Miracle-dz/UniFAR-FTUs huggingface dataset:
Injection Molding of Encapsulated Diffractive Optical Elements

Microstructuring techniques, such as laser direct writing, enable the integration of microstructures into conventional polymer lens systems and may be used to generate advanced functionality. Hybrid polymer lenses combining multiple functions such as diffraction and refraction in a single component become possible. In this paper, a process chain to enable encapsulated and aligned optical systems with advanced functionality in a cost-efficient way is presented. Within a surface diameter of 30 mm, diffractive optical microstructures are integrated in an optical system based on two conventional polymer lenses. To ensure precise alignment between the lens surfaces and the microstructure, resist-coated ultra-precision-turned brass substrates are structured via laser direct writing, and the resulting master structures with a height of less than 0.002 mm are replicated into metallic nickel plates via electroforming. The functionality of the lens system is demonstrated through the production of a zero refractive element. This approach provides a cost-efficient and highly accurate method for producing complicated optical systems with integrated alignment and advanced functionality.
Using Descriptive Analysis to Find Patterns and Trends: A Case of Car Accidents in Washington D.C.

—The descriptive analysis could be used to find the trends and patterns in historical data. In this article, descriptive analysis has been used to describe the car accidents in Washington, D.C., between 2009 and 2020. The dataset was downloaded from the District Department of Transportation (DDOT), the department responsible for car accidents in Washington, D.C. Multiple analytics and statistical models have been applied to find the relationships between different variables and the patterns and trends among the data, such as correlation analysis, confidence interval, One-Way-ANOVA, decision tree, and visualizations. The article aims to find the common reasons for accidents and help DDOT find ways to reduce and eliminate accidents in the area. The statistical and analytical tools examine multiple features to find the patterns and trends among the datasets. Four main findings were found after analyzing the data. First, the main reason for most crashes is drunken people, either drivers or pedestrians. The second finding is that the top reason which causes deadly accidents is speed. Also, we have found that most of the accidents are not dangerous. In addition, we found the top ten streets that contain the highest accident number, and we found that they are located on the north side of the town.
Fast impulsive reconnection and current sheet intensification due to electron pressure gradients in semi‐collisional plasmas

A numerical simulation of forced reconnection and current sheet growth due to inward boundary flows in semi-collisional plasmas is presented, and contrasted with the results of an incompressible resistive MHD simulation in the high-Lundquist-number regime. Due to the presence of electron pressure (or Hall currents) in the generalized Ohm's law, the reconnection dynamics makes an impulsive transition from a slow linear regime to a nonlinear regime characterized by fast reconnection and current sheet intensification at a near-Alfvenic rate. The current sheet spanning Y-points in the early nonlinear regime shrinks and approaches an X-point geometry. The spatial scale of the collisionless parallel electric field is the ion skin depth, and decoupled from the spatial scale of the parallel current which is much narrower and determined by the Lundquist number.
IJLM-02-2021-0131_proof 796..817

Purpose – This study investigates the impact of supplier visibility on the adoption of sustainable practices and supply chain performance. The paper applies contingent Resource-Based View to explain how information sharing with customers and suppliers and supply chain traceability has influenced visibility; visibility has impacted supply chain velocity sustainable practices, and finally, supply chain performance. Design/methodology/approach – This research analyses 263 survey responses from UK retail (grocery) stores’ executives and managers for perishable food during the COVID-19 pandemic. Findings – The research results indicate that both supply chain traceability and information sharing (customers) positively influence visibility. Further, visibility positively influences the adoption of sustainable practices and velocity, positively impacting supply chain performance. However, information sharing with the customer has no significant influence on performance, and information sharing with the supplier has no significant relationship with visibility. Originality/value – This research is the first attempt that explores the contingent Resource-Based View for the perishable food supply chain. Further, the empirical evidence provides meaningful insights for academics and industry by filling a crucial lacuna in the literature.
Video-based Heart Rate Estimation using Embedded Architectures

—Monitoring a driver’s heart rate is an important determinant to his health condition. The monitoring system must be accurate and non restrictive to the user’s actions. Estimating the driver’s change in his usual heart beat pattern can prevent undesirable outcomes. Several methods exist to estimate heart rate without any contact. In this paper, we are focusing on a method that uses remote photoplethysmography (rPPG). rPPG is a technique where heart rate is extracted from a PPG signal. The signal is extracted from the changes in blood flow that corresponds to the color variations recorded through an RGB camera. In this work, a different study that was based on an existing algorithm is presented to determine its processing time. The algorithm we proposed was divided into different global blocks and each block into different functional blocks (FBs). Though evaluating all the blocks’ processing time, it was possible to determine the most time consuming functional blocks. The results are implemented on different architectures: Desktop, Odroid XU4 and Jetson Nano to provide a higher performance.
Optimized Secure Federated Learning for Event Detection in Big Data using Blockchain Mechanism

