Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimisation of Reordering Points Considering Purchasing, Storing and Service Breakdown Costs

This paper focuses on the problem of the optimisation of reordering points through the usage of an educational version of a commercial Discrete Event Simulation software. A product for the optimisation of reordering points has been developed and successfully tested. After that, it has been used to optimise the reordering points of a plastic manufacturing company. The satisfactory results obtained have been useful not only to reduce the storage and backorder costs, but to train the purchasers of different plants. Most companies implement lean production principles. These are related to the minimisation of waste due to unneeded and inefficient operations, such as excessive buffering operations to serve the client or backorders (Narasimhan, Swink, & Wook Kim 2006). These two inefficiency types can be faced by defining a proper reordering point, as too low reordering points may worsen service rates whereas excessively high reordering points increase the storage costs. The optimisation of the reordering point of raw materials and maintenance spare parts is a problem extensively studied in the academic field, but poorly solved in the context of plant management. Thus, several authors (i.e. Namit & Chen 2007;Namit & Chen 2005;Taskin Gumus & Fuat Guneria 2009) study the problem of determining optimal reordering points. As a result, several tools for the development of reordering policies have been launched to the market; i.e. NSI developed a freeware tool (NSI 2004) for the optimisation of the reordering point of a single reference: to do that it is necessary to bring the monthly consumption of the reference during the last year, its lead time and the items per order of the element. As a result it offers different options of reordering points, each one with a determined expected service and confidence levels. Concerning the non freeware utilities, for example, Lokad (2008) offer its safety stock calculator, capable of interacting with the database of the company to determine proper safety stock levels. Nevertheless, practitioners state that most academical solutions developed do not reach SMEs. Tools as Lokad’s must 1
Extracting Sustainable Earnings from Profit Margins 1

Revenues and expenses are fundamentally proportional to one another, but are likely to be disproportionally affected by transitory items or economic shocks. We build on this observation and propose a new measure of sustainable earnings based on deviations from normal profit margins. While some other sustainable earnings metrics attempt to identify transitory components on a line-by-line basis, our measure, referred to as the intensity of core earnings, uses ratio analysis to extract the transitory portion of earnings from all line items. We find that the intensity of core earnings, as measured here, is positively associated with earnings persistence, better earnings predictability, and stronger market reaction to unexpected earnings. We also find that our measure is positively associated with postearnings announcement excess stock returns. Comparing our measure with an accrual-based measure of earnings quality, we find that, in general, the two metrics provide distinct incremental information relative to one another and in some instances our measure is better than an accrual-based measure in assessing earnings quality.
On Comparative Study of Breast Cancer Classification Using Ensembles in Statistical Modelling

Distinguishing different stages in cancer for medical professionals will require a reliable prediction methodology to diagnose cancer. To name a few, breast cancer is one of the most ordinary disease among women that leads to death. Hence, diagnosing it in earlier stage has become the certainty in cancer research,as to provide succeeding clinical approach to the patients. The classification of breast cancer is based on large number of parameters that characterize the tumour’s appearance. This helps the physician for diagnosis of breast cancer more easily. Hence automation of diagnostic system is needed for diagnosing tumours. Using automated computer tools and in particular machine learning to facilitate and boost medical investigation and detection is a promising and important area. A variety of these techniques, including Artificial Neural Networks (ANNs), Decision Trees (DTs) and Support Vector Machines (SVMs) and have been broadly applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Our Papers aims to use a modern and effective technique called Random Rotation Ensembles as well as Deep Learning . In this domain we show that the performance of this method is better than that of previous methods, therefore encouraging a more comprehensive and generic approach for cancer diagnosis.
Banknote Image Defect Recognition Method Based on Convolution Neural Network

There are shortcomings in the currently used traditional CCD imaging system which can automatically recognize banknote image defect, such as the need to manually extract the defect characteristics and low accuracy rate of detection results. This paper briefly introduced the advantage of convolution Neural Network (CNN) in image classification and designed a image defect identification method based on convolutional neural network (CNN). The experimental results on data sets show that the identification accuracy rate of this method is 95.6%, which is significantly better than traditional identification method.
Evaluation of Precipitation Estimation Accuracy in Reanalyses, Satellite Products, and an Ensemble Method for Regions in Australia and South and East Asia

