New Impossible Differential Characteristic of SPECK64 using MILP

Impossible differential attack is one of powerful methods for analyzing block ciphers. When designing block ciphers, it must be safe for impossible differential attacks. In case of impossible differential attack, the attack starts from finding the impossible differential characteristic. However, in the case of the ARX-based block cipher, these analyzes were difficult due to the addition of modulus. In this paper, we introduce 157 new six-round impossible differential characteristics of ARX-basef block cipher, SPECK64, using Mixed Integer Linear Programming (MILP) base impossible differential characteristic search proposed by Cui [3] etc

Depression and Type 2 Diabetes Mellitus: The Multiethnic Study of Atherosclerosis

Objective: To assess the cross-sectional association between depression and glucose tolerance status. Methods: We conducted a study of 6754 White, Black, Hispanic, and Chinese men and women aged 45 to 84 years in the Multiethnic Study of Atherosclerosis (MESA). Depression was defined as Center for Epidemiologic Studies Depression scale score of 16 and/or antidepressant use. Glucose tolerance status was defined as normal, impaired fasting glucose (IFG) or Type 2 diabetes mellitus (untreated and treated). Results: In the minimally adjusted model, although depression was not associated with a greater odds of IFG (odds ratio (OR) = 1.01; 95% confidence interval (CI): 0.87–1.18) or untreated diabetes (OR = 1.03; 95% CI: 0.74–1.45), it was associated with a greater odds of treated diabetes (OR = 1.57; 95% CI: 1.27–1.96). This persisted following adjustment for body mass index (OR = 1.52; 95% CI: 1.22–1.90), metabolic (OR = 1.54; 95% CI: 1.23–1.93), and inflammatory (OR=1.53; 95% CI: 1.21–1.92) factors, daily caloric intake and smoking (OR = 1.48; 95% CI: 1.16–1.88), and socioeconomic markers (OR = 1.47; 95% CI: 1.17–1.85). Among individuals with treated diabetes, median depression scores were higher in those with microalbuminuria compared with those without microalbuminuria (median = 7; interquartile range: 3–13 versus median = 6; interquartile range: 2–11; p = .046). Depression scores were not associated with homeostatic model assessment of insulin resistance among individuals without diabetes. Conclusions: In MESA, depression was significantly associated with treated diabetes. Further studies are needed to determine the temporality of this association.http://deepblue.lib.umich.edu/bitstream/2027.42/57784/1/Depression and Type 2 Diabetes MellitusThe Multiethnic Study of Atherosclerosis.pd

Examining a bidirectional association between depressive symptoms and diabetes.

CONTEXT: Depressive symptoms are associated with development of type 2 diabetes, but it is unclear whether type 2 diabetes is a risk factor for elevated depressive symptoms. OBJECTIVE: To examine the bidirectional association between depressive symptoms and type 2 diabetes. DESIGN, SETTING, AND PARTICIPANTS: Multi-Ethnic Study of Atherosclerosis, a longitudinal, ethnically diverse cohort study of US men and women aged 45 to 84 years enrolled in 2000-2002 and followed up until 2004-2005. MAIN OUTCOME MEASURES: Elevated depressive symptoms defined by Center for Epidemiologic Studies Depression Scale (CES-D) score of 16 or higher, use of antidepressant medications, or both. The CES-D score was also modeled continuously. Participants were categorized as normal fasting glucose ( or = 126 mg/dL or receiving treatment). Analysis 1 included 5201 participants without type 2 diabetes at baseline and estimated the relative hazard of incident type 2 diabetes over 3.2 years for those with and without depressive symptoms. Analysis 2 included 4847 participants without depressive symptoms at baseline and calculated the relative odds of developing depressive symptoms over 3.1 years for those with and without type 2 diabetes. RESULTS: In analysis 1, the incidence rate of type 2 diabetes was 22.0 and 16.6 per 1000 person-years for those with and without elevated depressive symptoms, respectively. The risk of incident type 2 diabetes was 1.10 times higher for each 5-unit increment in CES-D score (95% confidence interval [CI], 1.02-1.19) after adjustment for demographic factors and body mass index. This association persisted following adjustment for metabolic, inflammatory, socioeconomic, or lifestyle factors, although it was no longer statistically significant following adjustment for the latter (relative hazard, 1.08; 95% CI, 0.99-1.19). In analysis 2, the incidence rates of elevated depressive symptoms per 1000-person years were 36.8 for participants with normal fasting glucose; 27.9 for impaired fasting glucose; 31.2 for untreated type 2 diabetes, and 61.9 for treated type 2 diabetes. Compared with normal fasting glucose, the demographic-adjusted odds ratios of developing elevated depressive symptoms were 0.79 (95% CI, 0.63-0.99) for impaired fasting glucose, 0.75 (95% CI, 0.44-1.27) for untreated type 2 diabetes, and 1.54 (95% CI, 1.13-2.09) for treated type 2 diabetes. None of these associations with incident depressive symptoms were materially altered with adjustment for body mass index, socioeconomic and lifestyle factors, and comorbidities. Findings in both analyses were comparable across ethnic groups. CONCLUSIONS: A modest association of baseline depressive symptoms with incident type 2 diabetes existed that was partially explained by lifestyle factors. Impaired fasting glucose and untreated type 2 diabetes were inversely associated with incident depressive symptoms, whereas treated type 2 diabetes showed a positive association with depressive symptoms. These associations were not substantively affected by adjustment for potential confounding or mediating factors.http://deepblue.lib.umich.edu/bitstream/2027.42/78570/1/GoldenLazo2008_JAMA.pd

