Journal of Hunan University Natural Sciences

Temporomandibular joint (TMJ) internal derangement is a common cause of pain and functional limitation that can negatively affect quality of life. Arthrocentesis combined with intra-articular hyaluronic acid (HA) injection is widely used as a minimally invasive treatment; however, the additional benefit of oral glucosamine–chondroitin–methylsulfonylmethane (GCM) supplementation remains uncertain. This randomized clinical trial included 30 patients with TMJ internal derangement who were randomly allocated to two equal groups. The intervention group received arthrocentesis followed by intra-articular HA injection combined with oral GCM supplementation for three months, whereas the control group received arthrocentesis with HA injection alone. Pain intensity was evaluated using the Visual Analog Scale (VAS), and maximum mouth opening (MMO) was measured at baseline and at 1 week, 1 month, 3 months, and 6 months after treatment. Both groups demonstrated statistically significant reductions in pain and improvements in MMO over time (p < 0.001). Although absolute pain and MMO values differed between groups at certain follow-up intervals, comparisons of changes from baseline revealed no statistically significant differences between the two treatments (p > 0.05). Within the limitations of this study, arthrocentesis combined with intra-articular HA injection was effective in improving clinical outcomes in patients with TMJ internal derangement, whereas adjunctive oral GCM supplementation did not provide additional measurable benefit during the 6-month follow-up period.

Keywords: temporomandibular joint; TMJ internal derangement; arthrocentesis; hyaluronic acid; glucosamine; chondroitin; methylsulfonylmethane; maximum mouth opening; clinical trial.

DOI https://doi.org/10.55463/issn.1674-2974.53.5.9

This study analyzes the role of digital public service communication in supporting the performance of Online Single Submission (OSS) licensing services in Pangkep Regency, Indonesia. It addresses key challenges of digital governance in developing countries, particularly those related to public understanding, user participation, and service effectiveness. A qualitative approach was employed, combining a case study design with a phenomenological perspective to explore organizational practices and user experiences in depth. Data were collected through in-depth interviews with OSS officers and licensing applicants, participatory observation of the service process, and analysis of OSS guidance documents and internal procedures. Thematic analysis was used to identify patterns and relationships between communication practices and service performance. The findings show that although the Pangkep Regency Investment and One-Stop Integrated Service Office (DPMPTSP) uses various digital channels, including official websites, social media, OSS applications, and call centers, the quality and clarity of information remain inconsistent. Technical and formalistic content creates barriers for applicants with low digital literacy. Internal coordination among administrative units also affects service consistency and speed, as unclear or inconsistent information increases procedural errors and delays in permit issuance. Effective communication strategies, such as visual guides, video tutorials, infographics, and regular coordination meetings, were found to be important factors in improving service efficiency and applicant participation. This study emphasizes that the success of digital governance depends not only on technological infrastructure but also on the quality of public communication and internal coordination. The findings contribute to the digital governance literature and provide practical recommendations for improving OSS implementation at the local government level.

Keywords: digital governance; public service communication; Online Single Submission; OSS licensing system; organizational coordination; user participation; local government; Indonesia.

DOI https://doi.org/10.55463/issn.1674-2974.53.5.8

This study evaluates the environmental sustainability of the municipality of Palmira, Colombia, using the emergy accounting approach. It provides an updated assessment of the territory’s social-ecological metabolism for 2023 and offers a quantitative basis for planning in intermediate agro-industrial cities. The research is motivated by the increasing pressure that urbanization, agro-industrial expansion, and metropolitan integration place on the natural resources of intermediate municipalities, whose sustainability depends on the balance between the local ecological base and socioeconomic demands. In this context, the objective of the study is to evaluate the environmental performance of Palmira’s territorial system using emergy indicators that integrate ecological and economic flows.
The methodology is based on Odum’s emergy accounting framework. The system boundary was defined as the municipal territory of Palmira, and the main flows of energy, materials, food, goods, services, and economic transactions were identified. A system flow diagram was developed to represent these interactions. Physical and economic data for 2023 were obtained from official statistical sources and converted into solar emergy units (seJ) using emergy transformities reported in the scientific literature.
The results show a total emergy use of 3.20 × 10²² seJ/year, of which 41% corresponds to renewable resources, 34% to local non-renewable resources, and 25% to imports. The calculated indicators were EYR = 4.0, ELR = 1.44, and ESI = 2.78. These values suggest that Palmira has a relatively strong contribution from local resources and a moderate environmental loading level.
The study concludes that Palmira operates as an agro-industrial social-ecological system with a significant ecological base supporting its territorial metabolism. However, dependence on external inputs and soil degradation remain important challenges for its long-term sustainability.

