Results Of Evaluation Research Articles

Research of Green Innovation of Companies of Countries with Different Levels of Technological Development of Production

Abstract In the conditions of the transition to higher technological systems in the conditions of the implementation of the Sustainable Development Goals, the companies of the world face the question of effective management of green innovative development in their economic activities. The goal of the research is the formation of effective organizational and economic support for green innovations of companies of countries with different levels of technological development of production. This research contains: to describe the known positions of scientific schools regarding the managerial aspects of green innovations of companies of the world; to form the economic support of this administration, by developing an assessment of the potential of green innovations of companies of the world (the data of the Global Innovation Index Report were taken as a source of empirical data), to form criterion values of the potential of green innovations of companies of the countries for the purpose of their clustering. The research methods used include: system method; bibliometric analysis; method of assessing competitiveness; criterion method; method of generalization. The objects of evaluation include companies from five countries of Central and Eastern Europe. The main results of the study include: development of management theory; forming an assessment of the green innovation potential of companies from countries of the world with different levels of technological development of production (the data of the Global Innovation Index Report were taken as a source of empirical data); formation of criterion support for the results of evaluation of the green innovation potential of the countries’ companies. The conclusions of the study should include establishing the actualization of managing green innovations by companies of the world by involving the results of the assessment of the green innovation potential of companies of the world to the formation of the criterion values of this assessment in order to make effective management decisions on the part of all interested stakeholders. For further scientific research, to focus on the formation of effective communication links between different groups of stakeholders, the basis of this scientific direction is the criteria developed by the authors to ensure the level of green innovation potential of companies of countries in order to make effective decisions on the part of stakeholders (state bodies, investors, manufacturers, suppliers, clients, financial institutions) regarding organizational, social, economic support of green innovations of companies of countries with different levels of technological development of production. This study can be useful for the governments of countries in the development of international and national programs for the innovative development of the economy in terms of the implementation of the Sustainable Development Goals; to company managers when forming strategic and tactical plans for green innovative business development; to students and scientists in the implementation of scientific and educational programs.

Occluded apples orientation estimator based on deep learning model for robotic harvesting

Apple harvesting robot is in rapid development in the recent years due to the shortage of manual labor. Robotics vision system has been one of the main focus in the development to achieve successful grasping. Information such as the apple orientation is valuable for developing more effective fruit detachment strategies. Occlusions in the orchard environment and the lack of distinct features in an apple has made it challenging to identify the apple orientation. Our study presents a novel approach that aims to estimate the orientation of fruits by projecting 2D information onto a 3D space. This method specifically targets the field of robotic harvesting and leverages multiple well-established and reliable fundamental methods currently available that includes keypoint detection neural network and circle detection based on extracted line segments of occluded apple. Based on the extracted information, a unit vector that represents orientation of the apple can be computed and transformed according to the relative position between the apple and the camera. The proposed method is evaluated using a publicly available 3D point cloud data of 11 apple trees from an orchard farm that was additionally labeled manually with apple orientations. To verify the evaluation results, the proposed method is tested by integrating it into an apple harvesting robot to perform inspection. Results shows that the median angular error in the orchard setting is 17.6° whilst in the lab setting is 14.6°. The experimental results show that the performance of the proposed method is comparable to existing research when under heavy occlusion and thus it is suitable for harvesting robot operating in the orchard farm.

Penrose Drain for Carpal Tunnel Release: A Randomized Controlled Trial

Background: Surgical site hematoma is one of the factors that influence the results of the carpal tunnel release (CTR) and patients’ satisfaction; therefore, this study was designed to evaluate the effects of using Penrose drain to reduce the operation site hematoma and improve the results of surgery.
 Methods: Patients with a diagnosis of carpal tunnel syndrome (CTS) were randomly divided into intervention and control groups and then were evaluated by pain with the visual analog scale (VAS) and pinch power. The Penrose drain was placed for the intervention group and removed after two days. Three months after surgery, all patients were re-evaluated by VAS and pinch power.
 Results: Out of 46 patients, Penrose drains were placed for 21 patients. According to the results, the VAS and pinch power evaluations were better in patients with Penrose drain (P = 0.001) but in patients without Penrose drain, only the VAS evaluation results were improved, but a decrease in pinch power was detected.
 Conclusion: CTR surgery with or without the Penrose drain decreases pain in patients after three months of operation. CTR with Penrose drain improves pinch power and also decreases the pain, but in patients without Penrose drain, pinch power was reduced after this period.

