Preferred Input Research Articles

BodyFlow: An Open-Source Library for Multimodal Human Activity Recognition

Human activity recognition is a critical task for various applications across healthcare, sports, security, gaming, and other fields. This paper presents BodyFlow, a comprehensive library that seamlessly integrates human pose estimation and multiple-person estimation and tracking, along with activity recognition modules. BodyFlow enables users to effortlessly identify common activities and 2D/3D body joints from input sources such as videos, image sets, or webcams. Additionally, the library can simultaneously process inertial sensor data, offering users the flexibility to choose their preferred input, thus facilitating multimodal human activity recognition. BodyFlow incorporates state-of-the-art algorithms for 2D and 3D pose estimation and three distinct models for human activity recognition.

Determining the equilibrium efficient frontier by proportional frontier shifting for data envelopment analysis with fixed-sum outputs

The equilibrium efficient frontier data envelopment analysis (EEFDEA) has been extensively used to evaluate efficiencies of the decision-making units (DMUs) with fixed-sum outputs. This study develops a new EEFDEA approach based on a proportional frontier-shifting strategy. Our approach applies an iterative procedure to find the equilibrium efficient frontier (EEF). Each iteration uses a proportional frontier-shifting model to improve an inefficient DMU to the efficient frontier by increasing its fixed-sum outputs. Meanwhile, the DMUs on the efficient frontier decrease fixed-sum outputs proportionally to ensure the total fixed-sum outputs are unchanged. Our theoretical developments show that the proportional frontier-shifting strategy is feasible and can finally obtain a unique EEF. The new approach allows DMUs to use their preferred input and output weights when determining the EEF. This generates an EEF that better aligns with real-world practices and avoids the need to construct it as a single hyperplane, as required by conventional EEFDEA methods. It also avoids unfair adjustments in fixed-sum outputs among the DMUs and eliminates the problem of peculiar efficiency evaluation results (i.e., some DMUs obtain extremely high, or infinity, efficiencies). Finally, we apply our approach to a case study of Chinese vehicle industry companies to demonstrate its usefulness and compare it with the previous representative approach.

Isochrone fitting of the open cluster M67 in the era of Gaia and improved model physics

ABSTRACT The Gaia mission has provided highly accurate observations that have significantly reduced the scatter in the colour–magnitude diagrams of open clusters. As a result of the improved isochrone sequence of the open cluster M67, we have created new stellar models that avoid commonly used simplifications in 1D stellar modelling, such as mass-independent core overshooting and a constant mixing length parameter. This has enabled us to deliver a precise isochrone specifically designed for M67, available for download. We follow a commonly used qualitative approach to adjust the input physics to match the well-defined colour–magnitude sequence, and we test the model-predicted masses against a known eclipsing binary system at the main sequence turnoff of the cluster. Despite using improvements in photometry and stellar physics we cannot match the masses of both binary components with the same theoretical isochrone. A $\chi ^{2}$-based isochrone fitting approach using our preferred input physics results in a cluster age of $3.95^{+ 0.16}_{- 0.15}$ Gyr.

Research Project Topic Recommender System Using Generative Language Model

Education has become a driver of a person's continuous innovation to improve their quality. Currently, the use of artificial intelligence determines progress in education. In this research, artificial intelligence technology was applied to develop a web-based recommendation system to help students at the Faculty of Computer Science, Klabat University, choose appropriate research topics for their final assignments. To provide personalized and contextually relevant suggestions, the recommendation system leverages deep learning and generative language models, specifically GPT-3. The Rapid Application Development process model is employed to develop the system. Its key components include semantic search, rapid engineering, and an advanced vector database for effective data management and retrieval. The functions provided by the system include user account registration, login, input of major subject grades and research preferences, and personalized recommendation results. Some additional features such as profile management, previous recommendation history, and password reset options are also provided. All these functions have been tested using the black box method.

