Actual Input Research Articles

Precomputation Methods for Hash-Based Signatures on Energy-Harvesting Platforms

Energy-harvesting techniques can be combined with wireless embedded sensors to obtain battery-free platforms with an extended lifetime. Although energy-harvesting offers a continuous supply of energy, the delivery rate is typically limited to a few Joules per day. This is a severe constraint to the achievable computing throughput on the embedded sensor node, and to the achievable latency obtained from applications running on those nodes. In this paper, we address these constraints with precomputation. The idea is to reduce the amount of computations required in response to application inputs, by partitioning the algorithm in an offline part, computed before the inputs are available, and an online part, computed in response to the actual input. We show that this technique works well on hash-based cryptographic signatures, which have a complex key generation for each new message that requires a signature. By precomputing the key-material, and by storing it as run-time coupons in non-volatile memory, there is a drastic reduction of the run-time energy needs for a signature, and a drastic reduction of the run-time latency to generate it. For a Winternitz hash-based scheme at 84-bit quantum security level on a MSP430 microcontroller, we measured a run-time energy reduction of 11.9 $\times$ and a run-time latency reduction of 23.5 $\times$ .

Impact of clinical input variable uncertainties on ten-year atherosclerotic cardiovascular disease risk using new pooled cohort equations.

BackgroundRecently released American College of Cardiology/American Heart Association (ACC/AHA) guideline recommends the Pooled Cohort equations for evaluating atherosclerotic cardiovascular risk of individuals. The impact of the clinical input variable uncertainties on the estimates of ten-year cardiovascular risk based on ACC/AHA guidelines is not known.MethodsUsing a publicly available the National Health and Nutrition Examination Survey dataset (2005–2010), we computed maximum and minimum ten-year cardiovascular risks by assuming clinically relevant variations/uncertainties in input of age (0–1 year) and ±10 % variation in total-cholesterol, high density lipoprotein- cholesterol, and systolic blood pressure and by assuming uniform distribution of the variance of each variable. We analyzed the changes in risk category compared to the actual inputs at 5 % and 7.5 % risk limits as these limits define the thresholds for consideration of drug therapy in the new guidelines. The new-pooled cohort equations for risk estimation were implemented in a custom software package.ResultsBased on our input variances, changes in risk category were possible in up to 24 % of the population cohort at both 5 % and 7.5 % risk boundary limits. This trend was consistently noted across all subgroups except in African American males where most of the cohort had ≥7.5 % baseline risk regardless of the variation in the variables.ConclusionsThe uncertainties in the input variables can alter the risk categorization. The impact of these variances on the ten-year risk needs to be incorporated into the patient/clinician discussion and clinical decision making. Incorporating good clinical practices for the measurement of critical clinical variables and robust standardization of laboratory parameters to more stringent reference standards is extremely important for successful implementation of the new guidelines. Furthermore, ability to customize the risk calculator inputs to better represent unique clinical circumstances specific to individual needs would be highly desirable in the future versions of the risk calculator.Electronic supplementary materialThe online version of this article (doi:10.1186/s12872-016-0352-x) contains supplementary material, which is available to authorized users.

Abstract 5141: Determinants of metastatic competence in breast carcinoma: a role for immune cells