—Currently, cloud storage in blockchain and federated learning technology provides better security among data transmission and file access. But, in some of the cases, security issues arose. So, to avoid security problems and offer better protection in a cloud environment, a novel optimized buffalo-based Homomorphic SHA blockchain (OBHSB). In this model for accessing the cloud storage data with the key matching method, if any of the unauthenticated users are trying to access the file initially, the system checks the key matching parameter. The proposed model was developed to provide better security in big data presented in the cloud environment. However, the parameters in the proposed model were compared with the existing models to make sure better performance was attained through the proposed model. Attack was considered as an event in this research. In the performance analysis, the performance rate of the proposed model was validated. Subsequently, the case study was developed in this research to explain the working procedure of the proposed design; model performs hashing, encryption, decryption, and key matching mechanisms. The results proposed model is observed to have 100% confidentiality rate after attack.
Communicating encrypted information based on synchronized hyperchaotic maps

In this work, a communication scheme to transmit encrypted information based on synchronized hyperchaotic maps is presented. In particular, we appeal to model-matching approach from nonlinear control theory to synchronize two hyperchaotic R ssler maps. An application to secure communication of confidential information is also given. By using a hyperchaotic cryptosystem, we show that output synchronization of R ssler maps is indeed suitable for encryption, transmission, and decryption of confidential information which can be easily implemented on digital computers for use in E-mail.
THE VEGETATIVE GROWTH RESPONSES OF TAXITHELIUM OBLONGIFOLIUM (SULL. &LESQ.) Z. IWATS (FAMILY SEMATOPHYLLACEAE) CAUSED BY PHOTOPERIOD REGIMES

Bryophytes are useful objects for the investigation of stress-induced cellular responses in plant. Hence the purpose of this research was to study the effect that might occur in the lower plants where growing in condition of extreme climate change. This methodology was designed to examine the vegetative growth responses of Taxithelium oblongifolium (Sull.&Lesq.) Z. Iwats (pleurocarpic moss) to four photoperiods. These included 0 h light/24 h dark (as control), 8 h light/16 h dark, 12 h light/12 h dark and 16 h light/8 h dark in the controlled cabinets. Differences in their relative growth rate and some photosynthetic pigments of their shoot were analyzed among their photoperiod regimes. Maximum relative growth rate were observed for the 8-hour day length after incubating 14 and 21 days ( p <0.05). Whereby, the minimum relative growth rate was detected under 0 h light/24 h dark condition. Moreover, their chlorophyll content (total chlorophyll, chlorophyll a and ratio of chlorophyll a to b) which extracted from the leaves of their shoots did not show significant variations ( p <0.05) among the various photoperiod. However, the chlorophyll b quantity was statistically different significance ( p <0.05). Thus, these results suggested that photoperiod affected the vegetative growth and some photosynthetic pigmentation of the shoots in this lower plant. In addition to, chlorophyll b might be feasibility indicator of the ability of this species to contrasting photoperiod regimes.
Estimating the Directional Spread of Epidemics in Their Early Stages Using a Simple Regression Approach: A Study on African Swine Fever in Northern Italy

The early identification of the spreading patterns of an epidemic infectious disease is an important first step towards the adoption of effective interventions. We developed a simple regression-based method to estimate the directional speed of a disease’s spread, which can be easily applied with a limited dataset. We tested the method using simulation tools, then applied it on a real case study of an African Swine Fever (ASF) outbreak identified in late 2021 in northwestern Italy. Simulations showed that, when carcass detection rates were <0.1, the model produced negatively biased estimates of the ASF-affected area, with the average bias being about −10%. When detection rates were >0.1, the model produced asymptotically unbiased and progressively more predictable estimates. The model produced rather different estimates of ASF’s spreading speed in different directions of northern Italy, with the average speed ranging from 33 to 90 m/day. The resulting ASF-infected areas of the outbreak were estimated to be 2216 km2, about 80% bigger than the ones identified only thorough field-collected carcasses. Additionally, we estimated that the actual initial date of the ASF outbreak was 145 days earlier than the day of first notification. We recommend the use of this or similar inferential tools as a quick, initial way to assess an epidemic’s patterns in its early stages and inform quick and timely management actions.
An Analysis of Bias in Facial Image Processing: A Review of Datasets

