IntroductionInfluenza A (H5N1) remains a major public health concern due to its high pathogenicity and ongoing viral evolution, underscoring the need for novel antiviral candidates.MethodsIn this study, we performed an integrated in silico evaluation of organosulfur compounds derived from Allium ascalonicum L. (shallot) cultivated in the Tolaki-Mekongga region, Sulawesi, Indonesia, targeting key viral proteins including polymerase (PB2), nucleoprotein (NP), and neuraminidase (NA). ResultsDensity functional theory (DFT) analyses were conducted to characterize the electronic properties of the compounds, while PASS prediction indicated moderate potential antiviral activity for Propanethiol and Dipropyl disulfide. Pharmacokinetic profiling suggested acceptable ADMET properties for several candidates. Molecular docking revealed favorable binding conformations across all targets, with γ-glutamyl-S-propenylcysteine exhibiting the most favorable binding energies among the evaluated organosulfur compounds (PB2: -4.9 kcal/mol; NP: -5.8 kcal/mol; NA: -5.2 kcal/mol). These values were comparable to those of oseltamivir and favipiravir for NP and NA, although weaker binding was observed against PB2. Subsequent simulations of molecular dynamics demonstrated stable protein–ligand complexes over 100 ns, further supporting the predicted binding interactions. Consistently, MM-GBSA calculations indicated favorable binding free energies, particularly for γ-glutamyl-S-propenylcysteine (PB2: -30.52 ± 0.29 kcal/mol; NP: -22.76 ± 0.12 kcal/mol; NA: -26.13 ± 0.35 kcal/mol). ConclusionOverall, these findings suggest that shallot-derived organosulfur compounds, especially γ-glutamyl-S-propenylcysteine, exhibit potential for interaction with H5N1 viral targets and may warrant further investigation as antiviral candidates. Experimental validation through in vitro and in vivo studies is required to confirm their biological activity and therapeutic potential.
Sebagai pelaku bisnis diharapkan untuk memperhatikan dan mengukur kesejahteraan karyawan tidak hanya dari aspek ekonomi tetapi juga dari aspek sosial. Tujuan dari penelitian ini adalah mengkaji pengaruh faktor sosial ekonomi terhadap tingkat kesejahteraan karyawan. Suatu usaha seringkali dianggap memberikan dampak posifit terhadap karaywan, namun faktanya keberadaan usaha tidak selalu memberikan dampak positif karena dipengaruhi oleh beberapa faktor. Faktor ekonomi diukur berdasarkan jumlah pendapatan, sementara faktor sosial diukur berdasarkan tingkat kepuasan kerja dan keseimbangan kerja. Data penelitian diperoleh melalui skala likert yang akan dianalisis menggunakan regresi linier berganda, dengan responden sebanyak 30 orang mencakup seluruh karyawan di CV. Adnik. Dari hasil analisis, ditemukan bahwa pendapatan memengaruhi kesejahteraan karyawan pada tingkat signifikan (α=0,016 < 0,05), sedangkan variabel kepuasan kerja juga berkontribusi terhadap kesejahteraan karyawan pada tingkat signifikan (α=0,041 < 0,05) Namun, keseimbangan kerja tidak memiliki pengaruh terhadap kesejahteraan karyawan dengan tingkat signifikan (α=0,150 > 0,05). Faktor ekonomi dan sosial secara bersama-sama memiliki pengaruh terhadap tingkat kesejahteraan karaywan dengan nilai signifikan (α=0,000 > 0,05).
The dynamic and unpredictable nature of Distributed Denial of Service (DDoS) attacks continues to pose a critical threat to the availability of computer network services. Traditional security measures, including static firewalls and signature-based intrusion detection systems, have proven inadequate for identifying novel attack variants whose traffic characteristics closely mimic legitimate network activity. To address this challenge, the present study introduces an automated framework for DDoS detection and mitigation, which is built upon machine learning algorithms. Four distinct algorithms were benchmarked in this study: Decision Tree, Random Forest, Support Vector Machine (SVM), and XGBoost. Among these, Random Forest delivered the most superior performance. When evaluated on a 20% hold-out test subset, this optimal model achieved an accuracy of 98.80%, precision of 98.51%, recall of 99.10%, and an F1-score of 98.80%. The exceptionally high recall rate confirms the model's effectiveness in capturing nearly all malicious traffic while maintaining an extremely low false-negative rate, whereas the high precision indicates a minimal occurrence of false alarms. The primary novelty of this work rests on the seamless integration of accurate machine-learning-based detection with empirically quantifiable automated mitigation responses—a holistic approach that remains rarely validated experimentally within a single cohesive framework. Consequently, this integrated strategy offers a highly adaptive and dependable solution for reinforcing network security and ensuring sustained service availability against the ever-evolving landscape of DDoS attacks.
Environmental sustainability education in elementary schools is often delivered through global and abstract perspectives that are weakly connected to students’ local ecological and cultural contexts. This study aims to develop the Eco-Lokal Inquiry (ECO-LI) model, a culturally grounded inquiry-based learning framework that integrates Global Environmental Sustainability (GES) principles with Tengger Traditional Ecological Knowledge (TEK) to strengthen contextual and meaningful environmental learning. This study employed an integrative literature review design by systematically analyzing peer-reviewed journal articles, scholarly books, and international policy documents published between 2014 and 2024. Data were collected from Scopus, Web of Science, ERIC, and Google Scholar using explicit inclusion and exclusion criteria. An analytical instrument grid was used to extract key themes, which were then examined through thematic synthesis, comparative epistemological analysis, and conceptual model construction. The results of the analysis produced the ECO-LI model, which consists of five inquiry phases: contextual problem orientation, local ecological exploration, dialogic inquiry between indigenous knowledge and science, glocal reflection, and community-based environmental action. The findings reveal a strong conceptual alignment between GES dimensions—ecological integrity, social sustainability, and sustainability literacy—and Tengger ecological practices such as sacred forest protection, ritual-based conservation, and communal stewardship. This study demonstrates that inquiry-based learning can function as a dialogic space that bridges scientific sustainability frameworks and indigenous ecological worldviews without subordinating either knowledge system. The novelty of this research lies in its glocal inquiry framework, which positions TEK and science as complementary epistemologies within sustainability education.
This study investigates the geotechnical modeling and stability improvement of a 12-meter-high, 85-degree slope reinforced with a cantilever retaining wall integrated with a pile foundation system, analyzed using the GEO5 software suite. The research was motivated by a landslide incident in Bali, Indonesia, caused by prolonged heavy rainfall that generated high water flow energy at a drainage outlet located on a steep slope. The primary objective is to evaluate and enhance the global stability of the retaining structure under static and seismic conditions. The analysis was conducted in accordance with SNI 8460:2017 and Eurocode 2 (EN 1992-1-1) standards. The modeling involved defining soil stratigraphy, material properties, external loads, and staged construction. The design verification included assessments of overturning, sliding, bearing capacity, and global stability. Results showed that the cantilever wall alone met internal stability requirements but failed to meet the global stability criterion under static loading (FoS = 1.42 < 1.50) due to the steep slope condition. By integrating a pile foundation placed at counterfort positions, the global factor of safety improved to 1.60 > 1.50, while the pseudostatic seismic analysis yielded FoS = 1.25 > 1.10, satisfying design requirements. The findings highlight that the combination of a cantilever wall and pile foundation provides an effective and reliable geotechnical solution for stabilizing steep, high slopes under both static and dynamic loading conditions.
