Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and analysis of clover leaf‐shaped fractal antenna integrated with stepped impedance resonator for wireless applications

A novel multi‐band clover leaf‐shaped fractal antenna with integrated filtering to reduce higher order harmonics has been proposed. Existing microstrip filtering antennas endure limited gain, narrow impedance bandwidth, and undesired frequency radiations. The proposed antenna is designated to operate simultaneously in following operating bands: 3.35–3.6 GHz for WiMAX, 5.156–5.825 GHz for WLAN, 5.7–6.4 GHz for Intelligent Transport Systems (ITS), and 7.7–8.5 GHz for ITU‐R applications. The proposed patch is realized by combining two semi‐fractal ring microstrip patches like the geometry of a three‐leaf clover with dual‐layered stepped impedance resonator (SIR) integrated at bottom layer of the second substrate. The SIR bandpass filter exhibits odd and even modes suppresses higher order harmonics and undesired radiations. An aperture coupled feeding in the proposed patch offers better bandwidth and impedance matching. The number of iterations is increased to get the desired multiband characteristics. The proposed microstrip fractal antenna has the geometrical dimensions of 50 × 50 × 2 mm3. It is observed that both the simulated and measured results offers peak gain of >5 dBi and better return loss (S11), that is, 3.3–3.6‐GHz band offers S11 of −33.73 dB, 5.15–5.825‐GHz band offers S11 of −36.69 dB, and 5.795–6.4‐GHz band shows the S11 of −36.25 dB, and for 7.725–8.5 GHz, the return loss is −33.209 dB. Harmonics at 6.42 GHz (1.8f0), 7.37 GHz (1.5f0), and 9.04 GHz (1.73f0) has been suppressed and shows linear polarization in the WLAN band and circular polarization in the ITU‐R and WiMAX bands.
How Parents’ Stereotypical Beliefs Relate to Students’ Motivation and Career Aspirations in Mathematics and Language Arts

Despite progress, gender gaps persist in mathematical and language-related fields, and gender stereotypes likely play a role. The current study examines the relations between parents’ gender-related beliefs and their adolescent child’s motivation and career aspirations through a survey of 172 parent-child dyads. Parents reported their gendered beliefs about ability in mathematics and language arts, as well as their prescriptive gender role beliefs. Students reported their expectancies and values in these two domains, as well as their career aspirations The results of path models suggested that parents’ ability stereotypes about language boosted girls’ motivation for language arts, thereby nudging them away from STEM pathways. Girls’ career aspirations stemmed not only from their valuation of the corresponding domain, but also from their valuation of competing domains. Such findings highlight the need to consider multiple domains simultaneously in order to better capture the complexity of girls’ career decisions. For boys, parents’ language ability stereotypes were directly related to mathematical career aspirations. These results suggest that stereotypes that language arts is not for boys push them instead toward mathematics. Our study also highlighted the unique role of parental beliefs in traditional gender roles for boys’ motivation and career aspirations. Specifically, parents’ gender role stereotypes directly related to less interest in language arts only among boys. This highlights that research into gender gaps in female-dominated fields should consider stereotypes related to appropriate behavior and social roles for boys.
Assessment of Metabolic Adaptations in Periparturient Dairy Cows Provided 3-Nitrooxypropanol and Varying Concentrate Proportions by Using the GreenFeed System for Indirect Calorimetry, Biochemical Blood Parameters and Ultrasonography of Adipose Tissues

Methanogenesis in ruminants contributes to both greenhouse gas emissions and feed energy losses whereby the latter becomes specifically important in energy-deficient periparturient cows. It was hypothesized that increased concentrate feed proportions (CFP) and feeding with the methane inhibitor 3-nitrooxypropanol (3-NOP), as well as their potential synergism, improve the energy status of peripartal cows. Periparturient dairy cows were fed low or high dietary CFP either tested without or combined with 3-NOP. The GreenFeed system was used to calculate the metabolic respiration quotient (RQmetabolic) and tissue energy retention (ERtissue) by methods of indirect calorimetry. The calorimetrically estimated ERtissue coincided with a conventionally calculated energy balance except for the antepartal period. Neither CFP nor 3-NOP affected the ultrasonographically assessed lipomobilization in adipose depots. In the group fed 3-NOP and a high concentrate feed proportion, the RQmetabolic significantly rose over the course of the experiment and the ERtissue was also increased. Serum non-esterified fatty acid concentrations were lower in the 3-NOP groups albeit ß-hydroxybutyrate (BHB) remained unaffected. Higher CFP reduced BHB and increased blood glucose levels. In conclusion, 3-NOP and high CFP improved the energy budget of the cows in an interactive manner, which was, however, not apparent in all of the examined parameters. The application of the GreenFeed system for indirect calorimetry is a promising approach, which needs further validation in the future.
Air biking as a new way for stress testing

