Decision Policy Research Articles

A Novel Mirror Neuron Inspired Decision-Making Architecture for Human–Robot Interaction

Inspired by the role of mirror neurons and the importance of predictions in joint action, a novel decision-making structure is proposed, designed and tested for both individual and dyadic action. The structure comprises models representing individual decision policies, policy integration layer(s), and a negotiation layer. The latter is introduced to prevent and resolve conflicts among individuals through internal simulation rather than via explicit agent-agent communication. As the main modelling tool, Dynamic Neural Fields (DNFs) were chosen. Data was captured from human-human experiments with a decision-making task performed by either one or two participants. The task involves choosing and picking blocks one by one from seven wooden blocks to create an alpha/numeric character on a 7-segment. The task is designed to be as generic as possible. Recorded hand and blocks movements were used for developing DNF-based models by optimising parameters using a genetic algorithm. Results show that decision policies can be modelled and integrated with acceptable accuracy for individual performances. In the dyadic experiment, using only individual models without the negotiation layer, the model failed to resolve conflicts. However, with the implementation of a negotiation layer, this problem could be overcome. The proposed decision-making structure based on DNFs is developed and tested for a simple pick-and-place task. However, the main primitive underlying action of this task, pick-and-place, is indeed part of many more complex tasks people perform in their day-to-day life. Paired with the possibility to gradually evolve the architecture by adding new policies on demand, the architecture provides a general framework for modelling decision-making in joint action tasks.

Rethinking Local Economic Development for Fetal Alcohol Spectrum Disorder in Renosterberg Local Municipality, South Africa

Two towns in Renosterberg Local Municipality (RLM) in the Northern Cape Province of South Africa, Petrusville and Philipstown, have high Fetal Alcohol Spectrum Disorder (FASD) prevalence rates. FASD is linked to poverty and imposes high national economic costs. Thus, it is critical to understand the local economic development (LED) strategies used to mitigate the high prevalence of FASD. Moreover, there is sparse literature examining adult communities where FASD children reside. Understanding these adult communities is important because FASD cannot exist without adult gestational exposure to alcohol consumption. Using a mixed-method approach, this study uses a six-phase analytic approach to investigate the drinking culture and drinking motives in RLM, applied to two cross-sectional in-depth community needs assessments, five in-depth interviews, and three focus groups. This study also evaluates how the RLM targets FASD, as well as binge and risky drinking, in its municipal economic strategy by analysing its Integrated Development Plan (IDP) with respect to an eight-stage policy development process. The results indicate that 57% of respondents expressed concern regarding the unhealthy drinking culture in RLM, 40% felt that the residents of RLM drank in response to unemployment-related hopelessness, and 52% attributed the drinking culture to a lack of hobbies and recreational opportunities. The results of an analysis of the RLM IDP through the lens of Ryder's eight-stage policy development process suggest that the decisive policy development process is not open to the public and that, furthermore, FASD is neglected. A dedicated alcohol consumption census-style study is recommended to broadly capture alcohol consumption in RLM, allowing researchers to identify the exact alcohol consumption patterns and priority areas for the IDP and public health policy. RLM should directly publicise its policy development process so that its IDP is inclusively formulated to address FASD, risky drinking, binge drinking, and gestational alcohol consumption.

Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1

Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeService ScienceVol. 15, No. 1 Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1Guest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif BenjaafarGuest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif BenjaafarPublished Online:13 Mar 2023https://doi.org/10.1287/serv.2023.0317Rapid developments in city infrastructure and technologies are creating numerous opportunities and inspiring innovative and emerging urban services. Among these innovations, complex systems of urban transportation and logistics have embraced advances and have been reshaped significantly. They enable innovative new urban services, which are now booming and changing everyday life for urban residents.This special issue of Service Science explores perspectives on innovation in transportation-enabled urban services. We hope that the special issue will enhance the understanding of the planning, operation, and management of such services. Contributions are expected to demonstrate rigorous model development, economic/econometric analysis, and decision-making tools based on optimization and/or data-driven approaches. Research in this special issue addresses the novel challenges and opportunities that arise in these transportation/logistics systems and relevant transportation-enabled urban services.The articles in the special issue represent an exciting diversity in research settings, methodologies, and applications. The articles are composed into two parts. Part 1 of the special issue includes four papers. In what follows, we provide a brief description of the papers in Part 1. The introduction of papers in Part 2 is rendered to the journal issue scheduled for September 2023.Yan et al. (2023) study an incentive mechanism for private parking-sharing programs in an imperfect information setting. They propose a matching-and-pricing mechanism for a driver’s demand-reporting problem in parking-sharing programs in which owners share their private parking slots with other drivers. They generate a driver-slot matching solution and show that, under the assumption that drivers do not know with certainty whether other drivers will show up to compete for the parking slots, the mechanism proposed induces drivers to truthfully report their private information about the travel plans and guarantees some desirable properties.Schwamberger et al. (2023) study the dynamic customer contacting for e-fulfillment in times of crisis. Specifically, the outbreak of the coronavirus disease 2019 pandemic increased demand for online grocery orders for both click & collect (C&C) and attended home delivery (AHD) and outstripped delivery capacity by a wide margin. They investigate a new demand management concept of proactively contacting customers and develop a decision policy for the C&C fulfillment method to address the problem of when to contact customers. They also extend this approach to the AHD setting and propose a three-step procedure to solve this problem.van der Gaast and Arslan (2023) explore the last-mile logistics operations of instant grocery delivery services. They introduce an instant delivery problem to replicate and examine two widely adopted strategies in the rapid delivery market: the personal shopper system (PSS) and the inventory owned delivery (IOD) system. They propose a tailored rolling horizon framework that utilizes column generation to browse updated delivery plans for arriving customer orders. Computational studies show that the PSS is a highly competitive strategy compared with IOD, particularly when dealing with small-sized customer orders.Ke and Mo (2023) study a one-way car-sharing system with both station-based and free-floating service types, which apply the two types in different parts of the service area. They establish a mixed-integer linear-programming model to maximize the operator profit, considering both strategic decisions of service type and operational decisions of relocation and trip selection. They propose a heuristic algorithm to effectively solve the model. Computational results demonstrate that the integrated system achieves a larger profit and higher demand fulfillment rate compared with the single station-based or free-floating system.ReferencesKe H, Mo Y (2023) Station-based or free-floating? An integrated carsharing system with dual service types. Service Sci. 15(1):58–76.Google ScholarSchwamberger J, Fleischmann M, Strauss A (2023) Feeding the nation—dynamic customer contacting for e-fulfillment in times of crisis. Service Sci. 15(1):22–40.Google Scholarvan der Gaast JP, Arslan AM (2023) Personal shopper systems in last-mile logistics. Service Sci. 15(1):41–57.Google ScholarYan P, Cai X, Chu F, Ni D, He H (2023) An incentive mechanism for private parking-sharing programs in an imperfect information setting. Service Sci. 15(1):3–21.Google Scholar Back to Top Next FiguresReferencesRelatedInformation Volume 15, Issue 1March 2023Pages 1-78, C3 Article Information Metrics Information Published Online:March 13, 2023 Copyright © 2023, INFORMSCite asGuest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif Benjaafar (2023) Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1. Service Science 15(1):1-2. https://doi.org/10.1287/serv.2023.0317 PDF download

Interval type-2 Fuzzy control and stochastic modeling of COVID-19 spread based on vaccination and social distancing rates

Background and objectiveBesides efforts on vaccine discovery, robust and intuitive government policies could also significantly influence the pandemic state. However, such policies require realistic virus spread models, and the major works on COVID-19 to date have been only case-specific and use deterministic models. Additionally, when a disease affects large portions of the population, countries develop extensive infrastructures to contain the condition that should adapt continuously and extend the healthcare system's capabilities. An accurate mathematical model that reasonably addresses these complex treatment/population dynamics and their corresponding environmental uncertainties is necessary for making appropriate and robust strategic decisions. MethodsHere, we propose an interval type-2 fuzzy stochastic modeling and control strategy to deal with the realistic uncertainties of pandemics and manage the size of the infected population. For this purpose, we first modify a previously established COVID-19 model with definite parameters to a Stochastic SEIAR (S2EIAR) approach with uncertain parameters and variables. Next, we propose to use normalized inputs, rather than the usual parameter settings in the previous case-specific studies, hence offering a more generalized control structure. Furthermore, we examine the proposed genetic algorithm-optimized fuzzy system in two scenarios. The first scenario aims to keep infected cases below a certain threshold, while the second addresses the changing healthcare capacities. Finally, we examine the proposed controller on stochasticity and disturbance in parameters, population sizes, social distance, and vaccination rate. ResultsThe results show the robustness and efficiency of the proposed method in the presence of up to 1% noise and 50% disturbance in tracking the desired size of the infected population. The proposed method is compared to Proportional Derivative (PD), Proportional Integral Derivative (PID), and type-1 fuzzy controllers. In the first scenario, both fuzzy controllers perform more smoothly despite PD and PID controllers reaching a lower mean squared error (MSE). Meanwhile, the proposed controller outperforms PD, PID, and the type-1 fuzzy controller for the MSE and decision policies for the second scenario. ConclusionsThe proposed approach explains how we should decide on social distancing and vaccination rate policies during pandemics against the prevalent uncertainties in disease detection and reporting.

