Miracle-dz/UniFAR-FTUs huggingface dataset:
Selection of Food Identification System Features Using Convolutional Neural Network (CNN) Method

Purpose: The identification and selection of food to be consumed are critical in determining the health quality of human life. Our diet and the illnesses we develop are closely linked. Public awareness of the significance of food quality has increased due to the rising prevalence of degenerative diseases such as obesity, heart disease, type 2 diabetes, hypertension, and cancer. This study aims to develop a model for food identification and identify aspects that can aid in food identification.Methods : This study employs the convolutional neural network (CNN) approach, which is used to identify food objects or images based on the detected features. The images of thirty-five different types of traditional, processed, and western foods were gathered as the study’s input data. The image data for each type of food was repeated 100 times to produce a total of 3500 images. Using the color, shape, and texture information, the food image is retrieved. The hue, saturation, and value (HSV) extraction method for color features, the Canny extraction method for shape features, and the gray level co-occurrence matrix (GLCM) method for texture features, in that sequence, were used to evaluate the data in addition to the CNN classification method.Result:The simulation results show that the classification model’s accuracy and precision are 76% and 78%, respectively, when the CNN approach is used alone without the extraction method. The CNN classification model and HSV color extraction yielded an accuracy and precision of 51% and 55%, respectively. The CNN classification model with the Canny texture extraction method has an accuracy and precision of 20% and 20%, respectively, while the combined CNN and GLCM extraction methods have 67% and 69% success rates, respectively. According to the simulation results, the food classification and identification model that uses the CNN approach without the HSV, Canny, and GLCM feature extraction methods produces better results in terms of accuracy and precision model.Novelty: This research has the potential to be used in a variety of food identification applications, such as food and nutrition service systems, as well as to improve product quality in the food and beverage industry.
Exploring Yoga Behaviors among College Students Based on the Multi-Theory Model (MTM) of Health Behavior Change

During college years, perceived stress is the top reported hindrance to well-being and academic success. Data on the acceptance and perceived benefits of yoga among college students are limited. A purposive sample of college students (n = 79) from a course centered on Vinyasa Yoga and Mindfulness Meditation were recruited. Comprehensive yoga journaling data were collected, and a directed content analysis along the constructs of the multi-theory model (MTM) of health behavior change was utilized. The advantages of yoga that emerged were improved physical performance, reduced perceived stress, acceptance of oneself, better well-being, and improved coping. The identified disadvantages were time commitment, lack of motivation, and other competing interests. Learning through small steps, modifications, and identifying multiple sources of confidence helped build behavioral confidence. Practicing at home or at a yoga studio was a common theme for support in the physical environment. Directing negative emotions into purposes was helpful in maintaining the yoga practice. Sustained commitment to the practice also helped maintain the regular performance of yoga. Finally, social support from family, friends, and instructors was vital for continued practice. The study has important ramifications for the development of survey tools for descriptive studies and designing behavior-change yoga interventions in this target population.
Not My Business: How Individuals’ Role Identities Shape Sensegiving During Corporate Sustainability Initiatives

Managing organizational change toward corporate sustainability requires leaders to engage in sensegiving activities to alter organizational members’ interpretation of issues within and outside the organization. However, we still lack detailed insights into how efforts aimed at changing members’ cognitive frames through sensegiving are shaped by differences in members’ roles and role identities. To address this shortcoming, we draw on an 18-month longitudinal case study of a sustainability initiative within a medium-sized firm. We show that role identities shape the effectiveness of sensegiving, since they can lead individuals to dodge, delete, or disregard frame-challenging information. At the same time, persistent differences in frames across individuals within the organization may lead organizational members to constrain, criticize, or counteract others’ role adjustment. By developing a framework that shows how interactions between sensegiving, role identities, and cognitive frames shape organizational change, our work contributes to the literature on corporate sustainability, sensemaking/sensegiving, and role identities.
A Time Series Model of Interest Rates with the Effective Lower Bound

Modeling interest rates over samples that include the Great Recession requires taking stock of the effective lower bound (ELB) on nominal interest rates. We propose a flexible time– series approach which includes a “shadow rate”—a notional rate that is less than the ELB during the period in which the bound is binding—without imposing no–arbitrage assumptions. {{p}}The approach allows us to estimate the behavior of trend real rates as well as expected future interest rates in recent years.
The Impact of Green Marketing Mix on Brand Image of Unilever Indonesia