Precipitation estimates from reanalyses and satellite observations are routinely used in hydrologic applications, but their accuracy is seldom systematically evaluated. This study used high-resolution gauge-only daily precipitation analyses for Australia (SILO) and South and East Asia [Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE)] to calculate the daily detection and accuracy metrics for three reanalyses [ECMWF Re-Analysis Interim (ERA-Interim), Japanese 25-yr Reanalysis (JRA-25), and NCEP‐Department of Energy (DOE) Global Reanalysis 2] and three satellite-based precipitation products [Tropical Rainfall Measuring Mission (TRMM) 3B42V6, Climate Prediction Center morphing technique (CMORPH), and Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN)]. A depthfrequency-adjusted ensemble mean of the reanalyses and satellite products was also evaluated. Reanalyses precipitation from ERA-Interim in southern Australia (SAu) and northern Australasia (NAu) showed higher detection performance. JRA-25 had a better performance in South and East Asia (SEA) except for the monsoon period, in which satellite estimates from TRMM and CMORPH outperformed the reanalyses. In terms of accuracy metrics (correlation coefficient, root-mean-square difference, and a precipitation intensity proxy, which is the ratio of monthly precipitation amount to total days with precipitation) and over the three subdomains, the depth-frequency-adjusted ensemble mean generally outperformed or was nearly as good as any of the single members. The results of the ensemble show that additional information is captured from the different precipitation products. This finding suggests that, depending on precipitation regime and location, combining (re)analysis and satellite products can lead to better precipitation estimates and,thus, more accurate hydrological applications than selecting any single product.
Review of Optical Link Technology in Silicon Photonic Circuits

Silicon photonics is the new advancement in the field of highly integrated devices on a single semiconductor substrate. The wide availability of fabrication facilities, high-density integration of photonic devices and the high probability of monolithic integration with electronic circuits offer the prospect for future reliable, low cost and energy-efficient . Silicon integrated chips have the capability to generate, modulate and analyse the light signals. Hence the have a varied application in computing, communications and sensing and optical interconnects. The application of photonics in optical interconnects is discussed in this paper. Here in we review recent progress in the engineering of new devices and important elements in silicon photonics, including low-loss waveguides, micro-ring resonators, modulators, photodetectors, transmitter and receiver architecture. We summarise the various types of optical ring resonators and coupling methods, in the paper. An attempt is also made to demonstrate the simulation of the single and double ring waveguides in LUMERICAL software.
Numerical analysis of TDS spectra under high and low flux plasma exposure conditions

A recently developed numerical model, based on the dislocation-driven nucleation of gas bubbles, is used to analyse experimental results on deuterium retention in tungsten under ITER relevant plasma exposure conditions. Focus is placed on understanding the relation between exposure temperature and flux on primary features of thermal desorption spectra: peak positions and intensities of the desorption flux. The model allows one to relate the peak positions with the size of plasma induced deuterium bubbles and envisage exposure conditions (temperature and flux) for their formation. Based on the performed analysis, dedicated experimental conditions to validate the model are proposed.
Contextual support for innovation in an Australian financial services firm

Organizational learning can facilitate innovation and it is affected by internal and external contexts. Leaders can provide internal contextual support for learning to occur in the organization in order to respond to changes in external contexts. However, there are limited studies about how leaders affect innovation in financial services firms. This paper applies Crossan et al.’s (1999) 4I framework to examine the impact of internal and external factors on an organization’s learning process and the extent of its innovation. An on-going case study of a large Australian financial services firm is used to gain insights about contextual support for innovation. Competition in the industry has increased through the introduction of IT-enabled innovation but innovation is limited by bureaucracy, risk-aversion and regulatory-driven reliability. The study shows that leaders in such organisations can support IT-enabled innovation through vision, organizational culture, an agile methodology, activity-based working environments, and technology that supports innovation.
Effects of tidal states and time of day on the abundance and behavior of shorebirds utilizing tropical intertidal environment

A study was conducted in the mudflats area of Jeram and Remis Beaches, Selangor, Peninsular Malaysia from August 2013 until July 2014 to determine the effect of tidal states and time of the day on shorebird’s abundance and behavior. Direct observation techniques (with the aid of binoculars and a video recording) were used in this study. Twenty three species of shorebirds (Density = 413.3 indiv/ha) were recorded in both study areas. Mann-Whitney test showed a significant difference in shorebird abundance between high tide and low tide (W = 78.0, p < 0.005) and also between behavior of shorebirds with respect to the tidal state; feeding behavior (W = 222.0, p < 0.0001), preening behavior (W =204.5, p = 0.0017) and mobility behavior (W = 222.0, p < 0.0001). Friedman’s Two-Way ANOVA by Ranks proofs the significant difference occurred in shorebird’s distribution between ebbing, low tide peak, and rising tides (S = 17.17, p < 0.0001). χ2 tests were tested for all behaviors engaged by shorebirds during low tide period and the results showed a significant difference between the behaviors (χ2 = 1831.9, p < 0.0001). ANOVA analysis showed no significance difference in abundance of shorebirds between interval period (S = 487.0; p = 0.554). However, Friedman’s Two-Way ANOVA by rank test shows the significant difference in abundance of shorebirds in high tide (S = 8.788, p = 0.03) between interval period. On the contrary, the analysis of Friedman’s Two-Way ANOVA by ranks highlighted no significance difference in abundance of shorebirds in low tide (S = 4.526; p = 0.21) between interval time. Therefore, this study showed that the tide does influence the shorebird’s abundance and behavior whereas time of the day does not have significant effect on both shorebird’s abundance and behavior.
An Active Fault Tolerant Control Strategy based on Bond Graph Adaptive Observers

