Miracle-dz/UniFAR-FTUs huggingface dataset:
Pesticide distribution and ecotoxicological risk in the Ayuquila-Armería river

Pesticides make an important contribution to increased global agricultural production; however, their intensive use represents a risk to biota in both the short and long terms. The physical and chemical characteristics of these compounds, as well as their versatility in agricultural, domestic, and public health use, have led them to become widely present in the environment. The objectives of this study were to evaluate the spatiotemporal distribution of pesticides in the surface water of the Ayuquila-Armería river and to conduct an ecotoxicological risk assessment in algae, macroinvertebrates, and fish. The presence of 11 analytes was determined, being λ-cyhalothrin, ametrine, and malathion the pesticides with highest concentrations, and carbendazim, malathion, and glyphosate those with the highest frequency of detection (> 70 %). The number of pesticides detected per sample presented no significant temporal differences, and there was no significant effect of the crops or vegetation adjacent to the study sites on the number of pesticides detected per sample. The sampling conducted in November 2019 (wet season) presented the greatest difference in concentrations of most pesticides, compared to the other samplings. The ecotoxicological risk assessment showed that the macroinvertebrates (Daphnia magna) are the most vulnerable to the concentrations of pesticides in surface water, followed by fish and algae. Regulation of the importation, trade, and management of malathion and λ-cyhalothrin is crucial in order to reduce the presence of their residues and the risks associated with them.
Explicit Formulae for Noncommutative Deformations of CP^N and CH^N

We give explicit expressions of a deformation quantization with separation of variables for CP^N and CH^N. This quantization method is one of the ways to perform a deformation quantization of Kahler manifolds, which is introduced by Karabegov. Star products are obtained as explicit formulae in all order in the noncommutative parameter. We also give the Fock representations of the noncommutative CP^N and CH^N.
Characteristic Analysis of a Chipless RFID Sensor Based on Multi-Parameter Sensing and an Intelligent Detection Method

Chipless radio frequency identification (RFID) technology has been widely used in the field of structural health monitoring (SHM), but most of the current research mainly focuses on the detection of mechanical properties and there are few studies on the multi-physical parameters (for example, temperature and humidity) in the climatic environment around the structure. Thus, it is necessary to design a small and compact sensor for multi-parameter detection. This paper proposes a multi-parameter chipless RFID sensor based on microstrip coupling, which supports 4-bit ID code and integrates two detection functions of temperature and humidity. Through linear normalization fitting, the sensitivity of the sensor is about 2.18 MHz/RH in the ambient relative humidity test and the sensitivity of the sensor is about 898.63 KHz/°C in the experimental test of water bath heating from 24.6 °C to 75 °C. In addition, this paper proposes an engineering application detection method, designs a lightweight dynamic spectrum detection and wireless transmission platform based on a lightweight vector network analyzer (VNA) and realizes the real-time extraction and transmission of RFID spectrum sensing data. The means are more flexible and economical than traditional experimental scenarios.
Phytochemical screening and in vitro antioxidant activity analysis of leaf and callus extracts of Allophylus serratus (ROXB) KURZ.

Allophylus serratus is an important medicinal plant whose leaves, fruits, roots and stem have been in use in traditional medicines. The present study is aimed to carry out preliminary phytochemical analysis and to determine antioxidant activity of leaf and callus water, methanol, ethyl acetate, chloroform and petroleum ether extracts of A. serratus. The extracts were subject to various qualitative and quantitative tests for phytochemical analysis. An in vitro antioxidant activity of the extracts was investigated by DPPH (2,2-diphenyl-1-picrylhydrazyl), ABTS++,(2,2-azinobis(3-ethyl-benzothiozoline)-6-sulfonic acid) and FRAPS(Ferric reducing/ antioxidant power) assays. The qualitative phytochemical analysis confirmed the presence of alkaloids, phenols, flavonoids, glycocides, saponins, tannins, and steroids in both leaf and callus extracts. The quantitative analysis result showed higher yields of phenolics (104.73±4.3 mg) followed by flavonoids (69.63±6.35 mg) and soluble tannins (69.59±1.27mg) equivalent gallic acid/g of methanol, ethyl acetate and methanol, respectively. In case of callus extracts, the highest yield is phenols (54.42±6.59gm) followed by flavonoids (39.17±6.35 gm) equivalent gallic acid/g of methanol extracts. The antioxidant activity assays of both leaf and callus extracts demonstrated strong antioxidant activity which could be due to high content of phenolics and flavenoids. The methanol extract showed more antioxidant potential than the other extracts of both leaf and callus but leaf extracts show significant antioxidant activity than the callus extracts. Therefore, both leaf and callus extracts of Allophylus serratus contain phytochemicals that are potential source for antioxidant which suggested that the plant could serve as a source of useful drugs.
Multiferroics Are a Spintronics Game Changer