Data-driven supplier evaluation in complex product industry value chain for brand value co-creation

In the context of big data, it is an excellent challenge to evaluate suppliers under a brand value (BV) co-creation scenario due to the complexity of the BV co-creation process, as well as the nature of the multilayer complex network (MCN) relationships among suppliers in a complex product industrial value chain (CPIVC). To improve the BV satisfaction and accuracy of supplier evaluation results and to offer manufacturers effective decision-support in selecting suppliers that create BV, a data-driven supplier decision-making method comprising a two-stage model is proposed under BV orientation. In the first stage, the supplier relationship model is developed to elicit the autocorrelation importance based on MCN theory and supplier data. In the second stage, the supplier autocorrelation importance and industrial value chain data are integrated into the Quality Function Deployment (QFD) process to transform BV demands into value activities, and further into suppliers, with the purpose of calculating the importance of each supplier. Finally, the applicability and superiority of this method are illustrated by carrying out a case study using the real data collected from the air-conditioning industrial value chain. The proposed method takes into account the essence of MCN relationships among suppliers, making assessment results more accurate and realistic. It not only provides manufacturers with an effective decision-making tool to select suppliers for co-creating BV, and but also contributes to an innovative method for data-driven supplier evaluation of CPIVC.

Improving the quality assessment for Pacific white shrimp (Litopenaeus vannamei) stored at 0 °C

AbstractThe quality of Pacific white shrimp stored at 0 °C during the 10 days was evaluated by the combination of sensory, microbiological and biochemical measurements. Quality index methods (QIM) have been developed to evaluate sensory attributes of shrimp. Total visible count (TVC), K value, histamine, and pH are used to assess microbiological and biochemical indices. The results of TVC and QIM showed that shrimps showed signs of spoilage (lg cfu = 6.4; quality index (QI) = 21.37), which is unacceptable to consumers on day 9. QI quality score increased linearly with storage days. The remaining shelf life of shrimp was estimated from the linear regression equation. The results have found the linear regression equations between the QI, histamine, K value, and hypoxanthine. In addition, the results also showed that hypoxanthine could be considered as an independent quality index such as the K index. The quality of Pacific white shrimp could be classified into four grades: excellent, good, moderately acceptable, and just acceptable basing on the results of sensory and biochemical evaluation.

Prediction of unconfined compressive strength of lime treated soils

ABSTRACT Robust models based on Artificial Neural Network and simplified linear regression were proposed to predict the Unconfined Compressive Strength (UCS) of Lime Treated Soils (LTS). In total, an experimental database using 1120 test specimens was created. Critical examination of the collected experimental data suggested that there are eight key parameters that govern the attained strength gain. These input parameters are liquid limit, plastic limit, dry unit weight, water content, fine content, lime content, curing temperature and curing time whereas the only output dependent parameter is the UCS. The parameters of the proposed model including weights, biases and transfer functions were successfully converted to an explicit mathematical model relating the UCS with the key input parameters. Based on the results of the statistical evaluation, it was shown that a three-layered Artificial-Neural-Network model with 19 hidden neurons was capable to predict the UCS of (LTS) with a high degree of accuracy that was better than that achieved by the developed regression model. A coupling effect of the input parameters and weights analysis were conducted for the developed Artificial-Neural-Network-model to assess the importance of the key parameters. Analysis of weights of the model showed that curing time was the most significant factor affecting strength gain.

