Diverse Criteria Research Articles

Quaternary fluvial carbonate deposits of the Almonda River Valley, Central Portugal

This paper discusses the formation and preservation of a fluvial tufa system influenced by Atlantic climate based on stratigraphical, chronological (amino-acid racemization, AAR), sedimentological and stable-isotope analyses. On the southwestern Iberian Peninsula, the tufas and associated deposits of the Almonda River valley occur as isolated terraced bodies and reach 25 m thick. AAR dated most deposits to within the warm Marine Isotope Stage 5 (MIS-5). Two Holocene ages were reset within MIS-5 based on diverse criteria. Widely varied carbonate and minor allochthonous coarse detrital facies occur arranged in four simple vertical associations. The deposit geometry and facies association distribution correspond to a low- to moderate-sloped fluvial valley consisting of several short knickpoints and extensive flat areas between them. The latter are occupied by slow-flowing water facies (carbonate sand, lime mud, phytoclast and oncoid rudstones, and up-growing stem boundstones). Facies that formed in moderate- to high-slope substrates were stromatolite, moss and down-growing stem boundstones. The homogeneous Miocene bedrock lithology and gentle structural deformation propitiated this depositional architecture. Calcite δ13C and δ18O values suggest that the aquifer water provided the outflowing Almonda water with (1) 18O-enriched water, compared with present precipitation and groundwater δ18O values, and (2) 13C-depleted CO2 from bituminous rocks and vegetation cover in the catchment. The proximity to the Atlantic coast favoured the Mesozoic-rock aquifer recharge with 18O-enriched water precipitation, assuring water availability during the formation of the studied tufas. No evidence of frequent intense erosion phases might indicate stable precipitation regimes, which would have allowed the preservation of loose fine-grained and palustrine deposits.

Model of ecological resilience in Hyrcanian forests that combines the decision-making trial and evaluation method (DEMATEL) and system dynamics

The present study assessed the resilience model of Hyrcanian forests with Navroud-Asalem watershed as a case study. The Navroud-Assalem watershed was selected for study because of its special environmental characteristics and access to relatively acceptable information. In order to model resilience, the appropriate indices influencing resilience in Hyrcanian forests were identified and selected. The criteria of biological diversity and forest health and vitality were selected along with the indices of species diversity, forest-type diversity, mixed stands, and the infected area percentage forests with disturbance factors. Thirteen sub-indices and 33 variables were determined and the relationship between the indices and criteria was identified by developing a questionnaire using the decision-making trial and evaluation laboratory (DEMATEL) method. The weights of each index were estimated in Vensim software using the fuzzy analytic hierarchy process. Collecting and analyzing the regional information, the conceptual model was developed and formulated quantitatively and mathematically and was entered into Vensim for resilience modeling of the selected parcels. The DEMATEL method indicated that the species diversity indices and the percentage of affected forests had the greatest influence and interaction with other factors in the system. The parcels studied had different slopes and were affected by the input variables. They were considered as resilient if they were found to maintain current conditions. Avoidance of exploitation, infestation by pests, severe fires in the region, and increased livestock grazing compared to the existing situation were among the prerequisites for resilience in the region. Vensim modeling represents that in control parcel no. 232 the nondimensional resilience parameter is 3025 (the most resilient parcel), while in the disturbed parcel no. 278 this amount is 1775 (the least resilient parcel).

Influence of substitution model selection on protein phylogenetic tree reconstruction

Probabilistic phylogenetic tree reconstruction is traditionally performed under a best-fitting substitution model of molecular evolution previously selected according to diverse statistical criteria. Interestingly, some recent studies proposed that this procedure is unnecessary for phylogenetic tree reconstruction leading to a debate in the field. In contrast to DNA sequences, phylogenetic tree reconstruction from protein sequences is traditionally based on empirical exchangeability matrices that can differ among taxonomic groups and protein families. Considering this aspect, here we investigated the influence of selecting a substitution model of protein evolution on phylogenetic tree reconstruction by the analyses of real and simulated data. We found that phylogenetic tree reconstructions based on a selected best-fitting substitution model of protein evolution are the most accurate, in terms of topology and branch lengths, compared with those derived from substitution models with amino acid replacement matrices far from the selected best-fitting model, especially when the data has large genetic diversity. Indeed, we found that substitution models with similar amino acid replacement matrices produce similar reconstructed phylogenetic trees, suggesting the use of substitution models as similar as possible to a selected best-fitting model when the latter cannot be used. Therefore, we recommend the use of the traditional protocol of selection among substitution models of evolution for protein phylogenetic tree reconstruction.