Today, this environmental issue encourages companies to start looking for a marketing idea that prioritizes marketing campaigns to care for the environment, known as green marketing. The company strives to build an image in the minds of its customers by emphasizing the concept of environmental sustainability. This study aims to understand whether the green marketing mix affects the brand image of Unilever Indonesia. This research applied quantitative research with a survey method. The research proposes a conceptual model consisting of the following variables: green product, green price, green place, green promotion, and brand image. The results show that all independent variables partially affect the brand image variable. The most influential variables are green products and green prices. The target population for this study was Unilever’s customers, using a sampling technique in the form of purposeful sampling, with 160 respondents. The results of this study show that each independent variable affects the dependent variable brand image. Based on the findings, this study provides theoretical and managerial implications for companies building a positive and strong brand image. Keywords: green marketing, brand image, marketing strategy
Rule Design for Interpretable En Route Arrival Management via Runway-Flow and Inter-Aircraft Control

There are ongoing research efforts to implement En route arrival manager (AMAN), which decides arrival runways and controls cruise speed in en route airspace. Air traffic control operations that regulate arrival air traffic flows from en route airspace are considered effective in mitigating the congestion close to destination airports. Therefore, this study proposes a scientific system design for operationally feasible En Route AMAN assisting air traffic controllers (ATCos) through runway-flow and inter-aircraft control. Herein, we devise an airline-oriented runway assignment rule that selects a target minimizing arrival taxi time in case of over-demand according to the maximum estimated through the stochastic distribution of inter-aircraft time and runway occupancy time. We also formulate speed control rules based on inter-aircraft spacing using simulation-based optimization and decision tree analysis to visualize the distinct strategies and rules for the traffic responsible for each ATCo. Furthermore, an agent-based simulation is performed to evaluate the system effectiveness in reducing the arrival delay. The simulation indicates 20-d arrival and departure at the Tokyo International Airport, Japan, between 06:00 and 23:00. The results show that the designed IF–THEN rules reduce the total arrival sequencing delay time and arrival taxi time by 21% (median, 55.8 s) and 6.9% (median, 24.6 s). Our findings suggest that truly optimal scheduled time of arrival (STA) and operationally feasible rules for ATCos could promise congestion relief while ensuring the interpretability and possibility of En Route AMAN implementation.
Impact of Assimilating Aircraft Reconnaissance Observations on Tropical Cyclone Initialization and Prediction Using Operational HWRF and GSI Ensemble–Variational Hybrid Data Assimilation

This study evaluates the impact of assimilating high-resolution, inner-core reconnaissance observations on tropical cyclone initialization and prediction in the 2013 version of the operational Hurricane Weather Research and Forecasting (HWRF) Model. The 2013 HWRF data assimilation system is a GSI-based hybrid ensemble–variational system that, in this study, uses the Global Data Assimilation System ensemble to estimate flow-dependent background error covariance. Assimilation of inner-core observations improves track forecasts and reduces intensity error after 18–24 h. The positive impact on the intensity forecast is mainly found in weak storms, where inner-core assimilation produces more accurate tropical cyclone structures and reduces positive intensity bias. Despite such positive benefits, there is degradation in short-term intensity forecasts that is attributable to spindown of strong storms, which has also been seen in other studies. There are several reasons for the degradation of intense storms. First, a newly discovered interaction between model biases and the HWRF vortex initialization procedure causes the first-guess wind speed aloft to be too strong in the inner core. The problem worsens for the strongest storms, leading to a poor first-guess fit to observations. Though assimilation of reconnaissance observations results in analyses that better fit the observations, it also causes a negative intensity bias at the surface. In addition, the covariance provided by the NCEP global model is inaccurate for assimilating inner-core observations, and model physics biases result in a mismatch between simulated and observed structure. The model ultimately cannot maintain the analysis structure during the forecast, leading to spindown.
Deciphering the sensitivity of urban canopy air temperature to anthropogenic heat flux with a forcing-feedback framework

The sensitivity of urban canopy air temperature ( Ta ) to anthropogenic heat flux ( QAH ) is known to vary with space and time, but the key factors controlling such spatiotemporal variabilities remain elusive. To quantify the contributions of different physical processes to the magnitude and variability of ΔTa/ΔQAH (where Δ represents a change), we develop a forcing-feedback framework based on the energy budget of air within the urban canopy layer and apply it to diagnosing ΔTa/ΔQAH simulated by the Community Land Model Urban over the contiguous United States (CONUS). In summer, the median ΔTa/ΔQAH is around 0.01 K W m−2−1 over the CONUS. Besides the direct effect of QAH on Ta , there are important feedbacks through changes in the surface temperature, the atmosphere–canopy air heat conductance ( ca ), and the surface–canopy air heat conductance. The positive and negative feedbacks nearly cancel each other out and ΔTa/ΔQAH is mostly controlled by the direct effect in summer. In winter, ΔTa/ΔQAH becomes stronger, with the median value increased by about 20% due to weakened negative feedback associated with ca . The spatial and temporal (both seasonal and diurnal) variability of ΔTa/ΔQAH as well as the nonlinear response of ΔTa to ΔQAH are strongly related to the variability of ca , highlighting the importance of correctly parameterizing convective heat transfer in urban canopy models.
ROLE OF SELENIUM AND VITAMIN E IN LACTOSE BASED EXTENDER ON SEMEN CRYOPRESERVATION OF BUFFALO BULL (BUBALUS BUBALIS)