Assay Validation of Cell-Free DNA Shallow Whole-Genome Sequencing to Determine Tumor Fraction in Advanced Cancers

Blood-based liquid biopsy is increasingly used in clinical care of patients with cancer, and fraction of tumor-derived DNA in circulation (tumor fraction; TFx) has demonstrated clinical validity across multiple cancer types. To determine TFx, shallow whole-genome sequencing of cell-free DNA (cfDNA) can be performed from a single blood sample, using an established computational pipeline (ichorCNA), without prior knowledge of tumor mutations, in a highly cost-effective manner. We describe assay validation of this approach to facilitate broad clinical application, including evaluation of assay sensitivity, precision, repeatability, reproducibility, pre-analytic factors, and DNA quality/quantity. Sensitivity to detect TFx of 3% (lower limit of detection) was 97.2% to 100% at 1× and 0.1× mean sequencing depth, respectively. Precision was demonstrated on distinct sequencing instruments (HiSeqX and NovaSeq) with no observable differences. The assay achieved prespecified 95% agreement of TFx across replicates of the same specimen (repeatability) and duplicate samples in different batches (reproducibility). Comparison of samples collected in EDTA and Streck tubes from single venipuncture in 23 patients demonstrated that EDTA or Streck tubes were comparable if processed within 8 hours. On the basis of a range of DNA inputs (1 to 50 ng), 20 ng cfDNA is the preferred input, with 5 ng minimum acceptable. Overall, this shallow whole-genome sequencing of cfDNA and ichorCNA approach offers sensitive, precise, and reproducible quantitation of TFx, facilitating assay application in clinical cancer care.

A Study on a New Way of Thinking Based on Linear Programming to Analyze Civic Education in Colleges and Universities

Abstract This paper intends to construct a new multi-dimensional preference analysis model for learners by using the improved linear programming method of multi-dimensional preference analysis (LINMAP) to get the weight vector of attributes under the situation that the weights of attributes are completely unknown and for the uncertain multi-attribute decision-making problem that the elements of decision-making matrix are triangular fuzzy numbers, and the preference information is ordered pairs. The data of subjective or objective Civics education learning preferences are aggregated, and the statistical analysis of the data is carried out using SPSS.21 to generate the Civics education learning preference input degree weighting system. The results show that the offline behavioral input weight in the college Civics course is 0.5769, which is greater compared to the online input. And the attribute weights of course Civics preference are calculated to get Z max = 2.29, Z min = 0.73. Multiple feasible solutions can be obtained by taking any value between [0,1]. Based on the development law of the times, this paper makes a new thinking on ideological and political education and actively explores a new road suitable for the development of ideological and political education in colleges and universities.

User-centered design in mobile human-robot cooperation: consideration of usability and situation awareness in GUI design for mobile robots at assembly workplaces

Abstract Mobile cobots can increase the potential for assembly work in industry. For human-friendly automation of cooperative assembly work, user-centered interfaces are necessary. The design process regarding user interfaces for mobile human-robot cooperation (HRC) shows large research gaps. In this article an exemplary approach is shown to design a graphical user interface (GUI) for mobile HRC at assembly workplaces. The design is based on a wireframe developed to support situation awareness. An interactive mockup is designed and evaluated. This is done in two iterations. In the first iteration, a user analysis is carried out using a quantitative survey with n = 31 participants to identify preferred input modalities and a qualitative survey with n = 11 participants that addresses touch interfaces. The interactive mockup is developed by implementing design recommendations of the usability standards ISO 9241 – 110, 112 and 13. A heuristic evaluation is conducted with n = 5 usability experts and the measurement of situation awareness with n = 30 end users. In the second iteration, findings from the preceding iteration are implemented in the GUI and a usability test with n = 20 end users is conducted. The process demonstrates a combination of methods that leads to high usability and situation awareness in mobile HRC.

Excitation Flexibility of On-Antenna Power Combining

In this letter, a design methodology is proposed for on-antenna power combining with flexible input power ratios. Designing multi-feed antennas with direct power combining requires weighted driving signals to match the field distribution at the resonant mode of the antenna, imposing serious restrictions on their implementation. To overcome this drawback, we proposed a method to relax the excitation criterion by altering the impedance matching conditions of the multi-feed antenna, giving the RF designers a degree of freedom. First, an eigenanalysis method is employed to extract the active impedance values for each port of the antenna. Then, according to a preferred input power distribution, proper impedance matching is built based on the active impedance values. The resulting multi-feed antenna resembles the single-feed antenna in terms of the radiation performance with desired driving signals. A series of four-feed slot antennas were fabricated to validate the proposed method. Simulation and measurement results show that under different excitation scenarios the radiation performance of the antenna remains intact, demonstrating the flexibility for on-antenna power combining.

