Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of customer performance on IMC outcomes: firm size moderation in the inter-country context

PurposeTaking the customer-centric nature of integrated marketing communications (IMC), this article investigates the specific role of customer performance in IMC effectiveness in various size companies applying inter-country context.Design/methodology/approachThe sample consists of the primary data from developed (Spain) and developing (Belarus) economies. A total of 540 manager respondents participated in the survey. The article uses structural equation modeling and multi-group analysis for analysis.FindingsWhen taking into consideration, customer performance affects the IMC outcome on the market and financial performance. The customer performance role varies in firms of various sizes and small- and medium -sized enterprises (SMEs) operating both in developed and developing economies.Research limitations/implicationsThe research underlines the significant role of customer performance in IMC implementation, which stimulates further investigation on the topic. It also closes the gap in the IMC outcomes analysis in SMEs operating in developed and developing economies.Practical implicationsCustomer evaluation plays a vital role in the IMC outcomes for market growth and financial returns. SMEs and larger companies implement IMC with different levels of effectiveness. SMEs with IMC implementation can gain an advantage over larger rivals and improve their market position. Moreover, the study generalizes the results by applying inter-country context.Originality/valueThis is a pioneering study of the complex IMC outcomes model under firms' size moderate conditions. The research applies an inter-country context.
Stability Analysis and Control of Nonlinear Behavior in V 2 Switching Buck Converter

Mismatch between switching frequency and circuit parameters often occurs in industrial applications, which would lead to instability phenomena. The bifurcation behavior of V² controlled buck converter is investigated as the pulse width modulation period is varied. Nonlinear behavior is analyzed based on the monodromy matrix of the system. We observed that the stable period-1 orbit was first transformed to the period-2 bifurcation, which subsequently changed to chaos. The mechanism of the series of period-2 bifurcations shows that the characteristic eigenvalue of the monodromy matrix passes through the unit circle along the negative real axis. Resonant parametric perturbation technique has been applied to prevent the onset of instability. Meanwhile, the extended stability region of the converter is obtained. Simulation and experimental prototypes are built, and the corresponding results verify the theoretical analysis.
Effective Parameters on Fabrication and Modification of Braid Hollow Fiber Membranes: A Review

Hollow fiber membranes (HFMs) possess desired properties such as high surface area, desirable filtration efficiency, high packing density relative to other configurations. Nevertheless, they are often possible to break or damage during the high-pressure cleaning and aeration process. Recently, using the braid reinforcing as support is recommended to improve the mechanical strength of HFMs. The braid hollow fiber membrane (BHFM) is capable apply under higher pressure conditions. This review investigates the fabrication parameters and the methods for the improvement of BHFM performance.
Development of Cooling Load Demand Program for Building in Malaysia

Air conditioning is mainly to be used as human comfort medium. It has been use more often in country in which the daily temperatures are high. In scientific, air conditioning is defined as a process of controlling the moisture, cooling, heating and cleaning air. Without proper estimation of cooling load, big amount of waste energy been used because of unsuitable of air conditioning system are not considering to overcoming heat gains from surrounding. This is due to the size of the room is too big and the air conditioning has to use more energy to cool the room and the air conditioning is too small for the room. The studies are basically to develop a program to calculate cooling load. Through this study it is easy to calculate cooling load estimation. Furthermore it’s help to compare the cooling load estimation by hourly and yearly. Base on the last study that been done, the developed software are not user-friendly. For individual without proper knowledge of calculating cooling load estimation might be problem. Easy excess and user-friendly should be the main objective to design something. This program will allow cooling load able be estimate by any users rather than estimation by using rule of thumb. Several of limitation of case study is judged to sure it’s meeting to Malaysia building specification. Finally validation is done by comparison manual calculation and by developed program.
MULTIBODY DYNAMIC ANALYSIS OF MECHANISM FOR HEALTH EXCESCISE VIBRATOR

In this paper health exercise vibrator mechanism is designed for specific performance of exercise. The three dimensional conceptual model has developed first in CATIA V5. Later kinematics motions were verified using CATIA and also using ADAMS software. By using the ADAMS, the dynamic parameters of three-body vibrating mechanism are analyzed and the simple finite element model is established on the basis of these parameters in ANSYS. The main focus of this paper is to analyze behavior of mechanism in different loading conditions and also results are verified by manual equations. A good design of mechanism must be effectively. The mechanism shall not work effectively but also be reliable in its strength and durability yet not over-design. In order to optimize analysis for exercise mechanism, first we need to simulate the Multibody dynamics analysis for load and stress predictions. Therefore In this paper, we introduce methodology to simulate and analysis the whole contact range of health exercise mechanism Multibody dynamics analysis by using ADAMS, ANSYS14 and CATIA V 5.
Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction

