Search Results (96)

The increasing popularity of drones for Internet of Things (IoT) applications has led to significant research interest in autonomous navigation within unknown and dynamic environments. Researchers are utilizing supervised learning techniques that rely on image datasets to train drones for autonomous navigation, which are typically used for rescue, surveillance, and medical aid delivery. Current datasets lack data that allow drones to navigate in a 3D environment; most of these data are dedicated to self-driving cars or navigation inside buildings. Therefore, this study presents an image dataset for training drones for 3D navigation. We developed an algorithm to capture these data from multiple worlds on the Gazebo simulator using a quadcopter. This dataset includes images of obstacles at various flight altitudes and images of the horizon to assist a drone in flying at an appropriate altitude, which allows it to avoid obstacles and prevents it from flying unnecessarily high. We used deep learning (DL) to develop a model to classify and predict the image types. Eleven experiments performed with the Gazebo simulator using a drone and a convolution neural network (CNN) proved the database’s effectiveness in avoiding different types of obstacles while maintaining an appropriate altitude and the drone’s ability to navigate in a 3D environment. Full article

This article explores the maritime connections relating to Buddhism and diplomacy between polities in Sumatra, the Malay Peninsula, the Indian subcontinent, and China from the beginning of the 11th century up to the 12th century CE. It focuses on new epigraphic evidence from Muara Jambi in the form of two inscribed strips of copper mentioning the Cūḍāmaṇivarmavihāra, a monastery funded by the king of Śrīvijaya in Nagapattinam (South India), and the Bālādityavihāra, probably located in Nālandā (Northeastern India). These new findings are compared to archaeological and textual materials from elsewhere in the Buddhist world that cast light on the web of transregional connections between Nusantara, China, and India in the early centuries of the second millennium. Full article

Protein-energy wasting (PEW) increases the morbidity and mortality in maintenance hemodialysis (MHD) patients. The existing screening tools (e.g., Malnutrition-Inflammation Score (MIS)) are time-consuming and require expertise. Consequently, assessing a more practical and reliable tool such as handgrip strength (HGS) is important, as it strongly correlates with the PEW status in patients undergoing MHD, in whom increased protein and fat breakdown leads to muscle strength and function loss. A systematic search of five databases identified studies assessing HGS’s predictive value for PEW in patients undergoing MHD, using MIS as the reference. The quality of the studies was evaluated with the updated QUADAS tool. A diagnostic meta-analysis was carried out to estimate the pooled sensitivity, specificity, sROC, and sAUC using a two-level mixed-effects model. From 350 records, five studies were obtained which were included for analysis. The pooled sensitivity and specificity of the HGS among male patients were 68% (95%CI: 63–73%) and 66% (95%CI: 53–77%), respectively. Among female patients, the pooled sensitivity and specificity were 73% (95%CI: 62–82%) and 65% (95%CI: 48–79%), respectively. The positive likelihood ratio and negative likelihood ratio for male patients were 2.0 (95%CI: 1.4 to 2.9) and 0.48 (95%CI: 0.38 to 0.60), respectively. Meanwhile, among female patients, the positive likelihood ratio and negative likelihood ratio were 2.1 (95%CI: 1.4–3.1) and 0.41 (95%CI: 0.29–0.59), respectively. The sAUC for males and females was estimated to be 0.69 (95%CI: 0.65 to 0.73) and 0.75 (95%CI: 0.71–0.79). In summary, the sensitivity and specificity of the HGS were modest for all sexes, with females being more sensitive. HGS may be useful for triaging during daily screening and guiding further examination, but it requires supporting measurements to be employed as a diagnostic tool. PROSPERO: CRD42024595677 as of 1 October 2024. Full article