Queueing and Reliability Analysis of Unreliable Multi-server Retrial queue with Bernoulli feedback

The study consists of an unreliable multiserver retrial queue with feedback where clients may resist to join the orbit and the service provider is erratic. In this model we have considered c service providers at a time and if all the c servers are busy, the incoming clients wait in the retrial orbit. The model is unfolded using Matrix Geometric Method (MGM) to obtain the rate matrix and stationary probabilities. The system with retrials is modelled and the performance measures and reliability indices are procured. These derived quotients are then visualised and validated with the help of tables and graphs. Further, the cost analysis of the model is carried out and the optimal cost for the system is obtained using Particle Swarm Optimization (PSO) algorithm for matrix method. The optimal repair and retrial rates are obtained using PSO technique which helps the computer and manufacturing industry to work towards better decision policies.

Acuity-Based Allocation of ICU-Downstream Beds with Flexible Staffing

Intensive care units (ICUs) are crucial resources within hospitals, caring for the most critically ill patients. We propose a novel modeling framework that improves the outflow of ICU patients by anticipating unit interactions and resource sharing within the system. Across an arbitrary bipartite network of units, we consider two types of downstream staffing (baseline and flexible) and a two-stage decision process. In the first stage, we determine the level of flexible bed staffing using existing physical beds at downstream units in anticipation of incoming transfers from the ICUs. In the second stage, we determine the allocation of ICU patients to downstream beds. The goal of the model is to reduce inefficiencies and transfer delays causing ICU bed block due to lack of space in downstream units. We formulate a dynamic multiperiod model and analyze the dual of its (relaxed) stationary counterpart. Decomposing the relaxed stationary model into an ICU and downstream subproblems, we calculate the relative values of downstream beds and derive a practical acuity-based policy for the daily operational decisions. Using a large-scale simulation calibrated with historic hospital data, we demonstrate that our acuity-based policy reduces the number of long-run diverted ICU arrivals, particularly in high-demand scenarios, thus improving ICU throughput, when compared with a deterministic, a generalized randomized-most-idle, and static policies. History: Accepted by J. Paul Brooks, Area Editor for Applications in Biology, Medicine, & Healthcare. Funding: This work was partially supported by National Science Foundation [Grants CMMI-1635301/1635410/1635642]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1267 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0133 ) at ( http://dx.doi.org/10.5281/zenodo.7194693 ).

Long-term outcomes after a transcranial microsurgical approach to craniopharyngiomas: a 20-year clinical follow-up study

Craniopharyngioma (CP) is a mostly benign tumor that is nonetheless one of most formidable skull base lesions. CP tends to recur, and scarce clinical results are available regarding its long-term outcomes. From February 1996 to April 2002, craniopharyngiomas primarily resected by open surgery in a single-center and single-surgeon practice were screened. Medical information regarding patients' baseline, tumor parameters, endocrinological results, complications, and quality of life in those patients with a follow-up longer than 20years were reviewed. Nineteen out of 187 patients who met the inclusion criteria were eligible, and 78.9% of the patients were admitted due to visual deficits. The transcranial approach was mostly applied for the first attempt of opening resection, with 94.7% gross total resection. The size of the tumor ranged from 25 to 45mm with a mean maximal diameter of 34.7mm. Although 7 out of 19 patients received an extra procedure, 6 patients (31.5%) regained fertility, with 3 women bearing a total of 5 children and 3 men fathering a total of 4 children, during the 21.4-year follow-up (range: 20.0-23.3years). The mean Karnofsky Performance Status (KPS) score was 97.9 (range: 80-100) at the last follow-up, and the physical and mental 36-Item Short Form Health Survey (SF-36) scores were 88.0 and 80.5, respectively. The tumor sizes of the patients who regained fertility were not significantly different from those of the patients who remained infertile (t = 1.29, P > 0.2). The time interval from prior surgery to tumor resection for recurrent cases ranged from 0.3 to 17.4years (mean, 7.3years). There was no significant difference in the time until tumor recurrence in the patients who underwent a second surgery, a third surgery, or a fourth surgery. The satisfactory results in the present study revealed favorable long-term outcomes following the transcranial management of CPs, with acceptable endocrinological function and tumor-free survival. A decisive policy of open surgery with the objective of radical tumor removal remains a valid method for the primary treatment of CPs, aiming to avoid retreatment after tumor recurrence involving vital hypothalamic structures or hydrocephalus.