In the processing of remotely sensed data, classification may be preceded by feature extraction, which helps in making the most informative parts of the data emerge. Effective feature extraction may boost the efficiency and accuracy of the following classification, and hence various methods have been proposed to perform it. Recently, Singular Spectrum Analysis (SSA) and its 2-D variation (2D-SSA) have emerged as popular, cutting-edge technologies for effective feature extraction in Hyperspectral Images (HSI). Using 2D-SSA, each band image of an HSI is initially decomposed into various components, and then the image is reconstructed using the most significant eigen-tuples relative to their eigen-values, which represent strong spatial features for the classification task. However, instead of performing reconstruction on the whole image, it may be more effective to apply reconstruction to object-specific spatial regions, which is the proposed objective of this research. As an HSI may cover a large area, multiple objects are generally present within a single scene. Hence, spatial information can be highlighted accurately by specializing the reconstruction based on the local context. The local context may be defined by the so-called superpixels, i.e., finite sets of pixels that constitute a homogeneous set. Each superpixel may undergo tailored reconstruction, with a process expected to perform better than non-spatially-adaptive approaches. In this paper, a Superpixel-based SSA (SP-SSA) method is proposed where the image is first segmented into multiple regions using a superpixel segmentation approach. Next, each segment is individually reconstructed using 2D-SSA. In doing so, the spatial contextual information is preserved, leading to better classifier performance. The performance of the reconstructed features is evaluated using an SVM classifier. Experiments on four popular benchmark datasets reveal that, in terms of the classification accuracy, the proposed approach overperforms the standard SSA technique and various common spatio-spectral classification methods.
The Effects of Mixture Preparation and Trapped Residuals on the Performance of a Spark-Ignition Engine with Air-Shrouded Port Injectors, at Low Load and Low Speed

This paper is concerned with the steady state combustion performance of a port-injected spark-ignition engine operating at low load and low speed. The main areas of investigation were the effects of mixture quality and trapped combustion residuals on combustion performance. The behaviour of the inlet manifold fuel film was also investigated under various engine running conditions. The effects of injection timing were quantified, both with warm and hot engine coolant. Each of the six fuel injectors on the engine was equipped with an air-shrouded injector which improved fuel-spray atomization. Tests were carried out both with and without air flowing through the shrouds. A new method is also presented which provides a measure of the size of the inlet-manifold wall film under various engine running conditions. Finally, measurements of the amount of trapped combustion residuals are presented, and their effect on combustion performance is quantified.
Optical generation of a megahertz-linewidth microwave signal using semiconductor lasers and a discriminator-aided phase-locked loop

A discriminator-aided optical phase-locked loop (OPLL) with significantly enhanced frequency acquisition capability is presented. Its pull-in range is measured to be 300 MHz and can be easily extended further. Two grating-tuned external-cavity semiconductor lasers (ECSLs) were realized with more than 30-dB side-mode suppression ratio. These two lasers were allowed to beat on a fast detector and were offset phase locked. The generated microwave signal was found to be a replica of the reference RF signal close to the carrier, The noise level was measured to be -70 dBc/Hz close to the carrier and less than -100 dBc/Hz at 4 MHz away and beyond from the carrier. The total phase variance is 0.11 rad/sup 2/ over a 500-MHz bandwidth. The linewidth full width at half maximum (FWHM) of the locked signal was directly measured to be of order 1 mHz.
Quantitative Magnetic Resonance Imaging in Perianal Crohn’s Disease at 1.5 and 3.0 T: A Feasibility Study

Perianal Crohn’s Disease (pCD) is a common manifestation of Crohn’s Disease. Absence of reliable disease measures makes disease monitoring unreliable. Qualitative MRI has been increasingly used for diagnosing and monitoring pCD and has shown potential for assessing response to treatment. Quantitative MRI sequences, such as diffusion-weighted imaging (DWI), dynamic contrast enhancement (DCE) and magnetisation transfer (MT), along with T2 relaxometry, offer opportunities to improve diagnostic capability. Quantitative MRI sequences (DWI, DCE, MT and T2) were used in a cohort of 25 pCD patients before and 12 weeks after biological therapy at two different field strengths (1.5 and 3 T). Disease activity was measured with the Perianal Crohn’s Disease Activity index (PDAI) and serum C-reactive protein (CRP). Diseased tissue areas on MRI were defined by a radiologist. A baseline model to predict outcome at 12 weeks was developed. No differences were seen in the quantitative MR measured in the diseased tissue regions from baseline to 12 weeks; however, PDAI and CRP decreased. Baseline PDAI, CRP, T2 relaxometry and surgical history were found to have a moderate ability to predict response after 12 weeks of biological treatment. Validation in larger cohorts with MRI and clinical measures are needed in order to further develop the model.
Collaborative knowledge management in organisation from SECI model framework

