High-priority Demand Research Articles

Enhancing mechanical properties of 2024-Aluminium Alloy Matrix Composite strengthened by Y2O3 ceramic particles

The development of high-strength, low-weight composite materials has been a high-priority demand in the structural, aerospace, automotive, renewable energy, and sports equipment sectors. Micro-particle size Yttrium oxide (Y2O3) has been utilized to enhance the microstructure and mechanical characteristics of the 2024-Al alloy. The stir-casting technique is employed to prepare 2024-Al alloy and aluminum matrix composites (AMCs) with different Y2O3 weight fractions (1 %, 2 %, and 3 %). Scanning electron microscope ESM with EDS analysis and optical microscope imaging have been used to assess the development in the microstructure of AMCs. Furthermore, optical microscope images have been analyzed by using image processing software, ImageJ to evaluate development in microstructure grain size. Vickers' micro-hardness, tensile strength, elongation, and wear-resistant tests were conducted to assess the mechanical properties of AMCs. AMC image analysis results demonstrated a significant reduction in microstructure grain size. The highest reduction in grain size was recorded when adding 2 wt. % of Y2O3, which was approximately 22.5 % smaller than that of the plain alloy. Other mechanical test results demonstrated an increase in the hardness and tensile strength of AMCs compared with the plain alloy by approximately 58 % and 116 %, respectively, upon the addition of 2 wt. % of Y2O3, also tensile test shows increasing of AMCs elongation at failure point with increasing Y2O3 content, 132 % upon adding 3 wt. %. Finally, wear tests show a decrease in the AMC wear rate with an increasing Y2O3 weight fraction compared to the plain alloy.

Differentiated instruction for English learners: Teachers' understanding and practices

AbstractThe increase in the English learner (EL) student population in the United States and these students' placement in U.S. public schools have created high priority demands to address their language and learning needs. General education teachers, however, are often unprepared or underprepared to work with ELs, with limited knowledge and expertise to integrate differentiated instruction (DI) for them. This study examined two elementary teachers' understanding of differentiated instructional practices for EL students in a mainstream setting. Employing qualitative methods and following inductive data analytic processes, the study examined what these elementary teachers say facilitates DI for ELs. The researcher used semi‐structured individual interviews, a joint interview, and photo elicitation as the primary sources of data collection, and documents such as teachers' differentiated lesson plans and statements of teaching philosophy as secondary data sources. Findings revealed that a caring relationship between the teacher and ELs was essential and played a key role in student learning. By building caring relationships, the teachers learned about their EL students' needs and their interests and used that knowledge to implement DI for these students. Furthermore, teachers' proficiency in a language other than English and their metalinguistic awareness to bridge between one language and the other guided the teachers' differentiated instructional practices for their ELs.

Spatial Conservation Prioritization of Persian Squirrel Based on Habitat Suitability and Climate-Induced Forest Mortality Risk

The Zagros forests in Iran are currently experiencing an exacerbation of climate-induced mortality, placing the Persian squirrel, a keystone species reliant on these ecosystems, in jeopardy. Addressing this imminent threat, our research employed a spatial prioritization methodology, integrating assessments of habitat suitability and mortality risk. Utilizing a weighted ensemble approach, incorporating the strengths of diverse models and expert rules, we discerned that approximately 62% of surveyed forests are at risk, with 7% classified as high risk and 17% as very high risk. Notably, 83% of the forests exhibited varying degrees of habitat suitability, with 11% and 12% demonstrating high and very high suitability, respectively. Employing a conservation prioritization framework, we systematically categorized habitats into priority classes, with 8%, 17%, 29%, and 46% assigned to very high, high, moderate, and low conservation priority classes, respectively. Significantly, areas classified as very high priority demand immediate restoration efforts due to ongoing mortality, while other priority classes underscore the importance of protection and prevention in unaffected habitats. Acknowledging the irreversible nature of current climatic conditions in the Zagros forests, our strategic emphasis aligns with conservation triage principles, prioritizing the preservation of intact habitats yet to succumb to irreversible mortality.