Stress testing is an integral part of assessing cardiorespiratory fitness. Air bike is a cardio machine that has lately become quite popular among both professional athletes and general public. It is a type of stationary bike equipped with two handles allowing also upper-body activity. The aim of this study was to verify efficiency of stress ramp test to failure using air bike and to assess correlation between air bike performance and strength, endurance, and anthropometric parameters. The experimental group comprised 20 physically active individuals (average age: 22.1). The spiroergometric test protocol on air bike consisted of 3-minutes levels with no break. The testing led to high physiological values (HRmax = 189.7 bpm, RERmax: 1.12), the average test duration was 14 minutes. The most statistically significant connection (p = 0.01) with air bike performance was identified in fat-free mass (FFM) (0.86), back squat (0.83), bench press (0.84), 2 km row (−0.85). A less significant connection was discovered with VO2peak (0.68). Air bike was proven a suitable method for stress testing. Air bike performance depends mostly on FFM and strength parameters. Due to its specific usage (lower cadence, more strength focused work) that differs from running or stationary bike, it can be a suitable alternative for certain individuals or specific groups.
New Media Marketing Strategy Optimization in the Catering Industry Based on Deep Machine Learning Algorithms

With the in-depth development of new-generation network technologies such as the Internet, big data, and cloud intelligence, people can obtain massive amounts of information on mobile phones or mobile platforms. The era of unreachable big data has arrived, which raises questions for the development of corporate marketing. With the development of Internet technology, people use mobile terminals for longer and longer periods of time. New media has gradually become the mainstream of the media arena. It has distinctive features such as freedom to find audiences, diverse content forms, and timeliness of information release, which has changed the traditional. The marketing model has a profound impact on the development of the market. This article uses relevant theories, such as new media, marketing, and catering industry marketing strategies, studies the related concepts and characteristics of new media, clarifies the impact of the development of new media on the catering industry and audience groups, and studies the impact of the catering industry from multiple dimensions. Based on the development factors in the new media environment, combined with marketing theory, it puts forward suggestions for catering companies to use new media to carry out marketing planning in product innovation, improving information channels, creating network events and topics, and promoting innovation and health in the catering industry. And a marketing strategy is proposed based on deep machine learning algorithms; including a cloud server, the cloud server communicates with the e-commerce software platform and the input of physical sales is recorded. The adopted cloud server is connected with data collection, data processing, and communication module. The communication module is connected with a deep machine learning algorithm system; that is, deep machine learning algorithm system is connected with a sales platform in communication. The sales platform is connected with advertising settings and advertising, and the advertising is electrically connected with an algorithm of advertising delivery methods. Advertisement delivery method algorithm communication is connected to the cloud server. This article uses deep machine learning algorithms to process the data information to make the data information easy to view and clear. The advertisement delivery method algorithm calculates the best way of advertising and then calculates the advertisement to deliver.
Increasing Firm Value Based On External Governance And Modern Finance theory

It is based on the theory of firm and external agency that through investment, funding, and dividend decisions to achieve max firm value. The purpose of this study is to re-examine this model for cases in Indonesia. The sample population of this study comprised 18 companies listed on the Indonesia stock exchange that become the members of LQ-45 index from 2016-2019. The samples were selected by purposive sampling. This study used panel data regression analysis program with Eviews-9 application to answer the research objectives. The results indicated that investment decisions had a significant positive effect, funding decisions had a significant negative effect and dividend decisions had no significant negative effect on firm value.
Delayed Bronchial Obstruction following Esophageal Stent Implantation: A Case Report

Airway compression is a rare complication of esophageal stent placement. With the introduction of self-expanding metal stents, the incidence of bronchial obstruction by esophageal stents has decreased. Delayed external airway compression after esophageal stent implantation is rarely reported. We describe a case of left main bronchial obstruction after self-expandable esophageal stent placement. A 70-year-old patient with advanced esophageal cancer visited the emergency room (ER) with worsening cough and dyspnea. He had received palliative concurrent chemoradiotherapy after esophageal self-expanding metal stent (SEMS) insertion three months ago. One month before the ER visit, additional esophageal SEMS placement (stent-in-stent) was performed owing to the development of a tracheoesophageal fistula. After hospitalization, chest radiography revealed a patchy consolidation in the left lower lobe. A diagnosis of pneumonia was made, and the patient was treated with antibiotics. Seven days after antibiotic treatment, the patient developed a fever and severe dyspnea. Auscultation revealed the absence of breath sounds in the left hemithorax. A follow-up chest radiograph showed a white-out of the left hemithorax. Flexible bronchoscopy revealed luminal narrowing of the left main bronchus (LMB) due to external compression. Chest computed tomography further demonstrated compression of the LMB by esophageal stents. This case highlights that esophageal SEMS can present as an emergent and often life-threatening airway obstruction.
Needs Assessment: Integrating Medical Simulation in Residencies Programs in Saudi Arabia

