Optimisation of stochastic networks with blocking: a functional-form approach

This paper introduces a class of stochastic networks with blocking, motivated by applications arising in cellular network planning, mobile cloud computing, and spare parts supply chains. Blocking results in lost revenue due to customers or jobs being permanently removed from the system. We are interested in striking a balance between mitigating blocking by increasing service capacity, and maintaining low costs for service capacity. This problem is further complicated by the stochastic nature of the system. Owing to the complexity of the system there are no analytical results available that formulate and solve the relevant optimization problem in closed form. Traditional simulation-based methods may work well for small instances, but the associated computational costs are prohibitive for networks of realistic size. We propose a hybrid functional-form based approach for finding the optimal resource allocation, combining the speed of an analytical approach with the accuracy of simulation-based optimisation. The key insight is to replace the computationally expensive gradient estimation in simulation optimisation with a closed-form analytical approximation that is calibrated using a single simulation run. We develop two implementations of this approach and conduct extensive computational experiments on complex examples to show that it is capable of substantially improving system performance. We also provide evidence that our approach has substantially lower computational costs compared to stochastic approximation

Optimal Tradeoff Between Exposed and Hidden Nodes in Large Wireless Networks

Wireless networks equipped with the CSMA protocol are subject to collisions due to interference. For a given interference range we investigate the tradeoff between collisions (hidden nodes) and unused capacity (exposed nodes). We show that the sensing range that maximizes throughput critically depends on the activation rate of nodes. For infinite line networks, we prove the existence of a threshold: When the activation rate is below this threshold the optimal sensing range is small (to maximize spatial reuse). When the activation rate is above the threshold the optimal sensing range is just large enough to preclude all collisions. Simulations suggest that this threshold policy extends to more complex linear and non-linear topologies

Minimum target prices for production of direct acting antivirals and associated diagnostics to combat Hepatitis C Virus

Combinations of direct-acting antivirals (DAAs) can cure hepatitis C virus (HCV) in the majority of treatment-naïve patients. Mass treatment programs to cure HCV in developing countries are only feasible if the costs of treatment and laboratory diagnostics are very low. This analysis aimed to estimate minimum costs of DAA treatment and associated diagnostic monitoring. Clinical trials of HCV DAAs were reviewed to identify combinations with consistently high rates of sustained virological response across hepatitis C genotypes. For each DAA, molecular structures, doses, treatment duration, and components of retrosynthesis were used to estimate costs of large-scale, generic production. Manufacturing costs per gram of DAA were based upon treating at least 5 million patients per year and a 40% margin for formulation. Costs of diagnostic support were estimated based on published minimum prices of genotyping, HCV antigen tests plus full blood count/clinical chemistry tests. Predicted minimum costs for 12-week courses of combination DAAs with the most consistent efficacy results were: US$122 per person for sofosbuvir+daclatasvir; US$152 for sofosbuvir+ribavirin; US$192 for sofosbuvir+ledipasvir; and US$115 for MK-8742+MK-5172. Diagnostic testing costs were estimated at US$90 for genotyping US$34 for two HCV antigen tests and US$22 for two full blood count/clinical chemistry tests. Conclusions: Minimum costs of treatment and diagnostics to cure hepatitis C virus infection were estimated at US$171-360 per person without genotyping or US$261-450 per person with genotyping. These cost estimates assume that existing large-scale treatment programs can be established. (Hepatology 2015;61:1174–1182

Human Resource Management and Artificial Intelligence: A Bibliometric Exploration

The concept of artificial intelligence, a driving force behind human resource management, has recently gained popularity in the academic community. This study explores the intellectual structure of this field using the Scopus database in the subject area of business, management and accounting. Bibliographic analysis, a recent and rigorous method for delving into scientific data, is used in this investigation. The approach used is a structured and transparent process divided into four steps: (1) search criteria; (2) selection of database and documents; (3) selection of software and data pre-processing; and (4) analysis of findings. We employ bibliometric mapping to observe their numerous linkages and performance evaluation to learn about their structure. A total of 67 articles were collected from the Scopus database between 2015 and 2022 using certain keywords (artificial intelligence, expert systems, big data analytics, and human resource management) and some specific filters (subject–business, management and accounting; language-English; document–article, review articles and source-journals). Ten research clusters were identified: Cluster 1: multi-agent system; Cluster 2: decision support system; Cluster 3: internet of things; Cluster 4: active learning; Cluster 5: decision tree; Cluster 6: optimisation; Cluster 7: software design; Cluster 8: data mining; Cluster 9: cloud computing; Cluster 10: human-robot interaction. The findings could be helpful for researchers and practitioners in the HRM field to extend their knowledge and understanding of AI and HRM research. This study can provide notable guidance and future directions for quite a few firms in expanding the use of AI in HRM. Keywords: Artificial intelligence, human resource management, bibliometric analysi

Apparatus for recovery and use of waste thermal energy

Energy recovery apparatus for recovering and using thermal energy comprises multiple series of shape memory elements having decreasing transformation temperatures from one element to the next in the direction of a heating fluid to extract heat over a range of fluid temperatures to effect a shape change of each element. Cooling fluid is flowed alternately through each series of elements to cool the elements and effect another shape change. The cooling fluid exiting a preceding series of elements is used as the heating fluid of the next series of elements to extract further heat indirectly from the original heating fluid. The shape changes of the elements are converted to mechanical work