis appropriate to capturing the underlying uncertainties inherent to this process and a pricing mechanism to ensure its financial viability. We develop a non-preemptive multi-priority queueing model that optimally manages emergent surgeries and supports the resource allocation decision-making process. Specifically, we utilize queueing and discrete event simulation to develop empirical models for determining the required number of emergent operating rooms for a hospital surgical department. We also present algorithms that estimate the appropriate pricing for patient surgeries differentiated by priority level given the patient demand and the resources reserved to meet this demand.