The flexibility and rapid mobility of drones make them ideal for Internet of Things (IoT) applications, such as traffic control and data collection. Therefore, the autonomous navigation of 5G drones in unknown and dynamic environments has become a major research topic. Current methods rely on sensors to perceive the environment to plan the path from the start point to the target and to avoid obstacles; however, their limited field of view prevents them from moving in all directions and detecting and avoiding obstacles. This article proposes the deep learning (DL)-based autonomous navigation of 5G drones. This proposal uses sensors capable of perceiving the entire environment surrounding the drone and fuses sensor data to detect and avoid obstacles, plan a path, and move in all directions. We trained a convolution neural network (CNN) using a novel dataset we created for drone ascent and passing over obstacles, which achieved 99% accuracy. We also trained artificial neural networks (ANNs) to control drones and achieved a 100% accuracy. Experiments in the Gazebo environment demonstrated the efficiency of sensor fusion, and our proposal was the only one that perceived the entire environment, particularly above the drone. Furthermore, it excelled at detecting U-shaped obstacles and enabling drones to emerge from them. Full article

Background/Objectives: Natural polymer nanoparticles have potential as delivery systems, can enhance pharmacological activity, and can improve stability in the cosmetic field. In this research, we implemented a development approach for chitosan–alginate and chitosan–pectin nanoparticles. This study aimed to investigate effect of formulation, process variables, in vitro antiaging evaluation, and metabolite profiling of Toona sinensis leaf extracts. Methods: Polymeric nanoparticles have been prepared using the ionic gelation method (Temperature = 40 °C, time = 1 h and speed = 1000 rpm), in vitro antiaging evaluation using the Neutrophil Elastase Inhibitor Screening Kit method, and analysis of metabolite profiling with UHPLC–HRMS. Results: Research results found that the SLE and EAFSL nanoparticles that have good and stable characteristics before and after storage in a climatic chamber after 3 months are FIIA-NPSLE (0.75% chitosan and 1.25% Alginate), FIP-NPSLE (1% chitosan and 0.5% Pectin), FIIA-NPEAFSL (0.75% chitosan and 1.25% Alginate), and FIIIP-NPEAFSL (0.125% chitosan and 0.375% Alginate). Chitosan–alginate polymers, such as FIIA-NPEAFSL, have higher inhibition of the elastase enzyme than FIIA-NPSLE, with a % inhibition (IC₅₀) of FIIA-NPEAFSL being 87.30%, while the IC₅₀ of FIIA-NPSLE is 39.40%. Meanwhile, using chitosan–pectin polymers, such as FIP-NPSLE, results in lower inhibition of the elastase enzyme compared to the chitosan–alginate polymer, with an IC₅₀ of 27.28% while IC₅₀ FIIIP-NPEAFSL is 39.53%. SLE and EAFSL nanoparticles with chitosan–alginate and chitosan–pectin polymers resulted in a significant PDI during storage from 1.3 to 1.9, and zeta potential values were very low, ranging from −11 mV to −27 mV. Metabolite profiling using UHPLC–HRMS on T. sinensis leaf extracts revealed that the main compounds contained were glycitein, quercetin, quercetin-3β-D-glucoside, kaempferol, and ellagic acid, which has potential as an antiaging agent. Conclusions: It can be concluded that using chitosan, alginate, and pectin in the process of encapsulating extracts into nanoparticles with the same process variables affect evaluation of antiaging activity in elastase enzymes. Further research will develop these nanoparticles into nanohydrogels with antiaging activity. Full article