The Effect of Anthropogenic Activities on the Fish Fauna of the Devrez Stream (Turkiye)

In this study, fish species diversity and protection criteria, habitat evaluation, and threat elements of the ichthyofauna of the Devrez Stream were examined monthly between April 2020 and March 2021. Eight fish taxa (7 natives, 1 non-native) belonging to four families (Cyprinidae, Gobionidae, Leuciscidae, Gobiidae) were identified. Chondrostoma angorense Elvira, 1987 (endemic) from the mentioned species was reported for the first time in the Devrez Stream. Squalius sp. (32.5%), Chondrostoma angorense (24.4%), Capoeta tinca (23.9%), and Alburnoides freyhofi (11.1%) were the most common and abundant species while Pseudorasbora parva (0.5%), Barbus anatolicus (1.2%), Alburnus goekhani (2.4%) and Neogobius fluviatilis (4%) were the most rarely encountered species in the stream. According to our observations, drought, agricultural irrigation, and Devrez Kızlaryolu Dam, which is under construction, were considered major threats to the fish fauna.

문화다양성 정책 관점에서의 한국연극 동향 분석

This study aimed to figure out the trends of Korean theatre from the perspective of cultural diversity policies. The cultural diversity policies have mainly focused on the social minorities' culture and art activities and participation in Korea, which means that there is a relative shortage of discussion about the diversity of art. The study analyzed the achievements and limitations of cultural diversity policies in South Korea, and distinguished the diversity of art according to intrinsic and extrinsic criteria. Also analyzed in the study were reviews published in Critiques of Theatre for the last six years(2015~2020) based on the criteria of artistic diversity. The analysis results show that the diversity of art in the field not only accepted extrinsic diversity including the nationality, gender, and regional diversity of those who participated and enjoyed, but also realized the values of diversity including the content and formal diversity of artistic works.

Cartographic Modelling and Multi-Criteria Analysis (CMCA) for Rail Transit Suitability

Rail transportation planning is a continuous, multi-phase, functioning scale that is based on reconstructing the spatial system, thus contributing to build a more quality-focused, long-term-oriented and balanced approach. This paper presents cartographic modelling and multi-criteria analysis (CMCA) for the optimum route/station location in accordance with selected criteria and constraints for rail transit system planning. The methodology is based on overlay analysis (cartographic modelling) to combine diverse criteria maps and the analytic hierarchy process (AHP) method for criteria weighting. The purpose of the study is to examine a GIS multiple-criteria decision-making (MCDM) framework by considering Stream, Geology, Slope, Land use and Population decision criteria. The decision modelling framework conceives an additional prominent approach by co-producing with the public, especially for underserved areas of low socio-economic status and public user expectations in the railway transport planning process. This model applied to a candidate metro line referred to as M 18 (Başakşehir-Esenyurt-Beylikdüzü) with eight railway stations for the preliminary stage evaluation. The outcomes of this research will close the gap by establishing a novel suitability framework which can be used as a preliminary analysis for transforming and developing urban rail transit networks.

A Multi-Objective Crowding Optimization Solution for Efficient Sensing as a Service in Virtualized Wireless Sensor Networks

The Internet of Things (IoT) encompasses a wide range of applications and service domains, from smart cities, autonomous vehicles, surveillance, medical devices, to crop control. Virtualization in wireless sensor networks (WSNs) is widely regarded as the most revolutionary technological technique used in these areas. Due to node failure or communication latency and the regular identification of nodes in WSNs, virtualization in WSNs presents additional hurdles. Previous research on virtual WSNs has focused on issues such as resource maximization, node failure, and link-failure-based survivability, but has neglected to account for the impact of communication latency. Communication connection latency in WSNs has an effect on various virtual networks providing IoT services. There is a lack of research in this field at the present time. In this study, we utilize the Evolutionary Multi-Objective Crowding Algorithm (EMOCA) to maximize fault tolerance and minimize communication delay for virtual network embedding in WSN environments for service-oriented applications focusing on heterogeneous virtual networks in the IoT. Unlike the current wireless virtualization approach, which uses the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), EMOCA uses both domination and diversity criteria in the evolving population for optimization problems. The analysis of the results demonstrates that the proposed framework successfully optimizes fault tolerance and communication delay for virtualization in WSNs.