Keywords: emergy accounting; territorial sustainability; urban metabolism; agro-industrial systems; intermediate cities; environmental performance; social-ecological systems.

DOI https://doi.org/10.55463/issn.1674-2974.53.5.7

This study investigates how humble leadership, intrinsic motivation, and employee positive affect influence work engagement and creative performance among Millennial employees in Jakarta’s digital banking sector. As creativity becomes increasingly important in a rapidly changing digital environment, understanding the psychological and leadership-related factors that support innovation is essential. Using a quantitative survey approach, data were collected from Millennial employees working in three independent digital banks in Jakarta. The proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with a two-stage approach to assess reliability, validity, and structural relationships. The results show that humble leadership, intrinsic motivation, and employee positive affect each have a significant positive effect on work engagement. Furthermore, intrinsic motivation, employee positive affect, and work engagement significantly enhance creative performance. However, humble leadership does not directly affect creative performance, indicating that its influence operates indirectly through work engagement. Mediation analysis confirms that work engagement plays a key role in linking leadership humility, internal motivation, and employees’ emotional states to creative outcomes. These findings highlight the importance of fostering work engagement as a pathway to strengthening creativity among Millennial employees in the digital banking sector. The study contributes empirical evidence from the Indonesian digital banking context and provides practical insights for leaders seeking to build an innovative and psychologically empowered workforce.

Keywords: humble leadership; intrinsic motivation; employee positive affect; work engagement; creative performance; Millennial employees; digital banking; Indonesia.

DOI https://doi.org/10.55463/issn.1674-2974.53.5.6

Predicting event outcomes, particularly in sports, has attracted increasing research attention due to the growing availability of historical and performance-related data. Football match outcome prediction has traditionally relied on expert judgment, statistical analysis of past results, and qualitative assessments of team strengths and weaknesses; however, such approaches may be limited by subjectivity, incomplete feature representation, and restricted predictive consistency. This study develops and compares predictive models for football match outcomes using ensemble learning and deep learning algorithms applied to tabular sports data. A publicly available football match dataset obtained from Kaggle was used, and five algorithms were implemented: Deep Neural Network (DNN), TabTransformer, Neural Oblivious Decision Ensembles (NODE), XGBoost, and LightGBM. Model performance was evaluated using standard classification metrics, including precision, recall, F1-score, and accuracy. The results show that the deep learning models achieved moderate predictive performance, with accuracies ranging from 78% for NODE to 87% for the best-performing deep learning model. In contrast, XGBoost demonstrated strong performance across all metrics, achieving 0.96 precision, 0.96 recall, 0.95 F1-score, and 96% accuracy. LightGBM achieved the highest overall performance, with 0.98 precision, 0.98 recall, 0.98 F1-score, and 99% accuracy. These findings indicate that LightGBM is the most effective model for this tabular classification task, followed closely by XGBoost. Although the deep learning models, particularly TabTransformer, show potential, they did not outperform the boosting algorithms in this evaluation. The study recommends the use of ensemble-based algorithms for football match outcome prediction, especially when working with structured tabular datasets. Future research may extend this work by applying advanced hyperparameter optimization techniques, such as grid search, random search, or Bayesian optimization, to further improve the performance of LightGBM and XGBoost.

Keywords: football match prediction; match outcome classification; ensemble learning; deep learning; XGBoost; LightGBM; tabular data.

DOI https://doi.org/10.55463/issn.1674-2974.53.5.5