A conceptual neural model for business selection in multi business unit firms

Is it possible to transform stone into a technological and innovative device? The meeting with one of the main stone transformers in Europe produced the intention of a disruptive operation that could affect the strategy of the whole company. A contagious singularity. By intertwining LEAN methodologies and the human-centric approach of design thinking, we mapped the value creation in the company activating a dialogue with the workers and the management, listening to people, asking for ambitions, discovering problems and the potential of production. This qualitative and quantitative analysis conducted with a multidisciplinary approach by designers, architects and marketing strategists allowed us to define a new method. We used it to design a platform that could let all the players express their potential to the maximum. This is how the group's research laboratory was born, with the aim of promoting the relationship between humans and stone through product innovation. With this goal, we coordinated the new team, developing technologies that would allow creating a more direct relationship between man and surface, making the stone reactive. The result was the first responsive kitchen ever.

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Stochastic stability of a neural model for binocular rivalry

IEICE Proceeding Series ◽

10.15248/proc.1.739 ◽

2014 ◽

Vol 1 ◽

pp. 739-742

Author(s):

Tetsuya Shimokawa ◽

Kenji Leibnitz ◽

Ferdinand Peper

Keyword(s):

Binocular Rivalry ◽

Stochastic Stability ◽

Neural Model

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Business Development Independent Populist Economic Owned Enterprises as a Village

International Journal of Contemporary Research and Review ◽

10.15520/ijcrr/2018/9/06/520 ◽

2018 ◽

Vol 9 (06) ◽

pp. 20792-20799

Author(s):

I Ketut Sunada ◽

I Ketut Sandi

Keyword(s):

Human Resources ◽

Communication Networks ◽

Added Value ◽

Business Unit ◽

Management Support ◽

Economic Networks ◽

Bargaining Position ◽

Economic Infrastructure ◽

Economic Assets ◽

The Village

Bumdes is a village business unit whose capital comes from village-owned funds . The goal is to float the village potential into an independent village. Thus it takes effort and strategic and appropriate tips to achieve that goal. The first tip is to establish a forum for the development of economic networks in this case is the establishment of BUMDES. Some of the things that can be done are: (i) development of human resources capability so as to provide added value in the management of village economic assets, (ii) integrating rural economic products so as to have good bargaining position in market network, (iii) (iv) strengthening village economic institutions, (v) developing supporting elements such as micro-credit, market information, technological and management support, economic infrastructure and communication networks as well as support for guidance and regulation.

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A neural model for the anxiolytic effects of deep brain stimulation in patients with obsessive compulsive disorder

10.26226/morressier.5971be86d462b80290b5284a ◽

2017 ◽

Author(s):

Egill Axfjord Fridgeirsson

Keyword(s):

Deep Brain Stimulation ◽

Obsessive Compulsive Disorder ◽

Brain Stimulation ◽

Neural Model ◽

Obsessive Compulsive ◽

Compulsive Disorder ◽

Deep Brain

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Oil Saturation Log Prediction Using Neural Network in New Steamflood Area

Proc. Indon. Petrol. Assoc., Digital Technical Conference, 2020 ◽

10.29118/ipa20-g-307 ◽

2020 ◽

Author(s):

A. Syahputra

Keyword(s):

