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Problem definition: This paper examines whether and, if so, how much an online–off-line return partnership between online and third-party retailers with physical stores (or “location partners”) generates additional value to location partners. Academic/practical relevance: Online shoppers often prefer to return products to stores rather than mailing them back. Many online retailers have recently started to collaborate with location partners to offer the store return option to their customers, and we quantify its economic benefit to a location partner. Methodology: We analyze proprietary data sets from Happy Returns (which provides return services for more than 30 online retailers) and one of its location partners, using a panel difference-in-differences model. In our study, a treatment is the initiation of the return service at each of the location partner’s stores, and an outcome is the store and online channel performance of the location partner. We then explore the mechanisms of underlying customer behavior that drive these outcomes. Results: We find that the partnership increases the number of unique customers, items sold, and net revenue in both store and online channels. We identify two drivers for this improved performance: (1) the location partner acquires new customers in both store and online channels, and (2) existing customers change their shopping patterns only in the store channel after using the return service; in particular, they visit stores more often, purchase more items, and generate higher revenue after their first return service. Managerial implications: To our knowledge, we provide the first direct empirical evidence of value to location partners from a return partnership, and as these partnerships become more prevalent, our findings have important managerial implications for location partners and online retailers alike.

When gears change their distance along the off-line of action (OLOA) direction, this affects the distance between the working surfaces of the meshing teeth along the line of action (LOA). This effect is usually neglected in studies. To include this effect precise equations are derived for spur gears. The analysis is carried out for the general case of spur gears with shifted profiles frequently used in the industry. The influence of OLOA gear displacement on LOA direction is also a function of gears parameters. An analysis is conducted, and the impact of parameters such as module, pressure angle, gear ratio, and the number of teeth is determined. As an example, a simulation of a 12 DOF analytical model is presented. The movement of gears along OLOA is caused by a frictional force that can be high during tooth degradation e.g. scuffing. Results show that when the movement of gears along the OLOA direction is significant, its influence on the distance between the mating teeth should not be neglected.

The modality of apprehension and processing of metaphorical expressions in comparison with non-metaphorical ones has hitherto captivated numerous researchers in manifold fields of study, such as linguistics, psychology, and cognitive sciences. More specially, metaphors used in a one-sentence paragraph have been the subjects of many studies. However, cognitive functions of structural metaphors haven’t been entirely noteworthy in contrast with non-metaphorical expressions employed in textual context. In this study, the interrelationship between memory and conceptual metaphor in significant cognitive processes has been examined in a textual context. In this respect, the hypothesis, that conceptual metaphor as a value can assist with the recognition and recollection process and incorporate the quintessence of our cerebrations, has been put to test. To evaluate this assumption, the reaction time task is used. Each testable case has been subjected to analysis within two analogous contexts, in a metaphorical and non-metaphorical manner. Afterwards, terms were displayed, and the subjects needed to determine as swiftly as possible whether these vocabularies were exemplified or not. The results indicated that the terms pertaining to the schema and other terms included in metaphorical context would be processed faster than the one with non-metaphorical context. With regard to the obtained data, it seems that the conceptual metaphor generates semantic networks in the mind which will be more accessible to memory upon information retrieval.

For constrained linear parameter varying systems subject to bounded disturbances and noises, this article investigates an off-line output feedback robust model predictive control approach. The sub-observer gains with robust positively invariant sets, and sub-controller gains with robust control invariant sets are simultaneously off-line optimized and stored in a look-up table. According to real-time estimation error bounds and estimated states, the time-varying sub-observer gains and sub-controller gains are on-line searched. The proposed off-line output feedback robust model predictive control approach with the guarantee of nested robust positively invariant sets and robust control invariant sets in theory reduces the on-line computational burden.

In this work, is proposed an analytical method for the determination of nickel in natural water and sediment samples using solid-phase extraction with a mini-column of bamboo fibres modified with 5...