—Facial image processing is a major research area in digital signal processing. According to recent studies, most commercial facial image processing systems are prejudiced by bias towards specific races, ethnicities, cultures, ages, and genders. In some circumstances, bias may be traced back to the algorithms employed, while in others, bias can be elicited from the insufficient representations in datasets. This study tackles bias based on insufficient representations in datasets. To tackle this, the research undertakes an exploratory review in which the context of facial image dataset is analyzed to thoroughly examine the rate of bias. Facial image processing systems are developed using widely publicly available datasets since generating datasets are costly. However, these datasets are strongly biased towards Whites and Asians, and other geo-diversity such as indigenous Africans are underrepresented. In this study, 40 large publicly accessible facial image data sets were examined. The races of the datasets used for this study were visualized using the t-distributed Stochastic Neighbor Embedding (t-SNE) visualization method. Then, to measure the geo-diversity and rate of bias of the dataset, k-means clustering, principal component analysis (PCA) and the Oriented FAST and Rotated BRIEF (ORB) feature extraction techniques were used. The findings from this study indicate that these datasets seem to exhibit an obvious ethnicity representation bias, particularly for native African facial images; as a result, additional African indigenous datasets are required to reduce the bias currently present in the most publicly available facial image datasets.
Pre-proof Trustworthy Journalism Through AI

Quality journalism has become more important than ever due to the need for quality and trustworthy media outlets that can provide accurate information to the public and help to address and counterbalance the wide and rapid spread of disinformation. At the same time, quality journalism is under pressure due to loss of revenue and competition from alternative information providers. This vision paper discusses how recent advances in Artificial Intelligence (AI), and in Machine Learning (ML) in particular, can be harnessed to support efficient production of high-quality journalism. From a news consumer perspective, the key parameter here concerns the degree of trust that is engendered by quality news production. For this reason, the paper will discuss how AI techniques can be applied to all aspects of news, at all stages of its production cycle, to increase trust.
Metabolomics and Transcriptomics Revealed a Comprehensive Understanding of the Biochemical and Genetic Mechanisms Underlying the Color Variations in Chrysanthemums

Flower color is an important characteristic of ornamental plants and is determined by various chemical components, including anthocyanin. In the present study, combined metabolomics and transcriptomics analysis was used to explore color variations in the chrysanthemums of three cultivars, of which the color of JIN is yellow, FEN is pink, and ZSH is red. A total of 29 different metabolites, including nine anthocyanins, were identified in common in the three cultivars. Compared with the light-colored cultivars, all of the nine anthocyanin contents were found to be up-regulated in the dark-colored ones. The different contents of pelargonidin, cyanidin, and their derivates were found to be the main reason for color variations. Transcriptomic analysis showed that the color difference was closely related to anthocyanin biosynthesis. The expression level of anthocyanin structural genes, including DFR, ANS, 3GT, 3MaT1, and 3MaT2, was in accordance with the flower color depth. This finding suggests that anthocyanins may be a key factor in color variations among the studied cultivars. On this basis, two special metabolites were selected as biomarkers to assist in chrysanthemum breeding for color selection.
Review of Research on Coded Computing

By integrating the coding theory with distributed computing and exploiting flexible coding methods, coded computing manages to relieve the transmission burden and the negative effects of stragglers. In this way, it improves the overall performance of distributed computing systems. Meanwhile, coded computing schemes are also designed and used to provide security and privacy guarantees for distributed computing systems, where mechanisms, such as error-correcting and data masking, are generally adopted. Due to the advantages of coded computing in communication, storage and computational complexity, it has attracted extensive attention and has become a popular direction in the field of distributed computing. In this survey, the background of coded computing is reviewed with its definition and core ideology clarified. Afterward, the existing coding schemes for communication bottleneck, computation delay and security privacy are introduced and comparatively analyzed in detail. Finally, future research directions and technical challenges of coded computing are analyzed and introduced to provide valuable references for related researchers.
On Evaluation of Document Classifiers using RVL-CDIP

The RVL-CDIP benchmark is widely used for measuring performance on the task of document classification. Despite its widespread use, we reveal several undesirable characteristics of the RVL-CDIP benchmark. These include (1) substantial amounts of label noise, which we estimate to be 8.1% (ranging between 1.6% to 16.9% per document category); (2) presence of many ambiguous or multi-label documents; (3) a large overlap between test and train splits, which can inflate model performance metrics; and (4) presence of sensitive personally-identifiable information like US Social Security numbers (SSNs). We argue that there is a risk in using RVL-CDIP for benchmarking document classifiers, as its limited scope, presence of errors (state-of-the-art models now achieve accuracy error rates that are within our estimated label error rate), and lack of diversity make it less than ideal for benchmarking. We further advocate for the creation of a new document classification benchmark, and provide recommendations for what characteristics such a resource should include.
Effect of different herbicidal treatments on weeds, maize productivity and nutrient uptake by weeds and maize ( Zea mays L.)

