General structure and characteristics of quick response production system

Phenotypic relationships between maternal energy metabolism and lamb body weight development during lactation for pure- and crossbred sheep populations in low and high input production systems

In the signaling plane of an IMS (IP Multimedia Subsystem) network, the interrogating Call/Session Control Function (I-CSCF) is required to choose the serving CSCF (S-CSCF) for user requests arriving at the proxy CSCFs (P-CSCF). This requires S-CSCF selection in such a way that the response time is minimized. However, in an overloaded situation, the P-CSCF entry points for arriving requests must balance between rejecting new requests and having an acceptable response time from S-CSCF for those that are allowed to enter. In this setting, we consider a number of controls for congestion avoidance that work in conjunction with S-CSCF selection in an IMS Network. For balancing between rejection of requests and the response time, we also propose a composite metric. Through our studies on grid topologies, we observe that one of the avoidance algorithms, the bang-bang control, generally shows to be the most promising toward striking a good balance between rejection of requests and the response time.

Formal model for assigning human resources to teams in software projects

CMOS-compatible all-optical logic gates based on optical bistability are designed and numerically characterized. For this, graphene and H-BN-based hybrid plasmonic microdisk/waveguide structures have been used to achieve optical bistability at very low threshold power and with small dimensions. The simulation results and coupled-mode theory calculations show that, by adjusting the radius of the microdisk resonator, the threshold power and response time of the optical bistability can be tuned in a wide range. It is shown that bistable devices with overall dimensions of 2µm×2.2µm can easily be designed having either threshold powers as low as 0.79 µW (microdisk radius of 0.92 µm) or very short fall time and rise times of 1.24 and 1.53 ps (microdisk radius of 0.93 µm). The design procedure for the AND, NAND, OR, NOR, and NOT logic gates is discussed. Simulation results show that the proposed logic gates have much smaller footprints, lower power consumption, and higher speeds with acceptable response time, compared with the previously reported structures.

High performance medical image processing in client/server-environments

Electron Bombardment Induced Conductivity Including its Application to Ultra-violet Imaging in the Schuman Region

Aim. This paper presents the amelioration of maximum power point tracking using fuzzy logic methods for photovoltaic system supplying a standalone system. Method. The main role of the maximum power tracking is to force the system for working at the maximum point for each change of meteorological conditions. The classic technique Perturb and Observe is more attractive due to its simple and high efficiency. Sliding mode is a non-linear control technique; characterised by robustness against the parameters change or disturbances, it gives a good maximum power operation under different conditions such as changing solar radiation and photovoltaic cell temperature. Novelty. Fuzzy logic tracking technique is treated. Fuzzy rules construction is based on Perturb and Observe behaviour when the appropriate disturbance step is produced in order to obtain a fast system with an acceptable precision. We use in our study 60 W photovoltaic panel associated to boost chopper converter in order to supply a standalone system. Results. As show in results figures using fuzzy maximum power point tracking the ameliorate performances especially the very low oscillation rate (nearly 0.6 W), and very acceptable response time 0.1 s.

Brain-Computer Interface (BCI) systems are traditionally designed by taking into account user-specific data to enable practical use. More recently, subject independent (SI) classification algorithms have been developed which bypass the subject specific adaptation and enable rapid use of the system. A brain switch is a particular BCI system where the system is required to distinguish from two separate mental tasks corresponding to the on-off commands of a switch. Such applications require a low false positive rate (FPR) while having an acceptable response time (RT) until the switch is activated. In this work, we develop a methodology that produces optimal brain switch behavior through subject specific (SS) adaptation of: a) a multitrial prediction combination model and b) an SI classification model. We propose a statistical model of combining classifier predictions that enables optimal FPR calibration through a short calibration session. We trained an SI classifier on a training synchronous dataset and tested our method on separate holdout synchronous and asynchronous brain switch experiments. Although our SI model obtained similar performance between training and holdout datasets, 86% and 85% for the synchronous and 69% and 66% for the asynchronous the between subject FPR and TPR variability was high (up to 62%). The short calibration session was then employed to alleviate that problem and provide decision thresholds that achieve when possible a target FPR=1% with good accuracy for both datasets.