Sensor Fusion Approach for Multiple Human Motion Detection for Indoor Surveillance Use-Case.

Multi-human detection and tracking in indoor surveillance is a challenging task due to various factors such as occlusions, illumination changes, and complex human-human and human-object interactions. In this study, we address these challenges by exploring the benefits of a low-level sensor fusion approach that combines grayscale and neuromorphic vision sensor (NVS) data. We first generate a custom dataset using an NVS camera in an indoor environment. We then conduct a comprehensive study by experimenting with different image features and deep learning networks, followed by a multi-input fusion strategy to optimize our experiments with respect to overfitting. Our primary goal is to determine the best input feature types for multi-human motion detection using statistical analysis. We find that there is a significant difference between the input features of optimized backbones, with the best strategy depending on the amount of available data. Specifically, under a low-data regime, event-based frames seem to be the preferred input feature type, while higher data availability benefits the combined use of grayscale and optical flow features. Our results demonstrate the potential of sensor fusion and deep learning techniques for multi-human tracking in indoor surveillance, although it is acknowledged that further studies are needed to confirm our findings.

Analyzing time-to-first-spike coding schemes: A theoretical approach.

Spiking neural networks (SNNs) using time-to-first-spike (TTFS) codes, in which neurons fire at most once, are appealing for rapid and low power processing. In this theoretical paper, we focus on information coding and decoding in those networks, and introduce a new unifying mathematical framework that allows the comparison of various coding schemes. In an early proposal, called rank-order coding (ROC), neurons are maximally activated when inputs arrive in the order of their synaptic weights, thanks to a shunting inhibition mechanism that progressively desensitizes the neurons as spikes arrive. In another proposal, called NoM coding, only the first N spikes of M input neurons are propagated, and these “first spike patterns” can be readout by downstream neurons with homogeneous weights and no desensitization: as a result, the exact order between the first spikes does not matter. This paper also introduces a third option—“Ranked-NoM” (R-NoM), which combines features from both ROC and NoM coding schemes: only the first N input spikes are propagated, but their order is readout by downstream neurons thanks to inhomogeneous weights and linear desensitization. The unifying mathematical framework allows the three codes to be compared in terms of discriminability, which measures to what extent a neuron responds more strongly to its preferred input spike pattern than to random patterns. This discriminability turns out to be much higher for R-NoM than for the other codes, especially in the early phase of the responses. We also argue that R-NoM is much more hardware-friendly than the original ROC proposal, although NoM remains the easiest to implement in hardware because it only requires binary synapses.

Selective application of the accrual principle in the construction of government finance statistics: EU evidence

IMPACT This article will help preparers and users of public sector accounting information understand why there is a need to ‘translate’ accounting information into fiscal information, and how this translation takes place through several ‘adjustments’. Readers will learn that, in the EU, it is uncommon for fiscal information to be grounded on accrual-based accounting data—particularly in the central government where cash data remain the preferred input. Readers will also learn that, while cash accounting bases generate a somewhat greater need for adjustments before fiscal information is produced, no accounting basis fully embraces the selective interpretation of the accrual principle that underlies the construction of fiscal aggregates. The information produced by fiscal and accounting information systems is unlikely to overlap fully, even if the public sector were to adopt an accrual basis of accounting. ABSTRACT The authors studied the ‘translation’ of government accounting working balances into fiscal balances in the EU. Non-accrual-based working balances were found to represent the most frequent primary source of data that national statistical institutions use to calculate and report fiscal balances in Excessive Deficit Procedure notification tables. Compared to accrual-based working balances, non-accrual-based working balances impose significant adjustments on current revenues and expenses to obtain fiscal balances, yet they lead to limited adjustments to several long-term transactions. The authors argue that this result originates from the selective application of the accrual principle in the construction of Government Finance Statistics.