A s you read this article, billions of transistors are switching electrical currents on and off inside your computer, generating heat in the process. This “heating problem” is the inevitable result of energy dissipation associated with electrons moving around, and it threatens to hinder further development of silicon-based computer technology. A possible solution to the problem is to make novel electronic devices that take advantage of each electron’s intrinsic magnetic moment (spin). In an insulator (as opposed to a metal or a semiconductor), it is possible to transport these
Can ESG Performance Mitigate Information Asymmetry? Moderating Effect of Assurance Services

ABSTRACT This study examines whether ESG performance reduces information asymmetry and whether assurance services have a moderating effect on the association between ESG performance and information asymmetry. Given the increasing importance and benefits of ESG, examining how ESG performance improves the information environment is a critical question. Using ESG performance data, this study employs a panel data analysis of a large set of U.S. companies with 1,294 observations that are one-to-one matched between assured and unassured groups. The findings indicate that ESG performance reduces information asymmetry and that assurance services have a moderating effect on the negative relationship between ESG performance and information asymmetry. This study makes several contributions to the ESG literature by providing new evidence on ESG performance, employing the moderating variable of assurance services, and minimizing selection bias with propensity score matching. These findings have implications for the professionals and practitioners for information credibility of ESG and assurance services.
Assessment of variation between two blowfly species, Lucilia cuprina (Wiedemann) and Lucilia sericata (Meigen) (Diptera, Calliphoridae) using geometric morphometrics and cuticular hydrocarbon profiling

Lucilia cuprina and Lucilia sericata are two closely related species due to their similarity in morphology, habitat, distribution, and economic importance. Even though other methods have segregated the species, the aspect of comparative studies on geometric morphometrics and cuticular hydrocarbon composition in species variability is yet to be explored in these species. This study was conducted to assess variability between the two species and between the sexes. Wing shapes of 187 specimens of both species were analysed by geometric morphometric techniques. Landmarks 11, 10, 6 and 9, which corresponds to the intersection between the medial and the radial medial veins, medial and branched cubitus veins, distal end of radius vein (R2 + 3 vein) and curve point of medial vein, respectively, contributed significantly to the variability within and between species. Cuticular hydrocarbon profiles of four randomly collected individuals each of male and female L. cuprina and L. sericata , were assessed using GC-MS. Octadecene, Celidoniol, Hexatriacontane, Tetracontane and Tetracontane were identified as common for both species. 9-Octadecenal( z ) and Tetracosane-11-decyl being recorded as the most abundant hydrocarbons in male and female L. cuprina , and 13-methylheptacosane and Tetratetracontane in male and female L. sericata , respectively. Diagnostic characters indicating the variabilities can be used for the identification of the species. variability in wing shape cuticular hydrocarbon variability between the two the vein, intersection end radius vein curve
Asymmetric Arc Routing by Coordinating a Truck and Multiple Drones

Unmanned Aerial Vehicles, commonly known as drones, have been widely used in transmission line inspection and traffic patrolling due to their flexibility and environmental adaptability. To take advantage of drones and overcome their limited endurance, the patrolling tasks are parallelized by concurrently dispatching the drones from a truck which travels on the road network to the nearby task arc. The road network considered in previous research is undirected; however, in reality, the road network usually contains unidirectional arcs, i.e., the road network is asymmetric. Hence, we propose an asymmetric coordinated vehicle-drones arc routing mode for traffic patrolling. In this mode, a truck travelling on an asymmetric road network with multiple drones needs to patrol multiple task arcs, and the drones can be launched and recovered at certain nodes on the truck route, making it possible for drones and the truck to patrol the task in parallel. The total patrol time is the objective function that needs to be minimized given the time limit constraints of drones. The whole problem can be considered as an asymmetric arc routing problem of coordinating a truck and multiple drones. To solve this problem, a large-scale neighborhood search with simulated annealing algorithm (LNS-SA) is proposed. Finally, extensive computation experiments and a real case are carried out. The experimental results show the efficiency of the proposed algorithm. Moreover, a detailed sensitivity analysis is performed on several drone-parameters of interest.
Students’ beliefs and attitudes towards cooperative learning, and their relationship to motivation and approach to learning

