Future Directions Clause Samples
The "Future Directions" clause outlines the parties' intentions or plans for potential collaboration, development, or expansion beyond the current agreement. It typically describes areas where the parties may explore additional projects, enhancements, or joint ventures in the future, often setting a framework for ongoing discussions or negotiations. This clause helps ensure both parties are aligned on possible next steps and provides a basis for structured growth, while clarifying that such future activities are not yet binding commitments.
Future Directions. The results of this study suggest that inter- and intraobserver agreement on the presence of the NAPH pattern on CT scans is good. We also found that in approximately 22% of cases, there was disagreement as to whether the SAH matched the NAPH pattern. This implies that agreement may not be strong enough to suggest that the presence or absence of this pattern is reliable enough for a single observer’s opinion to be taken into account when deciding on the future management of the patient. If there is a low index of suspicion for the presence of an intracranial aneurysm, follow-up CT angiography, rather than DSA, can be used to accurately exclude aneurysms.8,9 In this study, we also found that in a most of cases, nonan- eurysmal SAH does not match the NAPH pattern. Given that such cases have not been as well characterized as NAPHs, it seems necessary to study these cases extensively so as to deter- mine whether one can develop an algorithm that could be used to reliably determine the best way to manage these cases.
Future Directions. PRECIOUS will review tools available for monitoring food intake and assess their recording accuracy, as well as gathering user feedback. The data collected will help to develop a user-friendly food intake tool for PRECIOUS, which will be enhanced by links to the virtual individual model, motivational tools and monitoring of other lifestyle aspects.
Future Directions. Cross-validation of arduous physical tasks and aerobic fitness
Future Directions. Work in progress is aimed at extending MVNE (i) to cope with dynamic update of graphs e.g., using asynchronous stochastic gradient descent (SGD) to update the latent space with the only newly added or deleted edges or nodes; and (ii) work with multi-modal networks that include richly structured digital objects (text, images, videos, etc).
Future Directions. While the main findings of this thesis are an important first step in understanding the relationship between syntactic annotation quality and machine translation performance, the results presented here raise some additional questions that bear further investigation. One slightly curious finding has to do with the disparity between tuning and test perfor- ▇▇▇▇▇. As we predicted, the new syntactic annotations resulted in higher BLEU scores on both the tuning and test data. However, in general, the tuning set improvements were quite a bit higher than those on the test set. This in itself is a relatively banal finding – optimizer overfitting is hardly an uncommon phenonomenon – but Table 4.3 shows a different ▇▇▇- ▇▇▇▇ of results from experiments where we improved the word alignments and held the trees constant. In these word alignment experiments, which are otherwise identical to the other MT experiments in this thesis, we saw tuning and test set improvements that were much closer together in magnitude, suggesting that the overfitting effect is stronger when parses are improved than when word alignments are improved. These results alone are probably not enough to be conclusive, but given the large recent interest in methods for overcoming MT optimizer instability (see for example ▇▇▇▇▇▇ et al., 2008; ▇▇▇▇▇▇▇▇ et al., 2008; ▇▇▇▇▇ et al., 2009; ▇▇▇▇▇ et al., 2011) , it seems to be worth investigating the interaction between parameter optimization and syntactic MT specifically. The final result of this thesis was the somewhat disappointing finding that the two basic approaches presented (statistical modeling to improve parser performance and tree trans- formations to improve agreement with alignements) do not automatically stack together to achieve even stronger MT performance. One possibility for this result is that the agree- ment score metric we defined is designed specifically for isolating problems that appear in monolingual parses. When the starting point is parses that were generated from a joint model, the initial agreement is already much higher, so perhaps the remaining disagree- ments that are relevant to MT performance are not adequately captured by continuing to optimize agreement score. However, we do see in Table 5.7 that the annotations with the highest agreement score (Joint PA + Transformation) also yield the highest BLEU score on the tuning set,1 so another possibility is that we’re just running up against the limits of the parameter optimiz...
Future Directions. The most important component of this study was the opportunity to help a wide range of SNAP-Ed schools across Georgia to improve the health and wellbeing of their students and families in the area of nutrition. Recommendations and individual school reports with detailed survey results will be provided to each school at HealthMPowers’ annual SNAP- ▇▇ ▇▇▇▇▇▇ trainings. At these trainings, HealthMPowers will provide school administrators and staff with individual school reports to encourage constructive discussion between staff. School staff will learn the specific areas that their schools are excelling in and areas of opportunity, and can use the data to inform future school nutrition policies, practices, and environmental goals. Respondents will also learn if there were any discrepancies in survey responses between administrators, grade level chairs, and nutrition managers and work toward resolving them. HealthMPowers will share the survey and its results with key stakeholders and explore ways to use the survey for measuring additional successes with policy, systems, and environmental changes in Georgia elementary schools. Currently there are at least three opportunities for further analysis using this dataset. First, a longitudinal analysis of schools led by HealthMPowers would be ideal, since a study of this nature could provide an assessment of long-term trends of school nutrition policies practices, and environments, as well as a deeper understanding of the impact and influence that HealthMPowers is having on schools. This longitudinal analysis could also involve surveying non-SNAP-Ed schools in Georgia. Second, there is potential to compare the results of this survey to national data from Bridging the Gap: Food and Fitness surveys, which the HealthMPowers School Setting Nutrition Survey was adapted from. Finally, HealthMPowers has a rich database of data in the areas of Georgia elementary school nutrition and physical activity, and there is an opportunity to link data from the HealthMPowers School Setting Nutrition Survey to other relevant datasets in the future. For example, it is possible to explore the relationship between nutrition and physical activity environments in Georgia SNAP-Ed elementary schools. Finally, future studies should involve large, nationally-representative samples and explicitly distinguish between policies, practices, and environments, since this can inform focused and targeted school-based interventions and programs. Additional...
