Leading & Enlightening Journal UMY
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From Coordination to Convergence: A Collaborative Governance Model for Multi-sectoral Stunting Reduction in Decentralized Contexts
No full textThis study seeks to develop a context-sensitive collaborative governance model to enhance multi-sectoral convergence in stunting reduction within decentralized public health systems. Despite the existence of national convergence policies in Indonesia, implementation at the district level remains fragmented due to sectoral silos, institutional ego, and limited cross-sector accountability. Drawing on a qualitative case study conducted in Sidenreng Rappang, an agriculturally productive yet nutritionally challenged district, this research employed in-depth interviews with 23 stakeholders across health, agriculture, education, social protection, and community sectors. Using thematic analysis supported by NVivo software and guided by Emerson and Nabatchi’s Collaborative Governance Regime (CGR) framework, the study identified three critical barriers: misalignment between national policy and local implementation, budget fragmentation, and the absence of shared performance indicators. Despite these constraints, informal collaboration among street-level actors emerged as a key enabler of convergence. The study proposes a governance model comprising institutional bridging forums, convergent resource mapping, and shared learning systems to integrate structural and relational mechanisms. This model offers a replicable strategy for institutionalizing multi-sector collaboration in stunting programs. The research highlights that successful convergence requires not only coordination structures but also trust, mutual incentives, and adaptive capacity. While limited to one district, the findings provide a transferable framework for other decentralized settings. Future studies should validate the model through comparative or multi-region analyses
Analysis of Translation Techniques in Japanese Diplomatic Content on Instagram
No full textThis study aims to describe the translation techniques used to translate Japan's diplomatic content in the Japan embassy's content on Instagram social media. The method used is a qualitative descriptive method. From the results of the study, it was found that 9 types of translation techniques were used in the translation of the Japan embassy's diplomatic content for Indonesia, namely transposition, amplification, modulation, established equivalents, reduction, borrowing, literal translation, generalization, borrowing, and description techniques. Modulation, literal translation, established equivalence, transposition, and reduction are the most widely used techniques. These five translation techniques are dominant because diplomatic Japanese is very dense and implicit. Modulation is widely used to adapt the Indonesian style and enable translators to convey diplomatic messages more persuasively. Established equivalence and literal translation are widely used to maintain the consistency of diplomatic terminology, facilitate readers' understanding of international concepts, and ensure the accuracy and standardization of diplomatic translations. Transposition and reduction are used because diplomatic Japanese texts have a complex and hierarchical structure, requiring translators to make shifts to produce more straightforward, informative, and linear Indonesian diplomatic sentences. Overall, the use of translation techniques in Japanese diplomatic content for Indonesia on Instagram social media shows that translators strive to balance semantic accuracy, pragmatic fluency, and the naturalness of Indonesian diplomatic style, which differs from the more formal, lengthy, and implicit Japanese style
The Clinical Course of Vulvar Lichen Sclerosus with Human Papillomavirus (HPV) Infection
No full textThe anogenital area is the main target of Lichen Sclerosus (LS) and is commonly diagnosed in postmenopausal women. Human Papillomavirus (HPV) infection increases the risk of LS, and treatment of LS with HPV infection requires special attention. The aim of this case report is to present the complications of HPV infection and the challenges associated with its treatment. We conducted a clinical interview to obtain details on symptoms, medical history, and patient complaints. The diagnosis was confirmed through a skin biopsy. A 56-year-old woman, with a history of Condyloma Accuminata, complained of white patches on the vaginal and anal area that felt stiff, itching (+) VAS 3/10. The physical examination showed ulcers with erythematous bases and hypopigmented macules, as well as sclerosis and atrophy. Histopathology revealed hyalinized eosinophilic collagen bundles. The patient was diagnosed with Vulvar et Perianal Lichen Sclerosus and was given Clobetasol propionate 0.05% ointment for four weeks. Improvement was noted during follow-up, with no clinical evidence of HPV reactivation observed. The occurrence of HPV-induced LS and its reactivation during treatment highlights the importance of close observation, personalized therapy, and immune system balance to manage both conditions successfully
Development of an Intelligent Ultrasound Therapy System with Fuzzy-PID Based Variable Frequency Control for Biological Response Optimization
