The National Research and Innovation Agency (BRIN) plays an important role in the development of energy research in Indonesia. In 2023, BRIN launched the “Call for Joint Collaboration (CFJC)” funding scheme, which allows researchers to submit proposals based on specific themes in the energy sector. This study examines administrative operability in the Proposal evaluation process at BRIN, with an emphasis on key stakeholder perspectives, including authority, institutional commitment, capability, and organizational support. Using a quantitative survey method and purposive sampling, data was gathered through questionnaires distributed to stakeholders. The results highlight institutional commitment, particularly leadership engagement and administrative staff involvement, as one of the most critical factors influencing successful proposal implementation. While the focus on energy research proposals at BRIN may limit the generalizability of the results, this study offers valuable insights into factors affecting successful research implementation in Indonesia’s energy sector, aligning with stakeholder theory in public administration. Further research is recommended to expand the scope and validate the findings.

Unmanned aerial vehicles (UAVs) are becoming more and more popular as a means of targeting terrorist and insurgent groups across the world due to a rise in terror threats, unconventional military threats, and geopolitical conflicts. The advantages are evident: lower risk to defense personnel's lives, targeted strikes to destroy hostile units, and cost-effectiveness. UAVs may now carry out a wider range of military and homeland security (HLS) missions, including combat strategies, surveillance, and reconnaissance, thanks to technological advancements in the field. UAVs have come a long way from being simple surveillance devices to becoming highly advanced military and peacekeeping equipment. There are various types of military drones, and it is important to have an effective method for selecting the best option for a given situation. In this study, we have introduced the LMAW and TOPKOR methods, which are integrated with the neutrosophic environment to efficiently handle uncertainty and ambiguity in choosing the best military UAV. An empirical analysis of a real-world scenario involving four initial military UAVs, evaluated by four specialists against five criteria, was conducted. The case study demonstrates the effectiveness and feasibility of the proposed evaluation process. We also performed sensitivity analysis and weighted analysis of the alternatives. Additionally, we used CRITIC, ENTROPY, MEREC, RAWEC, VIKOR and ARAS techniques for comparisons.

The global energy crisis has affected energy markets and policies around the world. In addition, the issue of global warming requires the world to switch to clean energy sources. The Indonesian government has committed to switching from fossil energy subsidies to renewable energy to achieve Net Zero Emissions (NZE) by 2060 or sooner. The National Research and Innovation Agency (BRIN) established the Energy and Manufacturing Research Organization (OREM) to support the NZE program. One of OREM's initiatives is the Call For Joint Collaboration (CFJC) which provides an opportunity for researchers to propose research proposals in various energy fields. However, the assessment of the CFJC proposal has not used scientific methods or criteria so that the results of the proposal evaluation are not optimal yet. Therefore, this study proposes optimization of evaluation criteria using the Patton-Sawicki Framework to determine the qualified proposal to be accepted. The research uses a qualitative approach method using CFJC proposal data on OREM in 2023. The results show that the proposed method is able to improve the quality of the proposal evaluation process by only accepting proposals that meet the requirements to support the development of NRE sector in Indonesia.

High quality laboratory results are critical for patient management. However, poor sample quality can impact these results and patient safety. To ensure reliable and accurate results laboratories must be aware of each analyte's stability under various storage conditions and matrices to guarantee correct and dependable outcomes. This knowledge allows laboratories to define the allowable delay between sample collection and centrifugation/analysis for all analytes to guarantee appropriate results quality and interpretation. The EFLM Working Group for the Preanalytical Phase (WG-PRE) therefore established a 4-step plan to tackle this issue, aiming to standardize and harmonize stability studies for improved comparison andmeta-analysis. The plan included the development of checklists and how-to guides for performing and reporting stability studies as well as a central resource of stability data. This manuscript deals with the issue of evaluating publications and incorporating them into a central resource. To evaluate stability studies, the CRESS checklist was used to structure 20 sections used to judge the quality of studies. Each section has 4 levels of quality, with scores converted to numerical values and weighted based on expert opinion. Based on this, a final score ranging from A to D was determined. The procedure was then tested on six manuscripts and checked for agreement between expert judgements. The results demonstrated that the proposed evaluation process is a useful tool to distinguish between best in class manuscripts and those of lower quality. The EFLM WG-PRE strongly believes that the provided recommendations and checklists will help improving stability studies both in quality and standardisation.