Review of machine learning methods in soft robotics

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots

The Epidemiology of Delirium: Challenges and Opportunities for Population Studies

Delirium is a serious and common acute neuropsychiatric syndrome that is associated with short- and long-term adverse health outcomes. However, relatively little delirium research has been conducted in unselected populations. Epidemiologic research in such populations has the potential to resolve several questions of clinical significance in delirium. Part 1 of this article explores the importance of population selection, case-ascertainment, attrition, and confounding. Part 2 examines a specific question in delirium epidemiology: What is the relationship between delirium and trajectories of cognitive decline? This section assesses previous work through two systematic reviews and proposes a design for investigating delirium in the context of longitudinal cohort studies. Such a design requires robust links between community and hospital settings. Practical considerations for case-ascertainment in the hospital, as well as the necessary quality control of these programs, are outlined. We argue that attention to these factors is important if delirium research is to benefit fully from a population perspective

Lagrangean approximation procedures for certain combinatorial optimization problems in production/manufacturing systems

We study three independent and self-contained topics on combinatorial optimization of certain production/manufacturing problems. First, a project selection problem for production planning is considered. The problem consists of determining a project selection schedule and a production-distribution-inventory-import schedule for each plant so as to meet the demands of multiregional markets at minimum discounted total cost during a discrete finite planning horizon. Through a problem reduction algorithm, the Lagrangean relaxation problem strengthened by the addition of a surrogate constraint reduces to at most two 0-1 integer knapsack problems, and yields a tight bound. Computational results are reported. Second, a nonpreemptive single stage manufacturing process with parallel, unrelated machines and multiple job types with setups (PUMS) is considered. We propose a hybrid bounding procedure where a Lagrangean relaxation dual and a Lagrangean decomposition dual are solved one after the other to generate a good lower bound of the makespan. Computational results are reported. Third, the framework of a parallelized Lagrangean approximation procedure (PLAP) is proposed for some combinatorial manufacturing problems which are decomposable into well-structured subproblems. The synchronous PLAP for the multistage dynamic lot-sizing problem is implemented on the Alliant FX/4 parallel computer. The promising computational results indicate that the Lagrangean approximation procedure is indeed well suited for vector-concurrent computations

SCHEDULING ALGORITHMS FOR MOBILE HARBOR: AN EXTENDED M-PARALLEL MACHINE PROBLEM

Mobile Harbor is a ship, or a movable floating structure with container loading/unloading equipment on board, so that it can work with a container ship anchored in sea. In some sense, a Mobile Harbor is equivalent to a berth with a quay crane of a conventional port. For a Mobile Harbor-based system to handle a large volume of containers, multiple units of Mobile Harbor are required. As such, operation schedule for the units is essential to the productivity of the system. In this paper, a method to compute a scheduling solution for Mobile Harbor is proposed. It determines the operation and time sequence of jobs by the units to minimize the makespan of container handling operation. This problem is formulated with Mixed Integer Programming (MIP), based on an m-parallel machine problem. In addition, a heuristic approach using a Genetic Algorithm is developed to obtain a near optimal solution with reduced computation time

TEST: Testing Environment for Embedded Systems Based on TTCN-3 in SILS

The Testing and Test Control Notation Version 3 (TTCN-3) is an internationally standardized language for defining test specifications for a wide range of computer and telecommunication systems. Since embedded systems software is frequently used in case that safety is a primary issue and reliability is critical in the systems, it is necessary for the embedded systems software to use a systematic testing method such as TTCN-3. Unfortunately, the difference of testing environment between embedded and PC-based software makes developers hard to test the software, and hence products not tested thoroughly could be released in the market, which can be a potential disaster. In this paper, we review the TTCN-3 testing system and suggest a modified TTCN-3 testing environment for embedded systems software in Software In the Loop Simulation (SILS). A simple example shows our demonstration of testing embedded systems software based on the proposed TTCN-3 testing system