No full textThis study develops an intelligent ultrasound therapy system based on Fuzzy-PID for optimizing biological responses. Ultrasound therapy is used to accelerate tissue regeneration, reduce inflammation, and alleviate pain. Conventional systems with fixed frequencies are limited in adapting therapy to the biological conditions of the patient. The Fuzzy-PID system adjusts the ultrasound frequency based on the physiological conditions of the patient, such as tissue temperature and inflammation. The ESP32 microcontroller controls the system, with an LCD Nextion interface to adjust therapy parameters. Tests on material thickness, therapy duration, power consumption, and temperature were conducted to evaluate system performance. The results show that ultrasound waves penetrate rubber silicone at 1.5 mm and 3 mm well, but at 4.5 mm, the transmission decreases. In the time test, the Fuzzy-PID system showed errors of 6.11% at 60 seconds, 1.44% at 300 seconds, and 1% at 900 seconds, indicating good stability in therapy duration. The power test showed efficient consumption, with 2.7 W on standby and 7.6 W when both probes were active. The temperature test showed the system maintained a safe temperature, increasing from 31.3°C after 1 minute to 34.8°C after 5 minutes. The Fuzzy-PID system demonstrated stable and responsive performance compared to conventional PID, with high energy efficiency and good temperature management, making it an optimal solution for medical treatment
Hybrid Administrative Accountability Model: Testing Indonesian-Brazilian Mining Discretion on Scientific General Principles
No full textThe recently established strategic partnership between Indonesia and Brazil, entailing a total investment of US$5 billion in the energy, mining, and sanitary/phytosanitary sectors, necessitates accountable governance in public administration to mitigate significant policy risks. Prior study has partially demonstrated the ineffectiveness of the State Administrative Law (HAN) tool. This dysfunction is apparent in the overlapping permits and inadequate compliance sanctions within the mining and coal industry, further intensified by the susceptibility of State-Owned Enterprise (SOE) hybrid governance in foreign collaborations. This discrepancy engenders a substantial accountability deficit. This essay seeks to evaluate and refine the Hybrid Administrative Accountability Model within the framework of state authority discretion. This work employs a normative legal methodology alongside a comparative administrative law approach to operationalize the Scientific Evidence Principle, which is robust within the SPS system, as a requisite General Principle of Good Governance (GPGG) applicable to all natural resource regimes. The findings indicate that the implementation of this scientific GPGG is essential for addressing the deficiency of legal clarity and regulating the discretion of administrative officials. The suggested paradigm ensures accountability, legal clarity, and transparency, so substantially mitigating the risk of failure in executing bilateral agreements
Antioxidant Activity Interaction of Rosella (Hibiscus sabdariffa L.) and Soursop Leaves (Annona muricata L.) Brew Combination Using the FRAP (Ferric Reducing Antioxidant Power) Method
Full text linkFree radical compounds can damage human body cells, triggering degenerative diseases. The prevention effort is to consume natural ingredients such as rosella and soursop leaves, which have the potential as antioxidants. This study is to determine the antioxidant activity of rosella and soursop leaf brew and their combination interaction. This research uses experimental quantitative methods. The samples used were F1 (rosella), F2 (soursop leaves) and their combination with concentration variations F3 (50:50), F4 (75:25), and F5 (25:75). Antioxidant activity values were determined by the FRAP method and analyzed using One-way ANOVA. The antioxidant activity value of rosella, soursop leaves and their combinations F3, F4 and F5 are 69.222 µg/mL; 133.701 µg/mL; 104.049 µg/mL; 57.382 µg/mL; and 115.438 µg/mL. Analysis: One-way ANOVA test of rosella and soursop leaf brew with several concentration variations has significant differences, sig. 0.000 <0.05 on the value of antioxidant activity. The antioxidant activity of the soursop leaf brew was higher than rosella brew. The combination interaction was categorized as antagonistic, with a % difference <0
Enhanced ELM Model Approach to Mitigate Multicollinearity of Lagged Independent Variables of ARMA Process
Full text linkThis paper presents a regularized Extreme Learning Machine (ELM) framework for identifying nonlinear dynamic systems affected by multicollinearity, with application to a Hammerstein-structured model of a Continuous Stirred Tank Reactor (CSTR). The model architecture employs a single-hidden-layer feedforward network (SLFN) for the static nonlinear block, and an autoregressive linear dynamic block whose order is determined using a Lipschitz quotient criterion. Traditional ELM models are known to suffer from instability when lagged input variables are highly correlated, a common occurrence in block-oriented system identification. To address this, the study investigates enhanced variants of ELM incorporating regularization, namely Ridge-ELM and Liu-ELM, which introduce biasing parameters to improve numerical stability and generalization. The proposed regularized ELM variants are evaluated against traditional ELM using performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Results show that Ridge-ELM and Liu-ELM significantly reduce parameter variance and improve predictive performance on datasets. Additionally, confidence intervals and condition number analysis demonstrate improved robustness in the presence of multicollinearity. Cross-validation is used to tune hyperparameters, and the Diebold-Mariano test confirms that the improvements are statistically significant. This approach offers a computationally efficient, scalable solution for robust nonlinear system identification in multivariate chemical processes and beyond
Inverse Kinematics via Acceleration-Level Quadratic Programming and Sliding Mode Control of an Aerial Manipulator for Crop Sampling