Oleuropein is the main active substance in foods or functional foods produced from olive (Olea europaea L.) leaves. In the present study, the combinative technology off line of HSCCC-PHPLC based on dual wavelength was used to separate highly purified oleuropein from oleuropein extract. Response surface methodology was used to optimize the conditions of HSCCC. Furthermore, a large amount of higher purified oleuropein was obtained through HSCCC at the wavelength of 254 nm, and oleuropein with the purity greater than 98.5% was obtained by PHPLC at the wavelength of 300 nm. Finally, the purity and structure identification of highly purified oleuropein were determined by various methods and its stability was investigated. As a result, oleuropein was stable in solution, and had good stability under the condition of dark storage at 4°C within a week or under the condition of dark storage at -20°C within one year. PRACTICAL APPLICATION: In this study, an efficient method for purification and refining of oleuropein by combinative technology off line of HSCCC-PHPLC based on dual wavelength was established. Oleuropein with the purity greater than 98.5% was macro-obtained via the technology. The highly purified oleuropein could be used to control the quality of olive products.

This work presents an analysis and a design methodology for a DC–DC LLC resonant converter applied to off-line LED driving. An investigation regarding the main design parameters that influence the LED low-frequency (LF) ripple has been carried out. The study was based on a mathematical analysis of the converter and employs numerical algorithms to solve a set of equations that accurately describe the converter behavior. Based on this analysis, a novel design methodology that allows for LF current ripple prediction has been devised. A numerical model has been developed to determine the LF current ripple transmission in DC–DC LLC resonant converters and provide a methodology for designing the bus capacitance in two-stage off-line LED power supplies. The work shows that the design of the LLC resonant converter for off-line LED driving is a trade-off between the converter efficiency and the LF ripple transmission. Experimental results were taken from two 46-W laboratory prototypes considering two different design goals: one for LF ripple reduction and another aiming at the highest converter efficiency and better power quality. Furthermore, the design of a two-stage LED driver based on an LLC converter taking into account the LF ripple transmission is also presented.

Tightnening emission regulations and increasing powertrain complexity lead car manufacturers to rely on novel testing methods in order to frontload development. Among these, Engine-in-the-Loop, that is, the coupling of a physical internal combustion engine (ICE) on a testbed with a virtual environment, shows great promise for emission- and consumption-related tasks. In particular, this study focuses on the driver model, a simple yet crucial component of the virtual environment. A longitudinal driver model is developed in Simulink based on the PI-regulation structure and augmented with anti-windup, cycle preview, and takeoff strategy. While the PI approach is generally chosen in the literature, this study details the implementation of the added functions, and proposes a method for the gains of the model to be tuned in simulation by considering engine dynamics, and using several performance indicators. The virtual driver is then tested in a complete EiL setup simulating an electric hybrid driveline and shows satisfactory cycle-following and overall behavior on a WLTC. Robustness of the tuning method is also studied by varying vehicle parameters on the EiL bench.

This paper investigates the off-line fault detectability of logical control networks (LCNs) by the method of semi-tensor product. Firstly, two concepts of off-line fault detectability, that is, wea...

Services such as delivery, warranty, and product returns that are bundled with various tangible products play a critical role in their market performance. As manufacturers strive to streamline operations and deliver consistent services across channels, this article analytically examines whether those who sell their products directly online and off-line through retailers should direct control, delegate to retailers, or outsource to third parties the delivery of these services. Our main findings are as follows. Manufacturers should not outsource demand-enhancing services to third parties if they do not ensure relatively low operating costs, as they reduce the level of service provided to customers. Assuming no channel partner has a cost advantage in service delivery, manufacturers should directly control the provision of services that are used identically by online and off-line customers. Manufacturers may not be able to directly and cost effectively offer services that are used differently by online and off-line customers. In such a context, depending on the relative sizes of off-line and online market bases, retailers may be considered either as single providers of the service for both online and off-line customers or jointly with manufacturers as providers for off-line customers only. We discuss the implications of these findings.

Dispatch security assessment is studied to develop a programmable algorithm in this field. The main objectives of the developed security program are : to incorporate network and environmental constraints, to provide results which allow the system operator to perform necessary corrective actions into the system dispatch for maintaining secure operation and to provide a fast solution for power flows within the system for any further study. The developed security program provides a solution rapid enough to allow sufficiently frequent repetition to follow changing system conditions and can be used on line or off line for maintaining secure system operation and reliability.