Future Directions. Given the fact that it is quite a complex architecture, the processing of requests could be a bit slower; cache techniques have been developed to address this problem. The technical choices which have been made will also require more work from the service providers as they will have to provide portlets for enabling access to their web services. Once the portal is ready, it will be moved to a production environment. The customization of its comportments will be a required step to have the best possible user experience. Liferay provides facilities such as clustering, load balancing and advanced caching techniques which will help in ensuring the robustness of this solution. This will likely be achieved before the end of the project.
Future Directions. Future proxy studies should continue the use of the mPSS as a tool to measure perceived stress in IWA and explore additional psychosocial variables that may affect the caregiver-proxy agreement on chronic stress. Assessing chronic stress and depression in people with acquired neurogenic communication disorders continues to be a complicated mission, as they are often excluded from studies that measure psychological disorders following a stroke (▇▇▇▇▇▇▇, ▇▇▇▇▇▇▇▇, & ▇▇▇▇▇▇, 1997). Therefore, future proxy studies with IWA as participants should include larger sample sizes and examine potential confounding or influential variables such as age, time post onset, aphasia severity, socioeconomic background, and individuals not in therapy. Additional research into mutuality and its impact on psychological disorders post-stroke between the IWA and their caregivers should be explored. A mutuality scale that is developed and normed for people who have acquire neurogenic communication disorders can be a tool used to help individualize treatment and help counsel IWA and their caregivers or family members. Future studies may want to explore the affect support groups may have on mutuality among the dyad and the long-term management of chronic stress. In addition, it can be used as a potential tool to help provide additional insight to potential discrepancies between the caregiver-proxy and patient report in subjective domains such as depression and chronic stress. Finally, the impact of caregiver’s stress and its influence on proxy ratings for IWA should be explored in more depth, as the results of the current study suggest that caregiver stress may have a negative impact on the level of agreement on proxy ratings. In addition, severity of aphasia should be looked into as another potential factor directly impacting level of agreement on proxy ratings. Past studies have highlighted how spousal caregivers for IWA have a difficult time adjusting to their new role as a caregiver (▇▇▇▇ & ▇▇▇▇▇▇, 1989; ▇▇▇▇▇▇▇▇▇, Le Dorze, & ▇▇▇▇▇▇▇▇▇, 2001). Future proxy studies should also explore the stress experienced by parent and child caregivers. Furthermore, other potential confounding variables for the caregiver, such as preexisting psychological conditions, alcohol or substance use, caregiver health, personality traits, age, and sex should be explored.
Future Directions. Our effort on ▇▇▇▇▇ opens up several interesting di- rections for further study. Two, in particular, that are worth emphasizing are ontologies and temporal constraints. First, based on the service exceptions that Enlil extracts, phrase classification algorithms can further organize these vocabularies into categories. For example, some exceptions refer to financial condi- tions such as nonpayment, and some refer to natural disasters such as earthquakes. On top of that, a tax- onomy of exceptions for a specific contract domain can potentially be generated automatically. It would be valuable to generate domain-specific ontologies of business service exceptions for potential use in evaluating contracts for completeness and authoring robust contracts. Second, Enlil may be readily enhanced to extract other types of information from contract text. In par- ticular, we observe that many business exceptions involve temporal constraints such as “late delivery of products” and “late payment.” A failure in the timely
Future Directions. The findings have implications for both research and education. The modifying effect of VSA should be taken into account when designing new research and analysis strategies, especially in the field of 3D technologies. For educational purposes, stereoscopic 3D AR models have a great potential to be effectively used in small-group teaching settings to stimulate active learning and peer-to-peer interaction by studying a synchronized anatomical 3D models. In addition to traditional ways of teaching, this new teaching tool can be used in the context of personalized learning to meet the students’ individual learning needs. Especially, the combination of stereoscopic 3D models and 2D anatomical atlas is worth further research. A possible synergic learning effect would be desirable since the level of anatomical knowledge among medical students still remain insufficient.