A field experiment was conducted at Advanced Centre for Rainfed Agriculture, Rakh Dhiansar of Sher-e- Kashmir University of Agricultural Sciences and Technology of Jammu during the Kharif seasons of 2016-2017 and 2017-2018 to identify the best herbicide option for weed management in maize ( Zea mays L.) The experimental field was highly infested with broad-leaved weeds: Amaranthus viridis and Solanum nigrum; grasses: Echinochloa colona, Digitaria sanguinalis, Acrachne racemosa, Eragrostis tenella, Eleusine aegyptium and sedges: Cyperus rotundus and Cyperus iria. The tank mix application of tembotrione 100 g/ha + atrazine 750 g/ha at 15-20 days after seeding (DAS) reduced the total weed density, biomass and nutrient uptake by weeds and increased the maize grain, stover yields and nutrient uptake by maize crop. It was at par with post-emergence application (PoE) of tank mixed tembotrione 100 g/ha + atrazine 500 g/ha 15-20 DAS; tembotrione 100 g/ha + halosulfuron 67.5 g/ha PoE 15-20 DAS; tembotrione 100 g/ha + halosulfuron 52.5g/ha PoE at 15 DAS and atrazine 1000 g/ha 0-3 DAS followed by ( fb ) tembotrione 100 g/ha15-20 DAS during both the years. Highest B: C ratio (3.11) was obtained with tembotrione 100 g/ha + atrazine 500 g/ha PoE at 15-20 DAS followed by the treatment of tembotrione 100 g/ha + atrazine 750 g/ha PoE at 15-20 DAS.
MIT Open Access Articles Relativistic corrections to nonrelativistic effective field theories

In this paper we develop a formalism for studying the nonrelativistic limit of relativistic field theories in a systematic way. By introducing a simple, nonlocal field redefinition, we transform a given relativistic theory, describing a real, self-interacting scalar field, into an equivalent theory, describing a complex scalar field that encodes at each time both the original field and its conjugate momentum. Our low-energy effective theory incorporates relativistic corrections to the kinetic energy as well as the backreaction of fast-oscillating terms on the behavior of the dominant, slowly varying component of the field. Possible applications of our new approach include axion dark matter, though the methods developed here should be applicable to the low-energy limits of other field theories as well.
Effect of health education supported with motivational ınterviewing on medication adherence of COVID-19 patients receiving home care

Objective : The objective of this study is to determine the effect of health education supported with motivational interviewing on medication adherence of COVID-19 patients receiving home care. Materials and Methods: This study was carried out in Gelibolu between the dates of June 2021 - January 2022. Participants who meet the inclusion criteria of the study were assigned to the intervention group (50) and control group (50) using the block randomization method. Data of the study were obtained with the “Sociodemographic Questionnaire Form” and “Medication Treatment Satisfaction Level” which were developed by the researchers according to the literature and also “Medication Adherence Reporting Scale”. The “Medicine Calendar” was used to track the drug use status of all participants. Data obtained were evaluated with the statistical analysis of percentage, frequency, mean, Mann Whitney U, Perason Ki Square and Wilcoxon test using SPSS 22.00 program. Results and Conclusion : At the end of the study, the mean “Medication Adherence Reporting Scale” score of the participants in the intervention group (16.12±5.37) was higher than the mean score of the participants in the control group (9.96±5.62) (p<0,05). In group comparison; ıt was observed that the mean score of the “ Medication Adherence Reporting Scale” (16.12±5.37) of the intervention group after the study was statistically significantly increased compared to the mean score of the pre-study scale (11.02±4.03) (p<0,05). However, it was evaluated that the mean score of the intervention group was below the cut-off point (20.00) and drug compliance was still at a low level. In the in-group comparison of the control group; It was observed that the mean score of the “Medication Adherence Reporting Scale” (9.96±5.62) after the study decreased statistically significantly compared to the mean score of the pre-study scale (11.40±4.93) (p<0,05). This has demonstrated that health education supported by motivational interviewing provided by nurses is an important component to improve medication adherence.
A Comparative Analysis of CET, IELTS and TOFEL for English Acquisition

English is the most widely distributed language in the world. With more and more international cultural exchanges, there are a huge number of people who learn English as a second language. English has always been the most popular second language in China. International language tests such as IELTS and TOEFL are becoming more common in China as more Chinese students go abroad for undergraduate or graduate studies. Compared with the two international English proficiency tests, CET-4 and CET-6 are basic English proficiency tests in China. This type of test primarily examines the student's basic vocabulary quantity to comprehend the situation and the grammar knowledge of the actual application. While IELTS focuses more on the assessment of logical thinking and language expression skills, reading and speaking tests are the most difficult assessment tasks. TOEFL attaches more importance to the students' listening, speaking, reading, and writing skills combined with the use of English level, and the difficulty of each part is evenly distributed, strong academically.