Green Drug Supply Chain Investigation by Time-Market Balance and Risk

The quality of pharmaceutical products plays a crucial role in healthcare systems such as hospitals for better patient services. Drug Supply Chain Management requires approaches to uncertainty and risk consideration. This study is a comprehensive multi-objective mathematical model considering the uncertainties and potential reserves in supply and medicine. The proposed model includes three general objective functions that minimize total production costs, including the costs of transportation, maintenance, breakdown, collection, and disposal of waste. The model also maximizes the quality of potential storage. The results show the proposed method has a high quality to solve the model and leads to the optimization of the results to provide the drug supply chain for the proposed example. We have identified three important risks and uncertainties in addressing drug supply planning: the indefinite duration of the licensing process, the risk of a forced brand change, and indefinite repayment levels that lead to varied demand diversification. The results of comparison with other multi-objective optimization methods in existing articles also show better performance of the proposed model. A significant cost reduction results from implementing our model instead of using the over-storage role to estimate the volume of active drug elements, as seen in today’s industry.

Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks:A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.

On the calibration of a shear stress criterion for rock joints to represent the full stress-strain profile

Conventional numerical solutions developed to describe the geomechanical behavior of rock interfaces subjected to differential load emphasize peak and residual shear strengths.The detailed analysis of preand post-peak shear stress-displacement behavior is central to various time-dependent and dynamic rock mechanic problems such as rockbursts and structural instabilities in highly stressed conditions.The complete stress-displacement surface(CSDS)model was developed to describe analytically the pre-and post-peak behavior of rock interfaces under differential loads.Original formulations of the CSDS model required extensive curve-fitting iterations which limited its practical applicability and transparent integration into engineering tools.The present work proposes modifications to the CSDS model aimed at developing a comprehensive and modern calibration protocol to describe the complete shear stressdisplacement behavior of rock interfaces under differential loads.The proposed update to the CSDS model incorporates the concept of mobilized shear strength to enhance the post-peak formulations.Barton’s concepts of joint roughness coefficient(JRC)and joint compressive strength(JCS)are incorporated to facilitate empirical estimations for peak shear stress and normal closure relations.Triaxial/uniaxial compression test and direct shear test results are used to validate the updated model and exemplify the proposed calibration method.The results illustrate that the revised model successfully predicts the post-peak and complete axial stressestrain and shear stressedisplacement curves for rock joints.

Strategies to achieve effective nitrogen activation

Ammonia serves as a crucial chemical raw material and hydrogen energy carrier.Aqueous electrocatalytic nitrogen reduction reaction(NRR),powered by renewable energy,has attracted tremendous interest during the past few years.Although some achievements have been revealed in aqueous NRR,significant challenges have also been identified.The activity and selectivity are fundamentally limited by nitrogen activation and competitive hydrogen evolution.This review focuses on the hurdles of nitrogen activation and delves into complementary strategies,including materials design and system optimization(reactor,electrolyte,and mediator).Then,it introduces advanced interdisciplinary technologies that have recently emerged for nitrogen activation using high-energy physics such as plasma and triboelectrification.With a better understanding of the corresponding reaction mechanisms in the coming years,these technologies have the potential to be extended in further applications.This review provides further insight into the reaction mechanisms of selectivity and stability of different reaction systems.We then recommend a rigorous and detailed protocol for investigating NRR performance and also highlight several potential research directions in this exciting field,coupling with advanced interdisciplinary applications,in situ/operando characterizations,and theoretical calculations.