EVALUASI KONSEP PENGEMBANGAN TAMAN BISNIS BERDASARKAN KARAKTERISTIK KAWASAN CAMPURAN

Mixed-use development has become common in many major cities around the world. The use of mixed-use development has also occurred in Indonesia since the 2000s, with the Jakarta Metropolitan Urban Area being one of the cities that has implemented it extensively. The implementation is intended to address urban issues caused by uncontrolled urbanization, such as traffic congestion, the need for proper housing, and the issue of limited land. One of the efforts for land efficiency and becoming a compact and sustainable city is the concept of mixed area development. One of the business areas that has been transformed from a warehouse area to a mixed area is CIBIS Business Park, which spans approximately 12 hectares and is located in the South Jakarta Administrative City area. Mixed area development should ideally meet all of the criteria for mixed area characteristics, but it turns out that there are many mixed area developments that do not meet the mixed area characteristic criteria, resulting in new problems both in the mixed area and surrounding it. The purpose of this study is to evaluate whether the CIBIS Business Park's concept of mixed-use development meets the criteria for mixed-use characteristics, specifically the diversity of use criteria and design criteria. The research method used in this study is quantitative research, and the evaluation method is based on the plan's concept (ex-ante evaluation). This evaluation method compares the CIBIS Business Park area's site plan to the criteria for mixed-use development characteristics, specifically the diversity of use criteria and design criteria. According to the evaluation results, the CIBIS Business Park development concept in general conforms to the characteristics of a mixed area, with 71.42% meeting the criteria for diversity of use and 57.14% meeting the design criteria

Optimizing storage assignment, order picking, and their interaction in mezzanine warehouses

In warehouses, order picking is known to be the most labor-intensive and costly task in which the employees account for a large part of the warehouse performance. Hence, many approaches exist, that optimize the order picking process based on diverse economic criteria. However, most of these approaches focus on a single economic objective at once and disregard ergonomic criteria in their optimization. Further, the influence of the placement of the items to be picked is underestimated and accordingly, too little attention is paid to the interdependence of these two problems. In this work, we aim at optimizing the storage assignment and the order picking problem within mezzanine warehouse with regards to their reciprocal influence. We propose a customized version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing the storage assignment problem as well as an Ant Colony Optimization (ACO) algorithm for optimizing the order picking problem. Both algorithms incorporate multiple economic and ergonomic constraints simultaneously. Furthermore, the algorithms incorporate knowledge about the interdependence between both problems, aiming to improve the overall warehouse performance. Our evaluation results show that our proposed algorithms return better storage assignments and order pick routes compared to commonly used techniques for the following quality indicators for comparing Pareto fronts: Coverage, Generational Distance, Euclidian Distance, Pareto Front Size, and Inverted Generational Distance. Additionally, the evaluation regarding the interaction of both algorithms shows a better performance when combining both proposed algorithms.

Political ecology explanations for ineffective environmental governance for sustainability in the Amazon: A comparative analysis of cases from Bolivia, Brazil, Colombia, and Peru

There is an extensive literature on environmental governance, which refers to multi-stakeholder processes to arrive at collective decisions about how natural resources will be managed. Recent work on environmental governance has focused on outcomes in terms of social-environmental sustainability. However, questions remain about the effectiveness of environmental governance in practice for yielding sustainable social or environmental outcomes. In cases where environmental governance processes prove ineffective, political ecology offers analytical approaches involving explanations that can account for unsustainable outcomes. In addition, an emergent literature on environmental governance provides frameworks to evaluate its effectiveness by unpacking it with regard to diverse criteria. These two literatures together permit analysis of how political ecology and other potential explanations can account for ineffective environmental governance in terms of specific unmet criteria. Analysis of ineffective environmental governance is likely to be especially valuable in a comparative perspective, in which multi-case studies can reveal the extent to which political ecology explanations predominate across cases. We focus on the Amazon, a large region with high social and biological diversity and where competing stakeholders engage in conflict over governance of natural resources. We pursue a comparative analysis of five cases where environmental governance has been ineffective in terms of sustainable outcomes. In each case, we identified five key explanations for ineffective environmental governance. We then coded those explanations with regard to whether they invoke issues highlighted by political ecology. We also coded them considering environmental governance evaluation frameworks to identify the unmet criteria for environmental governance to be effective. We then pursued a comparative analysis of similarities and differences across the cases. The findings indicate that political ecology issues are predominant among explanations for ineffective environmental governance across all five cases. The results also reveal which environmental governance evaluation criteria are most often unmet among the cases. The findings highlight the importance of political ecology for understanding ineffective environmental governance, and permit delineation of specific criteria for effective environmental governance that can be the focus of strategies to improve environmental governance for sustainability.