This paper outlines a proposal exploring the potential use of Large Language Models (LLMs), particularly GPT-4, in crafting realistic synthetic environments for self-driving scenarios. The envisioned approach involves dynamic scene generation within game engines, leveraging LLMs to introduce challenging elements for autonomous vehicles. The proposed evaluation process outlines assessments such as realistic testing, safety metrics, and user interaction, aiming to set the stage for potential improvements in self-driving system performance. The paper aims to contribute to the AI field by discussing how LLMs could be utilized to create valuable testing grounds for autonomous vehicles, potentially fostering the development of more robust self-driving technology. The envisioned impact is the eventual enhancement of road safety and the possible acceleration of the adoption of autonomous vehicles, paving the way for a future with safer and more efficient transportation.

Summarization tasks aim to summarize multiple pieces of information into a short description or representative information. A text summarization task is a task that summarizes textual information into a short description, whereas in an image collection summarization task, also known as the photo album summarization task, the goal is to find the representative visual information of all images in the collection. In recent years, scene-graph generation has shown the advantage of describing the visual contexts of a single-image, and incorporating external knowledge into the scene-graph generation model has also given effective directions for unseen single-image scene-graph generation. Following this trend, in this paper, we propose a novel scene-graph-based image-collection summarization model. The key idea of the proposed method is to enhance the relation predictor toward relationships between images in an image collection incorporating knowledge graphs as external knowledge for training a model. To evaluate the proposed method, we build an extended annotated MS-COCO dataset for this task and introduce an evaluation process that focuses on estimating the similarity between a summarized scene graph and ground-truth scene graphs. Traditional evaluation focuses on calculating precision and recall scores, which involve true positive predictions without balancing precision and recall. Meanwhile, the proposed evaluation process focuses on calculating the F-score of the similarity between a summarized scene graph and ground-truth scene graphs which aims to balance both false positives and false negatives. Experimental results show that the use of external knowledge in enhancing the relation predictor achieves better results compared with existing methods.

Data fabric and digital twins: An integrated approach for data fusion design and evaluation of pervasive systems

Abstract Herein, magnetic and conductive heterogeneous nickel‐matrix alloy is in situ constructed in the high‐entropy transition metal oxide matrix using a reductive circumstance, to form the high‐temperature resistant microwave absorbers. The ohmic, dielectric polarization and magnetic loss are enhanced synergistically, prompting the improvement of loss capacity and optimization of the impedance matching feature. The composites achieve over 90% absorption in the whole Ku band with a thickness of just 1.55 mm at room temperature. Moreover, the in situ high temperature measured microwave absorption keeps stable till 500 °C. In situ characterizations are employed to investigate the evolution processes and failure mechanisms. As the temperature elevates, there are three distinct stages. The absorber goes through minor chemical reactions, consequent elimination of magnetic loss, and a rapid increase in electroconductivity. These behaviors culminate in impedance mismatch, finally worsening its absorption performance at elevated temperatures. The proposed evaluation process reveals how the above irreversible and reversible behaviors affect high‐temperature microwave absorption, providing an effective theoretical basis for the design of high‐temperature microwave absorbers.

Cultural awareness reminds ABA service providers of the importance of considering the cultural practices of others when programming for behavior change. Decisions about the appropriateness of services may be difficult, however, when the values of the client conflict with the values of the culture(s) to which the client belongs or with the cultural biases of the practitioner. To minimize such conflicts, we propose a decision-making model that integrates client-centered and culture-centered assessments of habilitative validity. Throughout the proposed evaluation process, the behavior analyst and the recipients of services collaborate to refine program goals that will increase access to reinforcers for the client and their cultural groups. Given that cultures arrange reinforcers and punishers for the individual, assessing habilitative and social validity for the cultural groups affected by services is emphasized as an essential component of the model. We illustrate how the proposed model could be used to suggest appropriate courses of action by analyzing a situation that may involve conflicts of values.

Research evaluation is often understood as something similar to a competition, where an evaluation panel’s task is to award the most excellent researchers. This interpretation is challenging, in as far as excellence it is at best a multi-dimensional concept and at worst an ill-defined term because it assumes that there exists some ground truth as to who the very best researchers are and all that an evaluation panel needs to do is uncover this ground truth. Therefore, instead of focusing on competition, the Swiss National Science Foundation focused on active decision-making and sought inspiration in the deliberation proceedings of a jury trial for the design of a new evaluation procedure of an academic award. The new evaluation procedure is based upon fully anonymised documents consisting of three independent parts (achievements, impact and prominence). Before the actual evaluation meeting, the panel, which includes non-academic experts, pre-evaluates all nominations through a pseudo-randomly structured network, such that every nomination is reviewed by six members of the panel only. Evaluation decisions are based upon anonymous votes, structured discussions in the panel, ranking as opposed to rating of nominees and data-rich figures providing an overview of the positioning of the nominee along various dimensions and the ranking provided by the individual panel members. The proceedings are overseen by an academic chair, focusing on content, and a procedural chair, focusing on the process and compliance. Combined, these elements form a highly-structure deliberation procedure, consisting of individual steps, through which nominations proceed and which each either feed into the next step or into the final verdict. The proposed evaluation process has been successfully applied in the real world in the evaluation of the Swiss Science Prize Marcel Benoist, Switzerland’s most prestigious academic award.