A Distributionally Robust Optimization Scheduling Model for Regional Integrated Energy Systems Considering Hot Dry Rock Co-Generation

Hot dry rock(HDR)is rich in reserve,widely distributed,green,low-carbon,and has broad development potential and prospects.In this paper,a distributionally robust optimization(DRO)scheduling model for a regionally integrated energy system(RIES)considering HDR co-generation is proposed.First,the HDR-enhanced geothermal system(HDR-EGS)is introduced into the RIES.HDR-EGS realizes the thermoelectric decoupling of combined heat and power(CHP)through coordinated operation with the regional power grid and the regional heat grid,which enhances the system wind power(WP)feed-in space.Secondly,peak-hour loads are shifted using price demand response guidance in the context of time-of-day pricing.Finally,the optimization objective is established to minimize the total cost in the RIES scheduling cycle and construct a DRO scheduling model for RIES with HDR-EGS.By simulating a real small-scale RIES,the results show that HDR-EGS can effectively promote WP consumption and reduce the operating cost of the system.

Impact of Varying Blower Opening Degrees on Indoor Environment and Thermal Comfort

At present,air handling units are usually used indoors to improve the indoor environment quality.However,while introducing fresh air to improve air quality,air velocity has a certain impact on the occupants’thermal comfort.Therefore,it is necessary to explore the optimization of air-fluid-body interaction dynamics.In this study,the indoor air flow was changed by changing the opening and closing degree of the blower,and the thermal manikin is introduced to objectively evaluate the human thermal comfort under different air velocities.The main experimental results show that the air change rate increases with the increase of the opening and closing degree of the blower considering an ACH(air changes per hour)range between 3.8 and 10.For a better prediction,a linear correlation with a coefficient of 0.995 is proposed.As the blower’s opening is adjusted to 20%,25%,30%,35%,and 40%,the air velocity sensor positioned directly beneath the air inlet records average velocities of 0.19,0.20,0.21,0.28,and 0.34 m/s over four hours,respectively.Observations on thermal comfort and the average sensation experienced by individuals indicate an initial increase followed by a decline when the blower’s operation begins,with optimal conditions achieved at a 35%opening.These findings offer valuable insights for future indoor air ventilation and heat transfer design strategies.

Functionally graded structure of a nitride-strengthened Mg_(2)Si-based hybrid composite

The ex-situ incorporation of the secondary SiC reinforcement,along with the in-situ incorporation of the tertiary and quaternary Mg_(3)N_(2) and Si_(3)N_(4) phases,in the primary matrix of Mg_(2)Si is employed in order to provide ultimate wear resistance based on the laser-irradiation-induced inclusion of N_(2) gas during laser powder bed fusion.This is substantialized based on both the thermal diffusion-and chemical reactionbased metallurgy of the Mg_(2)Si–SiC/nitride hybrid composite.This study also proposes a functional platform for systematically modulating a functionally graded structure and modeling build-direction-dependent architectonics during additive manufacturing.This strategy enables the development of a compositional gradient from the center to the edge of each melt pool of the Mg_(2)Si–SiC/nitride hybrid composite.Consequently,the coefficient of friction of the hybrid composite exhibits a 309.3%decrease to–1.67 compared to–0.54 for the conventional nonreinforced Mg_(2)Si structure,while the tensile strength exhibits a 171.3%increase to 831.5 MPa compared to 485.3 MPa for the conventional structure.This outstanding mechanical behavior is due to the(1)the complementary and synergistic reinforcement effects of the SiC and nitride compounds,each of which possesses an intrinsically high hardness,and(2)the strong adhesion of these compounds to the Mg_(2)Si matrix despite their small sizes and low concentrations.

Optimizing a Transportation System Using Metaheuristics Approaches (EGD/GA/ACO): A Forest Vehicle Routing Case Study

The large-scale optimization problem requires some optimization techniques, and the Metaheuristics approach is highly useful for solving difficult optimization problems in practice. The purpose of the research is to optimize the transportation system with the help of this approach. We selected forest vehicle routing data as the case study to minimize the total cost and the distance of the forest transportation system. Matlab software helps us find the best solution for this case by applying three algorithms of Metaheuristics: Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Extended Great Deluge (EGD). The results show that GA, compared to ACO and EGD, provides the best solution for the cost and the length of our case study. EGD is the second preferred approach, and ACO offers the last solution.