System reliability has become one of the major concerns on industrial systems survey in order to guarantee a system life-cycle far from malfunctions, disturbances and dangers. In this context, Fault Tolerant Control systems are involved in automation engineering issues as a remedy to system reliability troubles. Thereby, we deal in this paper with a graphical Active Fault Tolerant Control ( AFTC ) law that compensates actuator fault effects, guarantees desirable closed-loop performances and system resilience. This tolerant control law is based on a linear Bond Graph ( BG ) adaptive observer to detect and estimate failures. This control framework and the adaptive observer are designed by a graphical concept using Bond Graph tool as a useful methodology for multidisciplinary systems and which is based on structural, causal and behavioural properties. To emphasis proposed controller efﬁciency, an hydraulic system with two tanks is modelled and controlled with and without fault scenarios.
Spoofing Detection for Airborne GNSS Equipment

Standards for the next-generation of civilian airborne GNSS equipment are now in development by RTCA and the European Organisation for Civil Aviation Equipment (EUROCAE). An initial version (for verification and validation) of Minimum Operational Performance Standards (MOPS) for dual-frequency multi-constellation (DFMC) GNSS airborne equipment is planned to be completed by 2020 and a final version by 2022. One objective for the next-generation airborne equipment is for improved resiliency in the presence of GNSS threats including spoofing. The current direction within RTCA and EUROCAE is for the threat of spoofing to be addressed primarily through the introduction of new requirements to detect the presence of * The contents of this document reflect the views of the authors and do not necessarily reflect the views of the Federal Aviation Administration (FAA) or the Department of Transportation (DOT). Neither the FAA nor the DOT makes any warranty or guarantee, expressed or implied, concerning the content or accuracy of these views. false GNSS signals within the airborne equipment so that so that an alternative means of navigation can be employed without a significant reduction in safety. This paper postulates a set of minimum high-level spoofing detection requirements for airborne GNSS equipment and assesses various methods against these requirements. One objective of this paper is to identify a number of issues for spoofing detection that are unique to certified airborne equipment for the purpose of fostering research to address these issues.
Effect of ethanolic extract of Mucuna pruriens on growth, metabolism and immunity of Labeo rohita (Hamilton, 1822) fingerlings.

The effect of dietary ethanolic extract of Mucuna pruriens on growth, metabolism and haematoimmunological parameters of an Indian Major Carp, Labeo rohita fingerlings were studied. Fishes were fed with a diet containing four graded levels (0.0, 0.06, 0.08 and 0.1 g/100 g diet) of Mucuna pruriens. Net weight gain, specific growth rate (SGR) and food conversion ratio varied significantly (p < 0.05) improved in Mucuna pruriens fed fish. Still the highest weight gain and specific growth rate (SGR) were recorded in 0.06 g/100 gm Mucuna pruriens supplemented diet. The increase in digestive enzymes like protease, amylase and lipase supported the results of increase in growth at treatment level 0.06g/100gm.The metabolic enzymes like LDH, MDH and G6PDH levels were decreased in herbal extract supplemented groups. Whereas, the enzymes of protein metabolism were found increased in herbal extract supplemented group in comparison to control. Further, the haematological parameters such as packed cell volume (PCV), haemoglobin concentration (Hb) and erythrocytes (RBC) were not significantly different in Mucuna pruriens supplemented diets as compared to control. A significant (P < 0.05) proliferation of the leukocytes (WBC) and improvement in respiratory burst activity. The results of this study proved the significant role of ethanolic extract of Mucuna pruriens in growth, metabolism and immunity defence mechanism of Labeo rohita fingerlings.
Inhomogeneous nuclear spin polarization induced by helicity-modulated optical excitation of fluorine-bound electron spins in ZnSe

Optically-induced nuclear spin polarization in a fluorine-doped ZnSe epilayer is studied by time-resolved Kerr rotation using resonant excitation of donor-bound excitons. Excitation with helicity-modulated laser pulses results in a transverse nuclear spin polarization, which is detected as a change of the Larmor precession frequency of the donor-bound electron spins. The frequency shift in dependence on the transverse magnetic field exhibits a pronounced dispersion-like shape with resonances at the fields of nuclear magnetic resonance of the constituent zinc and selenium isotopes. It is studied as a function of external parameters, particularly of constant and radio frequency external magnetic fields. The width of the resonance and its shape indicate a strong spatial inhomogeneity of the nuclear spin polarization in the vicinity of a fluorine donor. A mechanism of optically-induced nuclear spin polarization is suggested based on the concept of resonant nuclear spin cooling driven by the inhomogeneous Knight field of the donor-bound electron.
Perceptions on Authenticity in Chat Bots