Clinical significance of endocrine disorders in the development of early vascular aging in males with abdominal obesity and concomitant arterial hypertension: An observational cohort study

Background. Visceral obesity is a risk factor in the development of metabolic and endocrine disorders leading to arterial hypertension and cardiovascular complications. Their early predictors include increased vascular stiffness and early vascular aging. The current literature lacks studies into the effects of changes in testosterone, cortisol, and aldosterone levels in serum on vascular stiffness and the development of early vascular aging in patients with visceral obesity. Objective. To determine the relationship between hypercortisolemia, hyperaldosteronemia, and hypotestosteronemia and vascular stiffness and the presence of early vascular aging in male patients with visceral obesity. Methods. An observational cohort study of 78 males aged 35–45 years (mean age 38.1 ± 6.5 years) diagnosed with abdominal obesity and grade 1 arterial hypertension was conducted. The mean waist circumference ranged 105.5 ± 6.9 cm; systolic and diastolic blood pressure ranged 152.5 ± 5.0 and 92.5 ± 5.0 mm Hg, respectively. The vascular age of the studied patients (n = 78) comprised 44.1 ± 6.2 years, which was statistically higher than their passport age (р < 0.001). The studied patients were divided into subgroups according to both total testosterone (< 12.1 nmol/l in subgroup 1A (n = 49) and ≥ 12.1 nmol/L in subgroup 1B (n = 29)) and cortisol in the evening saliva portion (> 4.5 nmol/L in subgroup 2A (n = 24) and ≤ 4.5 nmol/L in subgroup 2B (n = 24)). All the patients completed the study. To assess the hormonal status, the total testosterone sex steroid-binding globulin (SSBP) and insulin in morning serum samples were investigated. Insulin resistance was assessed based on the NOMA-IR index. The concentration of total testosterone was determined by enhanced chemiluminescence (Ortho-Clinical Diagnostics, J&J); the SSBP and insulin levels were determined by delayed fluorescence. Aldosterone content was determined by radioimmunoassay; free cortisol and testosterone were measured by luminescent LIA. Free and bioavailable testosterone concentrations in serum were calculated using an online calculator (issam.ch/freetesto.htm). The cardio-ankle vascular index (CAVI) was determined using a VaSera VS-15000N device, which automatically calculated the vascular age. Statistical analysis was performed using the Statistica 10.0 Windows package (StatSoft, Inc., USA). Results. The vascular age of patients with hypogonadism was statistically significantly ( р < 0.001) higher than their passport age and the vascular age in males without hypogonadism. The CAVI and vascular age were also statistically significantly higher in males with functional hypercorticism (р < 0.001) compared with a subgroup of patients without hypercorticism. The vascular age and CAVI increased with an increase in the salivary cortisol concentration 2200 (r = 0.5; р < 0.05) and decreased with an increase in the salivary cortisol level 900 (r = –0.5; р < 0.05). These parameters decreased with an increase in serum aldosterone obtained in the morning (r = –0.4; p < 0.05) and increased with an increase in serum aldosterone in the evening (r = 0.4; p < 0.05). In 23% (n = 18), an inversion of the daily rhythm of cortisol production was observed; in these patients, salivary cortisol levels of 2200 exceeded salivary cortisol levels of 900. The vascular age of patients with the inversion of cortisol production (49.4 ± 4.4 years) was statistically significantly ( р < 0.001) different from that of patients with normal changes in salivary cortisol concentrations (41.9 ± 4.9 years). CAVI was also higher (р < 0.001) in males with inverted fluctuations in salivary cortisol levels (7.51 ± 0.62) compared to those with normal diurnal rhythm (6.45 ± 0.69). The results of aldosterone evaluation revealed that 17% of the patients (n = 13) had higher aldosterone levels in the evening serum portion compared to the morning serum portion. In these patients, the vascular age (45.8 ± 5.1 years) was higher ( р < 0.001) than that in males with normal physiologic changes in aldosterone levels (41.6 ± 5.7 years). A similar pattern was observed when comparing vascular stiffness indices. Thus, the CAVI in men with inverted changes in aldosterone concentration (6.9 ± 0.8) was significantly higher ( р < 0.001), compared to that in men with physiological changes in blood aldosterone levels (6.4 ± 0.8). Conclusion. Endocrinologic disorders in male patients with visceral obesity and concomitant arterial hypertension (functional hypogonadism, functional hypercorticism, inverted daily rhythms of cortisol and aldosterone production) contribute to the development of early vascular aging by increasing vascular stiffness.