The Impact of the Inclusion of MgO Nano-Fillers in a Polyethylene Matrix on Dielectric Strength and Resistance to Partial Discharges

One commonly used strategy to enhance polymers specific properties such as the resistance to partial discharges erosion is the incorporation into the polymeric matrix of inorganic micro or nanoparticles. This study focused on the dielectric properties of Low-Density Polyethylene (LDPE) filled with nano-sized Magnesium Oxide (MgO) particles compounded by thermo-mechanical process and one of the purposes was to establish appropriate processing parameters in order to reach the desired dielectric properties. LDPE was used as a matrix and was reinforced by MgO particles having a nominal average size of 30 nm. The MgO nanoparticles were treated with a silane coupling agent (3-Glycidyloxypropyl Trimethoxysilane). The samples were initially prepared in a melt-mixing chamber with a MgO content of 1% wt. These pre-mixed samples were further treated by the means of thermo-mechanical mixing in a conical co-rotating twin-screw extruder in order to improve the dispersion and distribution of the MgO particles. In this report, both lifetime under a PD activity and AC dielectric strength of pure and nano-filled LDPE samples have been measured and compared. Nano-filled LDPE samples were found to exhibit an improve lifetime, without any detrimental impact on their short-term dielectric strength. This suggests that nano-filled LDPE may be for electric applications for which the dielectric materials may be exposed to partial discharge activities. This is significant result for the use of MgO-reinforced PE as an insulating material for HV cables since the resistance to PD is closely related to treeing resistance which is the main electrical degradation mechanism that leads to failure for shielded extruded power cables.

Thermogravimetric analysis and kinetic modeling of the co-pyrolysis of a bituminous coal and poplar wood

The co-pyrolysis of coal and biomass has proven to be a promising route to produce liquid and gaseous fuels as well as specific value-added chemicals while contributing to mitigating CO_(2) emissions.The interactions between the co-processed feedstocks,however,need to be elucidated to support the development of such a thermochemical conversion process.In this context,the present work covers the kinetic analysis of the co-pyrolysis of a bituminous coal with poplar wood.In this research,biomass was blended with coal at two different mass ratios(10%(mass)and 20%(mass)).Thermogravimetric analyses were carried out with pure and blended samples at four heating rates(5,10,15 and 30℃·min^(-1)).A direct comparison of experimental and theoretical results(based on a simple additivity rule)failed to yield a clear-cut conclusion regarding the existence of synergistic effects.Kinetic analyses have therefore been achieved using two model-free methods(the Ozawa-Flynn-Wall and Kissinger-Akahira-Sunose models)to estimate the rate constant parameters related to the pyrolysis process.A significant decrease of the activation energy has thus been observed when adding wood to coal(activation energies associated with the blend containing 20%(mass)of biomass being even lower than those estimated for pure wood at low conversion degrees).This trend was attributed to the possible presence of synergies whose related mechanisms are discussed.The rate constant parameters derived by means of the two tested models were finally used to simulate the evolution of the conversion degree of each sample as a function of the temperature,thus leading to a satisfying agreement between measured and simulated data.

