Key Decisions Research Articles

Shovel allocation and scheduling for open-pit mining using deep reinforcement learning

ABSTRACT The open-pit production system is a highly dynamic and uncertain environment with complex interactions between haulage and loading equipment on a shared road network. One of the key decisions in open-pit short-term planning is the allocation sequence of shovels to mining faces to meet the production targets established by long- and medium-term strategic plans. Deep Reinforcement Learning(DRL) techniques are commonly used in dynamic production environments. In this approach, an agent is trained on a simulation of the production system to learn the optimal decisions based on the system’s current state. This paper proposes a DRL approach based on the Deep Q-Learning algorithm to obtain a robust shovel allocation plan for open-pit short-term planning. First, a discrete-event simulation of the mining production system incorporating trucks, shovels, crushers, waste dumps, and the road network is created. This simulation models the uncertainties of each component’s operating cycle based on historical activity records, and it is used to train the DRL agent. The goal is to learn a robust shovel allocation strategy for the next production quarter, 3 months, to meet the tonnes per hour (TPH) production target to be delivered to the crusher feeds by interacting with the production simulator. The framework is tested in an iron ore open-pit mine case study where the shovel allocation agent successfully learns a strategy that consistently delivers the desired production target.

Deciding where to put them: Sensitivity tests and independent evaluation are critical when using species distribution models to inform conservation translocations

Abstract Conservation translocations are an important tool for combating species declines and population losses. Species distribution models (SDMs) can facilitate the selection of suitable release sites for translocation programs. However, these models can be sensitive to several modelling decisions. In this study, we explore the impacts of three key modelling decisions on Maxent models developed to inform reintroductions of the long‐toed salamander (Ambystoma macrodactylum) in southwestern Alberta. We specifically test the sensitivity of model predictions to (1) the type of environmental variables used to generate models, (2) whether the background points used to calibrate the models reflects the potential bias in the input locality records and (3) the choice of geographic study extent. We use independent presence‐absence data from an extensive field survey to test the accuracy of models based on different decisions. Both model predictions and performance were sensitive to these modelling decisions. Models developed using local study extents were more accurate than those based on range‐wide extents. Both study extent and type of background points impacted model predictions and performance more than the set of environmental variables included in the models for this species. We further demonstrate the impacts of these decisions on the ranking of potential release sites and present a final set of recommendations that accounts for this uncertainty under both current and future climatic conditions. We specifically identify three sites that are expected to be suitable in both present and future time periods as potential release sites for salamander reintroductions in southwestern Alberta. Synthesis and applications: Our study adds to our understanding of how different modelling decisions impact SDMs and the downstream conclusions from these models while simultaneously demonstrating a rigorous approach for the use of SDMs in conservation translocation planning.

Using forest plots to interpret covariate effects in pharmacometric models.

The inclusion of covariates in pharmacometric models is important due to their ability to explain variability in drug exposure and response. Clear communication of the impact of covariates is needed to support informed decision making in clinical practice and in drug development. However, effectively conveying these effects to key stakeholders and decision makers can be challenging. Forest plots have been proposed to meet these communication needs. However, forest plots for the illustration of covariate effects in pharmacometrics are complex combinations of model predictions, uncertainty estimates, tabulated results, and reference lines and intervals. The purpose of this tutorial is to outline the aspects that influence the interpretation of forest plots, recommend best practices, and offer specific guidance for a clear and transparent communication of covariate effects.

Round-up of European Union trade marks in 2023

Abstract The purpose of this article is to provide a quick overview of the most significant European Union trade mark (EUTM) cases decided in 2023 by the Court of Justice of the European Union (CJEU) and the General Court (GC). This article has a practical bias and is aimed at readers who rapidly wish to find the key decisions of 2023.

Optimizing representations for integrative structural modeling using Bayesian model selection.