Neural Network ◽

Water Saturation ◽

Prediction Method ◽

Neural Model ◽

Training Model ◽

Acquisition Time ◽

Oil Saturation ◽

Full Field ◽

Remaining Oil ◽

Observation Wells

Surveillance is very important in managing a steamflood project. On the current surveillance plan, Temperature and steam ID logs are acquired on observation wells at least every year while CO log (oil saturation log or SO log) every 3 years. Based on those surveillance logs, a dynamic full field reservoir model is updated quarterly. Typically, a high depletion rate happens in a new steamflood area as a function of drainage activities and steamflood injection. Due to different acquisition time, there is a possibility of misalignment or information gaps between remaining oil maps (ie: net pay, average oil saturation or hydrocarbon pore thickness map) with steam chest map, for example a case of high remaining oil on high steam saturation interval. The methodology that is used to predict oil saturation log is neural network. In this neural network method, open hole observation wells logs (static reservoir log) such as vshale, porosity, water saturation effective, and pay non pay interval), dynamic reservoir logs as temperature, steam saturation, oil saturation, and acquisition time are used as input. A study case of a new steamflood area with 16 patterns of single reservoir target used 6 active observation wells and 15 complete logs sets (temperature, steam ID, and CO log), 19 incomplete logs sets (only temperature and steam ID) since 2014 to 2019. Those data were divided as follows ~80% of completed log set data for neural network training model and ~20% of completed log set data for testing the model. As the result of neural model testing, R2 is score 0.86 with RMS 5% oil saturation. In this testing step, oil saturation log prediction is compared to actual data. Only minor data that shows different oil saturation value and overall shape of oil saturation logs are match. This neural network model is then used for oil saturation log prediction in 19 incomplete log set. The oil saturation log prediction method can fill the gap of data to better describe the depletion process in a new steamflood area. This method also helps to align steam map and remaining oil to support reservoir management in a steamflood project.

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Toward a Neural Model of the Openness-Psychoticism Dimension: Functional Connectivity in the Default and Frontoparietal Control Networks

10.31234/osf.io/7uzmj ◽

2019 ◽

Author(s):

Scott D. Blain ◽

Rachael Grazioplene ◽

Yizhou Ma ◽

Colin G. DeYoung

Keyword(s):

Functional Connectivity ◽

Structural Equation ◽

Psychotic Disorders ◽

Neural Model ◽

Equation Modeling ◽

Control Networks ◽

Psychosis Proneness ◽

Human Connectome Project ◽

Intrinsic Connectivity Networks ◽

Personality Psychopathology

Psychosis proneness has been linked to heightened Openness to Experience and to cognitive deficits. Openness and psychotic disorders are associated with the default and frontoparietal networks, and the latter network is also robustly associated with intelligence. We tested the hypothesis that functional connectivity of the default and frontoparietal networks is a neural correlate of the openness-psychoticism dimension. Participants in the Human Connectome Project (N = 1003) completed measures of psychoticism, openness, and intelligence. Resting state functional magnetic resonance imaging was used to identify intrinsic connectivity networks. Structural equation modeling revealed relations among personality, intelligence, and network coherence. Psychoticism, openness, and especially their shared variance, were related positively to default network coherence and negatively to frontoparietal coherence. These associations remained after controlling for intelligence. Intelligence was positively related to frontoparietal coherence. Research suggests psychoticism and openness are linked in part through their association with connectivity in networks involving experiential simulation and cognitive control. We propose a model of psychosis risk that highlights roles of the default and frontoparietal networks. Findings echo research on functional connectivity in psychosis patients, suggesting shared mechanisms across the personality-psychopathology continuum.

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Hybrid Deep Neural Model for Duplicate Question Detection in Transliterated Bi-lingual Data

Recent Patents on Computer Science ◽

10.2174/2213275912666190710152709 ◽

2019 ◽

Vol 12 ◽

Author(s):

Seema Rani ◽

Avadhesh Kumar ◽

Naresh Kumar

Keyword(s):