Abstract 13604: Patient Centered Prescription Using Optimization Algorithms for Statin Users

Introduction: Clinical decision support (CDS) that does not optimize individual variability with best practice and treatment outcomes, cannot support patient-centered care. For example, a statin treatment CDS focused on cholesterol reduction without accounting for patient comorbidities, at risk lifestyle, or genetics, may well suggest a treatment that increases adverse effects, has a high cost, and has a low potential for improved outcome. We present a novel patient-centered decision support (PCDS) system for statin initiation that accounts for best practice and patient variation and ranks treatment options by their predicted ability to improve cardiac outcomes and reduce adverse effects for individual patients. Method: A total of 30 M de-identified claim records from 1.4 M adults receiving statin treatment over 1990-2020 are available in the OptumLabs® Data Warehouse and were used to train our counterfactual prediction algorithm. For a new patient, the PCDS system predicts LDL reduction, cardiac events, and other outcomes at 1 and 5 years, for every potential statin treatment. The PCDS then integrates doctor and patient preferences into a multi-objective optimization algorithm to generate a ranked list of treatment options. Conclusion: Our novel patient-centered decision support (PCDS) adds sophisticated AI modeling and optimization to account for evidenced-based best practice, patient variability, and adverse effect risk to produce a clear ranking of treatment options that can be re-ranked upon input of patient preferences and provider’s lifestyle, socioeconomics, culture, experience and environment factors. This PCDS approach leverages traditional CDS adjusted to individual preferences to aligned with the patient-centered care model.

Predicting Intention of Motion During Rehabilitation Tasks of the Upper-Extremity.

Rehabilitation promoting "assistance-as-needed" is considered a promising scheme of active rehabilitation, since it can promote neuroplasticity faster and thus reduce the time needed until restoration. To implement such schemes using robotic devices, it is crucial to be able to predict accurately and in real-time the intention of motion of the patient. In this study, we present an intention-of-motion model trained on healthy volunteers. The model is trained using kinematics and muscle activation time series data, and returns future predicted values for the kinematics. We also present the results of an analysis of the sensitivity of the accuracy of the model for different amount of training datasets and varying lengths of the prediction horizon. We demonstrate that the model is able to predict reliably the kinematics of volunteers that were not involved in its training. The model is tested with three types of motion inspired by rehabilibation tasks. In all cases, the model is predicting the arm kinematics with a Root Mean Square Error (RMSE) below 0.12m. Being a non person-specific model, it could be used to predict kinematics even for patients that are not able to perform any motion without assistance. The resulting kinematics, even if not fully representative of the specific patient, might be a preferable input for a robotic rehabilitator than predefined trajectories currently in use.

The cognitive comparison enhanced hierarchical clustering

The growth of online shopping is rapidly changing the buying behaviour of consumers. Today, there are challenges facing buyers in the selection of a preferred item from the numerous choices available in the market. To improve the consumer online shopping experience, recommender systems have been developed to reduce the information overload. In this paper, a cognitive comparison-enhanced hierarchical clustering (CCEHC) system is proposed to provide personalised product recommendations based on user preferences. A novel rating method, cognitive comparison rating (CCR), is applied to weigh the product attributes and measure the categorical scales of attributes according to expert knowledge and user preferences. Hierarchical clustering is used to cluster the products into different preference categories. The CCEHC model can be used to rank and cluster product data with the input of user preferences and produce reliable customised recommendations for the users. To demonstrate the advantages of the proposed model, the CCR method is compared with the rating approach of the analytic hierarchy process. Two recommendation cases are demonstrated in this paper with two datasets, one collected by this research for laptop recommendation and the other an open dataset for workstation recommendation. The simulation results demonstrate that the proposed system is feasible for providing personalised recommendations. The significance of this research is the provision of a recommendation solution that does not depend on historical purchase records; rather, one wherein the users’ rating preferences and expert knowledge, both of which are measured by CCR, is considered. The proposed CCEHC model could be further applied to other types of similar recommendation cases such as music, books, and movies.

Recognition of symptoms, mitigating mechanisms and self-care experiences of type 2 diabetes patients receiving insulin treatment in North-East Ethiopia