This study examined spatial–temporal variations in air pollutant levels across four provinces on Sumatra Island, Indonesia, utilizing data from the Sentinel-5P satellite equipped with TROPOMI and MODIS aboard NASA’s Terra and Aqua satellites from 2019 to 2021. Sentinel-5P data, with a spatial resolution of 3.5 × 5.5 km² and near-daily temporal coverage, were used to analyze the nitrogen dioxide (NO²), carbon monoxide (CO), and Aerosol Optical Depth (AOD) in North Sumatra, West Sumatra, Jambi, and Riau—regions selected for their distinct industrial, agricultural, and urban characteristics. The purpose of this study was to investigate seasonal trends, regional differences, and the impact of the COVID-19 pandemic on air pollution, aiming to provide insights for improved air quality management and policy development. The satellite data were validated using zonal statistics to ensure consistency and reliability. The findings revealed significant seasonal fluctuations in pollution, with elevated levels during the dry season, primarily due to land clearing and forest fires. Urban and industrial areas such as Medan, Pekanbaru, Jambi, and Padang consistently exhibited high levels of NO₂, primarily due to vehicular and industrial emissions. The regions affected by biomass burning and agriculture, particularly Jambi and Riau, displayed notably higher CO and AOD levels during the dry season. The COVID-19 pandemic provided a unique opportunity to observe potential improvements in air quality, with significant reductions in NO₂, CO, and AOD levels during the 2020 lockdowns. The NO₂ levels in urban centers decreased by over 20%, while the reductions in CO and AOD reached up to 29% and 64%, respectively, reflecting diminished human activities and biomass burning. This study underscores the need for enhanced air quality monitoring and targeted management strategies in Sumatra, Indonesia. Future research should aim to improve the resolution and validation of data with ground-based measurements and broaden the number of pollutants studied to better understand air quality dynamics and support effective policy development. Full article

This research, situated in the geographical and historical context of the Tangut and East Java, uncovers a significant aspect of the evolution of Buddhist art styles. A thangka of the goddess Vajravārāhī found in Khara Khoto, dated to the late 12th century, shows the bodhisattva decorated with a pearl-chain girdle and upper-arm bands. This form of pearl-chain jewellery, which appears on Vajravārāhī and other Sino-Tibetan-style bodhisattvas, also appears on three stone statues of the goddess Prajñāpāramitā in East Java, all of which depict a near identical use of this pearl-chain ornamentation, as well as on a statue of Prajñāpāramitā at the Muara Jambi Buddhist site in Sumatra. Maritime trade between the regions of China and Java was extensive. The commonality of such motifs in China and Java may highlight a convergence of cultural forces and perhaps shared styles originating from the maritime realm and traded via maritime routes; however, a direct or indirect influence of Sino-Tibetan styles on thangka paintings featuring this depiction of the jewellery perhaps occurred following dynamics of north–south exchange, highlighting the interrelated links along maritime and overland routes through the Pāla Buddhist kingdom in eastern India. Thus, I propose that the connection between the Vajravārāhī and other Tibetan thangka paintings was inspired by Northeast Indian influence from the Hexi corridor, eventually reaching East Java. Full article

Liberica coffee (Coffea liberica) thrives on peat soil, unlike Arabica and Robusta, making it an essential commodity in Jambi, Indonesia, where it is known as Liberika Tungkal Komposit (Libtukom) and serves as a primary livelihood. This low-caffeine coffee has a higher economic value than high-caffeine varieties. Adding spices such as temulawak (Curcuma zanthorrhiza) to Liberica coffee can create a unique, functional beverage with new aromas and health benefits, thanks to the curcuminoid antioxidants in Curcuma zanthorrhiza (C. zanthorrhiza). This research aims to develop a spice-infused Liberica coffee with enhanced sensory qualities and health benefits. Methods include spice coffee formulation; sensory evaluation; phytochemical screening; and analysis of phenolics, flavonoids, antioxidants, caffeine, and chlorogenic acid levels. Statistical analysis (ANOVA and Duncan’s post hoc test) reveals that C. zanthorrhiza-enhanced Liberica coffee is preferred by panelists, with spice coffee outperforming plain Liberica in sensory tests. This study showed that Liberica coffee mixed with C. zanthorrhiza affected the sensory performance of Liberica coffee, where the coffee most favored by panelists was spice coffee. Based on the cupping test results, spice coffee had excellent results, while Liberica coffee had very good results. The phytochemical analysis revealed that coffee–C. zanthorrhiza exhibited higher concentrations of total phenolics, flavonoids, and antioxidant activity than the original Liberica coffee. However, the caffeine and chlorogenic acid levels in all spiced coffee were lower compared to the original Liberica coffee. Full article