A significant issue in the domain of optical character recognition is handwritten text recognition. Here, two novel feature extraction techniques are proposed using a fixed-size sliding window, and also an edit distance-based architecture is suggested to recognize the off-line characters. These feature extraction techniques are designed for text recognition from the text images. It’s an off-line approach, that is why data from scanned documents or natural scenes are taken as input. In this paper, the freely available datasets, known as Chars74k and MNIST for English alphabets and digits are used. The proposed feature extraction technique for the off-line text images of characters as well as numbers generates the features successfully. The impact of the proposed method on text recognition accuracy is computed using several state-of-the-art machine learning algorithms. After that, these are again compared with the proposed Edit distance based text recognition system with the help of different conducted experiments. The proposed model has reached an accuracy of more than 96% for the MNIST dataset.

Nowadays, it is evident that signature is commonly used for personal verification, this justifies the necessity for an Automatic Verification System (AVS). Based on the application, verification could either be achieved Offline or Online. An online system uses the signature’s dynamic information; such information is captured at the instant the signature is generated. An offline system, on the other hand, uses an image (the signature is scanned). In this paper, some set of simple shaped geometric features are used in achieving offline Verification of signatures. These features include Baseline Slant Angle (BSA), Aspect Ratio (AR), and Normalized Area (NA), Center of Gravity as well as the line’s Slope that joins the Center of Gravities of the signature’s image two splits. Before the features extraction, a signature preprocessing is necessary to segregate its parts as well as to eliminate any available spurious noise. Primarily, System training is achieved via a signature record which was acquired from personalities whose signatures had to be validated through the system. An average signature is acquired for each subject as a result of incorporating the aforementioned features which were derived from a sample set of the subject’s true signatures. Therefore, a signature functions as the prototype for authentication against a requested test signature. The similarity measure within the feature space between the two signatures is determined by Euclidian distance. If the Euclidian distance is lower than a set threshold (i.e. analogous to the minimum acceptable degree of similarity), the test signature is certified as that of the claiming subject otherwise detected as a forgery. Details on the stated features, pre-processing, implementation, and the results are presented in this work.

Portable fluorescent test strips with off-line visual detection have become a hit in the field of target analysis. Yet, common fluorescent test strips are mainly based on filter papers, which usually suffer from poor visual detection effect owing to the “coffee ring effect”. Herein, an ingenious ratio fluorescent test strip was successfully prepared based on the polyvinylidene fluoride (PVDF) hydrophobic membrane for rapid, selective and off-line visual detection of 4-nitrophenol (4-NP). Interestingly, two surprising discoveries were made: one was, strategically used, the green-emission fluorescence polydopamine (F-PDA) function as both fluorescence functional monomer and response signal during the synthesis of ratiometric fluorescent probe (CdTe@SiO2@F-PDA/MIPs), which greatly shortened the detection time of 4-NP. Another was, the PVDF membrane with hydrophobicity and colorless fluorescent background effectively overcame the “coffee ring effect” and fluorescence background interference. Compared with other hydrophobic membranes, the PVDF hydrophobic membrane was rough enough to load more CdTe@SiO2@F-PDA/MIPs, which provided more recognition sites for 4-NP detection. Finally, the PVDF fluorescent test strip exhibited a much lower detection limit of 2.77 nM within 1.5 min (90 s). The visual detection comparison experiment showed that the color changes of the PVDF fluorescent test strip to 4-NP were lightly identifiable and more obvious than that with a traditional filter paper. Moreover, the detection reliability of CdTe@SiO2@F-PDA/MIPs was verified by the comparison experiment of high-performance liquid chromatography (HPLC), and satisfactory results were ultimately obtained.