Background: Compliance of patients with self-care practices is the mainstay of measures to manage diabetes. This study explored self-care practices of type 2 diabetes patients receiving insulin treatment in North-East Ethiopia. Methods: The study employed an interpretive phenomenological approach using purposive sampling. The data were collected from 24 (11 males and 13 females) participants (July 2019 to January 2020) using in-depth interviews till theoretical saturation. The data were analyzed thematically and organized using QDA Miner Lite v2.0.8. Results: The findings were categorized into: labeling diabetes, self-care maintenance experiences, recognition of symptoms, and mitigating mechanisms of symptoms. The self-care maintenance practice of patients was linked with diet input preferences and the effectiveness level of insulin. What guides the self-care behavior was the patients’ preferentiality of strictly adhering to their preferred dietary inputs. Barley and wheat were the most common preferential and non-preferential inputs, respectively. The patients strictly adhered to insulin treatment because they found it effective. The most common hyperglycemia symptoms to be managed by taking an additional dose of insulin, were frequent urination, increased thirst, and their consequences (dehydration). Excessive sweating (initial), shivering (middle), and falling (final), respectively in severity, were the most common symptoms of hypoglycemia which were perceived to be treated with sweet snacks. Originality: To our knowledge, this is the first research in Ethiopia to investigate the self-care experiences of type 2 diabetes patients receiving insulin using an interpretive phenomenological approach.

Recognition of symptoms, mitigating mechanisms and self-care experiences of type 2 diabetes patients receiving insulin treatment in North-East Ethiopia

Background: Compliance of patients with self-care practices is the mainstay of measures to manage diabetes. This study explored self-care practices of type 2 diabetes patients receiving insulin treatment in North-East Ethiopia. Methods: The study employed an interpretive phenomenological approach using purposive sampling. The data were collected from 24 (11 males and 13 females) participants (July 2019 to January 2020) using in-depth interviews till theoretical saturation. The data were analyzed thematically and organized using QDA Miner Lite v2.0.8. Results: The findings were categorized into: labeling diabetes, self-care maintenance experiences, recognition of symptoms, and mitigating mechanisms of symptoms. The self-care maintenance practice of patients was linked with diet input preferences and the effectiveness level of insulin. What guides the self-care behavior was the patients’ preferentiality of strictly adhering to their preferred dietary inputs. Barley and wheat were the most common preferential and non-preferential inputs, respectively. The patients strictly adhered to insulin treatment because they found it effective. The most common hyperglycemia symptoms to be managed by taking an additional dose of insulin, were frequent urination, increased thirst, and their consequences (dehydration). Excessive sweating (initial), shivering (middle), and falling (final), respectively in severity, were the most common symptoms of hypoglycemia which were perceived to be treated with sweet snacks. Originality: To our knowledge, this is the first research in Ethiopia to investigate the self-care experiences of type 2 diabetes patients receiving insulin using an interpretive phenomenological approach.

The democraticness of traditional political systems in Africa

ABSTRACT Traditional political systems (TPS) are an important part of the political landscape in Africa. They govern subnational communities and differ from nation states, both in their institutional set-up as well as in their legitimacy. Yet, we have little comparative knowledge on these political systems and, in particular, whether they can be described as democratic. In this article, we analyse the democraticness of TPS based on a new expert survey. Using confirmatory factor analysis (CFA), we show that the more than 140 ethnic groups we analyse vary meaningfully in their democraticness. Measures of public preference input and of political process control contribute particularly to a latent measure of democraticness. Furthermore, we find some indication for regionally interdependent institutions, with slightly more democratic systems in Southern Africa and less democratic systems in West Africa. Yet, no such interdependence exists between the state and the group level. Finally, we find that more hierarchically organized political systems, kings, and chiefs, as well as those organized in segments, are on average less democratic, while the presence of elders is associated with higher levels of democraticness.

Scaling-invariant maximum margin preference learning

One natural way to express preferences over items is to represent them in the form of pairwise comparisons, from which a model is learned in order to predict further preferences. In this setting, if an item a is preferred to the item b, then it is natural to consider that the preference still holds after multiplying both vectors by a positive scalar (e.g., 2a≻2b). Such invariance to scaling is satisfied in maximum margin learning approaches for pairs of test vectors, but not for the preference input pairs, i.e., scaling the inputs in a different way could result in a different preference relation being learned. In addition to the scaling of preference inputs, maximum margin methods are also sensitive to the way used for normalizing (scaling) the features, which is an essential pre-processing phase for these methods. In this paper, we define and analyse more cautious preference relations that are invariant to the scaling of features, or preference inputs, or both simultaneously; this leads to computational methods for testing dominance with respect to the induced relations, and for generating optimal solutions (i.e., best items) among a set of alternatives. In our experiments, we compare the relations and their associated optimality sets based on their decisiveness, computation time and cardinality of the optimal set.

The ASM Journals Committee Values the Contributions of Black Microbiologists.