Question Answering ◽

Neural Model ◽

Manhattan Distance ◽

Semantic Matching ◽

English Only ◽

Detection Model ◽

Proposed Model ◽

Reported Study ◽

Social Media Platforms ◽

Efficient Information

Background: Duplicate content often corrupts the filtering mechanism in online question answering. Moreover, as users are usually more comfortable conversing in their native language questions, transliteration adds to the challenges in detecting duplicate questions. This compromises with the response time and increases the answer overload. Thus, it has now become crucial to build clever, intelligent and semantic filters which semantically match linguistically disparate questions. Objective: Most of the research on duplicate question detection has been done on mono-lingual, majorly English Q&A platforms. The aim is to build a model which extends the cognitive capabilities of machines to interpret, comprehend and learn features for semantic matching in transliterated bi-lingual Hinglish (Hindi + English) data acquired from different Q&A platforms. Method: In the proposed DQDHinglish (Duplicate Question Detection) Model, firstly language transformation (transliteration & translation) is done to convert the bi-lingual transliterated question into a mono-lingual English only text. Next a hybrid of Siamese neural network containing two identical Long-term-Short-memory (LSTM) models and Multi-layer perceptron network is proposed to detect semantically similar question pairs. Manhattan distance function is used as the similarity measure. Result: A dataset was prepared by scrapping 100 question pairs from various social media platforms, such as Quora and TripAdvisor. The performance of the proposed model on the basis of accuracy and F-score. The proposed DQDHinglish achieves a validation accuracy of 82.40%. Conclusion: A deep neural model was introduced to find semantic match between English question and a Hinglish (Hindi + English) question such that similar intent questions can be combined to enable fast and efficient information processing and delivery. A dataset was created and the proposed model was evaluated on the basis of performance accuracy. To the best of our knowledge, this work is the first reported study on transliterated Hinglish semantic question matching.

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Who Should Select New Employees, Headquarters or the Unit Manager? Consequences of Centralizing Hiring at a Retail Chain

The Accounting Review ◽

10.2308/accr-52611 ◽

2019 ◽

Vol 95 (4) ◽

pp. 173-198 ◽

Cited By ~ 1

Author(s):

Carolyn Deller ◽

Tatiana Sandino

Keyword(s):

Data Availability ◽

Business Unit ◽

Difference In Differences ◽

Decision Rights ◽

Local Conditions ◽

New Employees ◽

Retail Chain ◽

Unit Manager ◽

Local Advantage ◽

Retail Company

ABSTRACT We examine how changing the allocation of hiring decision rights in a multiunit organization affects employee-firm match quality, contingent on a unit's circumstances. Our research site, a U.S. retail chain, switched from a decentralized hiring model (hiring by business unit managers—in our case, store managers) to centralized hiring (in this study, by the head office). While centralized hiring can ensure that enough resources are invested in hiring people aligned with company values, it can also neglect the unit managers' local knowledge. Using difference-in-differences analyses, we find that the switch is associated with relatively higher employee departure rates and, thus, poorer matches if the business unit manager has a local advantage; that is, if the store serves repeat customers, serves a demographically atypical market, or poses higher information-gathering costs for headquarters. In these cases, the unit manager may be more informed than headquarters about which candidates best match local conditions. Data Availability: The analyses presented in this study are based on data shared by a U.S. retail company. The data are confidential, according to a nondisclosure agreement between the company and the authors.

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Quality Evaluation of Potato Tubers Using Neural Image Analysis Method

Agriculture ◽

10.3390/agriculture10040112 ◽

2020 ◽

Vol 10 (4) ◽

pp. 112 ◽

Cited By ~ 1

Author(s):

Andrzej Przybylak ◽

Radosław Kozłowski ◽

Ewa Osuch ◽

Andrzej Osuch ◽

Piotr Rybacki ◽

...

Keyword(s):

Image Analysis ◽

Neural Model ◽

Assessment Method ◽

Analysis Method ◽

Potato Tubers ◽

Specialized Knowledge ◽

Analysis Techniques ◽

Quality Assessment Method ◽

Sorting Process ◽

Image Analysis Techniques

This paper describes the research aimed at developing an effective quality assessment method for potato tubers using neural image analysis techniques. Nowadays, the methods used to identify damage and diseases are time-consuming, require specialized knowledge, and often rely on subjective judgment. This study showed the use of the developed neural model as a tool supporting the evaluation of potato tubers during the sorting process in the storage room.

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