Many business situations can be called “games” because outcomes depend on multiple decision makers with differing objectives. Yet, in many cases, the payoffs for all combinations of player options are not available, but the ability to experiment off-line is available. For example, war-gaming exercises, test marketing, cyber-range activities, and many types of simulations can all be viewed as off-line gaming-related experimentation. We address the decision problem of planning and analyzing off-line experimentation for games with an initial procedure seeking to minimize the errors in payoff estimates. Then, we provide a sequential algorithm with reduced selections from option combinations that are irrelevant to evaluating candidate Nash, correlated, cumulative prospect theory or other equilibria. We also provide an efficient formula to estimate the chance that given Nash equilibria exists, provide convergence guarantees relating to general equilibria, and provide a stopping criterion called the estimated expected value of perfect off-line information (EEVPOI). The EEVPOI is based on bounded gains in expected utility from further off-line experimentation. An example of using a simulation model to illustrate all the proposed methods is provided based on a cyber security capture-the-flag game. The example demonstrates that the proposed methods enable substantial reductions in both the number of test runs (half) compared with a full factorial and the computational time for the stopping criterion.

Pasak bumi (Eurycoma longifolia Jack.) is a species belonging Simarobuaceae hasbeen long used as traditional medicine, which known very well as aphrosidiac. Thestudies have been conducted to reveal the benefits of E. longifolia throughethnobotany, phyto- chemical, and bioessay approaches, but the review articles on theits utilization and bioactivity limited. This article is based on scien- tific articlespublished on line or off line, then synthesized so that to be information the relationshipbetween use and bioactivity. Ethno- botanyof E. longifolia have been used as amedicine for fever, ma- laria, improve stamina, diabetic, cancer, aphrodisiac, erectiledys- function. Bioactivity of E. longifolia is antiosteoporotic, antimicro- bial,aphrodisiac, anticancer, angiogenesis, and hepatoprotective. Eurycomanone issecondary metabolites in E, longifolia has activi- ty as an antimalaria, antipyretic,aphrodisiac, and cytotoxic. The quassinoids, coumarin and glycosides of El haveactivity increase the production, quality, totality, synthesis and release of spermato-zoa. Eurycoma longifolia is very potential to be developed as an antiosteoporotic andaprosidiac drug, but until now most of it is harvested directly from the forest, so topreserve it, we need to study the cultivation method.

Introduction The minor phenolic constituents ofCyclopia pubescensEckl. & Zeyh. are unknown and one dimensional (1D) liquid chromatography (LC) is unable to provide sufficient separation. Methodology A two-dimensional (2D) LC method incorporating normal-phasehigh performance countercurrent chromatography (NP-HPCCC) in the first dimension (D-1) and reversed-phase ultra-high-performance liquid chromatography (RP-UHPLC) as the second dimension (D-2) was developed. The analytical HPCCC method was subsequently scaled up to semi-preparative mode and fractions pooled based on phenolic sub-groups. The phenolic compounds in selected fractions were subsequently isolated using RP-HPLC on a C(18)column. Isolated compounds were identified by nuclear magnetic resonance (NMR) spectroscopy. The absolute configurations of compounds were determined by optical rotation and electronic circular dichroism spectra. Sugars were identified by gas chromatography-mass spectrometry (GC-MS) analysis. Results The comprehensive off-line 2D CCC x LC method gave a good spread of the phenolic compounds. Orthogonality calculated using both the convex hull and conditional entropy methods were 81%. High-resolution mass spectrometric fragmentation spectra obtained from a quadrupole-time-of-flight instrument and ultraviolet-visible (UV-vis) spectral data were used to (tentatively) identify 32 phenolic compounds from the analytical CCC fractions. Of the seven isolated compounds, (2S)-5-O-[alpha-l-rhamnopyranosyl-(1 -> 2)-beta-d-glucopyranosyl]eriodictyol (3) and (2S)-5-O-[alpha-l-rhamnopyranosyl-(1 -> 2)-beta-d-glucopyranosyl]-5,7,3 ',4 '-tetrahydroxyflavan (4) were newly identified in all plants. The other isolated compounds were identified as (2S)-5-O-[alpha-l-rhamnopyranosyl-(1 -> 2)-beta-d-glucopyranosyl]naringenin (1),R-neo-eriocitrin (2), 3-O-alpha-l-arabinopyranosyl-3,4-dihydroxybenzoic acid (5), 4-O-beta-d-glucopyranosyl-Z-4-hydroxycinnamic acid (6) and 4-(4 '-O-beta-d-glucopyranosyl-4 '-hydroxy-3 '-methoxyphenyl)-2-butanone (7). Conclusions Among the 32 compounds (tentatively) identified, only six were previously identified inCyclopia pubescensusing 1D LC. Most of the isolated compounds were also identified for the first time inCyclopiaspp., improving the knowledge of the minor phenolic compounds of this genus.