Deep separation of arsenic and alkali from alkaline arsenic containing solution using hydrothermal lime precipitation method

A new strategy of lime precipitation of arsenic (As) under hydrothermal conditions was proposed for the treatment of alkaline arsenic-containing wastewater (AAW). On the basis of theoretical analysis, a study was conducted on the reaction law and mechanism of lime precipitation of arsenic in hydrothermal systems, as well as the environmental stability of precipitation products. Results of theoretical calculations showed that increasing the reaction temperature was beneficial the formation of insoluble Ca5(AsO4)3OH, which promoted the removal of arsenic. It is shown that the arsenic concentration could be reduced from 35.38 mg/L to 9.16 mg/L with increasing the reaction temperature from 90 °C to 210 °C. The experimental results further confirmed the above conclusion. When the reaction temperature was increased from 90 °C to 210 °C, the arsenic concentration in the final liquid decreased from 706.21 mg/L to 15.21 mg/L. The leaching toxicity evaluation results of the products showed that the stability of the arsenic precipitates was improved significantly with the hydrothermal conditions. The arsenic leaching concentration for the products obtained at 170 °C was only 2.1 mg/L, which was lower than the U.S. Environmental Protection Agency (USEPA) limit of 5 mg/L for solid waste containing hazardous arsenic. It is found that the improved stability of the arsenic precipitates could be attributed to the increased crystallinity and crystal size of the product obtained under hydrothermal conditions.

An embedded TensorFlow lite model for classification of chip images with respect to chip morphology depending on varying feed

AbstractTurning is one of the fundamental machining processes used to produce superior machine parts. It is critical to manage the machining conditions to maintain the desired properties of the final product. Chip morphology and chip control are crucial factors to be monitored. In particular, the selection of an appropriate feed has one of the most significant effects. On the other hand, machine learning is an advanced approach that is continuously evolving and helping many industries. Moreover, mobile applications with learning models have been deployed in the field, recently. Taking these motivations into account, in this study, we propose a practical mobile application that includes an embedded learning model to provide chip classification based on chip morphology. For this purpose, a dataset of chips with different morphological properties is obtained and manually labeled according to ISO 3685 standards by using 20 different feeds on AISI 4140 material. Accordingly, TensorFlow Lite is used to train a learning model, and the model is embedded into a real-time Android mobile application. Eventually, the final software is evaluated through experiments conducted on the dataset and in the field, respectively. According to the evaluation results, it can be stated that the learning model is able to predict chip morphology with a test accuracy of 85.4%. Moreover, the findings obtained from the real-time mobile application satisfy the success rate by practical usage. As a result, it can be concluded that such attempts can be utilized in the turning process to adjust the relevant feed conditions.

Peripherally inserted central-related upper extremity deep vein thrombosis and machine learning.

To establish a prediction model of upper extremity deep vein thrombosis (UEDVT) associated with peripherally inserted central catheter (PICC) based on machine learning (ML), and evaluate the effect. 452 patients with malignant tumors who underwent PICC implantation in West China Hospital from April 2021 to December 2021 were selected through convenient sampling. UEDVT was detected by ultrasound. Machine learning models were established using the least absolute contraction and selection operator (LASSO) regression algorithm: Seeley scale model (ML-Seeley-LASSO) and ML model. The information of patients with and without UEDVT was randomly allocated to the training set and test set of the two models, and the prediction effect of machine learning and existing prediction tools was compared. Machine learning training set and test set were better than Seeley evaluation results, and ML-Seeley-LASSO performance in training set was better than ML-LASSO. The performance of ML-LASSO in the test set is better than that of ML-Seeley-LASSO. The use of ML model (ML-LASSO and ML-Seeley-LASSO) in PICC-related UEDVT shows good effectiveness (the area under the subject's working characteristic curve is 0.856, 0.799), which is superior to the currently used Seeley assessment tool. The risk of PICC-related UEDVT can be estimated and predicted relatively accurately by using the method of ML modeling, so as to effectively reduce the incidence of PICC-related UEDVT in the future.