A multi-objective fuzzy facility location problem with congestion and priority for drone-based emergency deliveries

Emergency medical service (EMS) plays an essential role in modern emergency and health systems; however, the delivery service via traditional ground vehicles faces enormous challenges, e.g., traffic congestion and poor road conditions, especially for time-critical products. Fortunately, drones provide an alternative solution for EMS due to faster speed, fewer road restrictions, and fewer workforce requirements, compared to ground vehicles. This paper develops a drone-based queuing-location model with stochastic demands and congestion for EMS, where drones perform as mobile servers with a generally distributed service time. In practice, delivery decisions are often made in the presence of imprecise information, and customer requests often have different priorities. However, to our best knowledge, limited research has addressed these issues. Thus, this paper employs fuzzy theory to cope with the vague drone endurance and demand arrival rate under a priority queuing strategy. A multi-objective optimization approach is adopted to balance the total cost, system efficiency, and equitable response time. As the resulting model is challenging to solve, we apply chance-constrained, second-order conic, fuzzy, and weighted goal programming approaches to recast the model as a crisp mixed-integer second-order conic program, which can be efficiently solved via off-the-shelf solvers. Results based on a case study show that our method can help decision-makers to better balance various objectives, make more flexible decisions with desirable fuzzy degrees, and significantly improve the service level of high priority demands for EMS.

A Novel Method for Breakdown Prediction of Vehicle Clutch Using Multiple Linear Regression

The clutch is an important component of the transmission system in all aspects of car vehicle operation. The failure of a clutch in any vehicle has a direct impact on the vehicle's operation and, in some cases, on human safety as well. There are a variety of factors that might contribute to a clutch failure, including an overloaded vehicle, the use of the clutch in city traffic on a constant basis, and mistakes made during the gear shifting process. A high-priority demand is the ability to predict clutch failure, which is currently not achievable through vehicle diagnosis. This positional paper contributes to the use of multiple regression analysis technique to predict clutch life with the help of numerous vehicle parameters such as transmission oil temperature, vehicle speed, vehicle torque, vehicle engine speed, transmission oil level, accelerometer pedal position, parking brake status, and oil contamination. The consideration of many parameters adds to enhancing the accuracy of the forecast output by expanding the number of parameters considered. The proposed system's performance, which has an accuracy of 94 percent, is considered satisfactory. This technology can be used to notify drivers in Lehman language about projected consequences depending on the information provided by the system.

Multi-stage resilience scheduling of electricity-gas integrated energy system with multi-level decentralized reserve

Extreme events pose threats to secure and consistent supply of electricity-gas integrated energy system (EGIES), and adequate resilience is essential for improving risk defense abilities. Considering the constraints of power distribution network (PDN) and natural gas system (NGS), a multi-stage strategy integrating multi-level decentralized reserve is proposed for resilience enhancement in EGIES. Storages of multi-area community integrated energy systems (CIESs) serve as the secondary reserve while the primary reserve, i.e., independent electric power/natural gas system, fails to meet reserve requirements. Furthermore, the thermal storage of building air on the consumption side is taken as the tertiary reserve, so as to obtain a better emergency response effect. The scheduling framework consists of reserve calculation, economic scheduling, and fault restoration. On the basis of multi-area reserve calculation, day-ahead economic scheduling is conducted, maintaining adequate reserve with consideration of decentralized reserve constraints; thus, high-priority demands can be recovered in case of supply disruptions. In addition, the flexible topology of PDN is utilized to facilitate the restored loads by optimal islanding partition. Via conic relaxation conversion, the original nonconvex model is tracked into a unified mixed-integer second-order cone programming (MISOCP) formulation, which can be effectively and accurately solved. Finally, numerical study on a modified IEEE 33-bus test system and a modified 7-node gas system connecting multiple community integrated energy systems shows the effectiveness of the proposed resilience strategy.

Reduction Strategy for Electrical Loads Based on Demand Priority in Power System

Background: Most non-developing and under developing countries strive hard to tackle the situation of power crisis and to combat the imbalance between the power generation and load demand, especially in the case of increasing of the population. In this situation, the load shedding scheme has been extremely implemented as a fast solution for unbalance conditions. Thus, load shedding is crucial to investigate supply-demand balancing in order to protect the network from collapsing and to sustain stability as possible; however its implementation is mostly undesirable. Objective: prioritize the loads according to their importance and apply reduction strategy in the demands while the supplied power to the important loads such as health care and security installation are kept intact without any interruption as possible. Methods: The conventional methods of load shedding lead to over or under shedding and this may lead to many problems with the network. Under the scheme, these methods disconnect the load or the entire feeder without considering their priorities and may not perform as anticipated. In this work, we propose a logarithmic reduction method to reduce the load according to the priority and day life criticality. The method for shedding the load base on Reduction Matrix and which in turn depend on the priority demands. Results: The higher priority demands are fed with a reliable power source by the real time monitoring of the network accompanied with power reducing for the lower priority demands. Conclusion: We test a real data sample provided by the Iraqi national grid control center in Baghdad. Our simulation results prove effectiveness and practicality of the applied method paving the way for possible applications in power systems.