Integrative structural modeling combines data from experiments, physical principles, statistics of previous structures, and prior models to obtain structures of macromolecular assemblies that are challenging to characterize experimentally. The choice of model representation is a key decision in integrative modeling, as it dictates the accuracy of scoring, efficiency of sampling, and resolution of analysis. But currently, the choice is usually made ad hoc, manually. Here, we report NestOR (Nested Sampling for Optimizing Representation), a fully automated, statistically rigorous method based on Bayesian model selection to identify the optimal coarse-grained representation for a given integrative modeling setup. Given an integrative modeling setup, it determines the optimal representations from given candidate representations based on their model evidence and sampling efficiency. The performance of NestOR was evaluated on a benchmark of four macromolecular assemblies. NestOR is implemented in the Integrative Modeling Platform (https://integrativemodeling.org) and is available at https://github.com/isblab/nestor. Data for the benchmark is at https://www.doi.org/10.5281/zenodo.10360718.

Adaptation through knowledge coexistence: insights for environmental and sea lamprey stewardship

ABSTRACT Strategies for tackling environmental issues, including the consequences of invasive species and corresponding control efforts, are frequently approached through a Western scientific lens that often overlooks Indigenous rights and Indigenous knowledge systems. This can cause numerous issues from costly delays in implementing control programmes, overlooking vital ecosystem information and alternative options, legal action due to infringement on rights, and perpetuating systems of oppression. This research uses social science and Indigenous methodologies to understand the Denny’s Dam rehabilitation (DDR) as a case study for relationship-building and knowledge coexistence between the Saugeen Ojibway Nation and the Great Lakes Fisheries Commission in controlling sea lamprey (Petromyzon marinus), an invasive species in the Laurentian Great Lakes. To evaluate the successes and shortcomings of the project, virtual semi-structured interviews (n = 14) were conducted with key decision-makers and others involved in the rehabilitation of Denny’s Dam, a sea lamprey barrier. Analysis of these interviews identify four main factors that were crucial in the success of the DDR partnership: meaningful communication, funding and capacity, going beyond duty to consult requirements, and early engagement. The DDR shows how knowledge coexistence approaches, including Two-Eyed Seeing, can lead to equitable decision-making, foster collaboration, and contribute to addressing challenges like climate change, invasive species, and various environmental degradation.

Optimization of the sustainable food supply chain with integrative data envelopment analysis approach

ABSTRACT In this study, the supply chain system of the food supply chain (FSC) is assessed. Initially, the operations of farmers are examined using the Data Envelopment Analysis (DEA) technique. Then, locations of relatively efficient farmers are used as potential collection sites for the distribution at the downstream operations. Next, the Multi-Objective Optimization Model is proposed to examine location and distribution channels under the sustainability paradigm. That is, the economic criterion is primarily considered by focusing on minimizing the total supply cost. The social criterion is next deliberated by evaluating the equality aspect of farmers for the fair proportion of vegetable supply. Then, the environmental criterion is incorporated by assessing the CO2 emissions of food transportation activity. Additionally, a trade-off analysis is analyzed to investigate the conflicting behavior of model using the Non-Preemptive Programming method. The sensitivity analysis is further examined by varying the time-requirement parameter to verify the model functionalities. Finally, the regional case study in Thailand based on the food supply data is applied to validate the model. Given that farmers are distant from diverse locations, the results of our study can provide a strategic choice for key decision-makers in the FSC network under sustainability consideration.

Models of Strategic Management in Smes in The Period of The Covid-19 Pandemic in Metropolitan Lima

Purpose: The purpose of this research was to understand the adaptation of strategic management models by SMEs in Metropolitan Lima during the period of the COVID-19 pandemic. Theoretical Framework: Organizations must be agile and flexible to survive in a constantly changing business environment. During the pandemic, SMEs were forced to rethink their strategic management models, adopting a more authentic and adaptive approach. The ability to rethink strategies, diversify products and services, and embrace digitalization became crucial. Strategic management, an essential component of management models, encompasses the formulation, implementation, and evaluation of key decisions for organizations to achieve their objectives in a competitive and dynamic environment. This involves looking both internally at the organization's structure and operations and externally, evaluating opportunities and threats. Strategic management is how organizations not only face challenges, but also take advantage of emerging opportunities. Methodology: It was framed in the interpretative paradigm and supported by the hermeneutic methodology. Results and Discussion: The fundamental findings of the study relate to four primary categories and identify emerging facets within the expressions of social actors. This study highlights how, in times of crisis, resilience and creativity emerge as crucial qualities for success. It also highlights that adaptability and re-evaluation of previous approaches become essential for navigating turbulent terrain. Practical and social implication: The importance of understanding how strategies evolved to sustain the operation and generate valuable knowledge is highlighted. Originality and value: Originality and value lie in the depth of contextual analysis and the identification of concrete strategies that could inspire other companies in similar situations.