Abstract Oscillations are ubiquitous features of brain dynamics that undergo task-related changes in synchrony, power, and frequency. The impact of those changes on target networks is poorly understood. In this work, we used a biophysically detailed model of prefrontal cortex (PFC) to explore the effects of varying the spike rate, synchrony, and waveform of strong oscillatory inputs on the behavior of cortical networks driven by them. Interacting populations of excitatory and inhibitory neurons with strong feedback inhibition are inhibition-based network oscillators that exhibit resonance (i.e., larger responses to preferred input frequencies). We quantified network responses in terms of mean firing rates and the population frequency of network oscillation; and characterized their behavior in terms of the natural response to asynchronous input and the resonant response to oscillatory inputs. We show that strong feedback inhibition causes the PFC to generate internal (natural) oscillations in the beta/gamma frequency range (>15 Hz) and to maximize principal cell spiking in response to external oscillations at slightly higher frequencies. Importantly, we found that the fastest oscillation frequency that can be relayed by the network maximizes local inhibition and is equal to a frequency even higher than that which maximizes the firing rate of excitatory cells; we call this phenomenon population frequency resonance. This form of resonance is shown to determine the optimal driving frequency for suppressing responses to asynchronous activity. Lastly, we demonstrate that the natural and resonant frequencies can be tuned by changes in neuronal excitability, the duration of feedback inhibition, and dynamic properties of the input. Our results predict that PFC networks are tuned for generating and selectively responding to beta- and gamma-rhythmic signals due to the natural and resonant properties of inhibition-based oscillators. They also suggest strategies for optimizing transcranial stimulation and using oscillatory networks in neuromorphic engineering. Author Summary The prefrontal cortex (PFC) flexibly encodes task-relevant representations and outputs biases to mediate higher cognitive functions. The relevant neural ensembles undergo task-related changes in oscillatory dynamics at beta- and gamma frequencies. Using a computational model of the PFC network, we show that strong feedback inhibition causes the PFC to generate internal oscillations and to prefer external oscillations at similar frequencies. The precise frequencies that are generated and preferred can be flexibly tuned by varying the synchrony and strength of input network activity, the level of background excitation, and neuromodulation of intrinsic ion currents. We also show that the peak output frequency in response to external oscillations, which depends on the synchrony and strength of the input as well as the strong inhibitory feedback, is faster than the internally generated frequency, and that this difference enables exclusive response to oscillatory inputs. These properties enable changes in oscillatory dynamics to govern the selective processing and gating of task-relevant signals in service of cognitive control.

Route guidance ranking procedures with human perception consideration for personalized public transport service

The use of smartphone applications (apps) to acquire real time and readily available journey planning information is becoming instinctive behavior by public transport (PT) users. Through the apps, a passenger not only seeks a path from origin to destination, but a satisfactory path that caters to the passenger’s preferences at the desired time of travel. Essentially, apps attempt to provide a means of personalized PT service. As the implications of the Covid-19 pandemic take form and infiltrate human and environmental interactions, passenger preference personalization will likely include avoiding risks of infection or contagious contact. The personal preferences are enabled by multiple attributes associated with alternative PT routes. For instance, preferences can be connected to attributes of time, cost, and convenience.This work establishes a personalized PT service, as an adjustment to current design frameworks, by integrating user app experience with operators’ data sources and operations modeling. The work proceeds to focus on its key component: the personalized route guidance methodology. In addition to using the existing shortest path or k-weighted shortest path method, this study develops a novel, lexicographical shortest path method, considering a just noticeable difference (JND). The method adopts lexicographical ordering to capture passenger preferences for different PT attributes following Ernst Weber’s law of human perception threshold. However, a direct application of Weber’s law violates the axiom of transitivity required for an implementable algorithm, and thus, a revised method is developed with proven algorithms for ranking different paths. The differences between the three route-guidance methods and the effects of the JND perception threshold on the order of the alternative PT routes are demonstrated with an example.The developments were examined in a case study by simulation on the Copenhagen PT network. The results show that using the JND method reduces the value/cost of the most important attributes. Identical robust results are attained when JND parameters are not specified and default values are used. The latter may apply for the future with a mixture of specified and default preference input values. Finally, the computation time indicates a favorable potential for real-life applications. It is believed that the consideration of human threshold perception will encourage decision makers to establish new criteria to comply with this.