Abstract Dynamic interactions between cancer cells and their surrounding microenvironment influence tumor survival, growth and metastasis. The tumor microenvironment harbors a complex variety of cells including T lymphocytes and macrophages that produce a host of cytokines, chemokines, growth factors or interleukins, all of which presumably work in coordination to determine net tumor biology. Immune cells can offer a critical check-point in tumor progression. Interestingly however, infiltration of the tumor by immune cells appears to have dual functionality - they can reportedly either be pro-metastatic or anti-metastatic in their contribution. These clearly conflicting roles of immune cells limit our comprehension of their actual input to tumor advancement. Tumor infiltrating lymphocytes, in particular, the infiltration by T cells, has emerged as a good prognostic marker for a variety of cancers. However, the key molecular factors that regulate the cross-talk between tumor cells and T lymphocytes, and the underlying signaling pathways are still ill-defined. Moreover, the impact of T lymphocytes of the inflammatory tumor microenvironment, on the epithelial-mesenchymal plasticity and associated metastatic traits in breast cancer cells, is incompletely understood. We previously demonstrated that two key EMT/CSC factors - FOXC2 and Twist - are critical requirements for breast carcinoma cells to metastasize. The objective of the current study is to systematically investigate the role of regulatory T cells in EMT/CSC-dictated breast tumor progression, using two isogenic breast cancer cell lines (67NR and 4T1) that form primary mammary tumors similarly, but differ drastically in their ability to metastasize. Citation Format: Robiya Joseph, Rama Soundararajan, Anurag Paranjape, Sendurai Mani. Determinants of metastatic competence in breast carcinoma: a role for immune cells. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5141.

Prediction error, ketamine and psychosis: An updated model.

In 2007, we proposed an explanation of delusion formation as aberrant prediction error-driven associative learning. Further, we argued that the NMDA receptor antagonist ketamine provided a good model for this process. Subsequently, we validated the model in patients with psychosis, relating aberrant prediction error signals to delusion severity. During the ensuing period, we have developed these ideas, drawing on the simple principle that brains build a model of the world and refine it by minimising prediction errors, as well as using it to guide perceptual inferences. While previously we focused on the prediction error signal per se, an updated view takes into account its precision, as well as the precision of prior expectations. With this expanded perspective, we see several possible routes to psychotic symptoms – which may explain the heterogeneity of psychotic illness, as well as the fact that other drugs, with different pharmacological actions, can produce psychotomimetic effects. In this article, we review the basic principles of this model and highlight specific ways in which prediction errors can be perturbed, in particular considering the reliability and uncertainty of predictions. The expanded model explains hallucinations as perturbations of the uncertainty mediated balance between expectation and prediction error. Here, expectations dominate and create perceptions by suppressing or ignoring actual inputs. Negative symptoms may arise due to poor reliability of predictions in service of action. By mapping from biology to belief and perception, the account proffers new explanations of psychosis. However, challenges remain. We attempt to address some of these concerns and suggest future directions, incorporating other symptoms into the model, building towards better understanding of psychosis.

Improved Railway Track Irregularities Classification by aModel Inversion Approach

Over time railway networks have become complex systems characterized by manifold types of technical components with a broad range of age distribution. De facto, about 50 percent of the life cycle costs of railway infrastructures aremade up by direct and indirect maintenance costs. A remedy can be provided by a condition based preventive maintenance strategy leading to an optimized scheduling of maintenance actions taking the actual aswell as the expected future infrastructure condition into account. A prerequisite is, however, that the thousands of kilometers of railway tracks are almost continuously monitored. Thus, a promising approach is the usage of low-cost sensors, e.g. accelerometers and gyroscopes, which can be installed on common in-line freight and passenger trains. Due to ambiguous data records a credible classification of railway track irregularities directly from these data is challenging. Alternatively to this pure data-driven approach, in this paper a novel hybrid approach is presented. To this end, a simplified vehicle suspension model is applied for the purpose of railway track condition monitoring by analyzing the dynamic railway track - train interactions. The inversion of the model can be used to recalculate the actual inputs (irregularities) of the monitored system (rail surface) which have caused recorded system responses (dynamic vehicle reactions and acceleration data, respectively). These recalculated inputs are a sound basis of subsequent data-driven condition monitoring analyses.In this preliminary study, a classification algorithm is implemented to identify a simulated railway track irregularity automatically.

The Perception of Stress Pattern in Young Cochlear Implanted Children: An EEG Study.