Using a theory-based, customized video game as an educational tool to improve physicians’ trauma triage decisions: study protocol for a randomized cluster trial

Background Transfer of severely injured patients to trauma centers, either directly from the field or after evaluation at non-trauma centers, reduces preventable morbidity and mortality. Failure to transfer these patients appropriately (i.e., under-triage) remains common, and occurs in part because physicians at non-trauma centers make diagnostic errors when evaluating the severity of patients’ injuries. We developed Night Shift, a theory-based adventure video game, to recalibrate physician heuristics (intuitive judgments) in trauma triage and established its efficacy in the laboratory. We plan a type 1 hybrid effectiveness-implementation trial to determine whether the game changes physician triage decisions in real-life and hypothesize that it will reduce the proportion of patients under-triaged.MethodsWe will recruit 800 physicians who work in the emergency departments (EDs) of non-trauma centers in the US and will randomize them to the game (intervention) or to usual education and training (control). We will ask those in the intervention group to play Night Shift for 2 h within 2 weeks of enrollment and again for 20 min at quarterly intervals. Those in the control group will receive only usual education (i.e., nothing supplemental). We will then assess physicians’ triage practices for older, severely injured adults in the 1-year following enrollment, using Medicare claims, and will compare under-triage (primary outcome), 30-day mortality and re-admissions, functional independence, and over-triage between the two groups. We will evaluate contextual factors influencing reach, adoption, implementation, and maintenance with interviews of a subset of trial participants (n = 20) and of other key decision makers (e.g., patients, first responders, administrators [n = 100]).DiscussionThe results of the trial will inform future efforts to improve the implementation of clinical practice guidelines in trauma triage and will provide deeper understanding of effective strategies to reduce diagnostic errors during time-sensitive decision making.Trial registrationClinicalTrials.gov; NCT06063434. Registered 26 September 2023.

TBX3 reciprocally controls key trophoblast lineage decisions in villi during human placenta development in the first trimester

Human trophoblastic lineage development is intertwined with placental development and pregnancy outcomes, but the regulatory mechanisms underpinning this process remain inadequately understood. In this study, based on single-nuclei RNA sequencing (snRNA-seq) analysis of the human early maternal-fetal interface, we compared the gene expression pattern of trophoblast at different developmental stages. Our findings reveal a predominant upregulation of TBX3 during the transition from villous cytotrophoblast (VCT) to syncytiotrophoblast (SCT), but downregulation of TBX3 as VCT progresses into extravillous trophoblast cells (EVT). Immunofluorescence analysis verified the primary expression of TBX3 in SCT, partial expression in MKi67-positive VCT, and absence in HLA-G-positive EVT, consistent with our snRNA-seq results. Using immortalized trophoblastic cell lines (BeWo and HTR8/SVneo) and human primary trophoblast stem cells (hTSCs), we observed that TBX3 knockdown impedes SCT formation through RAS-MAPK signaling, while TBX3 overexpression disrupts the cytoskeleton structure of EVT and hinders EVT differentiation by suppressing FAK signaling.In conclusion, our study suggests that the spatiotemporal expression of TBX3 plays a critical role in regulating trophoblastic lineage development via distinct signaling pathways. This underscores TBX3 as a key determinant during hemochorial placental development.

Fulfilling fiduciary duties in the ai era: emerging risks and responsibilities in ai-assisted corporate financial oversight

This article examines emerging legal issues and theories of liability for directors involved in the management of AI financial instruments that are protected as trade secrets. The main question of the article is whether excessive delegation of functions or lack of transparency of AI algorithms can undermine the performance of fiduciary duties by directors. By reviewing case law in the context of strict oversight of past technological failures, the article proposes a renewed approach to the use of blockchain tools that will maintain efficiency benefits while ensuring necessary reporting and accountability. The study suggests that governance based on the principles of auditing AI performance and setting minimum standards for explainability can help strike a balance between driving innovation, addressing liability issues, and aligning with modern doctrines that hold boards accountable for key decision-making. new technologies. As algorithms become increasingly integrated into senior management decision-making processes, there is a need to further explore transparency mechanisms and monitoring processes that will support evolving fiduciary responsibilities in relation to evolving automation capabilities that impact shareholder interests.