Click Modification of a Metal-Organic Framework for Two-Photon Photodynamic Therapy with Near-Infrared Excitation.

The exploitation of effective strategies to develop materials bearing deep tissue focal fluorescence imaging capacity and excellent reactive oxygen species (ROS) generation ability is of great interest to address the high-priority demand of photodynamic therapy (PDT). Therefore, we use a rational strategy to fabricate a two-photon-active metal-organic framework via a click reaction (PCN-58-Ps). Moreover, PCN-58-Ps is capped with hyaluronic acid through coordination to obtain cancer cell-specific targeting properties. As a result, the optimized composite PCN-58-Ps-HA exhibits considerable two-photon activity (upon laser excitation at a wavelength of 910 nm) and excellent light-triggered ROS (1O2 and O2•-) generation ability. In summary, the interplay of these two critical factors within the PCN-58-Ps-HA framework gives rise to near-infrared light-activated two-photon PDT for deep tissue cancer imaging and treatment, which has great potential for future clinical applications.

Rationing policies in a spare parts inventory system with customers differentiation

We consider a spare parts inventory system controlled by a base-stock policy. We assume several demand classes, where the demands of each class follow a compound Poisson process, and a stochastic replenishment lead-time common to all classes. For this inventory system, we investigate two rationing policies that can be employed when the inventory level is critically low. The first policy is the reservation policy, where a part of the stock is reserved for high priority demand classes, whereas the second one is new, which we call a threshold policy, and accepts demands of a given class until the pipeline stock caused by the particular class (plus the amount of the incoming order) reaches a critical level. On average, for 8% (12%) of all instances, the threshold policy performs better than the reservation policy under the full (partial) lost sales case and when the objective is to minimise the average on-hand inventory subject to fill rate service requirements. Furthermore, in many cases where the reservation policy outperforms the threshold policy, the gap between the two policies is small under both service requirements and total cost minimisation criteria. This suggests that a trade-off between ‘fairness’ and ‘cost’ is required, and that when the performance of two policies are similar, the threshold policy is generally favoured.

A comparative study on allocation/rationing mechanisms operational with/without backorder clearing in divergent supply chains

The management of inventory in a divergent supply chain involves inventory allocation/rationing in addition to the determination of order policy parameters. In the case of a stock point feeding product(s) to several downstream members, rationing mechanism can be viewed as a special case of the allocation mechanism. In a supply chain with multi-period ordering cycles, a rationing decision ensures that the entire inventory available with the feeder stock point is rationed to downstream members, whereas an allocation decision need not allocate the entire inventory available, and it is at the discretion of the decision maker at the feeder stock point to retain inventory for possible high priority demands in future periods. In any supply chain permitting backordering of demands from downstream members, the clearing of backorders is a matter of concern. This study addresses the said issue by ensuring that the feeder stock point considers the current period demand for fulfilment only after clearing the backorders with respect to the downstream members. Through this study, an attempt is made to develop mathematical models for supply chains operating with installation-specific costs (holding and shortage) and ordering policy (base stock) over a finite time horizon with and without clearing backorders in the case of rationing as well as allocating inventory to downstream members. Specifically, this work appears to be the first comparative study on allocation and rationing mechanisms in association with/without backorder clearing mechanisms in divergent supply chains, and their impact on the total supply chain cost.

Demand Priority in a Power System With Wind Power Contribution Load Shedding Scheme Based

The load shedding scheme has been extensively implemented as a fast solution for unbalance conditions. Therefore, it's crucial to investigate supply-demand balancing in order to protect the network from collapsing and to sustain stability as possible, however its implementation is mostly undesirable. One of the solutions to minimize the amount of load shedding is the integration renewable energy resources, such as wind power, in the electric power generation could contribute significantly to minimizing power cuts as it is ability to positively improving the stability of the electric grid. In this paper propose a method for shedding the load base on the priority demands with incorporating the wind power generated. The higher priority demands are fed with a reliable wind energy resource in order to protect them from shedding under contingency condition such as high overloading by the real time monitoring of the network accompanied with power reducing for the lower priority demands. The simulation results prove effectiveness and practicality of the applied method paving the way for possible applications in power systems.