Bayesian Averaging Evaluation Method of Accelerated Degradation Testing Considering Model Uncertainty Based on Relative Entropy.

To evaluate the lifetime and reliability of long-life, high-reliability products under limited resources, accelerated degradation testing (ADT) technology has been widely applied. Furthermore, the Bayesian evaluation method for ADT can comprehensively utilize historical information and overcome the limitations caused by small sample sizes, garnering significant attention from scholars. However, the traditional ADT Bayesian evaluation method has inherent shortcomings and limitations. Due to the constraints of small samples and an incomplete understanding of degradation mechanisms or accelerated mechanisms, the selected evaluation model may be inaccurate, leading to potentially inaccurate evaluation results. Therefore, describing and quantifying the impact of model uncertainty on evaluation results is a challenging issue that urgently needs resolution in the theoretical research of ADT Bayesian methods. This article addresses the issue of model uncertainty in the ADT Bayesian evaluation process. It analyzes the modeling process of ADT Bayesian and proposes a new model averaging evaluation method for ADT Bayesian based on relative entropy, which, to a certain extent, can resolve the issue of evaluation inaccuracy caused by model selection uncertainty. This study holds certain theoretical and engineering application value for conducting ADT Bayesian evaluation under model uncertainty.

Heuristics on Call: The Impact of Mobile-Phone-Based Business-Management Advice

Abstract There is growing evidence that business training for micro-entrepreneurs can be effective. However, in-person training can be expensive and imposes costs on the target beneficiaries. This paper presents the results of a two-site randomized evaluation of a light-touch, mobile-phone-based business-training service for micro-entrepreneurs in India and the Philippines. The results show that the training had a statistically significant impact on the adoption of improved business practices, with an increase of 0.06 to 0.12 standard deviation points when considering a binary indicator of business practices. The study finds no evidence of impacts on business sales or profits, though the confidence intervals are wide enough to include meaningful effect sizes (positive or negative). These results suggest that mobile-phone-based training can be a cost-effective and scalable way to impart business skills to micro-entrepreneurs.

Evaluation Study of West Muna Kumite Karate Athlete Performance Coaching Program

Coaching achievements in karate athletes provides guidance to athletes starting from an early age, finding and achieving athlete talent in all sports in order to have maximum performance. This study aims to evaluate the implementation of the West Muna Kumite Karate achievement coaching program using the CIPP (Contexs, Input, Process, Product) evaluation model. This research is an evaluation research. The sample of this study was administrators, coaches and athletes of the West Muna Kumite Karate Dojo with a total of 27 people. Data was collected through questionnaires, interviews, observations and documentation. The data analysis techniques used in this study used quantitative and qualitative descriptive analysis. The results showed that the overall evaluation results of the West Muna Kumite Karate coaching program were still not good. In the evaluation of the Kumite Karate sports performance coaching program in West Muna, in terms of context, it has been going well. In terms of input, it is still not good because the facilities and infrastructure of the West Muna Kumite Karate sport are still very lacking. Likewise with funding that has not been distributed as a whole. In terms of input, there are coaches and athletes as expected. In terms of process, it is still very lacking because the implementation of the coaching and monev (monitoring and evaluation) program has not been carried out in accordance with the provisions, even though the implementation of the training program has been running well. In terms of overall product, the achievements that have been achieved have been good, it can be seen from the results of documentation of achievements that have been achieved by West Muna Kumite Karate athletes.