In July 2020 the Brazilian Terrestrial Television System Forum (SBTVD) has issued a Call for Proposals (CfP) for their next-generation digital TV system called TV 3.0. Fraunhofer IIS and ATEME have proposed the MPEG-H Audio system, based on the open international standard ISO/IEC 23008-3, MPEG-H 3D Audio, as a candidate technology for the Application Coding component of SBTVD TV 3.0. The submitted proposal specifies a new Application Programming Interface (API) enabling applications to make use of the nextgeneration interactivity features of the MPEG-H Audio system. This paper provides a detailed description of the proposed API, as well as the submitted JavaScript implementation, and the architecture of the prototype system. Additionally, the paper outlines the proposed evaluation process demonstrating how the MPEG-H Audio system fulfills the TV 3.0 Application Coding requirements for 3D object-based immersive audio interaction and emergency warning information delivery

Asphalt binder ageing alters its chemical and physical properties in a manner to make it brittle and more susceptible to thermal and fatigue cracking. The ageing rate of asphalt binders depends on a combination of different factors related to their chemical composition, environmental conditions and mix design. In order to prevent ageing, anti-ageing additives are used. The selection of the optimal additive should follow an iterative process. The additive choice is made concerning each case studied. Then, the selected modified asphalt binder is short-term aged by RTFOT and long-term aged by PAV and ultraviolet (UV) weathering. Rheology performance parameters are measured by DSR and BBR before and after ageing. Consequently, ageing indices are calculated to evaluate the appropriateness of the additive. The paper discusses several findings on the rheological level accordingly to the literature. Finally, a process is proposed to harmonise the research and the engineering practices in studying anti-ageing additives effects on asphalt binders.

An evidence-based process for the evaluation of teaching methods in nursing education, including classroom assignments, is not well described in the literature. Nurse educators are familiar with evidence-based teaching but may be less knowledgeable about evidence-based methodologies to evaluate the effectiveness of teaching methods. Global measures of students' success, such as passing a licensing or certification examination, are often used as benchmarks for nursing education programs. The authors suggest an evidence-based, 8-step evaluation process for evaluating the effectiveness of teaching methods. This process is demonstrated using simulation learning experiences in 2 courses in an advanced practice nursing education program. The 8-step evaluation process was found to be effective for evaluating teaching methods within a graduate-level nursing education program. The proposed evaluation process is applicable for evaluating teaching methods at all levels of nursing education. This systematic evaluation of teaching methods may ensure that students are engaged in learning activities leading to the attainment of assignment and course objectives.

This paper presents the first comprehensive review on the scientific utilization of earth observation data provided by the German TerraSAR-X mission. It considers the different application fields and technical capabilities to identify the key applications and the preferred technical capabilities of this high-resolution SAR satellite system from a scientific point of view. The TerraSAR-X mission is conducted in a close cooperation with industry. Over the past decade, scientists have gained access to data through a proposal submission and evaluation process. For this review, we have considered 1636 data utilization proposals and analyzed 2850 publications. In general, TerraSAR-X data is used in a wide range of geoscientific research areas comprising anthroposphere, biosphere, cryosphere, geosphere, and hydrosphere. Methodological and technical research is a cross-cutting issue that supports all geoscientific fields. Most of the proposals address research questions concerning the geosphere, whereas the majority of the publications focused on research regarding “methods and techniques”. All geoscientific fields involve systematic observations for the establishment of time series in support of monitoring activities. High-resolution SAR data are mainly used for the determination and investigation of surface movements, where SAR interferometry in its different variants is the predominant technology. However, feature tracking techniques also benefit from the high spatial resolution. Researchers make use of polarimetric SAR capabilities, although they are not a key feature of the TerraSAR-X system. The StripMap mode with three meter spatial resolution is the preferred SAR imaging mode, accounting for 60 percent of all scientific data acquisitions. The Spotlight modes with the highest spatial resolution of less than one meter are requested by only approximately 30 percent of the newly acquired TerraSAR-X data.