Background: Abuse of drugs is associated with several medical, forensic, toxicology and social challenges. “Drugs of abuse” testing is therefore an important issue. Objective: We propose a simple CE-based method for the quantification of amphetamine, codeine and morphine after direct injection of Exhaled Breath Condensate (EBC) by the aid of simple stacking mode and an off-line pre-concentration method. Methods: Using graphene oxide adsorbents, amphetamine, codeine and morphine were extracted from EBC in order to eliminate the proteins and other interferences. In addition to off-line method, an online stacking mode was applied to improve the analytical signal obtained from the instrument. Results: The validation parameters were experimented on the developed method based on the FDA guideline over concentration ranges of 12.5-100, 30-500 and 10-1250 ng/mL associated with amphetamine, codeine and morphine, respectively. Small volumes (around 100 μL) of EBC were collected using a lab-made setup and successfully analyzed using the proposed method where precisions and accuracies (within day and between days) were in accordance with the guideline (recommended less than 15 % for biological samples). The recovery tests were used to evaluate the matrix effect and data (94 to 105 %) showed that the proposed method can be applied in different EBC matrix samplings of subjects. Conclusion: The proposed method is superior for simultaneous determination of amphetamine, codeine and morphine over chromatographic analyses because it is fast and consumes fewer chemicals, with no derivatization step.

Dataset shifts are present in many real-world applications, since data generation is not always fully controlled and is subject to noise, degradation, and other natural variations. In machine learning, the lack of regularity in data can degrade performance by breaching error constraints. Different methods have been proposed to solve shifting problems; however, shifts in off-line learning mode are not as well examined. Off-line shifts consist of problems where drifts occur only with unlabeled data. Most methods aimed at dataset shifts consider that new labeled data can be received after training, which is not always the case. Here, a review on dataset shift characteristics and causes is presented as a tool for the analysis and implementation of machine learning methods targeting off-line mode dataset shift problems. In this context, a relationship between statistical learning risk functions and error degradation due to variation in data distribution was straightforwardly derived. Moreover, this paper provides a consistent survey of recent popular machine learning methods that address off-line mode dataset shift problems, focusing on the main characteristics of unlabeled data shifts.

The research proposes an efficient high-accuracy SVM-based handwritten Chinese character recognition system. The 304-dimensional feature vector of a handwritten Chinese character consists of the co...

In this paper, a novel off-line output feedback model predictive control strategy for linear parameter varying model is presented. This approach avoids the on-line optimization of the control law parameters, and simplifies the online computation mainly to refreshing the true state bounds (TSBs). In two previous works, the off-line output feedback model predictive control with ellipsoidal real-time TSB, and the on-line one with general real-time TSBs (both ellipsoidal and polyhedral), have been proposed. In this paper, the merits of both previous works are merged, so that the control performance can be greatly improved, with moderate increase of computational burden. Appropriate modifications in both on-line and off-line stages of the off-line MPC are taken, so that the closed-loop system is shown to be convergent. The effectiveness of the proposed approach is verified through numerical examples.

Communication through social networking platforms is central to the social experience of today’s emerging adults. In this study, we examined associations between emerging adults’ off-line social functioning (i.e., social skills and social anxiety) and online interactions on the social networking platform Facebook. A novel Facebook application was developed to systematically capture Facebook interactions of 92 students (59% female; Mage = 18.57 years). Trained coders rated online language for content related to social interactivity and emotional valence. Self-report questionnaires measured social skills and social anxiety. Endorsement of higher social skills was associated with posts more frequently referencing existing off-line relationships, initiating interactions specific to an online context, and referencing both oneself and others positively. Ratings of higher social anxiety were associated with posts more frequently initiating interactions online and posts praising/supporting other users. Study methodology highlights the feasibility of capitalizing on novel technology to better understand links between off-line and online interactions.