This study was designed to evaluate the supplementation of various concentrations of selenium and vitamin E and combinations in lactose egg-yolk-glycerol based extender on cryosurvival of buffalo bull semen. The semen of four healthy donor bulls and ejaculates (n= 80) were processed and pooled. The collected semen was extended with the experimental extenders and then frozen. The frozen straws of semen were thawed and analyzed for motility, viability, acrosomal integrity and functional integrity of sperm. The study indicated that combined use of selenium and vitamin E @ 2μg/ml + 0.75 mg/ml supplemented in extender had significantly (P<0.05) increased sperm motility, viability, acrosomal integrity and functional membrane integrity. Selenium (2μg/ml) in freezing extender also improved semen cryosurvival. The provision of selenium (3μg/ml) alone and in combination with Vitamin E (1 mg/ml) did not provide any beneficial impact on sperm cryosurvival. It was concluded that addition of selenium and vitamin E alone in fructose lactose egg yolk glycerol extender increased the cryosurvival of buffalo sperms.
Testing a Framework of Math Progress Indicators for ESSA: How Opportunity to Learn and Instructional Leadership Matter

Purpose: To advance a framework of indicators that promotes implementation of math standards under the Every Student Succeeds Act (ESSA), we tested a conceptual model of the resources and processes within schools that influence the opportunity to learn mathematics (OTL) in the classroom using a recent administration of the 2011 Trends in International Math and Science Study (TIMSS 2011). There is potential benefit to state departments and system-level practitioners from more information about how schools might influence student access to college and career-ready curriculum. Such information has the potential to shape the kinds of indicators stakeholders put in place to target problems and formulate solutions for math learning. Research Design: To test the fit of our hypothesized model of math content and instruction OTL, we applied structural equation modeling techniques to school and classroom data from 425 fourth-grade U.S. math teachers in TIMSS 2011. Findings: We found a direct influence of instructional leadership on OTL math instruction in the classroom and teacher participation in math professional development. Content-specific resources indirectly influenced both OTL math instruction and content through teacher preparedness. Conclusions: The results of this study demonstrate a potential framework through which school leaders can support the preparedness of teachers in providing students with equitable access to coherent, focused, and rigorous math content.
Geometric potential: An explanation of nanofiber’s wettability

Similar to Casimir force, nanofibers have a potential that attracts water molecules, while the porosity of the nanofiber mat produces a repelling force. Wetting property of a nanofiber mat is a result of combination of the forces and gravity. A new concept, the geometric potential or the boundary-induced force, is introduced to elucidate the basic property of wetting. Various nanofiber mats with different fiber morphologies are fabricated by the bubble electrospinning. The paper concludes that superhydrophobic properties of nanofiber mat depends upon mainly fiber morphology and porous structure of the mat, hydrophilic properties of ZnO nanorods will not affect the water contact angle much. Key word: Casimir force , nanofibers, ZnO nanorods, wetting properties
Added Value Plastic Waste Recycling Post-pandemic as a Circular Economy in Indonesia

Employee performance is crucial in assessing one’s capacity to compete in the workplace. Good performers will be able to help the business achieve its objectives. This investigation on employee performance takes the shape of a proposed study. This study aims to determine the effect of recruitment, training, and motivation on employee performance. Companies must be able to implement sound strategies and make changes to HR management because business conditions are constantly changing. The fulfillment of HR through recruitment will greatly determine the development of an organization because HR is the most important source of capital. In conducting recruitment, you must look for human resources with competencies that match the company needs. Furthermore, the company must have a clear and measurable training program so that the HR recruited are able to complete all their work properly. Moreover, the role of motivation also determines a person’s work as expected by the company. Many previous studies have stated that there is a link between recruitment, training, and motivation on employee performance. The results are still in the form of a proposal study; for more details whether there is an effect of each variable, it is necessary to analyze research data. Keywords: recruitment, training, motivation, employee performance
A 3D Multilevel Heterostructure Containing 2D Vertically Aligned MoS2 Nanosheets and 1D Sandwich C-MoS2-C Nanotubes to Enhance the Storage of Li+ Ions