Background/Objective: The utilization of maternal health services at the primary healthcare level is still considered an effective approach despite the critical role in improving maternal health outcomes. The study aimed to assess the influence of sociodemographic characteristics and interpersonal support on the use of maternal health services in three regions of the Province of Jambi, Indonesia. Methods: Using a mixed-methods sequential explanatory design, a quantitative survey of 432 women and qualitative focus group discussions with nine families were conducted. Quantitative data were analyzed using bivariate analysis, while the qualitative data were examined through conventional content analysis. Results: The research results show no significant association between sociodemographic factors (education, employment, residence) and the use of maternal health services for antenatal care and delivery. Direct support, such as accompaniment by spouses, and indirect support, including emotional encouragement and shared knowledge, were pivotal in influencing women’s decisions to seek care. Conclusions: Targeted interventions focusing on improving social support, addressing service accessibility barriers, and raising awareness about the benefits of community health centers are essential to enhancing maternal health outcomes. Policymakers and healthcare providers should integrate family-centered strategies to ensure women receive adequate maternal healthcare. Full article

This study evaluates bio-briquettes and bio-pellets made from ramie (Boehmeria nivea), sacha inchi (Plukenetia volubilis), and palm kernel shell (Elaeis guineensis) as renewable fuel sources. Proximate analysis was conducted to measure moisture, ash, volatile matter, fixed carbon, and calorific values, while combustion tests assessed boiling efficiency and burn time. Results reveal that bio-briquettes generally outperform bio-pellets in calorific value, with sample B-S8R2 achieving the highest at 6455 kcal/kg and the fastest boiling time of 14 min at 88 °C. This enhanced performance is attributed to its high fixed carbon (71.81%) and low volatile matter, optimizing combustion and energy yield. In contrast, bio-pellets like sample P-PO7R3, with a calorific value of 4212 kcal/kg, offer moderate heat and durability, making them suitable for household use. The high density and low moisture content across all samples support efficient combustion, while the bio-briquettes’ low ash production indicates a more environmentally friendly fuel. The findings suggest that bio-briquettes are optimal for high-energy applications due to their superior combustion efficiency and environmental benefits, whereas bio-pellets provide a viable option for moderate-energy needs. This research supports the development of sustainable biofuel from biomass waste, providing a promising alternative to traditional fossil fuels. Full article

This study aimed to investigate factors that predict Indonesian primary school teachers’ behavior of innovative teaching with technology (BITT). A survey instrument was adapted and validated through content validity, a pilot test, and a measurement model in partial least square structural equation modeling (PLS-SEM). We obtained data from 868 primary school teachers, analyzed through a structural model in PLS-SEM and multivariate analysis of variance (MANOVA) in SPSS. The structural model was computed with several statistical reports, including the path coefficient (β), effect sizes (f²), coefficient of determination (R²), and predictive relevance (Q²). MANOVA results informed t and p values. Findings indicated that four out of six hypotheses significantly predicted primary Indonesian teachers’ BITT. The most substantial relationship emerged between group learning and BITT. Meanwhile, the weakest correlation was between innovative culture and BITT. Two insignificant predictors of BITT were job autonomy and innovation compatibility. Most variables showed insignificant differences based on gender. However, some variables, such as benefits of innovation, innovation compatibility, innovative culture, group cohesion, and BITT, varied significantly based on location. The study may help teachers and policymakers understand BITT elements that encourage primary school teachers to use technology creatively. Full article