Temporal Dynamics Underlying Prelimbic Prefrontal Cortical Regulation of Action Selection and Outcome Evaluation during Risk/Reward Decision-Making.

Risk/reward decision-making is a dynamic process that includes periods of deliberation before action selection and evaluation of the action outcomes that bias subsequent choices. Inactivation of the prelimbic (PL) cortex has revealed its integral role in updating decision biases in the face of changes in probabilistic reward contingencies, yet how phasic PL signals during different phases of the decision process influence choice remains unclear. We used temporally specific optogenetic inhibition to selectively disrupt PL activity coinciding with action selection and outcome phases to examine how these signals influence choice. Male rats expressing the inhibitory opsin eArchT within PL excitatory neurons were well trained on a probabilistic discounting task, entailing choice between small/certain versus large/risky rewards, the probability of which varied over a session (50-12.5%). During testing, brief light pulses suppressed PL activity before choice or after different outcomes. Prechoice suppression reduced bias toward more preferred/higher utility options and disrupted how recent outcomes influenced subsequent choice. Inhibition during risky losses induced a similar profile, but here, the impact of reward omissions were either amplified or diminished, relative to the context of the estimated profitability of the risky option. Inhibition during large or small reward receipt reduced risky choice when this option was more profitable, suggesting these signals can both reinforce rewarded risky choices and also act as a relative value comparator signal that augments incentive for larger rewards. These findings reveal multifaceted contributions by the PL in implementing decisions and integrating action-outcome feedback to assign context to the decision space.SIGNIFICANCE STATEMENT The PL prefrontal cortex plays an integral role in guiding risk/reward decisions, but how activity in this region during different phases of the decision process influences choice is unclear. By using temporally specific optogenetic manipulations of this activity, the present study unveiled previously uncharacterized and differential contributions by PL in implementing decision policies and how evaluation of decision outcomes shape subsequent choice. These findings provide novel insight into the dynamic processes engaged by the PL that underlie action selection in situations involving reward uncertainty that may aid in understanding the mechanism underlying normal and aberrant decision-making processes.

Investment strategies and coordination for green food supply chain: a further research considering the inputs of the blockchain-based traceability system

PurposeTo study these issues, the authors chose a GFSC with one producer and one material supplier as research object, the supplier will offer green material to the producer and the producer will make green food using green production technology. Then, the authors proposed that consumers' perceived value was determined by the trustworthiness levels of the related green and quality-safety information provided by the supplier and the producer. Then, considering the trustworthiness levels of the green and quality information provided by the supplier and the producer, the authors improved the demand function. Afterwards, we constructed four investment models and their income models are built and then a cost-sharing and revenue-sharing contract (hereafter, CSRS) was adopted to coordinate the GFSC.Design/methodology/approachWith the growth of consumers environmental awareness and life level, consumers' requirements for green and high quality food are growing. In recently years, to increase consumers' perceived trustworthiness on the product greenness and quality levels, stakeholders in green food supply chain (hereafter, GFSC) start to adopt the blockchain-based traceability system (hereafter, BLTS). For investors, they need to know the investment conditions and how to coordinate the GFSC.Findings(1) When the revenue-sharing coefficient is less than three-fourths and higher then a certain vaule, the cost-sharing and revenue-sharing contract can make the GFSC coordinate. (2) The investment cost threshold of the BLTS has a positive relationship with the trustworthiness improvement levels of the green and quality information, the green degree of food products and the quality of food products. (3) In the proposed four investment situations, as the growth of consumers perceived credibility coefficient about the greenness information and the quality information, chain members' revenues will increase. In addition, comparing with co-investing the BLTS, benefits of chain members are lower than them in the sole investment model.Originality/value(1) The demand function we proposed can help chain members forecast market demand to support production or ordering decisions. (2) The investment decision policies can offer a theoretical reference for chain members to use the BLTS. (3) The CSRS will offer the theoretical reference for coordinating the supply chain after using the BLTS. Furthermore, our study method can be referenced by other scholars. (4) The study method can offer a method reference for researchers who do a similar discussion in a manufacturing supply chain. Although, our research cannot guide the industrial practices, it can serve as a reference of the similar research in industry.

