Effective medical surplus recovery

We analyze not-for-profit Medical Surplus Recovery Organizations (MSROs) that manage the recovery of surplus (unused or donated) medical products to fulfill the needs of underserved healthcare facilities in the developing world. Our work is inspired by an award-winning North American non-governmental organization (NGO) that matches the uncertain supply of medical surplus with the receiving parties’ needs. In particular, this NGO adopts a recipient-driven resource allocation model, which grants recipients access to an inventory database, and each recipient selects products of limited availability to fill a container based on its preferences. We first develop a game theoretic model to investigate the effectiveness of this approach. This analysis suggests that the recipient-driven model may induce competition among recipients and lead to a loss in value provision through premature orders. Further, contrary to the common wisdom from traditional supply chains, full inventory visibility in our setting may accelerate premature orders and lead to loss of effectiveness. Accordingly, we identify operational mechanisms to help MSROs deal with this problem. These are: (i) appropriately selecting container capacities while limiting the inventory availability visible to recipients and increasing the acquisition volumes of supplies, (ii) eliminating recipient competition through exclusive single-recipient access to MSRO inventory, and (iii) focusing on learning recipient needs as opposed to providing them with supply information, and switching to a provider-driven resource allocation model. We use real data from the NGO by which the study was inspired and show that the proposed improvements can substantially increase the value provided to recipients

Realistic Synthetic Financial Transactions for Anti-Money Laundering Models

With the widespread digitization of finance and the increasing popularity of cryptocurrencies, the sophistication of fraud schemes devised by cybercriminals is growing. Money laundering -- the movement of illicit funds to conceal their origins -- can cross bank and national boundaries, producing complex transaction patterns. The UN estimates 2-5\% of global GDP or \$0.8 - \$2.0 trillion dollars are laundered globally each year. Unfortunately, real data to train machine learning models to detect laundering is generally not available, and previous synthetic data generators have had significant shortcomings. A realistic, standardized, publicly-available benchmark is needed for comparing models and for the advancement of the area. To this end, this paper contributes a synthetic financial transaction dataset generator and a set of synthetically generated AML (Anti-Money Laundering) datasets. We have calibrated this agent-based generator to match real transactions as closely as possible and made the datasets public. We describe the generator in detail and demonstrate how the datasets generated can help compare different Graph Neural Networks in terms of their AML abilities. In a key way, using synthetic data in these comparisons can be even better than using real data: the ground truth labels are complete, whilst many laundering transactions in real data are never detected

Hotspot detection of SPEC CPU 2006 benchmarks with performance event counters

Hotspot is the part of a program where most execution time is spent. Detecting the hotspot enables the optimization of the program. The performance event counters embedded in modern processors provide the hardware support for the hotspot detection. By sampling the instruc- tion addresses of the running program with performance event counters, hotspot of the program can be statistically detected. This technical re- port describes our tool to find the sections of the code that are detected as the hotspot of the program with performance event counters. SPEC CPU 2006 benchmarks are tested with our tool and the results show the hotspot sections and overhead of the hotspot detection tool

Partner selection for reverse logistics centres in green supply chains: a fuzzy artificial immune optimisation approach

The design of reverse logistics networks has now emerged as a major issue for manufacturers, not only in developed countries where legislation and societal pressures are strong, but also in developing countries where the adoption of reverse logistics practices may offer a competitive advantage. This paper presents a new model for partner selection for reverse logistic centres in green supply chains. The model offers three advantages. Firstly, it enables economic, environment, and social factors to be considered simultaneously. Secondly, by integrating fuzzy set theory and artificial immune optimization technology, it enables both quantitative and qualitative criteria to be considered simultaneously throughout the whole decision-making process. Thirdly, it extends the flat criteria structure for partner selection evaluation for reverse logistics centres to the more suitable hierarchy structure. The applicability of the model is demonstrated by means of an empirical application based on data from a Chinese electronic equipment and instruments manufacturing company

Myocardial Infarction as a Presentation of Clinical In-Stent Restenosis

Abstract Background In-stent restenosis is considered to be a gradual and progressive condition and there is scant data on myocardial infarction (MI) as a clinical presentation. Methods and Results Of 2,462 consecutive patients who underwent percutaneous coronary intervention between June 2001 and December 2002, clinical in-stent restenosis occurred in 212 (8.6%), who were classified into 3 groups: ST elevation MI (STEMI), non-ST elevation MI (NSTEMI) and non-MI. Of the 212 patients presenting with clinical in-stent restenosis, 22 (10.4%) had MI (creatine kinase (CK) ≥2 × baseline with elevated CKMB). The remaining 190 (89.6%) patients had stable angina or evidence of ischemia by stress test without elevation of cardiac enzymes. Median interval between previous intervention and presentation for clinical in-stent restenosis was shorter for patients with MI than for non-MI patients (STEMI, 90 days; NSTEMI, 79 days; non-MI, 125 days; p=0.07). Diffuse in-stent restenosis was more frequent in MI patients than in non-MI patients (72.7% vs 56.3%; p<0.005). Renal failure was more prevalent in patients with MI than in those without MI (31.8% vs 6.3%, p=0.001). Compared with the non-MI group, patients with MI were more likely to have acute coronary syndromes at the time of index procedure (81.8% vs 56.8%, p=0.02). Conclusion Clinical in-stent restenosis can frequently present as MI and such patients are more likely to have an aggressive angiographic pattern of restenosis. Renal failure and acute coronary syndromes at the initial procedure are associated with MI. (Circ J 2006; 70: 1026 - 1029