Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceived Intensities of Normal and Shear Skin Stimuli Using a Wearable Haptic Bracelet

Our aim is to provide effective interaction with virtual objects, despite the lack of co-location of virtual and real-world contacts, while taking advantage of relatively large skin area and ease of mounting on the forearm. We performed two human participant studies to determine the effects of haptic feedback in the normal and shear directions during virtual manipulation using haptic devices worn near the wrist. In the first study, participants performed significantly better while discriminating stiffness values of virtual objects when the feedback consisted of normal displacements compared to shear displacements. Participants also commented that they could detect normal cues much easier than shear, which motivated us to perform a second study to find the point of subjective equality (PSE) between normal and shear stimuli. Our results show that shear stimuli require a larger actuator displacement but less force than normal stimuli to achieve perceptual equality for our haptic bracelets. We found that normal and shear stimuli cannot be equalized through skin displacement nor the interaction forces across all users. Rather, a calibration method is needed to find the point of equality for each user where normal and shear stimuli create the same intensity on the user’s skin.
Ayurvedic Plant Identification using Image Processing and Artificial Intelligence

Wireless networks provide small sensing, machine and wireless networking nodes. Different designs and implementation techniques were built based on the device requirements for wireless network sensors (WSN). Sensor networks are used in various applications, such as environmental monitoring, home automation, military applications, etc. In this study introduce an architectural survey and deployment of nodes in the Wi-Fi Sensor network in this article. The environmental features that can be added to the sensor networks are given. The program relies on the node installed in the WSN and is deterministic or random. But the biggest issue in both cases is the coverage of the region involved. Researcher also describe WSN routing protocols. In this paper, a new technique to deployment problem is proposed based on the artificial bee colony (ABC) algorithm which is enhanced for the deployment of sensor networks to gain better performance by trying to increase the coverage area of the network and energy consumption. The good performance of the proposed EABC algorithm shows that it can be utilized in the deployment of WSN.
Microstructure sensors

Surface micromachined polysilicon pressure sensors offer an attractive, cost-effective high-performance technology if material repeatability issues can be solved. The pressure ranges over which these devices are designable have been established and are set by the maximum allowable diaphragm stress and the minimum acceptable output level. The low pressure limitation can be eliminated by improving the transducer sensitivity. The substitution of the resonating force sensor, another polysilicon component, for piezoresistors can accomplish this but complicates processing. This approach is justified for high performance applications. The alternative is found in a differential transducer which is furnished with double-sided over-pressure stops. One of these stops is provided by the silicon substrate, the other by an electroplated air bridge which is formed by X-ray lithography and electroplating in the presence of a sacrificial layer. The end result is a rigid, thick, accurately spaced stop which results in devices which are rugged and can measure differentials in the 1" water range. <<ETX>>
Innovations of Loyalty Programs in the Hotel Industry

Tingtang Zhang is affiliated with University of Central Florida. Introduction Many hotels offer loyalty programs to increase customer revisits and to gain customer loyalty. Hoteliers see the important role of loyalty programs in obtaining the sustainability and staying competitive in the customer-oriented service market. Traditional loyalty programs usually adopt point-system for customers to gain through purchase and to redeem nights/food for rewards. However, as the loyalty programs become competitive among different hotels, it becomes more difficult to maintain customer loyalty and let customers stay with the hotel/brand instead of switching to other hotels/brands. On the other hand, some pioneering hotels or hotel groups launched innovative loyalty programs to engage with the customers, thus enhancing customer loyalty This case study presents a review of literature on loyalty programs and the role of social media and technology in loyalty programs. Then this case study analyzes and discusses some innovative loyalty programs offered by successful hotels/brands. Finally, discussions of such innovative adoptions and applications are conducted to provide insights for teaching purposes. Customer loyalty has experienced an evolution over time. Oliver (1999) proposed a progressive development of customer loyalty following cognitive-affective-conation pattern. Cognitive loyalty refers to the state that customers prefer one product/brand to another because of the product/brand itself (Oliver, 1999). Affective loyalty refers to the phase that customers like the product/brand and process a positive attitude towards the product/brand because of a continuous satisfactory experience (Oliver, 1999). Conation loyalty, also termed as action loyalty, refers to the stage that customers develop deep commitment to repurchase and the customers’ return intention has been “transformed into readiness to act” (Bowen & Chen, 2015: 418). Consistent with Oliver’s (1999) loyalty evolution stages, Dick and Basu (1994) classified customer loyalty into four dimensions: true loyalty, latent loyalty, spurious loyalty, and low loyalty. Figure 1 depicts the four dimensions of customer loyalty.
LEAKAGE CONTROL IN WATER DISTRIBUTION NETWORKS BY USING OPTIMAL PRESSURE MANAGEMENT: A CASE STUDY