Challenges and opportunities of the urban structures of holy cities: the case of old Najaf, Iraq

This paper shed light on the historic urban structure that the city of Najaf has, and the current challenges and circumstances that are facing its unique urban centre, its traditional architecture, and its identity as a heritage Islamic city. Discussing Najaf as a pilgrimage city, that experiences seasonal pilgrimages to its holy sites, might force unusual urban conditions that are rarely found in other Islamic cities. These conditions enforce unique urban design, planning decisions, and actions that respect and improve the intangible and tangible heritage of the old city. Using an analytical approach, this paper analyses official documents and reports, maps, and archival materials that regard the unfortunate loss of the unique construction identity, which was part of its tangible and intangible features, and how they have been lost or neglected in the reconstruction of the urban structure of the old centre of Najaf, to illustrate how the city’s identity has impacted by different approaches, changes, and decisions policies. The paper provides a closer look at understanding the city’s urban transformations and explores the new challenges, tactics, and policies. The paper concludes by emphasizing the need to strengthen the traditional urban structure and control the improper changes and replacement procedures whenever it takes to cover the needs (facilities and commercial activities). In addition, to determine the locations that will be changed or allocated for the new projects, to ensure maintaining the identity of the city.

Learning Re-grabbing Policies based on Grabbed Garbage Weight Estimation using In-bucket Images for Waste Cranes

The automation of waste cranes has been demanded to perform garbage incineration work with fewer workers efficiently. In particular, data-driven learning approaches are desirable for waste crane automation. When deciding whether to re-grab garbage by a waste crane to grab more garbage, human operators are efficiently making decisions based on visual information. However, the current automation system has to lift up the bucket to measure grabbed garbage weight to decide re-grab. The lifting motion after grabbing makes the crane motion inefficient. For this limitation, we propose a re-grabbing decision system with feedback from in-bucket camera images for the efficiency of waste crane automation by introducing the vision sensor in the bucket. To simplify the decision process of re-grabbing, we separate the re-grabbing decision system from the in-bucket image into grabbed garbage weight estimation and the re-grabbing decision policy based on estimated garbage weight. Moreover, the weight estimator model and the re-grabbing policy model are designed based on a Bayesian manner for data efficiency. The effectiveness is verified in a robotic waste crane system with an in-bucket camera. We confirmed the proposed method could learn re-grabbing decisions from the autonomously collected data. It achieved more efficient re-grabbing by waste cranes than the conventional automated system.

Machine Learning for Service Migration: A Survey

Future communication networks are envisioned to satisfy increasingly granular and dynamic requirements to accommodate the application and user demands. Indeed, novel immersive and mission-critical services necessitate increased computing and network resources, reduced communication latency, and guaranteed reliability. Thus, efficient and adaptive resource management schemes are required to provide and maintain sufficient levels of Quality of Experience (QoE) during the service life-cycle. Service migration is considered a key enabler of dynamic service orchestration. Indeed, moving services on demand is an efficient mechanism for user mobility support, load balancing in case of fluctuations in service demands, and hardware failure mitigation. However, service migration requires planning, as multiple parameters must be optimized to reduce service disruption to a minimum. Recent breakthroughs in computational capabilities allowed the emergence of Machine Learning as a tool for decision making that is expected to enable seamless automation of network resource management by predicting events and learning optimal decision policies. This paper surveys contributions applying Machine Learning (ML) methods to optimize service migration, providing a detailed literature review on recent advances in the field and establishing a classification of current research efforts with an analysis of their strengths and limitations. Finally, the paper provides insights on the main directions for future research.