No full textRecent advances in Unmanned Aerial Vehicles (UAVs) for Precision Agriculture have evolved from performing passive tasks, such as monitoring, mapping, and inspection, to active tasks including harvesting, crop sampling, and insect trap deployment. These new applications were carried out through the integration of robotic arms attached to the base of the platform, thereby forming unmanned aerial manipulators (UAMs). However, UAMs encounter several challenges related to the inverse kinematics problem ensuring a precise trajectory and motion under platform constraints and dynamic coupling. In this paper, the objective is to achieve a precise grasping of a plant sample for ex-situ analysis using a quadcopter equipped with a two-degree-of-freedom robotic arm. The main contribution is the development of an accelerationlevel quadratic programming (QP) approach to solve inverse kinematics and to minimize the end-effector tracking error by incorporating joint constraints, self-collision avoidance, and UAV orientation constraints, thereby exploiting the system’s redundancy. The coupled dynamics of the vehicle are formulated using the Euler-Lagrange formalism and a Sliding Mode Controller based on the super twisting algorithm is employed to ensure trajectory robustness and accuracy, while reducing chattering effects. The simulation results demonstrate high tracking accuracy the precision of (RMSEqp = 10−6m) in end-effector trajectory accuracy, with convergence achieved in 3 seconds and a 88% reduction in chattering compared to conventional SMC methods. Comparative analysis against hierarchical QP methods confirms superior trajectory precision and disturbance rejection. These contributions will enable crop collection tasks to be carried out accurately and with high stability and robustness
A Hybrid CNN–Vision Transformer Framework for Optimized Leaf Disease Detection Using Feature Fusion and Transfer Learning
No full textPlant disease of leaves drastically affects the yields of crops, which is a threat to food security and financial sustainability of agricultural enterprises. Early identification of diseases with accuracy is vital for precision farming, i.e., smart farming. Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) classifiers, being conventional, have already been employed for the sake of disease classification. These Machine Learning (ML) classifiers, nevertheless, are liable to be biased towards handcrafted features, sensitivity to lighting, background, and severity of disease, leading to lower accuracy and generalizability. To address these limitations, this study suggests a new hybrid deep learning architecture called Convolutional Neural Network – Vision Transformer (CNN-ViT), which unifies the spatial feature extraction of CNN with the Vision Transformers' global attention mechanism. As a result, the combined model can automatically capture hierarchical features and long-distance relationships from enhanced and preprocessed images of leaves, facilitating effective multiclass classification. Evaluation of the model is conducted based on a publicly available plant disease dataset, resulting in better performance than conventional ML techniques and individual deep learning models. The hybrid CNN–ViT framework combines the CNN’s ability to extract spatial features with the Vision Transformer’s long-range attention. We evaluated the framework on the Plant Village (banana subset) with a batch size of 32, learning rate 0.001, and 50 epochs. The hybrid framework was 8.4 % more accurate than comparable baseline CNN and 7.1 % and 6.5 % more precice and recall compared with the comparable baseline ViT, respectively. The model, while showing increased robustness, requires increased computational complexity and inference latency and is therefore key to scaleable real-time deployment. This combined methodology presents a scalable and efficient solution for real-time monitoring of leaf diseases and contributes towards the development of AI-powered smart agriculture
See things for what they are: Examining the financial condition on financial reporting quality in developed countries
No full textResearch aims: This study aims to determine the relationship between the Company's financial condition and the quality of financial reports.Design/Methodology/Approach: The classification of financial conditions in this study utilises the Altman Z-score model, which categorises companies into three categories: green zone, grey zone, and red zone. Researchers tested the hypothesis using the Generalised Least Squares (GLS) method to accommodate differences in data characteristics, heteroscedasticity, and multicollinearity diagnostic problems on 58,890 company-year observations from 47 developed countries between 2014 and 2023.Research findings: The results of this study support the hypothesis that a company's financial condition plays a role in determining the quality of its financial reports. Companies in the green zone and grey zone categories strive to maintain the quality of their financial reports and encourage an improvement in the quality of these reports. Meanwhile, companies in the red zone category tend to embellish the appearance of their financial report performance to conceal financial difficulties, which ultimately have the potential to compromise the quality of their financial reports.Theoretical contribution/Originality: This study offers insight into the implications of financial conditions on the quality of corporate financial reports in the international context of developed countries, which exhibit more advanced economic, social, and legal conditions.Research limitation/Implication: This research has practical implications for investors, creditors, external auditors, and regulators, as it suggests using bankruptcy zone assessment as an early warning system to evaluate the quality of financial reports