Anticipated rationing policy for inventory systems with two demand classes and backlogging costs

This paper studies a periodic-review, infinite-horizon, backlogging inventory model with two demand classes and a constant lead time, where inventory replenishment follows a base-stock policy. We consider an anticipated rationing policy which reserves inventory for future high-priority demands with higher backlogging costs by taking the coming delivery of the next period into consideration. Due to the lack of nice properties such as convexity, both the optimal base-stock level and the optimal critical level when minimising inventory costs have to be found by an exhaustive search. Instead, we study a single-period problem truncated from the original infinite-horizon problem and derive its optimal reservation level with a closed-form expression. Surprisingly, the solution form of the single-period problem coincides exactly with the anticipated rationing policy and hence this solution serves as a good approximation for the optimal critical level of the infinite-horizon problem. An empirical study further demonstrates that our closed-form approximation is quite attractive in both solution accuracy and computation efficiency based on spare parts inventory data from a petrochemical plant in China.

Managing Supply Chains with Expediting and Multiple Demand Classes

We consider a periodic review, single‐stage inventory system with multiple demand classes and a fixed replenishment lead time. Inventory expediting is allowed to alleviate demand–supply mismatches. Priority demands are commonly used in practice to provide differentiated services for customers, and inventory expediting is an effective strategy to improve the service levels of high‐priority demands. However, it is challenging to coordinate the inventory ordering, expediting and allocation decisions in supply chains. We partially characterize the structure of the optimal policy. We also derive various monotone properties of the optimal policy with limited sensitivities. Moreover, we show that a state‐dependent rationing level policy is optimal for inventory allocation and the optimal rationing levels are in fact independent of the backorder quantity of each demand class. We also show when some simple policies are indeed optimal. Numerically, we illustrate the optimal policy and investigate the performances of three proposed simple heuristics. Finally, we extend the results to more general systems, such as serial systems, systems with convex backordering costs, etc.

The value of maintenance delay flexibility for improved spare parts inventory management

Capital assets are typically maintained by replacing components with spare parts. Often, there is flexibility to delay planned, preventive maintenance when spare parts are required to perform more urgent unplanned, corrective maintenance. In this case, keeping a single stock of inventory to satisfy both types of demand can be beneficial. Therefore, we study a periodic review inventory system with a single stocking point and a zero replenishment lead time, used to meet lower-priority demands that are planned, as well as stochastic, high-priority demands. We study the impact of flexibility in performing planned maintenance on the optimal inventory policy. When planned maintenance may be delayed at most once, i.e., for one period, a myopic policy is optimal. When it may be delayed to the next period for an unlimited number of times, we characterize the structure of the optimal policy. We further propose two extensions, one in which delayed planned maintenance may become unplanned and one in which the total maintenance delay costs are increasing and convex in the number of delayed maintenance actions. Numerically, we find that allowing a single delay provides a system performance very close to that when unlimited delays are allowed. This result becomes even stronger in the context of the two model extensions. These results are important for practice, since maintenance managers are unlikely to accept the unlimited delay policy, while the one delay policy is likely to be more acceptable. Finally, we develop myopic heuristic policies and demonstrate that they perform quite close to optimal.

Markov Models of Systems with Demands of Two Types and Different Restocking Policies

Markov models of queuing-inventory systems with demands of two types are proposed. In these systems, two restocking policies are used: policies with fixed and variable demand size. High-priority demands have no access constraints while low-priority demands are accepted if the total number of demands in the system is less than a given threshold. Methods are developed to calculate performance measures of the systems under study and problems of their optimization are solved. Results of the numerical experiments are shown.

A continuous cost evaluation approach for periodic review inventory systems with threshold rationing policy

Abstract This study analyzes an inventory problem with two stochastic demand classes in a periodic review inventory setting, in which a firm reviews its inventory position periodically and fulfills two Poisson demand classes continuously during periods. This study simultaneously assumes positive lead time and full backorders. An inventory policy consisting of order-up-to policy and threshold rationing policy is adopted, in which the firm places an order to raise its inventory position to order-up-to level in a fixed length of review interval and the inventory is rationed with a threshold level. Specifically, the total demand is served on a first-come first-served basis (FCFS) if its available inventory is above the threshold level. Otherwise, only the high-priority demand class is served. With such an inventory policy, this study provides a novel approach to evaluate the exact long-run expected inventory costs by assuming a threshold backorder clearing mechanism. This evaluation approach can easily incorporate FCFS policy. Furthermore, a method is provided to find the optimal policy parameters. Numerical experiments show that, relative to the FCFS policy and separate policy, the rationing policy can generate significant cost savings.