This paper presents a web service named MAGIIC-PRO, which aims to discover functional signatures of a query protein by sequential pattern mining. Automatic discovery of patterns from unaligned biological sequences is an important problem in molecular biology. MAGIIC-PRO is different from several previously established methods performing similar tasks in two major ways. The first remarkable feature of MAGIIC-PRO is its efficiency in delivering long patterns. With incorporating a new type of gap constraints and some of the state-of-the-art data mining techniques, MAGIIC-PRO usually identifies satisfied patterns within an acceptable response time. The efficiency of MAGIIC-PRO enables the users to quickly discover functional signatures of which the residues are not from only one region of the protein sequences or are only conserved in few members of a protein family. The second remarkable feature of MAGIIC-PRO is its effort in refining the mining results. Considering large flexible gaps improves the completeness of the derived functional signatures. The users can be directly guided to the patterns with as many blocks as that are conserved simultaneously. In this paper, we show by experiments that MAGIIC-PRO is efficient and effective in identifying ligand-binding sites and hot regions in protein–protein interactions directly from sequences. The web service is available at and a mirror site at .

許容応答時間が存在する場合の多段階生産・在庫システムの解析 : 製品間で加工時間が異なる場合

The opportunity to learn from the best educators of the top universities at no cost has captured the imagination of millions who have registered for MOOCs across the United States and abroad. As MOOCs have gained momentum, critical debate is brewing on the pedagogical effectiveness of MOOCs. The high attrition rate of students who register at the beginning of a MOOC is a major cause of concern regarding the long-term success, impact, and sustainability of MOOCs. Having conducted many experiments and studies on the application of adaptive learning in personalized online learning, Synaptic Global Learning set out to develop the first adaptive MOOC (aMOOC) platform, providing a strong pedagogical framework and a personalized learning experience in a MOOC learning environment. In partnership with the Center for Innovation and Excellence in eLearning of the College of Advancing and Continued Studies, University of Massachusetts Boston, Synaptic Global Learning developed the first aMOOC in the area of computational molecular dynamics (CMD). This article describes the design, development, and deployment of this aMOOC. Part I of this case-study is focused on the pedagogical framework that allows four dimensions of learning and dynamic rendering of the content to accommodate five distinct learning strategies using the adaptive mobile learning (AMOL) platform. The AMOL platform is then implemented on the Amazon Web Services (AWS) cloud architecture and load-tested for the use of aMOOCs. The results indicate that even with dynamic rendering of the fine learning strategies and real-time feedback, the AWS-based scalable architecture is sufficient to provide high throughput and acceptable response time.

In this paper we deal with an algorithm for the facility placement problem relevant to one of the most commonly used layout schemes in flexible manufacturing systems: the single-row layout problem. The objective function that we want to minimize is the backtracking. As a result we obtain a heuristic easy to implement which generates good quality solutions in acceptable response times. It turns out that, in particular cases, it outperforms an alternative algorithm recently developed to the same end.

Optical method for detecting oxygen via the chromogenic reaction catalyzed by polyphenol oxidase

An Application of Multi-parametric Programming in Integrated Circuit Automation (Slotting Problem)

The increasing demand for efficient and accessible academic services has led higher education institutions to adopt innovative digital solutions. At STMIK Mulia Darma, students often experience delays and limited access to academic information due to manual service systems and limited staff availability. To address these challenges, this research proposes the development of an academic chatbot using Natural Language Processing (NLP) to automate and enhance student services. The chatbot is designed to understand and respond to student inquiries in Bahasa Indonesia, providing real-time information related to course schedules, registration procedures, tuition deadlines, and other academic matters. By integrating NLP with the institution’s academic information system, the chatbot delivers personalized and context-aware responses. The system was developed using a rule-based NLP model enhanced with intent classification and entity recognition techniques. Testing results indicate that the chatbot successfully answered more than 90% of user queries with acceptable response time and accuracy. This solution demonstrates the potential of NLP-powered chatbots to improve service efficiency, reduce administrative workload, and support the implementation of a smart campus ecosystem.