Quadcopter is an important way for the human to explore the physical world. The brain-computer interface (BCI) technology is used to control the quadcopter flight in order to help disabled persons communicate with the external world freely. In this study, a quadcopter control system using a hybrid BCI based on off-line optimization and enhanced human-machine interaction was designed to control the quadcopter flight in 3D physical space. The proposed system implemented the control of quadcopter moving up/down, forward/backward, left/right by six different SSVEP, and turning left/right by left-hand and right-hand motor imagery. Meanwhile, the optimization of the control system and the human-machine interaction enhancement improved practicability in real-time use. Five subjects participated in an on-line experiment to control the quadcopter flight in real-time. The average classification accuracy of EEG-based commands in the on-line experiment was 87.09±2.82% and information transfer rate (ITR) was 0.857±0.085 bits/min. The results demonstrated the feasibility of multidirectional control of quadcopter flight in 3D space by using hybrid BCI technology and revealed the practicality and operability of the hybrid BCI control system based on off-line optimization and human-machine interaction enhancement.

Abstract writing is a very important since it is the piece of formal writing which should attract the attention of the reader and make him/her read the rest of the paper. Thus, it must be clear, concise, coherent, and powerful enough to attract the reader and provide him/her with the key information about the content of a research paper. All university students have to write an abstract in English in their qualification papers. However, non-native students of English usually have difficulties in writing an abstract in English. For example, the Czech students lack formal writing skills in English since the skill of formal writing is not developed enough in their native language, i.e. Czech. Therefore, the aim of this article is to discuss the use of an off-line scaffolding tool, which can help students develop an abstract in English, as well as to demonstrate how this tool can be incorporated in the whole Course of Practical English taught at the Faculty of Informatics and Management of the University of Hradec Kralove in the Czech Republic. The application was developed for students as support to a face-to-face class instruction, which proved to be insufficient in teaching students how to write an abstract in English. The application reflects the structure and sentence functions of the abstract and provides students with a model abstract, as well as with additional formulations. Overall, the application was designed with the purpose to enhance student’s skill of writing an abstract in English, as well as to lead students to successfully complete the task by making the application user-friendly - easy-to-use, accessible and transferrable as far as the knowledge is concerned. The application aims to enhance students’ autonomous learning by tailoring the content to their specific needs and making it accessible anytime and from anywhere.

In this work, we propose the use of Siamese Neural Networks to help solve the off-line handwritten signature verification problem with random forgeries in a writer-independent context. Our proposed solution can be used on new signers without the need for any additional training. Also, we have analyzed three types of synthetic data to increase the amount of samples and the variability needed for training deep neural networks: augmented data samples from GAVAB dataset, a proposal of compositional synthetic signature generation from shape primitives and the GPDSSynthetic dataset. The first two approaches are “on-demand” generators and they can be used during the training stage to produce a potentially infinite number of synthetic signatures. In our approach, we initially trained Siamese Neural Networks using signatures from GAVAB dataset and different combinations of synthetic data. The best verification results were obtained when combining original and synthetic signatures for training. Additionally, we tested our approach on the GPSSynthetic, MCYT, SigComp11 and CEDAR datasets demonstrating the generalization capabilities of our proposal.

Solid-state disk (SSD) deduplication refers to the identification and deletion of duplicate data stored in an SSD. The reliability of SSDs is improved by deduplication. At present, the common data deduplication of SSDs is based on online data deduplication with Field Programmable Gate Array (FPGA) acceleration. The disadvantage is that FPGA, which has a complex structure. An off-line deduplication method for the SSD based on hot and cold data was proposed in this study to simplify the structure of an SSD deduplication, reduce the cost, and improve the efficiency of deduplication and access performance of SSDs. First, the wear-leveling algorithm was employed in the SSD to divide the data into cold and hot. Then, the corresponding fingerprint was generated for the cold data. Second, the fingerprint was compared, and the cold data with the same fingerprint were deleted. Finally, the cold and hot data were exchanged after deduplication. Results demonstrate that the duplicate recognition rate of the proposed method is 5% - 38%, which is close to that of the online deduplication method. In terms of access performance, the performance of SSDs using the proposed method is improved by 20% compared with that of traditional SSDs and is near the access performance of SSDs using online deduplication. This study provides certain reference for improving the reliability of existing SSDs.

