Nanopesticides are novel plant protection products offering numerous benefits. Because nanoparticles behave differently from dissolved chemicals, the environmental risks of these materials could differ from conventional pesticides. We used soil–earthworm systems to compare the fate and uptake of analytical‐grade bifenthrin to that of bifenthrin in traditional and nanoencapsulated formulations. Apparent sorption coefficients for bifenthrin were up to 3.8 times lower in the nano treatments than in the non‐nano treatments, whereas dissipation half‐lives of the nano treatments were up to 2 times longer. Earthworms in the nano treatments accumulated approximately 50% more bifenthrin than those in the non‐nano treatments. In the non‐nano treatments, most of the accumulated material was found in the earthworm tissue, whereas in the nano treatments, the majority resided in the gut. Evaluation of toxicokinetic modeling approaches showed that models incorporating the release rate of bifenthrin from the nanocapsule and distribution within the earthworm provided the best estimations of uptake from the nano‐formulations. Overall, our findings indicate that the risks of nanopesticides may be different from those of conventional formulations. The modeling presented provides a starting point for assessing risks of these materials but needs to be further developed to better consider the behavior of the nanoencapsulated pesticide within the gut system.
Diamond-like carbon (DLC) homogeneous thin films were deposited from cyclohexane (Ccyclohexane (Ccyclohexane (Ccyclohexane (C cyclohexane (Ccyclohexane (Ccyclohexane (C cyclohexane (Ccyclohexane (C 6H12 ) liquid by using a plasma jet system which operates with alternating high voltage 7.5 which operates with alternating high voltage 7.5which operates with alternating high voltage 7.5which operates with alternating high voltage 7.5 which operates with alternating high voltage 7.5which operates with alternating high voltage 7.5which operates with alternating high voltage 7.5 which operates with alternating high voltage 7.5which operates with alternating high voltage 7.5 which operates with alternating high voltage 7.5which operates with al
... Show MoreIn this study, a different design of passive air Solar Chimney(SC)was tested by installing it in the south wall of insulated test room in Baghdad city. The SC was designed from vertical and inclined parts connected serially together, the vertical SC (first part) has a single pass and Thermal Energy Storage Box Collector (TESB (refined paraffin wax as Phase Change Material(PCM)-Copper Foam Matrix(CFM))), while the inclined SC was designed in single pass, double passes and double pass with TESB (semi refined paraffin wax with copper foam matrix) with selective working angle ((30o, 45o and 60o). A computational model was employed and solved by Finite Volume Method (FVM) to simulate the air i
... Show MoreAdverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD
... Show MoreTraffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c
... Show MoreThe research aimed: 1. Definition of family climate for the university students. 2. Definition of statistical significance of differences in family climate variable depending on the sex (males - females) and specialization (Scientific - humanity). 3. Definition of academic adjustment for university students. 4. Definition of correlation between climate and academic adjustment. The research sample formed of (300) male and female students by (150) male of scientific and humanitarian specialization and (150) female of scientific and humanitarian specialization randomly selected from the research community. To achieve the objectives of the research the researcher prepared a tool to measure family climate. And adopted the measure (Azzam 2010)
... Show MoreThin films of CuPc of various thicknesses (150,300 and 450) nm have been deposited using pulsed laser deposition technique at room temperature. The study showed that the spectra of the optical absorption of the thin films of the CuPc are two bands of absorption one in the visible region at about 635 nm, referred to as Q-band, and the second in ultra-violet region where B-band is located at 330 nm. CuPc thin films were found to have direct band gap with values around (1.81 and 3.14 (eV respectively. The vibrational studies were carried out using Fourier transform infrared spectroscopy (FT-IR). Finally, From open and closed aperture Z-scan data non-linear absorption coefficient and non-linear refractive index have been calculated res
... Show MoreThe rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient com
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