From Non-Optimal Routing Protocols to Routing on Multiple Optimality Criteria

At a suitable level of abstraction, all that standard routing protocols do is iterate extension and election operations on path attributes. An extension operation composes the attribute of a path from those of a link and another path, while an election operation produces the most preferred attribute of a set of candidate attributes, given a total order on them. These protocols are guaranteed to compute optimal paths only if the extension operation and the total order are entwined by the algebraic property of isotonicity, which states that the relative preference between two attributes is not inverted when both are extended by a third attribute. We solve the problem of computing and routing on optimal paths with generality by recognizing that every total order contains a partial order for which isotonicity holds. Then, we design a partial-order vectoring protocol where every election operation produces a subset of attributes from a set of candidate attributes, rather than a single attribute as is the case with a standard vectoring protocol; the election operation is derived from the partial order, ensuring that no attribute of the set of candidate attributes is preferred to an attribute of the elected subset. Moreover, we show how partial-order vectoring protocols can be designed to allow routing on optimal paths concurrently for diverse optimality criteria. Our evaluation over publicly available network topologies and attributes, covering both intra- and inter-AS routing, evince that the sizes of elected subsets of attributes are surprisingly small and that the partial-order vectoring protocol converges fast, sometimes faster than a standard vectoring protocol operating in the absence of isotonicity.

A SPECTRAL-SPATIAL AUGMENTED ACTIVE LEARNING METHOD FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Abstract. In this paper, a new classification technique for hyperspectral images (HSIs) based on an augmented active learning (AL) is introduced. The proposed method consists of two main steps: first, a 2-D non-subsampled shearlet transform (NSST) is applied to each spectral band of HSIs to extract the spatial features. After that, the kernel minimum noise fraction (KMNF) is used to reduce the spectral dimension. Second, the classification task using an augmented active learning technique is performed. For this purpose, an iterative process is considered. At each iteration, a discriminative sample selection and augmentation are used to create the training set. Then, the support vector machine (SVM) is iteratively applied to the training set. In the proposed method, the most informative samples are selected by a new query function combination of a posterior probability-based uncertainty and angle-based diversity criteria. The augmentation strategy during the training process is chosen by two-sample Kolmogorov-Smirnov test and the existing outliers are removed by k-means clustering. Finally, the proposed algorithm is applied to the real datasets and compared with three state-of-the-art AL algorithms. The obtained results show that the proposed method significantly increases accuracy considering the most informative samples.

Stakeholder’s practices for the sustainability assessment of professional urban agriculture reveal numerous original criteria and indicators

With the rapid growth of professional intra-urban agriculture (PIUA) projects in the Global North, sponsors, projects leaders, and experts developing these projects are seeking to evaluate their sustainability. As existing assessment tools are not adapted to PIUA projects, they establish their own assessment practices. Our study examines these practices to identify their original features, criteria, and indicators used. To this end, we analysed 19 case studies of different PIUA projects. We identified four dimensions underpinning sustainability assessment, namely, internal sustainability, external sustainability, the project leader’s credibility, and the innovative nature of the project. We also shed light on the wide diversity of the 67 assessment criteria identified, as well as the qualitative nature of 78% of indicators used. In addition, our study highlights that assessment practices evolve over time as the project progresses from ideation to implementation, according to the variety of assessment situations. Our study is the first to provide an in-depth exploration of PIUA stakeholders’ sustainability assessment practices and to shed light on their specific features. Our results afford a better understanding of the way the sustainability of PIUA projects is assessed and contribute to reflection on the design of a flexible assessment tool, considering the diverse criteria and practices used by stakeholders to assess the sustainability of PIUA.

An Enhanced Dynamic Ensemble Selection Classifier for Imbalance Classification With Application to China Corporation Bond Default Prediction