Designing Interprofessional Online Learning Experiences for Future Healthcare Professionals

The design case details the collaborative work of a design team—three faculty members, one instructional designer, and one educational resource specialist—to create a simulation-based interprofessional education (IPE) experience for future healthcare professionals. Before the COVID-19 pandemic that caused the shutdown of campuses across the country/world, this simulation-based learning experience was always offered in a face-to-face format. Thus, this case highlights the challenges and opportunities of designing this online learning experience within a limited period of time. Further, this case documents what theories or evidence-based practices were instrumental in designing this learning experience, along with the design team’s narrative regarding key design decisions and moves. It also includes a design narrative focusing on the description of the design process, such as key design judgments, decisions, and concrete examples of the design process outcome. Lastly, the design case highlights unique design features: Scalability of instruction through accessibility and usability, authenticity, interprofessional collaboration, and reflection. The design was guided by the TEACH (Team Education Advancing Collaboration in Health) core curriculum in the state of Indiana that foregrounds interprofessional practice competencies and teamwork in preparing future healthcare professionals.

‘Manage up’ your board to get more buy‐in on key decisions

When it comes to advocating for a particular program or strategy, or making a specific change in operations, nonprofit CEOs and executive directors are at the mercy of their boards to some extent. After all, the system is designed such that boards manage the EDs, not the other way around.

Draft co‐champion to help ED with strategic plan

These days, most nonprofit leaders agree that having a strong strategic plan in place—and, importantly, following its guidance—is crucial to a nonprofit's success. But still, many organizations either don't follow their plans when making key decisions, or don't have one in place at all.

Climate Driver Influences on Prediction of the Australian Fire Behaviour Index

Fire danger poses a pressing threat to ecosystems and societies worldwide. Adequate preparation and forewarning can help reduce these threats, but these rely on accurate prediction of extreme fire danger. With the knowledge that climatic conditions contribute heavily to overall fire danger, this study evaluates the skill with which episodes of extreme fire danger in Australia can be predicted from the activity of large-scale climate driver patterns. An extremal dependence index for extreme events is used to depict the historical predictive skill of the Australian Bureau of Meteorology’s subseasonal climate prediction system in replicating known relationships between the probability of top-decile fire danger and climate driver states at a lead time of 2–3 weeks. Results demonstrate that the El Niño Southern Oscillation, Southern Annular Mode, persistent modes of atmospheric blocking, Indian Ocean Dipole and Madden-Julian Oscillation are all key for contributing to predictability of fire danger forecasts in different regions during critical fire danger periods. Northwest Australia is found to be particularly predictable, with the highest mean index differences (>0.50) when certain climate drivers are active, compared with the climatological index mean. This integrated approach offers a valuable resource for decision-making in fire-prone regions, providing greater confidence to users relying on fire danger outlooks for key management decisions, such as those involved in the sectors of national park and forest estate management, agriculture, emergency services, health and energy. Furthermore, the results highlight strengths and weaknesses in both the Australian Fire Danger Rating System and the operational climate model, contributing additional information for improving and refining future iterations of these systems.

Article: The Issue of Consumer Compensation Before Antitrust Authorities: Commitments, Cooperation and Competence: The Hungarian Experience

The compensation of consumers for damages stemming from a breach of EU competition law rules has been in the forefront of discussions in the last years. A recent important aspect of the debate is whether a mixing of public and private enforcement efforts could enhance the effectiveness of consumer compensation. In Hungary, consumer compensation for breach of another field of EU law (unfair commercial practices) has been championed by a public authority, the Hungarian Competition Authority, which has achieved significant results to the direct benefit of consumers in this respect. This paper reviews the authority’s recent practices, key decisions and main achievements in this area (including some earlier cases concerning antitrust) and then provides a detailed assessment of the corresponding advantages and disadvantages. The authors argue that some key achievements in Hungary in this respect are very well suited for further use in the development of consumer compensation in the field of antitrust law on both an EU and national level. public enforcement, private enforcement, antitrust, consumer protection, damages, EU, Hungary

Knowledge and practice of childhood immunisation among parents in Kelantan, Malaysia: A cross-sectional study.