Deep Reinforcement Learning for Time-Energy Tradeoff Online Offloading in MEC-Enabled Industrial Internet of Things

Mobile edge computing (MEC) has recently emerged as a promising technology to boost the integration ability of sensing, transmission and computation in industrial Internet of Things (IIoT). This paper investigates an MEC-enabled IIoT system, where multiple industrial devices may offload computation-intensive tasks to an edge server through wireless communication. We focus on the online offloading problem to optimize the tradeoff of the task accomplishing time and energy consumption. Time-varying wireless channels, random targeted task data sizes and dynamically changing residual energy as well as adaptively adjusted tradeoff weights make this problem highly challenging. Conventional optimization methods may lead to inefficient or even infeasible solutions. To efficiently tackle this problem, we leverage the deep reinforcement learning (DRL) technology to propose a time-energy tradeoff online offloading algorithm called TETO. In TETO, the online offloading decision policies are empirically learned via a well-designed DRL framework. TETO algorithm incorporates a stochastic strategy, the crossover and mutation technology and a novel feasible suboptimal offloading method to expand the offloading action search space with the provable feasibility guarantee. Extensive experimental results based on a real-world dataset show that, our TETO algorithm performs better than existing baseline algorithms, and obtains near-optimal performance with low CPU execution latency.

A mathematical framework for estimating prototyping cost considering transition of quality states under Markovian setting

PurposeThis research aims to aid product development managers to estimate the expected cost associated with the development of cost-intensive physical prototypes considering transitions associated with pertinent states of quality of the prototype and corresponding decision policies under the Markovian setting.Design/methodology/approachThe authors evolve two types of optimization-based mathematical models under both deterministic and randomized policies. Under the deterministic policy, the product development managers take certain decisions such as “Do nothing,” “Overhaul,” or “Replace” corresponding to different quality states of prototype such as “Good as new,” “Functional with minor deterioration,” “Functional with major deterioration” and “Non-functional.” Under the randomized policy, the product development managers ascertain the probability distribution associated with these decisions corresponding to various states of quality. In both types of mathematical models, i.e. related to deterministic and randomized settings, minimization of the expected cost of the prototype remains the objective function.FindingsEmploying an illustrative case of the operator cabin from the construction equipment domain, the authors ascertain that randomized policy provides us with better decision interventions such that the expected cost of the prototype remains lower than that associated with the deterministic policy. The authors also ascertain the steady-state probabilities associated with a prototype remaining in a particular quality state. These findings have implications for product development budget, time to market, product quality, etc.Originality/valueThe authors’ work contributes toward the development of optimization-driven mathematical models that can encapsulate the nuances related to the uncertainty of transition of quality states of a prototype, decision policies at each quality state of the prototype while considering such facets for all constituent subsystems of the prototype. As opposed to a typical prescriptive study, their study captures the inherent uncertainties associated with states of quality in the context of prototype testing, etc.

A Neural Network approach for integrating banks’ decision in shipping finance

Forecasting refers to the process of predicting future trends by lying on data from the past. An error in forecasting can lead to significant business losses especially in banking industry where decisions are taken in a highly volatile and uncertain environment due to the dynamic changes in world economy. In this paper, we study both the effectuations of the exogenous factors in the tanker shipping-related financial market and the modulation of the credibility coefficient as an internal factor in shipping banks that may affect their decision to either increase or decrease loans within tanker shipping sector by adopting the artificial neural network technique. Within this context, we modeled a unique network that adjusts 88 macroeconomic indices to the real data of 89 shipping banks within a period of T = 5 years time. The main contribution of this study is the understanding of the relation between bias and either exogenous or unpredictable factors in the market as a key factor in the financing decision policy of a shipping bank for the forthcoming year T + 1.

Deep Recurrent Q-Network Methods for mmWave Beam Tracking systems

This article studies a reinforcement learning (RL) approach for beam tracking problems in millimeter-wave massive multiple-input multiple-output (MIMO) systems. Entire beam sweeping in traditional beam training problems is intractable due to prohibitive search overheads. To solve this issue, a partially observable Markov decision process (POMDP) formulation can be applied where decisions are made with partial beam sweeping. However, the POMDP cannot be straightforwardly addressed by existing RL approaches which are intended for fully observable environments. In this paper, we propose a deep recurrent Q-learning (DRQN) method which provides an efficient beam decision policy only with partial observations. Numerical results validate the superiority of the proposed method over conventional schemes.