China corporation bond default prediction is important and can be formulated as an imbalance classification problem solved by static ensemble classifiers. However, dynamic ensemble selection (DES) classifiers have not been applied to this typical problem in the context of business research. DES classifiers are capable of selecting an ensemble classifier for each test sample, leading to better classification performance than static ensemble classifiers. Most existing DES classifiers can not address imbalance classification optimally and only use single criteria of competent for classifier selection, resulting in sub-optimal classification performance. In this paper, we propose an enhanced DES classifier, named META-DES-Diversity, that inherits strengths of data sampling, meta-learning, criteria of diversity, and dynamic weighting fusion scheme to alleviate such limitations. Specifically, the synthetic minority over-sampling technique is initially used to balance the training set before generating a candidate classifier pool. To select an ensemble classifier with a highly competent, the meta-learning framework META-DES is utilized to consider multiple criteria of competence. In complement with the meta-leaning framework, a two-phase selection strategy is utilized to perform competence and diverse ensemble classifier selection. Note that a competence-driven decision fusion scheme is employed to effectively fuse classification results from selected ensemble classifiers. Experiments on 14 two-class imbalanced data sets from the KEEL repository and one self-collected China corporation bond data set show the effectiveness and superiority of the proposed enhanced DES classifier.

Active Learning for Hyperspectral Image Classification Using Kernel Sparse Representation Classifiers

Active learning is one of the popular approaches that can mitigate some of the drawbacks of supervised classification. Although sparse representation classifier has already proven to be a robust classifier and successfully used in many applications, it is seldom used jointly with active learning. In this letter, we propose a novel active learning technique for sparse representation classifiers. In the proposed model, the query function is designed by combining uncertainty and diversity criteria, both of which are defined by using the sparse representation classifier in kernel space. The proposed technique outperforms other state-of-the-art methods in terms of classification performance.

Multiplayer reputation-based coalition game model (MRCGM) with dominant strategy analysis for detecting malicious attacks in wireless sensor networks

Wireless Sensor Networks (WSNs) are turning into an essential portion of our lives. Devoid of assuring WSNs security (WS), there remain no broad implementations of WSNs. Because of sensor nodes’ constrained capacities concerning computation, communication, and energy, giving protection to WSNs remains competitive. Indeed, the procedure of applying WS remains adaptable and vibrant that develops consistently. Attack-defend’s crux in WS could be conveyed by collaborative schemes of interdependency when Game Theory (GT) could be employed to consider communications amidst schemes of logical decision-makers. Hence, learning WS alongside GT possesses greater logic. This study proffers a Multiplayer Reputation-based Coalition Game Model (MRCGM) for seeking the false data injection amidst the nodes. An attacker attempts to sustain an ideal level of belief by correlating the identifications within the network (NW) to initiate a victorious transfer. Concurrently, the attacker should acquire a few charges for sustaining the trustability of its identifications. This proffered procedure employs this notion and turns the attack expensive by fixing a global threshold for nodes or identifications to be trustable and functional within the NW. Additionally, the beneficial function of the attacker and defender is as well described. The MRCGM has been correlated with 2 advanced methodologies like security-aware routing scheme employing repeated game (SARSRGM) paradigm and a Game-based Fuzzy Q-learning (GBFQL) scheme concerning diverse criteria. Consequently, the proffered MRCGM attains 124 kbps of Packet Drop Rate, 76.14% of energy efficiency, 96.1% of detection accuracy, 25.45% of energy consumption, and 102.45 ms of routing latency.

Securing wireless sensor network from node capture attack using an efficient optimization defense strategy model

Wireless Sensor Networks (WSNs) are a promising evolving technology for recent real-world applications in collaborative settings. These applications ranging from household monitoring to crucial military applications transmit critical data and thus more challenging approaches are required for providing security. In WSNs, a node capture attack (NCA) is a more serious attack among various attacks which supports an impostor to perform different network (NW) operations, and thereby the security of the entire NW is compromised easily. For conquering the surpassing issues of sensitivity-related techniques and for enhancing the NCA’s AE, we establish the NCA technique amalgamating several objectives like speed and location to lessen comprehensive intricacy. For securing the NW out of the NCA, this study proffers Improved Flower Pollination Optimization Defense Strategy Algorithm (IFPODSA) intending to compute the multiple objective functions for effectual chief administration procedure employing Hankel matrix formation. This proffered IFPODSA is correlated with two prevailing methodologies like Fruit Fly Optimization Algorithm (FFOA) and Finding Robust Assailant Optimization-Particle Swarm Optimization and Genetic Algorithm (FRAOPSOGA) concerning diverse criteria. Consequently, it has been observed that the proffered IFPODSA attains 458.7 kbps of throughput, 81.56% of PDR, 18.2% of energy consumption, and 64.36% of overhead.

Evaluation criteria for health apps supporting medication adherence in early-stage technology development - a scoping review.