Evaluation of IoT Measurement Solutions from a Metrology Perspective

To professionally plan and manage the development and evolution of the Internet of Things(IoT),researchers have proposed several IoT performance measurement solutions.IoT performance measurement solutions can be very valuable for managing the development and evolution of IoT systems,as they provide insights into performance issues,resource optimization,predictive maintenance,security,reliability,and user experience.However,there are several issues that can impact the accuracy and reliability of IoT performance measurements,including lack of standardization,complexity of IoT systems,scalability,data privacy,and security.While previous studies proposed several IoT measurement solutions in the literature,they did not evaluate any individual one to figure out their respective measurement strengths and weaknesses.This study provides a novel scheme for the evaluation of proposed IoT measurement solutions using a metrology-coverage evaluation based on evaluation theory,metrology principles,and software measurement best practices.This evaluation approach was employed for 12 IoT measure categories and 158 IoT measurement solutions identified in a Systematic Literature Review(SLR)from 2010 to 2021.The metrology coverage of these IoT measurement solutions was analyzed from four perspectives:across IoT categories,within each study,improvement over time,and implications for IoT practitioners and researchers.The criteria in this metrology-coverage evaluation allowed for the identification of strengths and weaknesses in the theoretical and empirical definitions of the proposed IoT measurement solutions.We found that the metrological coverage varies significantly across IoT measurement solution categories and did not show improvement over the 2010–2021 timeframe.Detailed findings can help practitioners understand the limitations of the proposed measurement solutions and choose those with stronger designs.These evaluation results can also be used by researchers to improve current IoT measurement solution designs and suggest new solutions with a stronger metrology base.

Failure Prediction and Intelligent Maintenance of a Transportation Company’s Urban Fleet

The present work deals with intelligent vehicle fleet maintenance and prediction. We propose an approach based primarily on the history of failures data and on the geographical data system. The objective here is to predict the date of failures for a fleet of vehicles in order to allow the maintenance department to efficiently deploy the proper resources;we further provide specific details regarding the origins of failures, and finally, give recommendations. This study used the Société de transport de Montréal (STM)’s historical bus failure data as well as weather data from Environment Canada. We thank Facebook’s Prophet, Simple Feed-forward, and Beats algorithms (Uber), we proposed a set of computer codes that allow us to identify the 20% of buses that are responsible for the 80% of failures by mean of the failure history. Then, we deepened our study on the unreliable equipments identified during the diffusion of our computer code This allowed us to propose probable predictions of the dates of future failures. To ensure the validity of the proposed algorithm, we carried out simulations with more than 250,000 data. The results obtained are similar to the predicted theoretical values.

ADS-B Reception Error Correction Based on the LSTM Neural-Network Model

Standard automatic dependent surveillance broadcast (ADS-B) reception algorithms offer considerable performance at high signal-to-noise ratios (SNRs). However, the performance of ADS-B algorithms in applications can be problematic at low SNRs and in high interference situations, as detecting and decoding techniques may not perform correctly in such circumstances. In addition, conventional error correction algorithms have limitations in their ability to correct errors in ADS-B messages, as the bit and confidence values may be declared inaccurately in the event of low SNRs and high interference. The principal goal of this paper is to deploy a Long Short-Term Memory (LSTM) recurrent neural network model for error correction in conjunction with a conventional algorithm. The data of various flights are collected and cleaned in an initial stage. The clean data is divided randomly into training and test sets. Next, the LSTM model is trained based on the training dataset, and then the model is evaluated based on the test dataset. The proposed model not only improves the ADS-B In packet error correction rate (PECR), but it also enhances the ADS-B In terms of sensitivity. The performance evaluation results reveal that the proposed scheme is achievable and efficient for the avionics industry. It is worth noting that the proposed algorithm is not dependent on conventional algorithms’ prerequisites.

Microstructure refinement,mechanical and biocorrosion properties of Mg–Zn–Ca–Mn alloy improved by a new severe plastic deformation process

In this study,the microstructural evolution,mechanical properties and biocorrosion performance of a Mg–Zn–Ca–Mn alloy were investigated under different conditions of heat treatment,extrusion,one pass and two passes of half equal channel angular pressing(HECAP)process.The results showed significant grain refinement of the homogenized alloy after two passes of HECAP process from 345μm to 2μm.Field emission scanning electron microscopy(FESEM)revealed the presence of finer Mg_(6)Zn_(3)Ca_(2)phase as well asα-Mn phase after HECAP process.The results also showed that mechanical characteristics such as yield strength,ultimate tensile strength and elongation of the HECAPed samples improved by~208%,~144%and~100%compared to the homogenized one,respectively.Crystallographic texture analysis indicated that most of the grains at the surface were reoriented parallel to the(0001)basal plane after HECAP process.Electrochemical corrosion tests and immersion results indicated that the sample with two passes of HEACP had the highest biocorrosion resistance confirming that the basal planes had the lowest corrosion rate compared to the non-basal ones.The mechanical behavior and bio-corrosion evaluation demonstrated that the HECAPed Mg–Zn–Ca–Mn alloy has great potential for biomedical applications and a mechanism was proposed to explain the interrelations between the thermomechanical processing and bio-corrosion behavior.