ABSTRACT This study investigates the beliefs and attitudes about group work of students entering their first year of university and beginning a training program based on cooperative learning activities. Its objective is to determine the extent to which these beliefs and attitudes depend on students’ prior experience with group work, their motivation for studying, and their approach to learning. The study is based on the responses of 172 first-year university students to a questionnaire combining closed- and open-ended questions. The study shows that most students had a positive perception of group work, but did not spontaneously mention as benefits knowledge learning or the development of some crosscutting skills put forward by research on cooperative learning. It was found that the degree of reluctance towards group work is related to a low perception of its learning gains and the idea that it faces organizational constraints. The results also offer new insights into the origins of these beliefs and attitudes. The frequency of prior experience of group works is positively correlated with the perceived learning gains and negatively correlated with reluctance to work in groups. When considering motivation and deep approach to learning and their sub-dimensions, reflective approach to learning was found to be the main factor determining the perceived learning gains. These results suggest two avenues for teaching based on cooperative learning: discuss and clarify with students the mechanisms of cooperative learning, and structure the teaching to engage them in discussions to collectively self-regulate their activities.
Efektivitas Forgiveness Therapy untuk Memperbaiki Tingkat Self-Acceptance pada Wanita Penderita Kanker Payudara: Literature Review

The number of deaths from breast cancer reached 684,996 people, which is 15.5% of the total deaths from cancer. In 2018 in Indonesia, breast cancer was ranked 7th of all types of cancer. The women with breast cancer generally do chemotherapy as a process that is believed to cure, however, chemotherapy has side effects in the form of physical and psychological. Women with breast cancer also experience more then one changes in their routine, related to the way they perceive their own bodies (body image). This literature review attempts to elucidate the effectiveness of forgiveness intervention in improve self-acceptance in females whose suffering breast cancer. A writing thecnique that uses a literature review by gathering various data from a described phenomenon in the journal, book, or some alternative type of reference that is relevant with the topic. The results obtained are forgiveness therapy has effectiveness in increasing acceptance in women with breast cancer. Keywords: Forgiveness Therapy; Self-acceptance; Breast Cancer
Intelligent Epileptic Seizure Detection and Classification Model Using Optimal Deep Canonical Sparse Autoencoder

Simple Summary Epileptic seizures are a chronic and persistent neurological illness that mainly affects the human brain. Since the traditional way of diagnosing epileptic seizures is laborious and time-consuming, automated tools using machine learning (ML) and deep learning (DL) models may be useful. This paper presents an intelligent deep canonical sparse autoencoder-based epileptic seizure detection and classification (DCSAE-ESDC) model using EEG signals. The proposed DCSAE-ESDC technique involves two major processes, namely, feature selection and classification. The DCSAE-ESDC technique designs a novel coyote optimization algorithm (COA)-based feature selection technique for the optimal selection of feature subsets. Moreover, the DCSAE-based classifier is derived for the detection and classification of different kinds of epileptic seizures. Finally, the parameter tuning of the DSCAE model takes place via the krill herd algorithm (KHA). Abstract Epileptic seizures are a chronic and persistent neurological illness that mainly affects the human brain. Electroencephalogram (EEG) is considered an effective tool among neurologists to detect various brain disorders, including epilepsy, owing to its advantages, such as its low cost, simplicity, and availability. In order to reduce the severity of epileptic seizures, it is necessary to design effective techniques to identify the disease at an earlier stage. Since the traditional way of diagnosing epileptic seizures is laborious and time-consuming, automated tools using machine learning (ML) and deep learning (DL) models may be useful. This paper presents an intelligent deep canonical sparse autoencoder-based epileptic seizure detection and classification (DCSAE-ESDC) model using EEG signals. The proposed DCSAE-ESDC technique involves two major processes, namely, feature selection and classification. The DCSAE-ESDC technique designs a novel coyote optimization algorithm (COA)-based feature selection technique for the optimal selection of feature subsets. Moreover, the DCSAE-based classifier is derived for the detection and classification of different kinds of epileptic seizures. Finally, the parameter tuning of the DSCAE model takes place via the krill herd algorithm (KHA). The design of the COA-based feature selection and KHA-based parameter tuning shows the novelty of the work. For examining the enhanced classification performance of the DCSAE-ESDC technique, a detailed experimental analysis was conducted using a benchmark epileptic seizure dataset. The comparative results analysis portrayed the better performance of the DCSAE-ESDC technique over existing techniques, with maximum accuracy of 98.67% and 98.73% under binary and multi-classification, respectively.
Improved Ant Lion Optimizer for Coverage Optimization in Wireless Sensor Networks