In 1950, Alan Turing proposed his concept of universal machines, emphasizing their abilities to learn, think, and behave in a human-like manner. Today, the existence of intelligent agents imitating human characteristics is more relevant than ever. They have expanded to numerous aspects of daily life. Yet, while they are often seen as work simplifiers, their interactions usually lack social competence. In particular, they miss what one may call authenticity. In the study presented in this paper, we explore how characteristics of social intelligence may enhance future agent implementations. Interviews and an open question survey with experts from different fields have led to a shared understanding of what it would take to make intelligent virtual agents, in particular messaging agents (i.e., chat bots), more authentic. Results suggest that showcasing a transparent purpose, learning from experience, anthropomorphizing, human-like conversational behavior, and coherence, are guiding characteristics for agent authenticity and should consequently allow for and support a better coexistence of artificial intelligence technology with its respective users.
Crisis Intervention Teams may prevent arrests of people with mental illnesses

Historically, as many as 7–10% of US police contacts have involved persons with mental illnesses, with a disproportionate amount of these encounters resulting in arrest, usually for minor offenses. Crisis Intervention Teams (CIT) were created, and have proliferated, to ameliorate this problem by offering a specialized response and serving – at least informally – as a liaison between mental health services and police departments. Because preventing unnecessary arrests is one of CIT’s principal objectives, this study examined the arrest rates of persons with mental illnesses and the number of arrests that might have been prevented after the implementation of a CIT program in a large county in Central Florida. The arrest rate after CIT implementation was found to be very low and even declined across time providing evidence that CIT programs may indeed be useful in reducing discretionary arrests among persons with mental illnesses.
Application of Aligned Carbon Nanotube-Reinforced Polymer Composite to Electrothermal Actuator

Electrothermal bimorph actuators have been widely researched, comprising two layers with asymmetric expansion that generate a bending displacement. Actuation performance greatly relies upon the difference of the coefficients of thermal expansion (CTE) between the two material layers. Since traditionally used bimorph materials have positive CTE values, the generated displacements are restricted because of their relatively low CTE difference. Currently, the synthesis and characterization of carbon nanotube (CNT)/polymer composite actuators are topics of intense research activity. CNTs have been attracting much interest because of their superior electrical, thermal and mechanical properties. In addition, the negative CTE value of CNTs in the axial direction has been investigated analytically, leading one to expect that the CTE of the composites in a direction parallel to the CNT alignment will drastically decrease by containing the aligned CNTs into polymer materials. In this chapter, an experimen‐ tal method for determining the CTE of a CNT in the axial direction is discussed. Based on this result, we demonstrate an electrothermal bimorph actuator having a large bending displacement and high force output using an aligned CNT-reinforced epoxy composite and thin aluminum foil. Performance characteristics including power and work output per unit volume versus frequency are also reviewed.
Optimal Learning for Sequential Decision Making for Expensive Cost Functions with Stochastic Binary Feedbacks

We consider the problem of sequentially making decisions that are rewarded by "successes" and "failures" which can be predicted through an unknown relationship that depends on a partially controllable vector of attributes for each instance. The learner takes an active role in selecting samples from the instance pool. The goal is to maximize the probability of success in either offline (training) or online (testing) phases. Our problem is motivated by real-world applications where observations are time-consuming and/or expensive. We develop a knowledge gradient policy using an online Bayesian linear classifier to guide the experiment by maximizing the expected value of information of labeling each alternative. We provide a finite-time analysis of the estimated error and show that the maximum likelihood estimator based produced by the KG policy is consistent and asymptotically normal. We also show that the knowledge gradient policy is asymptotically optimal in an offline setting. This work further extends the knowledge gradient to the setting of contextual bandits. We report the results of a series of experiments that demonstrate its efficiency.
Optimal Monetary Policy in the Presence of Pricing-to-Market

This paper presents a general-equilibrium framework to revisit the issues of optimal monetary policies and international policy coordination in a two-country model, focusing on the role of a pricing-to-market (PTM) policy by firms. Both countries may be different with respect to PTM. Using the set-up developed by Corsetti and Pesenti (2001a) and Betts and Devereux (2000a,b), we show that (i) for a given Foreign monetary stance, a Home monetary expansion is beneficial for both countries only if Home PTM is at an intermediate range; (ii) in a world Nash equilibrium Home and Foreign welfare are bell-shaped in the degrees of PTM; (iii) relative welfare crucially depends on the degrees of PTM; (iv) there is a welfare gain from cooperation even in the cases of no and full PTM.