Argentina

Early decisive policy implementation by the new economic team was critical to stabilizing markets and begin rebuilding confidence in the run-up to the second review. Domestic demand has since slowed in response to tighter macroeconomic policies, with high frequency indicators pointing to a further moderation in inflation, a contraction in goods imports, and improvements in the trade balance. Nonetheless, and against a more challenging external and domestic backdrop, the situation remains fragile. Inflation is still high and unanchored, reserves are low, and confidence needs further strengthening. Moreover, social discontent has risen amid spending restraint and some decline in real wages. Review discussions focused on strengthening macroeconomic policies to safeguard stability and achieve program objectives, especially a durable reduction in inflation and improvement in reserve coverage.

A multi-stage stochastic programming model for adaptive biomass processing operation under uncertainty

Variations of physical and chemical characteristics of biomass reduce equipment utilization and increase operational costs of biomass processing. Biomass processing facilities use sensors to monitor the changes in biomass characteristics. Integrating sensory data into the operational decisions in biomass processing will increase its flexibility to the changing biomass conditions. In this paper, we propose a multi-stage stochastic programming model that minimizes the expected operational costs by identifying the inventory level and creating an operational decision policy for equipment speed settings. These policies take the sensory information data and the current biomass inventory level as inputs to dynamically adjust inventory levels and equipment settings according to the changes in the biomass’ characteristics. We ensure that a prescribed target reactor utilization is consistently achieved by penalizing the violation of the target reactor feeding rate. A case study is developed using real-world data collected at Idaho National Laboratory’s biomass processing facility. We show the value of multi-stage stochastic programming from an extensive computational experiment. Our sensitivity analysis indicates that updating the infeed rate of the system, the processing speed of equipment, and bale sequencing based on the moisture level of biomass improves the processing rate of the reactor and reduces operating costs.

Time for upgrades? In time for consumers and competition

An upward line extension is a new, improved product within an existing product category for the high-end market. Many manufacturers pursue line-extension strategies, but they carry risks due to uncertain consumer taste, the internal competition between products, and the external competition between firms. We use game theory to answer when a firm should allocate production capacity for an upward line extension under competition and consumer taste uncertainty. The firm can either act early when taste is still uncertain to benefit from the first-mover advantage or wait until consumer taste realizes. We measure consumer taste risk by the correlation between the average consumer taste and the density of consumer taste, with a strong correlation meaning a considerable risk exposure, foreshadowing a more likely hit-or-miss result for the new product. The firm’s competitive advantage is based on both firms’ expected marginal revenues of capacity allocation, considering the internal and external competition. We show how the competitive advantage will amplify exponentially in the face of consumer taste uncertainty and characterize the firm’s decision policy by deriving a threshold for the risk index against the competitive advantage index. When the threshold is exceeded, it implies that the firm’s competitive advantage is not significant enough, so it should postpone allocating capacity. Otherwise, it should act early. We apply our results to two industry examples and provide practical guidelines to manufacturers for determining the right capacity timing for an upward line extension.

Lessons for Coastal Applications of IMTA as a Way towards Sustainable Development: A Review

Integrated multi-trophic aquaculture (IMTA) systems integrate the cultivation of species from different trophic levels. Uneaten feed, solid wastes, and dissolved nutrients are turned into harvestable and healthy food, making IMTA a driver for ecologically sustainable aquaculture. Its wider sustainability potentials arise from social, environmental, and economic sustainability enhancement options. Biological and economic outcomes are promising, while social equity and acceptance remain to be further investigated in the context of the long-term viability of aquaculture. Sustainable coastal and marine aquaculture development requires a holistic approach that involves social/cultural, economic, as well as environmental sustainability. This article examines IMTA as a pathway to socially, environmentally, and economically sustainable development. We collate evidence that shows that IMTA can minimize the negative environmental effects of aquaculture, assist local economies, and boost competitiveness and long-term economic viability. Available analyses of socio-economic and cost-effectiveness reveal positive prospects for IMTA systems, through product diversification, faster production cycles, and IMTA product prices and show a divergence between financial returns at the level of the entrepreneurial unit and economic returns at the macro level, which inhibits the uptake of IMTA. We conclude that the lack of governance analysis or inappropriateness of institutional development, in terms of aquaculture governance and management laws and regulations, is at the core of the hitherto weak engagement with IMTA. Unsuitable policies, regulations, and public and private sector decision policies and implementation, underlined by the scarcity of analyses of aquaculture governance institutions, are part of the reason for this. The evidence we have aggregated indicates that the relative scarcity of commercially successful coastal IMTA undertakings is not so much an intrinsic feature of the IMTA approach but is likely to have been generated by missing or inappropriate governance structures and procedures in the coastal realm.