Coverage optimization is an important research topic in wireless sensor networks (WSNs). By studying the coverage optimization problem, the coverage rate of the network can be improved, and the number of redundant sensors can be reduced. In order to improve the coverage performance of wireless sensor networks, we propose an improved ant lion optimizer (IALO) to solve the coverage optimization problem in a WSN. Firstly, we give a network coverage optimization model with the objective of maximizing network coverage rate. Secondly, we alternately execute Cuckoo Search (CS) and Cauchy mutation to update the positions of the ants to enhance population diversity and accelerate convergence speed. Then, we introduce differential evolution (DE) to mutate the population of antlions to improve the convergence accuracy of our algorithm. We compare IALO with the original ant lion optimizer (ALO) and other algorithms on 9 benchmark functions to verify its effectiveness. Finally, IALO is applied to the coverage optimization in wireless sensor networks. Simulation results show that, compared with previous works, IALO provides higher coverage rate, makes the sensor distribution more uniform, and effectively reduces the deployment cost.
Measuring the concentration of some hormones in women with polycystic ovary syndrome

This study was conducted at the Women and Children’s Hospital in Al-Qadisiyah from 1/10/2021 to 2/1/2022, and 50 patients were followed up for women suffering from polycystic ovaries on the third day of the menstrual cycle, and compared to the control group, which included 50 normal cases, which were confirmed to be free. Of the chronic diseases such as heart disease, thyroid, diabetes and blood pressure, the concentration of hormones was measured for both infected and non-infected women, including PRL, LH and FSH. A significant increase in The results showed a significant increase in prolactin and FSH, LH was observed in women with PCOS compared to the control group.
The Psychology of Financial Giving: Values Congruence and Normative Organizational Commitment as Predictors of Alumni Monetary Donations to Higher Education

Many universities around the world depend on financial donations to maintain and enhance their operations. We propose that donating money to an organization is a specific form of organizational citizenship behavior. We theorize that perceived values congruence between alumni and their colleges/universities and normative organizational commitment each provide motivation for people to donate money. We tested our hypotheses using alumni from a private college in the USA, measuring both the amount as well as the frequency of their donations to the college. We also measured alumni self-reports of values congruence and normative organizational commitment. We found empirical support for a positive relationship between values congruence and commitment and between commitment and financial giving. Values congruence, however, was not related to giving behavior; normative organizational commitment fully mediated the relationship between values congruence, and the alumnus’ financial giving behaviors. Higher education organizations that depend on donations from members to sustain their operations might focus advancement efforts on developing a perception of values congruence in potential donors, and/or by stimulating a sense of obligation to give back to the colleges from which they have previously derived benefits.
Brain–Computer Interface in Europe: the thirtieth anniversary

ABSTRACT This paper reviews the development of Brain–Computer Interface research, covering period from 1973 when the term Brain–Computer Interface was introduced, till the last year of the twentieth century, 1999. The focus is on the first two brain–computer interface demonstrations done in Europe, in 1988. This paper written in 2018 marks the thirtieth anniversary of those two events. The first event was control of a computer buzzer using EEG contingent negative potential variation, and the second event was control of a physical object with a mass, a robot, using EEG alpha rhythm amplitude variation. Movement of a physical object with a mass through signals emanating from a human brain was named psychokinesis and before 1988 it was in the realm of science fiction. The paper describes the two events in chronological order, from the basic idea to the engineering realization.
Transition of care for stroke patients: an integrative review

Objective: to analyze, based on scientific evidence, how the transition of care for people with stroke occurs after hospital discharge. Methods: integrative literature review, conducted in the databases Scopus, Web of Science, Pubmed/MEDLINE, Latin American and Caribbean Literature on Health Sciences, Nursing Database and The Cumulative Index to Nursing and Allied Health Literature. A total of 21 articles were included in the final sample, which were then descriptively analyzed. Results: factors related to the adequate transition of care included: organizational barriers of the health services involved in the transition of care, the importance of health information, and the adequate coordination of care. Conclusion: the studies express limits experienced by individuals, family members and health professionals in systems that are fragmented and rule out the possibility of integral care in times of transition. Contributions to practice: the study reveals the necessary articulation of those who provide health care to people with this grievance and their families, to recognize points to be improved in their health practices and minimize complications arising from failures in care transitions.