Parents are key decision-makers in the immunisation practice and compliance of children. This study aimed to determine the knowledge and practice of immunisation among parents in Kelantan, Malaysia, and their associated factors. A cross-sectional study was conducted using a validated online questionnaire from May to June 2021. An invitation was distributed to parents attending a university hospital and extended families of staff through online platforms. A total of 311 parents participated in the study. The questionnaire consisted of 10 questions each on knowledge and practice and three questions on vaccination status. Descriptive analysis was performed. The associations between the sociodemographic characteristics and knowledge and practice scores were determined using the chi-square test, and predictive factors were identified using logistic regression analysis. Most respondents were Malay (94.2%), Muslim (94.5%), women (79.7%) and married (96.1%). The median score for immunisation knowledge and practice was 8 (interquartile range [IQR]=2) and 7 (IQR=3), respectively. Multiple logistic regression revealed that parents who were unmarried or single, less educated, and had lower incomes were predicted to have poor knowledge of childhood vaccination (P<0.05). Conversely, those living outside Kota Bharu, less educated, and younger parents were predicted to have poor vaccination practice of childhood vaccination (P<0.05). Most respondents (97.8%) indicated completing their children's vaccination schedule. Parental education and household income are associated with immunisation knowledge and practice. Improving access to information about childhood vaccination among targeted groups may further boost immunization coverage.

A performance management framework for smart health-care supply chain based on industry 4.0 technologies

Purpose The study aims to envisage upon conceptualizing and developing the scales of smart health-care supply chain (HCSC) performance in the era of the fourth industrial revolution. Design/methodology/approach This study has implemented structural equation modelling to analyse the survey data. To analyse the collected data from the field investigation involving a sample size of 323, the IBM SPSS AMOS 26 software package is considered to implement exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) in this study. Findings The measurement model of the study developed using EFA and CFA has resulted in validating 32 items out of the 42 items. Resultantly, the analysis using the above-mentioned tools and the parsimony of items to scale development makes it more susceptible to contributing significantly to the current HCSC literature. Research limitations/implications The HC providers need to consider a holistic and systematic approach while taking into account the constructs of smart HCSC performance, specifically, the effect of HCSC responsiveness and industry 4.0 between the independent and dependent variables. The scales are validated from the perspectives of developing countries such as India, and hence, their generalizability with respect to first-world countries is practically limited. Originality/value The scales validated in this study would facilitate managers and key decision-makers to apply the various elements of HCSC practices, gauge the application of these scales and monitor the performance of health-care facilities.

Correction to: Designing for Learning: Key Decisions for an Open Online Math Tutor for Elementary Students

Correction to: Designing for Learning: Key Decisions for an Open Online Math Tutor for Elementary Students

Derivation of no significant risk levels for three lower acrylates: Conclusions and recommendations from an expert panel

A panel of toxicology, mode of action (MOA), and cancer risk assessment experts was engaged to derive no-significant-risk-levels (NSRLs) for three lower acrylates: methyl acrylate (MA), ethyl acrylate (EA), and 2-ethylhexyl acrylate (2EHA) using the best available science, data, and methods. The review was structured as a five-round, modified Delphi format, a systematic process for collecting independent and deliberative input from panel members, and it included several procedural elements to reduce potential sources of bias and groupthink. Input from the panel for key decisions in the dose-response assessments resulted in NSRL values of 530 μg/day (330–800 μg/day), 640 μg/day (280–670 μg/day), and 1700 μg/day (1300–2700 μg/day) for MA, EA, and 2EHA, respectively. Novel to this approach were the use of nonneoplastic lesions reported at point of contact where tumors have been reported in laboratory rodents, along with nonlinear extrapolation to low doses (uncertainty factor approach) based upon panel recommendations. Confidence in these values is considered medium to high for exposures applied to the routes of exposure tested (inhalation for MA and EA, dermal for 2EHA), but confidence is considered lower when applied to other routes of exposure.