More than thirty percent of drinking water loss through water distribution systems (WDS) in Iran has made a major concern for Iranian water utilities, which has been located in a semi-arid area. In many cases, invisible leakage in the pipelines of WDS, particularly old WDSs, causes a high proportion of total annual losses. Pressure management is a well known and useful tool for reducing invisible leakage since leak is a pressure dependent function. This paper presents an optimization model for pressure management by optimizing pressure reducing valves (PRV) locations and settings. The PRVs location and setting is formulated here as an optimization problem with: (1) the objective function of maximizing the coverage of end-node users’ pressures in an appropriate range; (2) the objective function of minimizing total leakage in the network. Genetic algorithm is used here as the optimization model, in which EPANET toolkit is used as the simulation engine. The model is applied to a case study in Iran known as Mahalat WDS which has assigned a high value of water loss in the networks. The results show that the system is able to considerably moderate the high pressure values and total value of drinking water which is annually lost.
How do prosperity and aspiration underlie leisure tourism expenditure patterns?

This research advances the current knowledge of tourism expenditure by adapting a new analytical approach to understand expenditure differentials along their conditional distributions, based on multiple segmentation criteria. Using data from survey and secondary sources, we approximate tourists’ required utilities via prosperity at their countries of residence, a macro-level criterion, and individual-travel aspirations, a micro-level criterion. Subsequently, expenditure differentials between more and less prosperous/aspired tourists are decomposed into two components. First, group differences in expenditure covariates that represent tourists’ relative consumption behaviors and, second, differences in the estimated returns to those covariates, measuring potential third-degree price discrimination. Our results guide policy makers in the tourism industry to develop pricing strategies capable of generating mark-ups within all viable segmentations.
Predictive modeling of drilling rate index using machine learning approaches: LSTM, simple RNN, and RFA

Abstract Drilling rate index (DRI) is a fundamental parameter in the investigation of rock drillability, as drillability is considered one of the main problems in rock engineering. Several researchers have continuously tried to analyze and correlate rock DRI, but the problem remains unchanged. This study elucidates the machine learning approaches, namely long short term memory (LSTM), simple recurrent neural network (RNN) and random forest algorithm (RFA) to predict DRI of rocks using multivariate inputs, that is, uniaxial compressive strength in MPa; Brazilian tensile strength (BTS) in MPa; brittleness value (S20); Sievers’ J value (S j); modulus ratio (MR); shore hardness (SH), porosity (n) in %; shimazeks F abrasitivity in N/mm; and equivalent quartz content in %. For all proposed methods, the original dataset was divided into 70% for training and the remaining 30% for testing. Next, the performance indices, such as correlation coefficient (R 2), root mean square error (RMSE), variance accounts for (VAF) and a-20 index of each proposed method were determined to examine the accuracy of the predicted data. In this study, according to the results of LSTM, simple RNN and RFA methods, the LSTM revealed the best prediction output for DRI with the strongest R 2, the lowest RMSE, the largest VAF and an appropriate a-20 index values as 0.999, 0.13416, 0.997, and 0.999 in the training stage and 0.998, 0.19479, 0.996, and 0.997 in the testing stage, respectively. Therefore, LSTM is an applicable machine learning approach that can be applied to accurately predict the DRI.
Maternal and Child Health Services in Rural Settings of The Gambia: Contextual Determinants of Postnatal Care from Mothers’ Perspectives—A Community-Based Analytical Cross-Sectional Study