In the age of web collaboration and sharing, enabled by Web 2.0 and Linked Data, many organisations have adapted themselves to collaborative knowledge management and established that they gain benefits from creating, accessing, sharing, learning and reusing resources from this aspect. In order to understand knowledge transfer in organisation, the SECI model framework of Nonaka and Takeuchi has been adopted by a lot of research. However, little of the research mentions technologies like Web 2.0 and Linked Data which bring major changes to the interface and the structure of knowledge management system. Also, it is rarely found to be implemented in real-life application. Consequently, the present research aims to contribute a case of real-life application to bridge the gap between the theoretical framework of the SECI model and upcoming technologies like Web 2.0 and Linked Data.
Fall Detection by Embedding an Accelerometer in Cellphone and Using KFD Algorithm

Summary The fall is a risky event in the elderly people’s daily living, especially the independent living, it often cause serious injury both in physiology and psychology. Wearable sensor based fall detection system had been proved in many experiments for its feasibility and effectiveness, but there remain some crucial problems, include: the people maybe forget to wear the clothes with micro sensors, which device standard should be selected between medical device standard and mass market standard, and how to control the false alarm probability to fit the individualized requirements. To deal with these problems, we think it is a reasonable design to combine micro sensors with an ambulatory daily using device which has a common network interface, and adjust the classification parameters via a remote server. In this paper, we embed a tri-axial accelerometer in a cellphone, connect to Internet via the wireless channel, and using 1-Class SVM (Support Vector Machine) algorithm for the preprocessing, KFD (Kernel Fisher Discriminant) and k-NN (Nearest Neighbour) algorithm for the precise classification. And there were 32 volunteers, 12 elders (age 60-80) and 20 younger (age 20-39), attended our experiments, the results show that this method can detect the falls effectively and make less disturbance to people’s daily living than the general wearable sensor based fall detection systems.
Shrimp shells-derived biochar for efficient adsorption of Pb2+ in aqueous solutions

Oxidation modification is an important technique for the development of novel adsorbents and has attracted considerable interest in recent years. A new and efficient Pb(II) adsorbent was developed in this study using simple and cost-effective protocols. Shrimp shell biochar (SSC) was obtained by pyrolysis of abandoned shrimp shells and subsequently treated with mixed acid to obtain modified biochar (SSC-M). The structure and adsorption properties of SSC and SSC-M were investigated for Pb(II) adsorption using a variety of characterisation methods. Both SSC and SSC-M exhibited excellent aqueous Pb(II) adsorption capacities and achieved maximum adsorption capacities of 94.16 and 156.48 mg g–1 at the optimal condition, respectively. The adsorption of lead ions by SSC and SSC-M followed a pseudo-second-order kinetics model and a monolayer adsorption process. Moreover, the prominent adsorption performance of SSC-M for Pb(II) was mainly attributed to electrostatic interactions and surface complexation. SSC-M can be a promising and affordable adsorbent for Pb(II) ions owing to its significant adsorption capacity.
Parallel processes of temporal control in the supplementary motor area and the frontoparietal circuit.

Durations in the several seconds' range are cognitively accessible during active timing. Functional neuroimaging studies suggest the engagement of the basal ganglia (BG) and supplementary motor area (SMA). However, their functional relevance and arrangement remain unclear because non-timing cognitive processes temporally coincide with the active timing. To examine the potential contamination by parallel processes, we introduced a sensory control and a motor control to the duration-reproduction task. By comparing their hemodynamic functions, we decomposed the neural activities in multiple brain loci linked to different cognitive processes. Our results show a dissociation of two cortical neural circuits: the SMA for both active timing and motor preparation, followed by a prefrontal-parietal circuit related to duration working memory. We argue that these cortical processes represent duration as the content but at different levels of abstraction, while the subcortical structures, including the BG and thalamus, provide the logistic basis of timing by coordinating the temporal framework across brain structures.
MANAGING CONCURRENTLY THE PROCESSES OF KNOWLEDGE EXPLORATION AND EXPLOITATION: THE INFLUENCE OF KNOWLEDGE EXCHANGE DETERMINANTS AND THE INTRODUCTION OF AN INTRANET

In this paper we address the question how organizational factors and the introduction of an intranet influence vertical and horizontal knowledge exchange processes within a firm in terms of knowledge exploration and exploitation. To this end, we develop a conceptual framework and propositions indicating how organizational factors like search practices, incentives, linking-mechanisms, prior knowledge, and the organization form impact on intracorporate knowledge exchange processes in terms of exploration and exploitation. Special attention is paid to the influence of the introduction of an intranet in this respect. The two contexts of ‘organization-enabled’ and ‘web-enabled’ knowledge exchange are suggested to be complementary for achieving both strategic goals of knowledge exploration and exploitation.
Shear Lag Analysis due to Flexure of Prismatic Beams with Arbitrary Cross-Sections by FEM