Several machine learning and deep learning frameworks have been proposed to solve remaining useful life estimation and failure prediction problems in recent years. Having access to the remaining useful estimation or the likelihood of failure in the near future help operators to assess the operating conditions and, therefore, making better repair and maintenance decisions. However, many operators believe remaining useful life estimation and failure prediction solutions are incomplete answers to the maintenance challenge. They would argue that knowing the likelihood of failure in a given time interval or having access to an estimation of the remaining useful life are not enough to make maintenance decisions which minimize the cost while keeping them safe. In this paper, we present a maintenance framework based on off-line deep reinforcement learning which instead of providing information such as likelihood of failure, suggests actions such as “continue the operation” or “visit a repair shop” to the operators in order to maximize the overall profit. Using off-line reinforcement learning makes it possible to learn the optimum maintenance policy from historical data without relying on expensive simulators. We demonstrate the application of our solution in a case study using NASA C-MAPSS dataset.

BACKGROUND The off-line extracorporeal photopheresis (ECP) procedure requires photosensitization in an external cell therapy laboratory as per the French regulatory requirement. This regulation results in higher time and costs compared with the on-line alternative performed entirely at the patient's bedside. Recently, full in situ execution of the off-line procedure has been implemented in the Pitie-Salpetriere Hospital Hemobiotherapy Department (Paris, France). This report summarizes the center's experience regarding the organizational and costs impacts of this change compared with the on-line procedure. MATERIAL AND METHODS ECP was broken down into stages, and several procedures were monitored prospectively in real-life settings. The total costs associated with both procedures were the sum of the fixed costs and variable costs related to all stages of the procedures, nursing-time costs, property costs, and patient-related production loss costs. RESULTS Eight off-line ECP and fourteen on-line ECP procedures were monitored during five consecutive days. Procedure duration was not different (median 137.5 vs 154.0 minutes, P = .29). Times and costs associated with nursing were higher but offset by lower fixed costs of the off-line ECP. Total direct costs per procedure associated with using the off-line ECP were significantly lower than those of the on-line procedure (459.6 ± 7.1 EUR vs 953.8 ± 6.5 EUR; P = .0002). Similar results were observed when including the costs of patient production loss. CONCLUSIONS As a competitive time procedure, the in situ off-line method proved to be cost-efficient by effectively offering similar patient treatment per year compared with the on-line procedure.

