Optimizing Approach of Water Allocation to Off-Takes During Reduced Flows

Multi-objective optimization models with an index were developed based on farmers’ preferences, local requirements, supplies available at the head of the canal, system losses, crop demand about different growth stages, and field soil moisture balance. The models were applied using linear programming. The Model 1 determines the cropping pattern by maximizing net economic benefits using a monthly basis lumped volume available at the head of the canal and is set to the minimum and maximum area constraints along with the constraint of minimum main crop area. The areas for different crops given by the first model form input for the Model 2. The other inputs of Model 2 included periodic supply available at the head of the primary canal (7-day period in this study), root growth depth, demand, and soil moisture constants. The Model 2 optimizes the sum of relative yields of all the crops and provide the irrigation levels of various crops for specified periods. Finally, the distributed area and irrigation levels determined by Model 2 are used in conjunction with the losses to decide flow rates of off takes. The complete program was implemented in the West branch irrigated area of Mirpurkhas subdivision. The results showed that the resources were allocated to off-takes in a competitive and conflict-free manner.

     

Effect of the filler grade on the characteristics and the sublingual permeability of atropine sulfate fast disintegrating sublingual tablets

Abstract

Context: AS FDSTs will provide an accessible alternative for AS autoinjector (ATROPEN^®), and a noninvasive first-aid antidote for the treatment of organophosphate (OP) poisoning and reduce the number of fatalities due to nerve gas attacks or OP pesticide poisoning.

Objective: The effects of changing the filler grade on the characteristics of atropine sulfate (AS) fast disintegrating sublingual tablets (FDSTs) and AS sublingual permeability were investigated in order to optimize the formulation of AS FDSTs and, therefore, AS sublingual permeability.

Methods: Two batches of AS FDSTs containing AS 8?mg were formulated and manufactured using two different filler grades: microcrystalline cellulose (MCC) UF-702 (formulation A) and MCC PH-301 (formulation B). Several United States Pharmacopeia (USP) and non-USP physical tests were performed to evaluate the AS FDSTs’ characteristics. The AS permeability from the two AS FDST batches were evaluated using Franz cells through excised porcine sublingual membranes. Results were statistically compared at p?<?.05.

Results: Both batches passed the content uniformity and friability tests. Formulation A tablets were significantly different from formulation A tablets and resulted in better powder flowability, higher breaking force, faster disintegration, faster dissolution rate, higher water uptake, and higher AS permeability.

Conclusion: The selection of the filler grade to be used in the formulation of AS FDSTs can significantly impact their characteristics and significantly affect AS sublingual permeability, which can be used to improve the sublingual delivery of AS and the potential of using AS FDSTs as an alternative dosage form for the first-aid treatment of OP poisoning.     

A Neural Network-Based Trust Management System for Edge Devices in Peer-to-Peer Networks

Edge devices in Internet of Things (IoT) applications can form peers to communicate in peer-to-peer (P2P) networks over P2P protocols. Using P2P networks ensures scalability and removes the need for centralized management. However, due to the open nature of P2P networks, they often suffer from the existence of malicious peers, especially malicious peers that unite in groups to raise each other's ratings. This compromises users' safety and makes them lose their confidence about the files or services they are receiving. To address these challenges, we propose a neural network-based algorithm, which uses the advantages of a machine learning algorithm to identify whether or not a peer is malicious. In this paper, a neural network (NN) was chosen as the machine learning algorithm due to its efficiency in classification. The experiments showed that the NNTrust algorithm is more effective and has a higher potential of reducing the number of invalid files and increasing success rates than other well-known trust management systems.     

A High-Energy Asymmetric Supercapacitor Based on Tomato-Leaf-Derived Hierarchical Porous Activated Carbon and Electrochemically Deposited Polyaniline Electrodes for Battery-Free Heart-Pulse-Rate Monitoring

A simple and scalable method to fabricate a novel high-energy asymmetric supercapacitor using tomato-leaf-derived hierarchical porous activated carbon (TAC) and electrochemically deposited polyaniline (PANI) for a battery-free heart-pulse-rate monitor is reported. In this study, TAC is prepared by simple pyrolysis, exhibiting nanosheet-type morphology and a high specific surface area of ≈1440 m² g⁻¹, and PANI is electrochemically deposited onto carbon cloth. The TAC- and PANI- based asymmetric supercapacitor demonstrates an electrochemical performance superior to that of symmetric supercapacitors, delivering a high specific capacitance of 248 mF cm⁻² at a current density of 1.0 mA cm⁻². The developed asymmetric supercapacitor shows a high energy density of 270 µWh cm⁻² at a power density of 1400 µW cm⁻², as well as an excellent cyclic stability of ≈95% capacitance retention after 10 000 charging–discharging cycles while maintaining ≈98% Coulombic efficiency. Impressively, the series-connected asymmetric supercapacitors can operate a battery-free heart-pulse-rate monitor extremely efficiently upon solar-panel charging under regular laboratory illumination.