3‐{2‐[4‐(Dimethylamino)phenyl]‐2,3‐dihydro‐1 H ‐1,5‐benzodiazepin‐4‐yl}‐4‐hydroxy‐6‐methyl‐2 H ‐pyran‐2‐one (DMABD) was synthesized through a Schiff base reaction between dehydroacetic acid and orthophenylenediamine. The characterization of DMABD involved determining the melting point, mass spectrometry, and various techniques such as NMR, IR, and UV–visible spectroscopy. The corrosion inhibition properties of DMABD on XC48 steel in 0.5 M HCl were evaluated using electrochemical methods and weight loss measurements at temperatures ranging from 298 to 318 K. Surface analysis was performed using AFM, SEM, EDX, XRD, and contact angle measurements. The results demonstrated that DMABD achieved an inhibition efficiency of 93% at a concentration of 10 −3 M, functioning as a mixed‐type inhibitor according to the Langmuir isotherm model. Kinetic and thermodynamic parameters related to inhibition and metal dissolution were analyzed. Additionally, density functional theory (DFT) and molecular dynamics simulations (MD) were employed to assess quantum parameters and the interfacial arrangement of DMABD with Fe(110)/H 2 O.
In France, there have been repeated warnings in recent years about the effects of pesticides on agricultural workers’ health, as well as cases of intoxication. Yet it is still hard to know if farmers are really dangerously exposed to pesticides. The literature on social manufacturing of ignorance focuses either on industries’ strategies to foster doubt or on the role of official tools to examine the risks, and tends to overlook the role of farmers in impeding the production of knowledge. Based on an empirical study of the supply chains of apples and potatoes, and through the lens of the sociology of decision-making, this article seeks to understand how the farmers’ close dependency on pesticides participates in downplaying the toxicity of pesticides. It shows that the practical use of pesticides involves a constant trade-off between legal, economic, and agricultural risks. This leads farmers to develop a culture of secrecy around real pesticide use, in which they hide irregularities. Such open secrecy, shared by a multitude of actors, causes agricultural workers to underestimate the danger of pesticides.
The majority of IoT implementations demand sensor nodes to run reliably for an extended time. Furthermore, the radio settings can endure a high data rate transmission while optimizing the energy-efficiency. The LoRa/LoRaWAN is one of the primary low-power wide area network (LPWAN) technologies that has highly enticed much concentration. The energy limits is a significant issue in wireless sensor networks since battery lifetime that supplies sensor nodes have a restricted amount of energy and neither expendable nor rechargeable in most cases. A common hypothesis is that the energy consumed by sensors in sleep mode is negligible. With this hypothesis, the usual approach is to consider subsets of nodes that reach all the iterative targets. These subsets also called coverage sets, are then put in the active mode, considering the others are in the low-power or sleep mode. In this paper, we address this question by proposing an energy consumption model based on LoRa and LoRaWAN, which optimizes the energy consumption of the sensor node for different tasks for a period of time. Our energy consumption model assumes the following, the processing unit is in on-state along the working sequence which enhances the MCU unit by constructing it in low-power modes through most of the activity cycle, a constant time duration, and the radio module sends a packet of data at a specified transmission power level. The proposed analytical approach permits considering the consumed power of every sensor node element where the numerical results show that the scenario in which the sensor node transfers data to the gateway then receives an acknowledgment RX2 without receiving RX1 consumes the most energy; furthermore, it can be used to analyze different LoRaWAN modes to determine the most desirable sensor node design to reach its energy autonomy where the numerical results detail the impact of scenario, spreading factor, and bandwidth on power consumption.
The paper proposes a new class of nonlinear operators and a dual learning paradigm where optimization jointly concerns both linear convolutional weights and the parameters of these nonlinear operators. The nonlinear class proposed to perform a rich functional representation is composed by functions called rectified parametric sigmoid units. This class is constructed to benefit from the advantages of both sigmoid and rectified linear unit functions, while rejecting their respective drawbacks. Moreover, the analytic form of this new neural class involves scale, shift and shape parameters to obtain a wide range of activation shapes, including the standard rectified linear unit as a limit case. Parameters of this neural transfer class are considered as learnable for the sake of discovering the complex shapes that can contribute to solving machine learning issues. Performance achieved by the joint learning of convolutional and rectified parametric sigmoid learnable parameters are shown to be outstanding in both shallow and deep learning frameworks. This class opens new prospects with respect to machine learning in the sense that main learnable parameters are attached not only to linear transformations, but also to a wide range of nonlinear operators.
La quantité d’antibiotiques utilisés dans les filières monogastriques (porcs, volailles et lapins) a chuté fortement à partir des années 2000, et connaît une relative stabilisation depuis quelques années. Les plans EcoAntibio successifs ont renforcé la dynamique et contribué à réduire drastiquement l’usage des antibiotiques critiques. Cette évolution est la résultante combinée d’évolutions réglementaires, d’actions volontaires privées mises en œuvre dans les filières de production, et de démarches professionnelles collectives et individuelles. Différentes actions ont été mises en place, reposant sur une approche multifactorielle de la santé, l’établissement d’un diagnostic fin des troubles sanitaires de l’élevage, et un travail sur leurs causes sous-jacentes pour définir des mesures préventives adaptées. L’accent est mis sur la conduite d’élevage, l’assainissement vis à vis d’agents pathogènes particuliers, la biosécurité, la vaccination, la nutrition, et l’usage de substances alternatives. Les pratiques d’antibiothérapie ont aussi évolué, avec la mise en place de guides de bonnes pratiques consensuels, la généralisation de l’examen bactériologique et de l’antibiogramme, la bonne observance des posologies, et le suivi précis de la santé pour adapter les traitements. La mise en place de ces évolutions repose par ailleurs sur un bon rapport de confiance entre éleveur, vétérinaire et technicien d’élevage, l’accompagnement des éleveurs ayant aussi été renforcé via des dispositifs de sensibilisation et de formation. La poursuite de la rationalisation des usages reposera sur le ciblage des exploitations à risque au regard des usages d’antibiotiques et la mise en place d’actions sur-mesure.