Towards more reliable evaluation in pedestrian detection by rethinking “ignore regions”

It remains a challenging task to detect pedestrians in crowds and it needs more efforts to understand why the detectors fail. When we perform an error analysis based on the traditional evaluation strategy, we find that it produces many misleading false positives, which in fact cover occluded pedestrians. The reason for this is that we usually have two kinds of annotations in the dataset: regular pedestrians (detection targets) labeled by full-body boxes and ignored pedestrians (NOT detection targets) labeled by visible boxes. Ignored pedestrians are labeled as an additional category termed the “ignore region”. Nevertheless, our detectors always predict a full-body box for each pedestrian. This gap results in the following case: when a detector successfully predicts a full-body box for those ignored pedestrians, a false positive is triggered due to the low overlap between the predicted full-body box and the labeled visible box for the ignored pedestrian. This becomes even more harmful as the detector improves and becomes more capable of locating occluded pedestrians. To alleviate this issue, we devise a new pedestrian detection pipeline, which considers the additional visible box at both the detection and evaluation stages. During detection, we predict an extra visible box apart from the full-body box for every instance; during evaluation, we employ visible boxes instead of full-body boxes to match the “ignore region”. We apply the new pipeline to dozens of detection methods and validate the effectiveness of our pipeline in reducing the over-reporting of false positives and providing more reliable evaluation results.

Study on the Regeneration of Waste FCC Catalyst by Boron Modification.

Regeneration has been considered as an ideal way for the post-treatment of waste FCC catalyst (ECat). In this work, the degeneration mechanism of ECat was firstly researched and attributed to the increasing of strong acid sites accessibility of ECat in contrast with fresh FCC catalyst by adsorption FTIR. Based on the proposed degeneration mechanism, ECat was successfully regenerated through suitable weakening for strong acid sites by boron modification. Characterization and evaluation results suggested that, the strong acid sites of regenerated ECat (R-ECat) were apparently decreased by boron modification which had significantly improve the heavy oil catalytic cracking performance of R-ECat. Because of the excellent performance, R-ECat in this work could successfully substitute for partial fresh FCC catalyst in FCC unit, which would provide a practicable way for the reutilization of ECat.

Joint Optimization on Trajectory, Data Relay, and Wireless Power Transfer in UAV-Based Environmental Monitoring System

In environmental monitoring systems based on the Internet of Things (IoT), sensor nodes (SNs) typically send data to the server via a wireless gateway (GW) at regular intervals. However, when SNs are located far from the GW, substantial energy is expended in transmitting data. This paper introduces a novel unmanned aerial vehicle (UAV)-based environmental monitoring system. In the proposed system, the UAV conducts patrols in the designated area, and SNs periodically transmit the collected data to the GW or the UAV. This transmission decision is made while taking into account the respective distance between both the GW and the UAV. To ensure a high-quality environmental map, characterized by a consistent collection of a satisfactory amount of up-to-date data while preventing energy depletion in the SNs and the UAV, the UAV periodically decides on three types of UAV operations. These decisions involve deciding where to move, deciding whether to relay or aggregate the data from the SNs, and deciding whether to transfer energy to the SNs. For the optimal decisions, we introduce an algorithm, called DeepUAV, using deep reinforcement learning (DRL) to make decisions in UAV operations. In DeepUAV, the controller continually learns online and enhances the UAV’s decisions through trial and error. The evaluation results indicate that DeepUAV successfully gathers a substantial amount of the current data consistently while mitigating the risk of energy depletion in SNs and the UAV.

A novel self-supervised contrastive learning based sentence-level attribute induction method for online satisfaction evaluation

Satisfaction evaluation provides valuable support for procurement decisions and product improvement on e-commerce platforms. As an important task of satisfaction evaluation, attribute induction is essential to identify relevant aspects and demands of user-generated evaluations. However, previous methods have often directly predicted typical attributes using individual words, neglecting valuable sentence-level information that could significantly enhance effectiveness. To alleviate this issue, this article introduces the concept of representations of potential attributes (PARs) to explicitly capture sentence-level information regarding product attributes in customer reviews. It further develops a novel self-supervised contrastive learning based sentence-level attribute induction method (SSCL-SAI) to train these PARs. Recognizing that sentences are the basic units of human expression, this approach mitigates information loss. Then, an aspect-category sentiment analysis model is used to transform the unstructured online textual reviews into linguistic 2-tuple for obtaining final satisfaction evaluation result. These 2-tuples provide a representation of sentiment orientations on a continuous numerical scale, enabling precise linguistic operations. Afterwards, an illustrative example conducted on real-world laptop product satisfaction analysis testify the validity of the approach. Finally, the comparative analysis shows the effectiveness of the proposed SSCL-SAI, and experiments on the impact of hyperparameter settings demonstrate that SSCL-SAI can be applied in various scenarios.