ObjectivesThe use of simulation in the curricula of training programs has expanded significantly over the last decade. This study conducted a simulation needs assessment on diverse ongoing residency training programs supervised by the Saudi Commission for Health Specialties (SCFHS) in Saudi Arabia. The goal was to introduce a standardized methodological approach to integrate simulation as a teaching tool for any ongoing training programs.Methoda mixed-methods approach is used in four steps to focus on top educational needs and integrate simulation within the curriculum. In the first step, thirty-eight residency training programs underwent a selection process using the scoring criteria tool. Nine residency training programs were selected to be target programs. This step was followed by champions recruitment, where two faculty representatives from each specialty were oriented about the steps to be process facilitators. The third step is targeted audience needs assessment, consisting of four phases: curriculum review, targeted audience survey, a focus group of stakeholders' interview, and selecting the top identified educational needs. A metrics-based approach was used to rank the list of skills and behaviors by invited stakeholders. The last step was to build common themes using an integration simulation sheet to facilitate integrating simulation into the program curriculum.ResultsOut of 38 programs, nine top-ranked specialties completed the process, and their roadmaps were developed. A critical appraisal facilitated by champions and simulation experts helped the stakeholders to identify the top needed skills and behaviors in each of the nine specialties. The final score proportion for each skill and behavior of the entire combined list was calculated then ranked. The proportion of top needed list of skills and behaviors were obtained as followings: Obstetrics and Gynecology 10 skills and behaviors concluded out of 84 (10/84), Emergency Medicine (ER) 80/242, Intensive care unit (ICU) 20/139, Internal Medicine (IM) 37/102, Pediatric 82/125, Ear Nose Throat (ENT) 49/125, General Surgery (GS) 55/114, Plastic Surgery 24/165, and Family Medicine (FM) 59/168.ConclusionsFindings from this process could provide a standardized approach to supervising bodies at a country level and support decision-makers with prioritizing criteria to be used in the needs assessment to integrate simulation in any other ongoing residency training programs.
The MARSYAS Software: An Efficient Tool for the Rational Design of Assembly Systems

Today, companies have to face an ever growing competition. In order to optimize production costs, many research efforts have been made for the last two decades. However, most of the resulting methods and techniques are only dedicated to components fabrication. Assembly activities were quite put aside. Particularly, there is not any complete method for rationally designing flexible assembly systems. Since many years, one of the aims of the LAB is to improve such a method. This paper presents its last significant results in this field. The LAB method divides the design process in two main stages: the first one determines the assembly plans and the second one progressively defines the structure of the assembly system. The first stage is today well known, and does not interest us in this paper, while the second stage, the synthesis process for the assembly systems, is our purpose. It is divided into three distinct steps : - the static study is the description of every operating mode which may be adopted to perform every candidate assembly operation. In the same way, it defines the entire list of the equipment that are able to perform the elementary tasks, which result of these operating modes - the dynamic study consists in a systematic combination of the different possibilities of every local materialization. It classifies the different resulting solutions by comparison of their respective temporal performances and costs - the layout study defines the spatial organization of the resulting assembly systems. The MARSYAS software was written in order to support the dynamic study. On the one hand, this paper describes the pertinent criteria and discusses their order of using into the software, in attempt to restrain the number of candidate assembly system structures. On the other hand, this paper presents the results of an experiments campaign about the software and discusses its application fields and its validity limits.
Genomic signatures of host-specific selection in a parasitic plant