Experimental study of path planning problem using EMCOA for a holonomic mobile robot

In this paper,a comparative study of the path planning problem using evolutionary algorithms,in comparison with classical methods such as the A*algorithm,is presented for a holonomic mobile robot.The configured navigation system,which consists of the integration of sensors sources,map formatting,global and local path planners,and the base controller,aims to enable the robot to follow the shortest smooth path delicately.Grid-based mapping is used for scoring paths efficiently,allowing the determination of collision-free trajectories from the initial to the target position.This work considers the evolutionary algorithms,the mutated cuckoo optimization algorithm(MCOA)and the genetic algorithm(GA),as a global planner to find the shortest safe path among others.A non-uniform motion coefficient is introduced for MCOA in order to increase the performance of this algorithm.A series of experiments are accomplished and analyzed to confirm the performance of the global planner implemented on a holonomic mobile robot.The results of the experiments show the capacity of the planner framework with respect to the path planning problem under various obstacle layouts.

Towards Optimizing a Personal Cooling Garment for Hot and Humid Deep Mining Conditions

Workers exposed to hot and humid conditions suffer from heat stress that affects their concentration and can potentially lead to an increase in workplace accidents. To minimize heat stress, protective equipment may be worn, such as personal cooling garments. This paper presents and discusses the performances, advantages and disadvantages of existing personal cooling garments, namely air-cooled, liquid-cooled, phase change, hybrid, gas expansion and vacuum desiccant cooling garments, and a thermoelectric cooling technology. The main objective is to identify the cooling technique that would be most suitable for deep mining workers. It appears that no cooling technology currently on the market is perfectly compatible with this type of mining environment. However, combining two or more cooling technologies into a single hybrid system could be the solution to an optimized cooling garment for deep mines.

Seismic fragility assessment of low-rise stone masonry buildings

Many historic buildings in old urban centers in Eastern Canada are made of stone masonry reputed to be highly vulnerable to seismic loads.Seismic risk assessment of stone masonry buildings is therefore the first step in the risk mitigation process to provide adequate planning for retrofit and preservation of historical urban centers.This paper focuses on development of analytical displacement-based fragility curves reflecting the characteristics of existing stone masonry buildings in Eastern Canada.The old historic center of Quebec City has been selected as a typical study area.The standard fragility analysis combines the inelastic spectral displacement,a structure-dependent earthquake intensity measure,and the building damage state correlated to the induced building displacement.The proposed procedure consists of a three-step development process：（1） mechanics-based capacity model,（2） displacement-based damage model and（3） seismic demand model.The damage estimation for a uniform hazard scenario of 2% in 50 years probability of exceedance indicates that slight to moderate damage is the most probable damage experienced by these stone masonry buildings.Comparison is also made with fragility curves implicit in the seismic risk assessment tools Hazus and ELER.Hazus shows the highest probability of the occurrence of no to slight damage,whereas the highest probability of extensive and complete damage is predicted with ELER.This comparison shows the importance of the development of fragility curves specific to the generic construction characteristics in the study area and emphasizes the need for critical use of regional risk assessment tools and generated results.