Children with sensorineural hearing loss may (re)gain hearing with a cochlear implant—a device that transforms sounds into electric pulses and bypasses the dysfunctioning inner ear by stimulating the auditory nerve directly with an electrode array. Many implanted children master the acquisition of spoken language successfully, yet we still have little knowledge of the actual input they receive with the implant and specifically which language sensitive cues they hear. This would be important however, both for understanding the flexibility of the auditory system when presented with stimuli after a (life-) long phase of deprivation and for planning therapeutic intervention. In rhythmic languages the general stress pattern conveys important information about word boundaries. Infant language acquisition relies on such cues and can be severely hampered when this information is missing, as seen for dyslexic children and children with specific language impairment. Here we ask whether children with a cochlear implant perceive differences in stress patterns during their language acquisition phase and if they do, whether it is present directly following implant stimulation or if and how much time is needed for the auditory system to adapt to the new sensory modality. We performed a longitudinal ERP study, testing in bimonthly intervals the stress pattern perception of 17 young hearing impaired children (age range: 9–50 months; mean: 22 months) during their first 6 months of implant use. An additional session before the implantation served as control baseline. During a session they passively listened to an oddball paradigm featuring the disyllable “baba,” which was stressed either on the first or second syllable (trochaic vs. iambic stress pattern). A group of age-matched normal hearing children participated as controls. Our results show, that within the first 6 months of implant use the implanted children develop a negative mismatch response for iambic but not for trochaic deviants, thus showing the same result as the normal hearing controls. Even congenitally deaf children show the same developing pattern. We therefore conclude (a) that young implanted children have early access to stress pattern information and (b) that they develop ERP responses similar to those of normal hearing children.

Probing diagnostic fracture injection tests in unconventional reservoirs

Abstract Diagnostic fracture injection tests (DFIT) are commonly used to characterize stress and reservoir properties in unconventional reservoirs. Although simple in concept, interpreting DFIT results can be difficult because several factors can cause results to deviate from ideal DFIT behavior. Some examples of non-ideal DFIT behavior include steep pressure declines after shut-in, absence of pseudo-linear and pseudo-radial flow, and excessive storage indications. Such deviations from ideal DFIT behavior challenge our ability to estimate formation properties reliably. Potential drivers of non-ideal behavior include heterogeneous rock properties, complex rock/fluid interaction, multiphase effects, and natural fractures. The objective of this study is to investigate how these factors can impact DFIT results and interpretations. A comprehensive approach was taken using a combination of pressure transient analysis, frac modeling, analytical leak-off modeling, and detailed numerical simulation of DFIT behavior. The application was for a horizontal well, completed in a shale gas reservoir, which included an actual field DFIT. Detailed modeling included full wellbore transients and storage, hydraulic behavior through induced fractures, as well as complex interactions between rock, fluids, and natural fractures. It was determined that the actual DFIT showed indications of a complex network created by the pump-in. Closure pressure estimates were found to be reliable, between the simulation cases and DFIT analysis. However, a consistency check on initial reservoir pressure had to be used to obtain reasonable estimates compared to the simulation input. Finally, estimates of reservoir conductivity were highly uncertain compared to the actual simulation inputs. Furthermore, the actual DFIT estimates of reservoir pressure and conductivity were found to be overoptimistic. In fact, the estimated ultimate recovery using DFIT-based permeability-thickness product turned out to be more than twice the estimated ultimate recovery using the integrated core, log, and a pressure-buildup test.

수리온 연구개발 사업의 경제적 파급효과 분석

수리온 연구개발 사업의 성과분석을 위해 투입 비용을 조사하고, 산업파급효과 계수를 적용하여 생산유발효과, 부가가치유발효과 및 고용창출효과 분석을 실시하였다. 그리고 기술적 유사도와 공헌도 조사를 통하여 항공우주산업, 방위산업과 민간산업 시장에 대한 향후 기술파급효과를 측정하였다. This paper deals with the economic effects for the Surion(KUH-1) research and development program. The economic effects was measured by Inter-industry analysis method for production-inducing effects, value-added inducement effects, and job-creation effects and by estimating actual domestic input cost. In addition, technical riffle effects was analyzed for aerospace industry, defense industry and commercial industry market through similarity and contribution of technology.