Premise Parasitic plants and their hosts are model systems for studying genetic variation in species interactions across environments. The parasitic plant Striga hermonthica (witchweed) attacks a range of cereal crop hosts in Africa and exhibits substantial variation in performance on different host species. Some of this variation is due to local adaptation, but the genetic basis of specialization on certain hosts is unknown. Methods To identify genomic regions that are strongly differentiated between parasites attacking different host species, we present an alignment-free analysis of S. hermonthica population diversity using whole genome sequencing (WGS) data for 68 individuals from western Kenya. We validate our findings with germination experiments and analyses based on a de novo assembled draft genome. Results Reference-free and reference-based analyses suggest that only a small portion of the S. hermonthica genome is strongly differentiated by host species in populations from western Kenya. Analysis of host-associated k-mers implicated genes involved in development of the parasite haustorium (a specialized structure used to establish vascular connections with host roots) and a potential role of chemocyanins in molecular host-parasitic plant interactions. Conversely, no phenotypic or genomic evidence was observed suggesting host-specific selection on parasite response to strigolactones, hormones exuded by host roots and required for parasite germination. Conclusions This study demonstrates the utility of WGS for plant species with large, complex genomes and no available reference. Contrasting with theory emphasizing the role of early recognition loci for host specificity, our findings support host-specific selection on later interaction stages, recurring each generation after homogenizing gene flow.
Assessment of primary stability and bone density of maxillary implant overdenture following Osseo densification concept.

Objectives: To evaluate the effect of osseodensification concept using Densah bur on implant primary stability and peri-implant bone density in maxillary implant overdenture. Materials and Methods: Ten completely edentulous patients were selected and two implants were inserted in the maxilla for each patient in a split mouth study design; one implant with the conventional drilling system and one implant with the Densah bur drilling system. Implant primary stability was assessed using ostell device at the time of implant placement and at prosthetic rehabilitation phase (4m). Bone density around the implants was measured using CBCT at 3,6,12 months intervals. Results: Significant difference was found regarding Osstell values as well as bone density measurements between both groups throughout the study period Conclusions: Within the limitation of this study, Densah drilling burs that adopt the osseodensification concept. Have displayed more primary stability compared to the conventional drilling protocol in terms of primary stability as well as bone density for delayed loaded implants used to retain and support maxillary overdentures.
Optimal reset law design based on guaranteed cost control method for Lipschitz nonlinear systems

This article proposes a systematic approach for optimal reset law design of a class of nonlinear systems. By using the guaranteed cost control method, sufficient conditions for the design of optimal reset law are derived in terms of linear matrix inequalities. In an offline design procedure, the reset law is computed that minimizes the upper bound of a quadratic cost function. The proposed method can be implemented for real‐time applications even with small sampling time. The simulation results verify the efficacy and effectiveness of the proposed theoretical results.
Smart Materials Prediction: Applying Machine Learning to Lithium Solid-State Electrolyte

Traditionally, the discovery of new materials has often depended on scholars’ computational and experimental experience. The traditional trial-and-error methods require many resources and computing time. Due to new materials’ properties becoming more complex, it is difficult to predict and identify new materials only by general knowledge and experience. Material prediction tools based on machine learning (ML) have been successfully applied to various materials fields; they are beneficial for modeling and accelerating the prediction process for materials that cannot be accurately predicted. However, the obstacles of disciplinary span led to many scholars in materials not having complete knowledge of data-driven materials science methods. This paper provides an overview of the general process of ML applied to materials prediction and uses solid-state electrolytes (SSE) as an example. Recent approaches and specific applications to ML in the materials field and the requirements for building ML models for predicting lithium SSE are reviewed. Finally, some current obstacles to applying ML in materials prediction and prospects are described with the expectation that more materials scholars will be aware of the application of ML in materials prediction.
URGING COTTON PLANTS TO OVERCOME THE SALT STRESS CONDITIONS IN THE RECLAIMED LAND USING A COMBINATION OF INNOVATIVE AGRICULTRAL FACTORS II-THE RELATIONSHIP BETWEEN YIELD AND SOME CHARACTERS IN EGYPTIAN COTTON

The technique of stepwise multiple regression and correlation analysis was employed to determine the contribution of the various traits to the variation in cotton yield. Data were obtained from yield trials carried out in El-Tahrir Province, El-Behera Governorate, Egypt. during 2009 and 2010 seasons. The mean squares of regression analysis were high for nine acceptance variables (traits), i.e., the number of leaves per plant, leaf area per plant, dry weight of leaves per plant, chlorophyll b, total carotenoids, phosphorus, iron, manganese and lint percentage out of eighteen ones. The total contribution of the previously mentioned traits over all variation in seed cotton yield per plant was 99.46 %. However, the residual effect of other nine traits reduced 0.54 % of the total variation in cotton yield. Seed cotton yield was significantly positive and had the greatest values of correlation coefficients with the number of leaves per plant (0.980), leaf area per plant (0.974) and chlorophyll b (0.970). High increase in the range of performances was detected for nine mentioned traits due to the creation of variation via different treatments under study. Finally, as increased number of leaves per plant, leaf area per plant and chlorophyll b may induce positive impacts on cotton yield under salinity conditions.