Background. Maternal and child health in The Gambia is a trending public health burden. The postnatal period is critical and vital to the lives and health of the maternal mothers and their newborns, and pieces of evidence deduced that a majority of maternal and newborn deaths occur during this period. Hence, this research aimed to assess the prevalence and determinants of postnatal care utilization across women in rural Gambia. Methods. A community-based cross-sectional design was conducted in January 2020, with 265 women of childbearing age were recruited from the selected households in rural communities across the Upper River Region (URR), Gambia, using multistage sampling technique. A structured-interview questionnaire was developed to elicit contextual sociodemographic characteristics regarding the use of postnatal care. Percentages, chi-square/Fisher’s exact test for variables with p value ≤0.15 were considered for inclusion into the logistic regression model. The significance level was set at p < 0.05 . The adjusted odds ratios (aOR) with 95% confidence interval (CI) were computed to declare significance. Results. The study revealed that 90% of women utilize postnatal care (PNC) services in the rural settings of URR, Gambia. Factors such as women’s occupation, location, number of pregnancies, attending in last pregnancy, current uptake of family planning, and complication(s) in last labor were found to be associated with PNC utilization. Women who attended antenatal care (ANC) services in their last pregnancy were 10.8 times (aOR = 10.795, 95% CI: 1.025–113.694), current users of FP were 10.7 times (aOR = 10.708, 95% CI: 1.379–83.152), women’s number of pregnancies increases by a factor of 4.6 times (aOR = 4.649, 95% CI: 1.518–14.243) more likely to utilize PNC while women’s number of children alive were less likely by 76.1% (aOR = 0.239, 95% CI: 0.071–0.810) as compared to those who were not utilizing PNC services. Conclusion. The findings showed that attending ANC services, current uptake of FP, women’s number of pregnancies, and number of children alive were associated with PNC. Strengthening women empowerment and designing responsive ANC programs especially for rural women to improve PNC services are required.
Pollinators: Their Relevance in Conservation and Sustainable Agro-Ecosystem

Survival and reproduction of several wild plants and crops is mostly by insects pollinator, their recognition and importance have been increased in this climatic changing scenario, which affects the various aspects of their life cycle. According to an estimate, approximately 30,000 species of bees are known in entomology, and about 190 species of bees have been reported to be associated with pollination. There can be an established link between seed production and pollinator diversity, for the plants with a generalist pollination system. The increasing of human habitation affects insect pollinators in various ways, i.e. of habitat destruction, results in low availability of food sources, nesting, oviposition, resting, and mating sites. Pollinator availability restraints the geographical distribution of plant species, i.e. to develop an ecological niche of certain plant species. Failure of pollinator- plant interaction mutualism results in lower seed production and sometimes extirpation of plant population has been recorded. The declining pollinators’ population strengthens existing plant-pollinator interaction or allows new pant pollinator interaction to form. Maintaining the commercial and wild pollinator populations and preventing future shortages of pollination services, therefore, is extremely significant.
A Speed-Sensorless Start-Up Method of an Induction Motor Driven by a Modular Multilevel Cascade Inverter (MMCI-DSCC)

This paper presents theoretical and experimental discussions on a practical speed-sensorless start-up method for an induction motor driven by a modular multilevel cascade inverter based on double-star chopper cells (MMCI-DSCC) from standstill to middle speed. This motor drive is suitable, particularly for a large-capacity fan- or blower-like load. The load torque is proportional to a square of the motor mechanical speed. The start-up method is characterized by combining capacitor-voltage control with motor-speed control. The motor-speed control with the minimal stator current plays a crucial role in eliminating a speed sensor from the drive system and in reducing an ac-voltage fluctuation occurring across each dc capacitor. Experimental results obtained from the 400-V 15-kW downscaled system with no speed sensor verify that the motor-speed control proposed for the DSCC-based drive system can enhance the start-up torque by a factor of three under the same ac-voltage fluctuation. Several start-up waveforms show stable performance from standstill to middle speed with different load torques.
Optimization of ligament parameters on a subject-specific computational human knee model in a dynamic knee simulator using design of experiments