BackgroundThe Institute of Medicine has called for approaches to help maximize the return on investments (ROI) in cancer clinical trials. Value of Research (VOR) is a health economics technique that estimates ROI and can inform research prioritization. Our objective was to evaluate the impact of using VOR analyses on the clinical trial proposal review process within the SWOG cancer clinical trials consortium.MethodsWe used a previously developed minimal modeling approach to calculate VOR estimates for 9 phase II/III SWOG proposals between February 2015 and December 2016. Estimates were presented to executive committee (EC) members (N = 12) who determine which studies are sent to the National Cancer Institute for funding consideration. EC members scored proposals from 1 (best) to 5 based on scientific merit and potential impact before and after receiving VOR estimates. EC members were surveyed to assess research priorities, proposal evaluation process satisfaction, and the VOR process.ResultsValue of Research estimates ranged from −$2.1B to $16.46B per proposal. Following review of VOR results, the EC changed their score for eight of nine proposals. Proposal rankings were different in pre‐ vs postscores (P value: 0.03). Respondents had mixed views of the ultimate utility of VOR for their decisions with most supporting (42%) or neutral (41%) to the idea of adding VOR to the evaluation process.ConclusionsThe findings from this pilot study indicate use of VOR analyses may be a useful adjunct to inform proposal reviews within NCI Cooperative Clinical Trials groups.

A-7 ロジックモデルを活用したメンバの人事目標設定及び評価の提案(新産業創出と方法論/ビジネスモデル開発トラック,サービス社会における新産業創出と方法論:三次元プリンターのインパクトと課題を探る」〜市場と公益が共存できるビジネスモデル創出を目指すプログラムマネジメント〜)

Core competence evaluation standards for emergency nurse specialist: Developing and testing psychometric properties

Object proposal algorithms have shown great promise as a first step for object recognition and detection. Good object proposal generation algorithms require high object detection recall rate as well as low computational cost, because generating object proposals is usually utilized as a preprocessing step. The problem of how to accelerate the object proposal generation and evaluation process without decreasing recall is thus of great interest. In this paper, we propose a new object proposal generation method using two-stage cascade support vector machines (SVMs), where in the first stage linear filters are learned for predefined quantized scales/aspect-ratios independently, and in the second stage a global linear classifier is learned across all the quantized scales/aspect-ratios for calibration, so that all the windows from the first stage can be compared properly. The windows with highest scores from the second stage are kept as inputs to our new efficient proposal calibration algorithm to improve their localization quality significantly, resulting in our final object proposals. We explain our scale/aspect-ratio quantization scheme, and investigate the effects of combinations of l1 and l2 regularizers in cascade SVMs with/without ranking constraints in learning. Comprehensive experiments on VOC2007 dataset are conducted, and our method is comparable with the current state-of-the-art methods with much better computational efficiency.

Purpose: In this paper, we developed a reliability index (RI) to efficiently compare reliability of products based on the design, development and production information such as reliability tests, quality, product life-cycle management. RI also can be applied to reliability prediction of a novel product as well as comparison evaluation among existing products. Methods: For evaluating RI, we proposed evaluation process which is composed of five steps. Target modules are selected based on warranty data and correlation analysis. Scores of selected target modules are calculated by scoring function. Finally, weights of RI model are determined by optimization method. Results: This paper presented an empirical analysis based on failure data of mobile devices. In this case study, we demonstrated that there is a direct correlation between evaluated RI and field failure probability of each product. Conclusion: We proposed the index for comprehensive and effective assessment of product reliability level. From the procedure of this study, we expected to be applied for reliability estimation of novel products and deduction of field failure-related factors.

Abstract A newly expanded digital resource exists for tracking decisions on all nomenclature proposals potentially contributing to Appendices II–VIII of the International Code of Nomenclature for algae, fungi, and plants. This system owes its origins to the Smithsonian Institution's “Proposals and Disposals” website created by Dan H. Nicolson to track conservation/rejection proposals, but now also treats proposals to suppress works or requests for binding decisions. The new resource was created to accommodate the steadily expanding content of the Appendices in relation to the main body of the Code. A database is now available to generate these Appendices, as has been done for the Melbourne Code. A web interface allows users to query database content in various ways to review proposal histories or to extract all or part of the Appendices. An analysis of the underlying data was conducted to make comparisons between proposals submitted for the various editions of the Code. These include the type of nomenclatural remedy sought, the major group concerned, the numbers of names involved, the timeliness of the proposal evaluation process, the proposal success rate, and the diversity of proposal authorship. The success of proposals was also evaluated by the type of remedy sought and by major groups.