Optimal Production Control of Hybrid Manufacturing/Remanufacturing Failure-Prone Systems under Diffusion-Type Demand

The problem of production control for a hybrid manufacturing/remanufacturing system under uncertainty is analyzed. Two sources of uncertainty are considered: machines are subject to random breakdowns and repairs, and demand level is modeled as a diffusion type stochastic process. Contrary to most of studies where the demand level is considered constant and fewer results where the demand is modeled as a Poisson process with few discrete levels and exponentially distributed switching time, the demand is modeled here as a diffusion type process. In particular Wiener and Ornstein-Uhlenbeck processes for cumulative demands are analyzed. We formulate the stochastic control problem and develop optimality conditions for it in the form of Hamilton-Jacobi-Bellman (HJB) partial differential equations (PDEs). We demonstrate that HJB equations are of the second order contrary to the case of constant demand rate (corresponding to the average demand in our case), where HJB equations are linear PDEs. We apply the Kushner-type finite difference scheme and the policy improvement procedure to solve HJB equations numerically and show that the optimal production policy is of hedging-point type for both demand models we have introduced, similarly to the known case of a constant demand. Obtained results allow to compute numerically the optimal production policy in hybrid manufacturing/ remanufacturing systems taking into account the demand variability, and also show that Kushner-type discrete scheme can be successfully applied for solving underlying second order HJB equations.

Production Planning of a Failure-Prone Manufacturing/Remanufacturing System with Production-Dependent Failure Rates

This paper deals with the production-dependent failure rates for a hybrid manufacturing/remanufacturing system subject to random failures and repairs. The failure rate of the manufacturing machine depends on its production rate, while the failure rate of the remanufacturing machine is constant. In the proposed model, the manufacturing machine is characterized by a higher production rate. The machines produce one type of final product and unmet demand is backlogged. At the expected end of their usage, products are collected from the market and kept in recoverable inventory for future remanufacturing, or disposed of. The objective of the system is to find the production rates of the manufacturing and the remanufacturing machines that would minimize a discounted overall cost consisting of serviceable inventory cost, backlog cost and holding cost for returns. A computational algorithm, based on numerical methods, is used for solving the optimality conditions obtained from the application of the stochastic dynamic programming approach. Finally, a numerical example and sensitivity analyses are presented to illustrate the usefulness of the proposed approach. Our results clearly show that the optimal control policy of the system is obtained when the failure rates of the machine depend on its production rate.

Multi-Frequency Interference Detection and Mitigation Using Multiple Adaptive IIR Notch Filter with Lattice Structure

Radio Frequency Interferences (RFI), such as strong Continuous Wave Interferences (CWI), can influence the Quality of Service (QoS) of communications, increasing the Bit Error Rate (BER) and decreasing the Signal-to-Noise Ratio (SNR) in any wireless transmission, including in a Digital Video Broadcasting (DVB-S2) receiver. Therefore, this paper presents an algorithm for detecting and mitigating a Multi-tone Continuous Wave Interference (MCWI) using a Multiple Adaptive Notch Filter (MANF), based on the lattice form structure. The Adaptive Notch Filter (ANF) is constructed using the second-order IIR NF. The approach consists in developing a robust low-complexity algorithm for removing unknown MCWI. The MANF model is a multistage model, with each stage consisting of two ANFs: the adaptive IIR notch filter Hl(z) and the adaptive IIR notch filter HN(z), which can detect and mitigate CWI. In this model, the ANF is used for estimating the Jamming-to-Signal Ratio (JSR) and the frequency of the interference (w(0)) by using an LMS-based algorithm. The depth of the notch is then adjusted based on the estimation of the JSR. In contrast, the ANF HN(z) is used to mitigate the CW interference. Simulation results show that the proposed ANF is an effective method for eliminating/reducing the effects of MCWI, and yields better system performance than full suppression (kN=1) for low JSR values, and mostly the same performance for high JSR values. Moreover, the proposed can detect low and high JSR and track hopping frequency interference and provides better Bit error ratio (BER) performance compared to the case without an IIR notch filter.