To overcome the disadvantages of the MoS2 anode for LIBs in terms of low intrinsic conductivity, poor mechanical stability, and adverse reaction with electrolytes, a 3D multilevel heterostructure (VANS-MoS2-CNTs) has been successfully prepared by a simple hydrothermal method followed by thermal treatment. VANS-MoS2-CNTs are made up of 2D vertically aligned MoS2 nanosheets (VANS) and 1D sandwich C-MoS2-C nanotubes (CNTs). The sandwich-like nanotube is the core part, which is made up of the MoS2 nanotube covered by carbon layers on both side surfaces. Due to the special heterostructure, VANS-MoS2-CNTs have good conductivity, high structured stability, and excellent Li+/electron transport, resulting in high discharge capacity (1587 mAh/g at a current density of 0.1 A/g), excellent rate capacity (1330 and 730 mAh/g at current densities of 0.1 and 2 A/g, respectively), and good cyclic stability (1270 mAh/g at 0.1 A/g after 100 cycles).
Stop chasing unicorns of climate inaction: Annulling the value–action gap, introducing persons

In this article, we argue that it is necessary to cut the Gordian knot that is the value–action gap in climate action research. We do this theoretically by demonstrating the link connecting research findings on the gap with mainstream environmental psychology’s implicit theory of human conduct, and we do it empirically by demonstrating the field’s own finding: that this theory of human conduct is a poor fit with reality. We present an existing, alternative theory of human conduct and illustrate the value of this alternative with empirical data. We show that (a) there are theories that offer a better fit with reality, one of them being the person perspective and (b) according to these theories and the empirical example, people do not show value–action gaps (i.e., no void) but rather very meaningful considerations of and prioritizations among their many intertwined motives.
SUBJECT VS. OBJECT RELATIVES: WHAT CAN ROMANIAN CHILDREN TELL US ABOUT THEIR ACQUISITION?

The present study addresses the question of intervention effects in subject and object relative clauses and aims at evaluating to what extent Romanian children make use of syntactic and morphological material in order to disambiguate between the two structures. The comprehension of relative clauses was tested with a sentence-scenario matching task in two experiments that manipulated the type of DP appearing as intervener in object relatives: the subject ('the intervener') was either a full nominal expression, or a null subject. In both cases, the phi-features of the subject matched those present on the head of the object relative clause ('the target'). The results obtained show that Romanian children (aged 4 to 6) comprehend subject relatives better than object relatives. This suggests that children have difficulties computing structures that involve movement across an intervener whose argumental features are properly included in the set of features of the target.
Effectiveness of m-health based self-management on self-efficacy in patients with cancer: A systematic review and meta-analysis

Introduction: M-health has been developed and tested through studies in various settings and found was useful for providing knowledge and experience for nurses in cancer care settings. However, none has synthesized the effectiveness of m-health on self-management of patients with cancer. To evaluate the effect of interventions using mobile-based application on patient’s self-management. Outcome measures were patients’ medical adherence, self-efficacy and self-management level and health literacy. Method: The review is reported in accordance with the guidelines of the PRISMA statement. A systematic was conducted in five databases. Randomized controlled trials and quasi-experimental trial were eligible for inclusion. Results: Eight studies were including. There was no significant effect on self-efficacy after interventions using mobile-based applications (SMD = 0.36, CI 95%, [ 0.16, 0.56 ], p < 0.00006). Qualitative synthesis shows that the use of m-health can improve changes in health behavior, health literacy and physical activity. Conclusion: M-health app interventions may improve self-efficacy in cancer patients. Meanwhile, changes in health behavior in patients can be significantly improved using m-health based self-management. M-health can be integrated into health services for the management of patients with cancer.
Storage Quality Changes in Craft and Industrial Blueberry, Strawberry, Raspberry and Passion Fruit-Mango Sorbets

Sorbets are a popular dessert, especially during hot summer days. They can also have health-promoting qualities, mainly due to the nutritional value of the fruit from which they are made. The production technology can also have an impact on the final nutritional quality of the sorbets. This paper presents a comparative assessment of the quality of industrial fruit sorbets and their craft analogs. Sorbets with the following flavors were selected for the research: blueberry, strawberry, raspberry, and passion fruit with mango. An organoleptic evaluation was performed, and the overrun, melting resistance, active acidity (pH), color in the CIE Lab system, antiradical activity (DDPH method), and content of vitamin C and total polyphenols were determined. The research revealed the differences between sorbets produced from different fruits as well as the differences depending on the production method between products made of the same type of fruit. Craft sorbets were found to be better than industrial sorbets, and storage time had a significant effect on the sorbets’ quality. In terms of organoleptic characteristics, craft mango-passion fruit sorbet turned out to be the best; in terms of antioxidant properties, craft raspberry and strawberry sorbets were the best, and these two sorbets also showed good, stable overrun and melting resistance values during storage.