Health apps offer an approach to improve the patients' management of their medication. Although the Digital Healthcare Act (DVG) has created a claim in the statutory health insurance (SHI), the large number of health apps available and their varying quality make it difficult for service providers and especially for medical laypersons to select an adequate high-quality medication app. Manufacturers need guidance for the development of high-quality apps right from the start. Various general evaluation concepts for health apps have been available to date. However, the requirements that should be met by healthcare depend largely on the field of application and the type of apps. This article aims to provide an overview of the international evidence on specific criteria for the evaluation of medication apps. Within the framework of a scoping review, a systematic search was conducted in PubMed and EMBASE on January 29, 2020. The search was limited to publications from 2007 onwards as well as to English and German articles. Additionally, a semi-systematic research of reference lists of the previously included articles as well as a structured search of websites of relevant stakeholders were conducted. Inclusion criteria were the following: the publication deals with health apps that can be used on smartphones and focus on supporting medication intake; the publication does not refer to evaluation criteria for a single app exclusively. The included publications were examined in a qualitative content analysis searching for evaluation criteria and categorizing them according to the framework criteria of the DVG and the Digital Health Applications Ordinance (DiGAV). 2,542 articles were identified in the systematic search (999 in PubMed, 1,543 in EMBASE, 560 duplicates). A total of 16 studies met the inclusion criteria. The semi-systematic research and the structured search identified one further study. A catalog of criteria was developed based on the included 17 studies. This catalog covers the general topics "patient orientation" (data protection and security, consumer protection, user friendliness) and "quality/core functions of medication apps" (reminder, self-monitoring, (drug) information, motivation to change behavior, drug/patient safety, robustness) as well as "interoperability/cooperation". Due to its specific importance for medication apps, the subcategory "motivation for behavioral change" stands out beneath the general topic "quality/core functions of medication apps". This category aims to evaluate the design of individual functions with regard to their potential to actually change the behavior of app users. The criteria for the evaluation of health apps mentioned in the DiGAV intersected with the criteria identified in the literature research. However, the area of positive health care effects was hardly covered by the included studies. In the development of the criteria catalog, it was not possible to weight the identified criteria. Therefore, the catalog should be understood as a supporting checklist for service providers, manufacturers, and/or users. A large variety of possible evaluation criteria for medication apps could be shown. Future research should focus on the possibilities of weighting these diverse evaluation criteria, using not only clinical studies but also methods to identify preferences.

Are software projects evaluated using software teams’ success criteria? A systematic literature review

As diverse as software project stakeholders are, so are their project needs and interests. Furthermore, the criteria to measure project success are based on the stakeholder groups’ various needs. It is on the basis of these diverse measurement criteria that project success has become an elusive moving target. Despite this, the measurement and achievement of project success remains a critical milestone in project management for the satisfaction of stakeholders, including software project teams (SPTs). Several research studies have reported project success or failure from various stakeholders’ perspective. However, recent studies have projected SPTs as the most neglected key stakeholder group by software project managers and researchers in the project management field. Given this backdrop, conducting a systematic literature review (SLR) research study to determine if there are empirical studies that have evaluated software project success from SPTs’ perspective would be of interest to many in the field. According to the study's authors there is no study that has been conducted to determine this. The study found one (1) paper, which evaluated a project based on the SPTs’ success criteria, thus showing a research gap and indicating the neglect of this group of stakeholders when it comes to establishing whether software projects meet their needs or not. This study recommends that empirical studies be conducted to close this research gap.

An Enhanced Research Productivity Monitoring System for Higher Education Institutions (HEI's) with Natural Language Processing (NLP)

The HEI in the Philippines is entrusted with three mandates by the Commission on Higher Education (CHED): teaching, research, and extension service. Among these mandates, research productivity plays a crucial role in fostering knowledge generation, production, and transfer within the community. However, monitoring research productivity has been a persistent challenge due to the diverse evaluation criteria set by local and international accrediting organizations, which directly impact an HEI's educational quality. This study has selected an academic institution and aims to 1) Design a system model as research data collection; 2) employ Natural Language Processing (NLP)-based techniques to facilitate the monitoring and retrieval of information on research productivity and 3) generate visualizations and analytics based on research data. The extensive review of relevant literature, visualization and analysis of the institution's research data were carried out in this study. NLP techniques was employed to enable mapping of the institution's research output to the government and organizations research agenda, facilitating the identification of emerging topics through clustering techniques and modeling. Systems evaluation through Technology Acceptance Model (TAM) was gained 4.56 mean results from the respondent corresponding to “Strongly Agree” which indicates that system is able to assist in monitoring the institution's research productivity.