This paper presents the use of a finite element method (FEM) to analyze the shear lag effect due to the flexure of beams with an arbitrary cross-section and homogeneous elastic material. Beams are constrained by the most common types of supports, such as fixed, pinned, and roller. The transverse, concentrated, or distributed loads act on the beams through the shear center of the cross-section. The presented FEM transforms the 3D analysis of the shear lag phenomenon into separated 2D cross-sectional and 1D beam modeling. The characteristics of the cross-section are firstly derived from 2D FEM, which uses a 9-node isoparametric element. Then, a 1D FEM, which uses a linear isoparametric element, is developed to compute the deflection, rotation angle, bending warping parameter, and stress resultants. Finally, the stress field is obtained from the local analysis on the 2D-cross section. A MATLAB program is executed to validate the numerical method. The validation examples have proven the efficiency and reliability of the numerical method for analyzing shear lag flexure, which is a common problem in structural design.
Shear transfer in cracked reinforced concrete

Synopsis Tests of a new type are described in which reinforced concrete plate specimens are cracked in direct tension. The tensile force is maintained while in-plane shear forces are applied across the crack. Measurements are taken of the shear stiffness and subsequent crack widening, together with the tensile forces in the reinforcement, using strain gauges embedded within the bars. A technique of resin injection is used to study the variation in the width of a crack beneath the surface of the concrete. Contrary to the results of previous studies, no significant variation was observed. The shear stiffness of cracked reinforced concrete specimens was compared with results from previous aggregate interlock and dowel action tests. It can be predicted from available theoretical models, provided that the anchorage stiffness of the reinforcement is known.
Framework of Intelligent System for Machine Learning Algorithm Selection in Social Sciences

The ability to generate data has never been as powerful as today when three quintile bytes of data are generated daily. In the field of machine learning, a large number of algorithms have been developed, which can be used for intelligent data analysis and to solve prediction and descriptive problems in different domains. Developed algorithms have different effects on different problems.If one algorithmworks better on one dataset,the same algorithm may work worse on another data set. The reason is that each dataset has different features in terms of local and global characteristics. It is therefore imperative to know intrinsic algorithms behavior on different types of datasets andchoose the right algorithm for the problem solving. To address this problem, this papergives scientific contribution in meta learning field by proposing framework for identifying the specific characteristics of datasets in two domains of social sciences:education and business and develops meta models based on: ranking algorithms, calculating correlation of ranks, developing a multi-criteria model, two-component index and prediction based on machine learning algorithms. Each of the meta models serve as the basis for the development of intelligent system version. Application of such framework should include a comparative analysis of a large number of machine learning algorithms on a large number of datasetsfromsocial sciences.
Autonomous placement of a range sensor for acquisition of optimal 3-D models

In this paper, the authors describe the system they have implemented which automatically determines an optimized next range sensor position and orientation during the reconstruction of a three-dimensional model. The system they have developed reconstructs a model consisting of surfaces which have been viewed and volumes occluded from the camera's view. Ideally, a sensor pose determined by a "best-next-view" system will reveal the greatest quantity of previously unknown scene information. We will present results from the most intelligent of the algorithms we have developed. This algorithm attempts to intelligently cluster the occluded data and orient the sensor on the centroid of the largest cluster. A system which finds a solution to the best-next-view problem may find application in the contexts of robot navigation, manufacturing and hazardous materials handling. The methods we implement take advantage of no a priori information in finding the best-next-view position.
Phase-shedding in Boundary Conduction Mode Converter with Optimal Transition Load-level

Phase-shedding, one famous technology for multi-phase converters, is implemented in a bi-directional multi-phase converter for ESS. It reduces active operating modules at light load to compensate efficiency. Shedding point, the load level where the converter changes the number of active modules, is important factor that affects the effect of phase-shedding. Loss analysis is done for determining shedding point. Phase-shedding hysteresis is applied so that excessive phase transition is avoided. This paper proposes shedding point correction where the shedding point is adaptively corrected by calculating a new shedding point.
Application of Wavelet Packet Analysis in Damage Alarming for Steel-Frame Structure

The method of damage alarming based on wavelet packet analysis which applied on steel-frame structure is researched. Firstly, the method of damage identification based on wavelet packet analysis is introduced. Secondly, in view of the dependability of the method on the excitation, virtual impulse response function is brought in to enhance robustness of the method to the excitation. Lastly, through the steel-frame structure experimentation of damage alarming, the two damage modes of the structure are identified by the method based on wavelet packet energy spectrum. The experimentation results show that the effect of damage alarming to the steel-frame structure is completely obvious by wavelet packet analysis. Accordingly, this method has much application value for engineering.