An expert system for financial performance assessment of health care structures based on fuzzy sets and KPIs

Interest in the field of performance assessment of health care structures has grown in recent decades. In fact, the possibility of determining overall performances of health care structures plays a key role in the optimization of resource allocation and investment planning, as it contributes to reducing the uncertainty of future performance. In this context, key performance indicator (KPI) tools have been developed to assess the performance of health care structures from process, organizational, cost, financial, and output points of view. In practice, they are periodically calculated, and the effect of several KPIs on the overall performance of health care structures is determined by management through human judgment or software that provides synthetic dashboards. Given their non-stationary nature, performance assessment and forecasting are generally tackled by employing adaptive models, but these approaches cannot reflect the holistic nature of performance itself, nor take into account the impact of KPIs on the overall performances. In order to overcome these shortcomings, this study presents an expert system whose engine relies on fuzzy sets, in which the input–output relations and correlations have been modeled through inference rules based on time-series trends. The focus is on the financial performance assessment of a health care structure, such as a hospital. The approach is of an interdisciplinary kind, as several indicators were taken as inputs that relate to output, process, and cost KPIs, and their impact on the output measure, which is of a financial kind (namely the total reimbursement). The output measure calculated by the expert system was then compared with that predicted using only adaptive forecasting models, and the error with respect to the actual value was determined. Results showed that measures determined by fuzzy inference, able to effectively model actual input–output relations, outperform those of adaptive models.

Consideration of plant behaviour in optimal servo-compensator design

ABSTRACTWhere the most prevalent optimal servo-compensator formulations penalise the behaviour of an error system, this paper considers the problem of additionally penalising the actual states and inputs of the plant. Doing so has the advantage of enabling the penalty function to better resemble an economic cost. This is especially true of problems where control effort needs to be sensibly allocated across weakly redundant inputs or where one wishes to use penalties to soft-constrain certain states or inputs. It is shown that, although the resulting cost function grows unbounded as its horizon approaches infinity, it is possible to formulate an equivalent optimisation problem with a bounded cost. The resulting optimisation problem is similar to those in earlier studies but has an additional ‘correction term’ in the cost function, and a set of equality constraints that arise when there are redundant inputs. A numerical approach to solve the resulting optimisation problem is presented, followed by simulations on a micro–macro positioner that illustrate the benefits of the proposed servo-compensator design approach.

An effective analytical model of selective laser melting

ABSTRACTSelective Laser Melting (SLM) is a laser-based powder bed Additive Manufacturing process that can produce near net shape products with metallic powders according to Computer-Aided Design models. In this paper, a useful analytical model of SLM is proposed by investigating the energy requirement of the process. Results from the experiments on SLM of pure nickel and pure tin are reported. By compiling process parameter data from various literatures and experiments, this model has shown to enable predictions of the energy input required to process different metallic materials to an order of magnitude despite the many assumptions made. Possible explanations for the deviation in predictions and actual energy inputs are also discussed.

Control of Flexible Joint Robot Using Integral Sliding Mode and Backstepping

The control of flexible joint robot is getting more attentions because its applications are more frequently used for robot systems in these days. This paper proposes a robust impedance controller for flexible joint robots by using integral sliding mode control and backstepping control. The sliding mode control decouple disturbances completely but requires matching condition of disturbances. The dynamic model of FJR is divided into motor side and link side and the disturbance of the link side does not satisfy matching condition and cannot be decoupled directly by the actual input in the motor side. To overcome this difficulty, backstepping control technique is used with sliding mode control. The mismatched disturbance in the link side is changed into matched one in the respect to virtual control input which is the state controlled by actual input in the motor side. Integral sliding mode control is used to preserve the impedance control performance and the improved robustness at the same time.