The objective of this study was to optimize ligament parameters used in a dynamic threedimensional subject-specific computational knee model. The parameters of interest are the zero load length ( 0 L ), stiffness ( k ) and the strain level where the force length relationship of the ligament goes from the nonlinear “toe” region to the linear region ( l  ). Experimental testing was performed at the University of Kansas Experimental Joint Biomechanics Research Laboratory (Lawrence, Kansas) in the Kansas Knee Simulator (KKS) under various loading profiles while the kinematics of the knee was recorded. A model of the knee in the KKS was created using previously validated modeling techniques. The experimental and model simulation kinematics for a ten second walk cycle were compared in a two level partial factorial design and a three level full factorial design and the ligament parameters were optimized based on the kinematic RMS error. The resulting parameter values are 80 % of the experimentally derived zero load length values, 99.71% of the published ligament stiffness values, and 100% of the published l  . These values are reasonable, and are validated by the stiffness values from the optimization closely matching published values for each ligament’s stiffness.
Evaluation of Seed Yield Stability of Faba Bean (Vicia faba L.) Genotypes Using GGE Biplot Analysis

To investigate adoptability and pattern of G × E interaction, 15 faba bean lines as well as four check cultivars (including Barekat, Saraziri, Baloochi and Zereshki) were evaluated using a randomized complete block design with three replications in four agricultural research field stations of Gorgan, Dezful, Brojerd and , Iranshahr, Iran during two cropping seasons (2015-2016 and 2016-2017). Combined analysis of variance showed significant effects of location, year, year × location interaction, genotype × location interaction, genotype × year interaction and year × location × genotype interaction on grain yield. Stability in performance of the 19 genotypes was tested using GGE-Biplot approach across eight environments. GGE- Biplot analysis using a genotype × environment interaction (GEI) model explained 91.5% of total interaction effect variance. View of polygon graph revealed three superior mega-environments and the compatible genotypes were determined for each mega-environment; Gorgan (Line G15), Brojerd-Iranshahr (Line G3) and Iranshahr (Line G19). Lines G15, G7 and G13 with average seed yield of 3439, 3128 and 3094 kg ha -1 , respectively, had higher seed yield and yield stability. Based on GEI and GGE- Biplot analysis, Gorgan and Brojerd experimental environments had good differentiation ability. Finally, genotypes G12, G14 and G15 were the most stable genotypes with wider adaptation to all the tested environments and can be recommended as the superior genotypes for being released as new commercial faba bean cultivars.
Characterization and Evolution of a Digital Economy Ecosystem Based on an Interspecies Competition Model