Chemical Composition, Physicochemical Properties, and Sensory Attributes of Pawpaw-Banana Mixed Fruit Leather

Fruits offer nutrient‐dense and satiety choice for a healthy diet; However, they are highly perishable and prone to progressive undesired changes if stored untreated. Post-harvest losses in fruits vary between 15-90% in developing countries. In order to reduce fruits post-harvest losses, it is important to process them into shelf stable products such as fruit leather. Thus, this study assessed the quality attributes of pawpaw-banana mixed fruit leather. The fruit leather was produced by blending varying proportions of banana puree (0 – 40%) with pawpaw puree, and evaluated for chemical and physicochemical properties, carotenoid contents, antioxidant activities, microbiological characteristics and sensory attributes. The vitamin C contents (13.07 mg/100 g) and brix level (12.0 obrix) of the 70%pawpaw leather were the highest among the mixed fruit leather. The beta-carotene and lycopene contents of the mixed fruit leather ranged from 19.78 – 31.88 mg/100g and 70.47- 91.95 mg/100g, respectively. The total phenolic and the FRAP contents of the mixed fruit leather are 0.65 – 0.86 mg/GAE g and 2.34 – 3.84 mmolFe2+/g respectively, while that of the plain pawpaw leather was 0.61 mg/GAE g and 4.11 mmolFe2+/g for total phenolic and the FRAP. The total bacteria count in the fruit leather were within the safe level, while the total coliform and total fungi were not detected. The sensory evaluation results showed that 70%pawpaw leather was highly rated for flavor, taste, texture and overall acceptance. Thus, 70% Pawpaw leather is recommended due to its high nutritional value and sensory attributes.

Relationship between brightness perception and skin color influenced by experimental method

AbstractFacial brightness perception is affected by skin color. It was reported that reddish skin was matched with a higher lightness scale stimulus than yellowish skin among Japanese observers. However, this trend is inconsistent among Thai, Korean, Chinese, and European observers, implying the influence factors, such as ethnicity, environment, and judgment criteria. In this study, we investigated the influence of the experimental method on facial brightness perception using test stimulus images with constant lightness and different hue angles. We examined two instructions (“Appearance match” vs. “Brightness match”) and the scale stimulus for matching (a uniform color patch vs. a face image). We further conducted an impression evaluation experiment of the whole face. The matching results from the Japanese showed a similar trend in both instructions and scale stimuli under most conditions, suggesting that matched brightness is not influenced by instruction and the complexity of scale stimuli. The impression evaluation results were similar for Thai, Japanese, and Chinese observers, with reddish faces being evaluated with higher scores in brightness. Our findings suggest the influence of experimental methods on facial brightness judgment. The holistic judgment of facial brightness would be universal among different East Asian groups, at least for the range of variation in skin color we used.

A Study of Components and Indicators of Being Quality Organization of Phrapariyattidhamma School, General Education Department, in the Northeast

The purpose of this study was to 1) study the components and indicators of being quality organization of Phrapariyattidhamma School, General Education Department, in the Northeast, and 2) evaluate the suitability of the components and indicators of being quality organization of Phrapariyattidhamma School, General Education Department, in the Northeast. The phases were divided into 2 phases: 1) studying documents and related works on the components of being quality organization and synthesize received data of components of being quality organization with 7 experts, and 2) evaluating the suitability of components and indicators of being quality organization employing suitability test verified by the experts on question details. The data analysis was by computer program using Rating Scale, Average, and Standard Deviation.
 
 The findings of the study were found that the components and indicators of being quality organization Phrapariyattidhamma School, General Education Department, in the Northeast contained 5 components and 43 indicators: 1) 9 indicators of quality learners, 2) 8 indicators of professional teachers, 3) 9 indicators of high-performance school, 4) 5 indicators of strategic management, 5) 8 indicators of community cooperation and the results of the overall evaluation of the components and indicators were at a higher level.