Study on Heat Transfer Calculation Method of Ground Heat Exchangers Based on Heat Pump Unit Operation Characteristics

For ground source heat pump (GSHP) system, the dynamic heat rejected to or extracted from the ground is an important parameter to analyze the heat transfer performance of ground heat exchanger (GHE). Coupled with dynamic indoor load and actual operation characteristics of heat pump unit, a method to calculate actual dynamic heat load input of GHEs was introduced and a calculation program was developed. In an actual GSHP system project, the dynamic heat load input of GHEs was calculated and used as a boundary condition, a three-dimensional heat transfer model of this GHE field was established, and then the water temperature variation of GHEs was obtained. The actual water temperature variation of GHEs was tested and it was similar to the theoretical calculation results, thus the method was validated and could be applied in the actual engineering projects.

Abstract A130: Immune regulation of EMT and metastatic competence in breast tumor progression

Abstract Dynamic interactions between cancer cells and their surrounding microenvironment influence tumor survival, growth and metastasis. The tumor microenvironment harbors a complex variety of cells including lymphocytes and macrophages that produce a host of cytokines, chemokines, growth factors or interleukins, all of which presumably work in coordination to determine net tumor biology. Immune cells can offer a critical check-point in tumor progression. Interestingly however, infiltration of the tumor by immune cells appears to have dual functionality - they can reportedly either be pro-metastatic or anti-metastatic in their contribution. These clearly conflicting roles of immune cells limit our comprehension of their actual input to tumor advancement. The presence of tumor infiltrating lymphocytes has emerged as a good prognostic marker for a variety of cancers. However, the key molecular factors that regulate the cross-talk between tumor cells and lymphocytes, and the underlying signaling pathways are still ill-defined. Moreover, the impact of lymphocytes of the inflammatory tumor microenvironment, on the epithelial-mesenchymal plasticity and associated metastatic traits in breast cancer cells, is incompletely understood. We previously demonstrated that two key EMT/CSC factors - FOXC2 and Twist - are critical requirements for breast carcinoma cells to metastasize. The objective of this study is to systematically investigate the role of immune cells in EMT/CSC-dictated breast tumor progression using two isogenic breast cancer cell lines (67NR and 4T1) that form primary mammary tumors similarly, but differ drastically in their ability to metastasize. Citation Format: Robiya Joseph, Rama Soundararajan, Anurag Paranjape, Sendurai Mani. Immune regulation of EMT and metastatic competence in breast tumor progression. [abstract]. In: Proceedings of the CRI-CIMT-EATI-AACR Inaugural International Cancer Immunotherapy Conference: Translating Science into Survival; September 16-19, 2015; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(1 Suppl):Abstract nr A130.

Response: No need to match: a comment on Bach, Nicholson, and Hudson's "Affordance-Matching Hypothesis".

Response: No need to match: a comment on Bach, Nicholson, and Hudson's "Affordance-Matching Hypothesis".

Improved design of all-optical photonic crystal logic gates using T-shaped waveguide

In this paper, we propose new structures of all-optical logic gates based on two-dimensional photonic crystal of square type lattice with Si rods in air background. The proposed structures are based on T-shaped waveguide with optimized edge rod radius. An extra reference input port is included in the structure along with the actual input ports required for a logic gate. The simulation results show that the proposed T-shape waveguide can work as a NOT gate and a dual T-shape waveguide can work as NOR, XNOR and NAND gate with proper change in the phase values of logic ‘1’ input. The proposed structures have fast response time with value not more than 0.35 ps. The size of these structures is also small. It is expected that these new T-shaped structure based all-optical logic gates are suitable for large scale optical integration and can potentially be used in on-chip photonic integrated circuits.