This paper provides an in-depth study and analysis of the characterization of the digital economy ecosystem and the mechanism of eye-flowering through the method of interspecies competition. The evolutionary game model of symbiotic decision-making in the entrepreneurial ecosystem is constructed, the evolutionary process of symbiotic decision-making of subjects is analyzed through mathematical derivation, and the symbiotic decision-making process of subjects is simulated through computer simulation to answer how the subjects of the entrepreneurial ecosystem make symbiotic decisions and explore the mechanism of symbiotic formation of the entrepreneurial ecosystem. Then, based on the ecological perspective, the symbiotic evolution model of entrepreneurial ecosystem subjects is constructed from the subject level, the equilibrium point of the evolution of entrepreneurial ecosystem subjects, the stability conditions, and the relationship between the equilibrium point and the symbiosis model are analyzed, and the symbiotic evolution paths of entrepreneurial ecosystem subjects under different symbiosis modes, initial population size, maximum size, and natural growth rate are presented with simulation experiments, respectively. The main characteristics and manifestations of the dynamic evolution of the platform ecosystem are analyzed, and the key competitive factors that determine the dynamic evolution of the platform ecosystem are depicted. Then, according to the inherent characteristic laws of the platform ecosystem, the complex network approach is applied to construct a dynamic evolution model with originality and wide applicability for the change of bilateral user scale. Based on the dynamic evolution process, the relationship between model parameters and business performance is explored, and the trajectory of bilateral user size change over time and the range of parameters are derived by numerical calculation. Finally, using Monte Carlo simulation methods, the dynamic evolution model is used to predict the future operating conditions of platform enterprises, providing a valuation basis for investors to make investment decisions and helping platform managers to formulate business strategies.
A human measure: structure, meaning and operation of the ‘Lu Ban’ foot-rule (鲁班尺) of the Dong carpenters

The Dong carpentry rule is reflective of the Dong's culture, traditions and construction methods related to human measurement. It is dimensioned by a comparable set of lucky and unlucky units instead of abstract geometries, indicating the favorable and unfavorable units that can be applied in construction. As a measurement system derived from the human body, the units celebrate more critical sections such as the head, feet or joints, relating to the proportions of the ‘master craftsman's body. Thus, on a construction site, the representation of the human body acts to convey scale and measurement, and particularly, this ruler holds the human proportion for sacred and public buildings, specifically Drum Towers and ‘Wind and Rain’ bridges. In this paper, the measuring system will be explored to show how it is made and assists the carpenters in dimensioning their buildings. This measurement system establishes a relationship between the construction and their beliefs.
Learning Airfoil Manifolds with Optimal Transport

The manifold hypothesis forms a pillar of many modern machine learning techniques. Within the context of design, it proposes that valid designs reside on low dimensional manifolds in the high dimensional design spaces. Our previous research—BézierGAN—suggests learning the low dimensional parameterization of valid airfoil designs can indeed accelerate aerodynamic optimizations. However, it incurs problems such as misalignment, long training time, and the trouble of finding the latent dimensionality. In this work we present the optimal-transport-based sibling of BézierGAN that surpass its predecessor in terms of both manifold approximating precision and learning speed, and provide methodology that helps determine the intrinsic dimension of the design manifold beforehand.
Effect of different dietary fibre sources on the zootechnical performance, feeding behaviour and intestinal physiology of growing and finishing pigs

The aim of this study was to determine the effect of different sources of fibre in the diets of fattening pigs on performance, feeding behaviour and intestinal physiology. A total of 60 barrows and gilts (initial body weight 28.4 ± 0.4 kg) were allotted to four dietary treatments: control (CON), lignocellulose (LC), mycelium (MYC) and corn gluten feed (CGF). Diets were calculated to provide balanced available nutrient contents. Including MYC in the diet resulted in an increased average daily gain (P < 0.05) compared to CON and CGF, and improved gain to feed ratio (P < 0.05) compared to LC. Pigs in CON (P < 0.05) ate the fewest but largest meals, whereas treatment CGF (P < 0.05) showed the opposite effect, resulting in the same daily feeder occupation time. Regarding intestinal physiology, in ileum, no differences were observed between the contents of short chain fatty acids (SCFA), lactic acid and biogenic amines. In the colon, MYC showed an increased concentration of acetic acid (P < 0.05) as well as the total content of SCFA (P < 0.05), compared to LC and CGF. Distinct fermentation profiles of ammonia were recorded in ileal and colonic digesta, although contents remained below harmful concentrations. Morphometrical measurements showed differences between the fibre sources LC and MYC, as well as LC and the CON in all investigated gut sections. These results provide evidence that the inclusion of specific dietary fibre sources/contents can positively influence the gut morphology and performance of pigs. However, further studies are needed regarding the mode of action and physico-chemical characteristics of the different fibre sources as a precondition for their successful application in pig diets.