En la actualidad es muy importante para las marcas poseer una buena presencia on-line, que funcione en sinergia con sus plazas físicas, estrategias offline, sus ideales y objetivos, para poder construir una comunidad de marca fuerte que les permita convertirse en una lovemark para sus consumidores, Para muchas marcas ha sido difícil establecer esa comunicación con sus consumidores mientras que otras han logrado construir grandes comunidades de marca que giran en torno a la empresa y sus productos, llegando incluso a permitir una retroalimentación para la marca. Hoy es un gran desafío y una gran necesidad para las marcas desarrollar estrategias integrales que les permitan vincular todos sus canales de comunicación de forma efectiva, sumado al hecho de que la nueva normalidad que trajo consigo la pandemia ha impulsado a emprendedores a buscar la mejor forma de vender sin descuidar sus canales físicos de comunicación, da a entender que este panorama deja a las empresas con 2 opciones o cerrar o reinventarse. De acuerdo a lo anterior esta investigación pretende comprender como marcas exitosas han desarrollado estrategias eficaces que les han permitido posicionarse en la mente de sus seguidores, y convertirse en una lovemark, para esto se aborda el ejemplo de la marca Mattelsa, la cual ha conseguido a través de los años convertirse en una de las marcas con mayor crecimiento del país. Realizando un análisis al histórico de Mattelsa, y su entorno Off line y On line se busca responder la pregunta de ¿Qué acciones de lovemark genera la marca de ropa Mattelsa en la generación centennials a través de la experiencia On line y Off line? El presente trabajo brinda la oportunidad de conocer la experiencia de Mattelsa la cual siendo una empresa joven encontró una fórmula ideal de un ecosistema mediático que trabaja en sinergia los elementos off line y on line, este conocimiento permite a actuales y futuros publicistas y emprendedores conocer algunas estrategias efectivas que la empresa emplea para cautivar a su público objetivo y convertirse en una lovemark para ellos. Currently it is very important for brands to have a good online presence, which works in synergy with their physical places, offline strategies, their ideals and objectives, in order to build a strong brand community that allows them to become a lovemark for your consumers, For many brands it has been difficult to establish this communication with their consumers, while others have managed to build large brand communities that revolve around the company and its products, even allowing feedback for the brand. Today it is a great challenge and a great need for brands to sell without neglecting their physical communication channels, it makes sense that this panorama leaves companies with 2 options: close or reinvent themselves. According to the above, this research aims to understand how successful brands have developed effective strategies that have allowed them to position themselves in the minds of their followers, and become a lovemark, for this the example of the Mattelsa brand is addressed, which has managed to over the years to become one of the fastest growing brands in the country. By analyzing the history of Mattelsa, and its Off line and On line environment, we seek to answer the question of What lovemark actions does the Mattelsa clothing brand generate in the centennial generation through the On line and Off line experience? This work provides the opportunity to learn about Mattelsa experience, which being a young company found an ideal formula of a media ecosystem that works in synergy with offline and online elements, this knowledge allows current and future advertisers and entrepreneurs to know some effective strategies that the company employs to captivate its target audience and become a lovemark for them Proyecto de grado (Publicista)-- Universidad Autónoma de Occidente, 2022 Pregrado Publicista

In two experiments, we investigated the correspondences between off-line word segmentation and on-line segmentation processing during Chinese reading. In Experiment 1, participants were asked to read sentences which contained critical four-character strings, and then, they were required to segment the same sentences into words in a later off-line word segmentation task. For each item, participants were split into 1-word segmenters (who segmented four-character strings as a single word) and 2-word segmenters (who segmented four-character strings as 2 two-character words). Thus, we split participants into two groups (1-word segmenters and 2-word segmenters) according to their off-line segmentation bias. The data analysis showed no reliable group effect on all the measures. In order to avoid the heterogeneity of participants and stimuli in Experiment 1, two groups of participants (1-word segmenters and 2-word segmenters) and three types of critical four-character string (1-word strings, ambiguous strings, and 2-word strings) were identified in a norming study in Experiment 2. Participants were required to read sentences containing these critical strings. There was no reliable group effect in Experiment 2, as was the case in Experiment 1. However, in Experiment 2, participants spent less time and made fewer fixations on 1-word strings compared to ambiguous and 2-word strings. These results indicate that the off-line word segmentation preferences do not necessarily reflect on-line word segmentation processing during Chinese reading and that Chinese readers exhibit flexibility such that word, or multiple constituent, segmentation commitments are made on-line.

Neuro-evolution is an appealing approach to generating collective behaviors for robot swarms. In its typical application, known as off-line automatic design, the neural networks controlling the robots are optimized in simulation. It is understood that the so-called reality gap, the unavoidable differences between simulation and reality, typically causes neural network to be less effective on real robots than what is predicted by simulation. In this paper, we present an empirical study on the extent to which the reality gap impacts the most popular and advanced neuro-evolutionary methods for the off-line design of robot swarms. The results show that the neural networks produced by the methods under analysis performed well in simulation, but not in real-robot experiments. Further, the ranking that could be observed in simulation between the methods eventually disappeared. We find compelling evidence that real-robot experiments are needed to reliably assess the performance of neuro-evolutionary methods and that the robustness to the reality gap is the main issue to be addressed to advance the application of neuro-evolution to robot swarms., info:eu-repo/semantics/published