Design and modeling of a novel translational and angular micro-electromechanical accelerometer

A continuous model is presented for a combined translational/angular micro capacitive accelerometer. New design includes two rectangular micro-beams, square shaped proof mass and four pairs of parallel capacitors, which are located in an appropriate construction. In order to find the transverse/torsional vibration effects of micro-beams on the proof mass, the Euler–Bernoulli beam theory is used to derive the governing partial differential equations of the problem which are then solved using modal analysis. Effects of squeeze film on the motion of the proof mass, including both spring and dissipative forces/moments, are considered using nonlinear isothermal Reynold's equation. Afterwards, these equations are solved using HPM method. Electrostatic forces/moments of capacitors are also included in the modeling analysis. Finally, the system of two second-order time-dependent differential equations is obtained. Appropriate formulation in order to correlate the output voltages of the system to the actual input accelerations is presented. Furthermore, output responses are precisely calibrated to form input accelerations. Results for different types of inputs are delineated as five case studies. Apart from transient part of the responses, the model predictions for both angular and translational accelerations show reasonably acceptable convergence with input accelerations of the system. Moreover, results revealed that for various combinations of input accelerations, the system presents reasonably accurate output predictions for each one.

Efficiency of the knowledge triangle policy in the EU member states: DEA approach

This paper aims to compare relative efficiency of the knowledge triangle policy in 25 EU member states providing evidence on the knowledge triangle technical efficiency calculated relying on output-oriented Data Envelopment Analysis (DEA) model assuming variable returns to scale (VRS). Moreover, the knowledge triangle efficiency indices are calculated, utilising the concept of actual and target inputs. Results obtained under output-oriented VRS model may signify overall effectiveness of the EU knowledge triangle policy. A more detailed analysis of output-oriented knowledge triangle efficiency indices reveals more differentiation among some countries. There are also significant variations among efficient decision-making units (DMUs) revealed through reference share analysis. Outcomes of this research may suggest the need of modification and/or strengthening of the knowledge triangle policy in some EU member states. The results obtained should however be treated with caution because they are influenced by the choice of variables.

Asynchronous update based networked predictive control system using a novel proactive compensation strategy

Networked predictive control system (NPCS) has been proposed to address random delays and data dropouts in networked control systems (NCSs). A remaining challenge of this approach is that the controller has uncertain information about the actual control inputs, which leads to the predicted control input errors. The main contribution of this paper is to develop an explicit mechanism running in the distributed network nodes asynchronously, which enables the controller node to keep informed of the states of the actuator node without a priori knowledge about the network. Based on this mechanism, a novel proactive compensation strategy is proposed to develop asynchronous update based networked predictive control system (AUBNPCS). The stability criterion of AUBNPCS is derived analytically. A simulation experiment based on Truetime demonstrates the effectiveness of the scheme.

Improving the Energy Efficiency of Pipelined Delay Lines Through Adaptive Granularity

A calibrated delay line is a key component in many modern digital systems. Traditionally, these lines are designed as real-time pipelines with static granularity, fine enough to handle a worst case input rate. However, due to their rigid structure, they have suboptimal energy for low- and varying-rate input streams. We introduce a complete methodology for designing reconfigurable delay lines that dynamically adapt their granularity to actual input traffic, on-the-fly, without stalling or disturbing normal operation. Two or more modes can be used, with different granularities to handle different traffic densities. During sparser traffic, the system is reconfigured to the proper coarser-grain mode, thereby reducing total energy, and it reverts to fine-grain mode during denser traffic. In each case, overall delay is preserved. This strategy is especially beneficial for applications where input traffic is highly varied. The particular focus of this paper is one promising domain, continuous-time digital signal processors (CT DSPs), a new class of processors targeting low-energy applications. The proposed system includes two lightweight asynchronous control blocks: a digital controller to continuously monitor input traffic, and a micropipeline to dynamically reconfigure the entire delay line. Design approaches for bimodal and trimodal adaptive lines are presented, which are then implemented in a 0.13- $\mu \text{m}$ IBM CMOS technology. Simulations for these delay lines demonstrate savings in overall dynamic power up to 45.5% and 71.1%, respectively, when compared with a nonadaptive design, with only minimal area overhead (1.5% and 3%, respectively, for a targeted configuration). Using extensions to more configuration modes, further power reductions can be achieved. While results are presented for CT DSPs, significant benefits are also expected in many other domains, where the delay lines are used.