Combining water supply and flood control purposes in the Coghinas Basin (Sardinia, Italy)

ABSTRACTThe increased level of impact on life and infrastructure and the environment caused by flooding in Sardinia (Italy) has resulted in the need for the Flood Risk Management Plans (FRMP). The FRMP has been developed by Sardinian Region Administration that selected the Coghinas Basin (North Sardinia) as a pilot basin in according with the EU and National legislation. Recently, the European Flood Directive 2007/60/CE has stated that the flood-risk evaluation should include a cost–benefit analysis and an integrated decision-making tool should help optimize operating rules for multipurpose reservoirs, primarily with regard to the mitigation of flood risks and high priority demand supplies, and selected flood control works. The main purpose of this study was to develop an integrated flood management model to predict flood inundation in the flood-prone areas for various probabilities of occurrence, identify the flood operating rules in a multipurpose reservoir which accomplish a reduction in flood risk, and assess the total damage due to flooding. The FRMP is currently assessing the impacts of new reservoir operating rules reservoir and new works for flood damage mitigation. Based on a calibrated benefit–cost analysis, the proposed model suggested the best scenario of reservoir operating rules and flood control works with a significant saving of money compared to the actual scenario in the FRMP.

COMMUNITY-BASED NATIONWIDE CAPACITY BUILDING FOR GIS-BASED INTEGRATED FLOOD MANAGEMENT

Abstract. After the development of a flood action plan (FAP) in 1988, the Bangladesh government has led practical actions for nationwide flood management. However, Bangladesh is on its way to improve the capacity and knowledge of sustainable infrastructure and non-structural countermeasures. Local government and communities are also struggling to mitigate flood risk and severe damage. Therefore, a government-driven strategy of capacity building on sustainable flood risk management is an inevitable process to support local authorities’ responsibility under the leadership. This pilot study introduced the GIS-based integrated flood management (GiFM) for improving nationwide flood risk management at different levels from communities. First, we proposed the conceptual framework of GiFM to strengthen local government-based capacity focusing on a cost-effective way under the FAP. Then the proposed GiFM employed hydro-GIS geo-database and Earth Observation (EO) data for sharing information on the current flood risk situation including flood monitoring, forecasting and river embankment erosion in transboundary river basins. For flood risk reduction, the prototype of GiFM showed a good example of consultation and action plan to realize high-priority demands at all levels of multi-stakeholders, from decision makers to residents who are living in vulnerable low-lying floodplains of Bangladesh.

IEEE Access Special Section Editorial: Mission Critical Public-Safety Communications: Architectures, Enabling Technologies, and Future Applications

Disaster management organizations such as fire brigades, rescue teams, and emergency medical service providers have a high priority demand to communicate with each other and with the victims by using mission-critical voice and data communications [item 1) in the Appendix]. In recent years, public safety agencies and organizations have started planning to evolve their existing land mobile radio system (LMRS) with long-term evolution (LTE)-based public safety solutions which provides broadband, ubiquitous, and mission-critical voice and data services. LTE provides high bandwidth and low latency services to the customers using internet protocol-based LTE network. Since mission critical communication services have different demands and priorities for dynamically varying situations for disaster-hit areas, the architecture and the communication technologies of the existing LTE networks need to be upgraded with a system that has the capability to respond efficiently and in a timely manner during critical situations.

Dynamic inventory rationing: How to allocate stock according to managerial priorities. An empirical study

In spare part distribution, contracts characterized by different unit prices, lead times, minimum order quantities are usually offered to different types of customers. In this context, one of the main problems is the decision on the allocation of the on–hand inventory to the different customers in order to minimize the total back–order cost. In this paper, we propose two allocation policies that use a dynamic critical level to reserve on-hand inventory for high priority customers. The first policy sets the critical level according to a target service level for high priority demand, while the other sets the critical level according to the back-order costs for the different classes of customers. The effectiveness of the two policies with respect to no rationing or stock piling polices is shown using the data of a European spare part company. Moreover, the relationship between the two policies is evaluated from a managerial perspective.