More than 40% of the total oil palm area in Indonesia is owned and managed by smallholders. For large plantations, guidelines are available on so-called ‘best management practices’, which should give superior yields at acceptable costs when followed carefully. We tested a subset of such practices in a sample of smallholder plantations, aiming to increase yields and profitability. We implemented improved practices (weeding, pruning, harvesting, and fertiliser application) in 14 smallholder plantations of 13–15 years after planting in Jambi province (Sumatra) and in West-Kalimantan province (Kalimantan) for a duration of 3 to 3.5 years. During this period, we recorded yields, measured palm leaf parameters and analysed leaf nutrient contents. Yield recording then continued for an additional two years. In the treatment plots, the yields did not increase significantly, but the size of the bunches and the size of the palm leaves increased significantly and substantially. The tissue nutrient concentrations also increased significantly, although after three years, the potassium concentrations in the rachis were still below the critical value. Because of the absence of yield increase and the additional costs for fertiliser inputs, the net profit of implementing better management practices was negative, and ‘business as usual’ was justified financially. Some practices, such as harvesting at 10-day intervals and the weeding of circles and paths, were received positively by those farmers who could implement them autonomously, and were applied beyond the experiment. It is challenging to find and implement intensification options that are both sustainable and profitable, that have a substantial impact on yield, and that fit in the smallholders’ realities. On-farm experimentation and data collection are essential for achieving sustainable intensification in smallholder oil palm plantations. Full article

A student-centered learning (SCL) method has been applied to improve Teaching English as a Foreign Language (TEFL) students’ 21st-century skills at the English department of a university. Therefore, this study aimed to investigate the effect of SCL application on TEFL students’ 21st-century skills performance. To achieve this objective, a total of 220 questionnaires were distributed to TEFL students, and ten course designs were obtained from the department. Content analysis on course designs showed that hard skills were more prioritized than soft skills, while character slightly ebbed in learning design. Furthermore, SCL application through Group, Independent, and Online learning methods significantly increased TEFL students’ 21st-century skills. Hard and soft skills were most and slightly associated with cumulative grade point average (CGPA), respectively. These results showed that SCL should be properly applied to deliver course content and improve 21st-century skills performance. Full article

Advanced metering infrastructures (AMIs) aim to enhance the efficiency, reliability, and stability of electrical systems while offering advanced functionality. However, an AMI collects copious volumes of data and information, making the entire system sensitive and vulnerable to malicious attacks that may cause substantial damage, such as a deficit in national security, a disturbance of public order, or significant economic harm. As a result, it is critical to guarantee a steady and dependable supply of information and electricity. Furthermore, storing massive quantities of data in one central entity leads to compromised data privacy. As such, it is imperative to engineer decentralized, federated learning (FL) solutions. In this context, the performance of participating clients has a significant impact on global performance. Moreover, FL models have the potential for a Single Point of Failure (SPoF). These limitations contribute to system failure and performance degradation. This work aims to develop a performance-based hierarchical federated learning (HFL) anomaly detection system for an AMI through (1) developing a deep learning model that detects attacks against this critical infrastructure; (2) developing a novel aggregation strategy, FedAvg-P, to enhance global performance; and (3) proposing a peer-to-peer architecture guarding against a SPoF. The proposed system was employed in experiments on the CIC-IDS2017 dataset. The experimental results demonstrate that the proposed system can be used to develop a reliable anomaly detection system for AMI networks. Full article

Trapezoidal positive/negative fuzzy numbers have no single definition; instead, various authors define them in relation to different concepts. This means that arithmetic operations for trapezoidal fuzzy numbers also differ. For the operations of addition, subtraction, and scalar multiplication, there are not many differences; for multiplication, however, there are many differences. In general, multiplication is divided into various cases. For the inverse operation, there is not much to define; in general, for any trapezoidal fuzzy number $\tilde{u},$ $\tilde{u}\otimes \frac{1}{\tilde{u}}=\tilde{i}=(1,1,0,0)$ does not necessarily apply. As a result of the different arithmetic operations for multiplication and division employed by various authors, several researchers have tackled the same problem and reached different solutions, meaning that the application will also produce different results. To date, many authors have proposed various alternatives for the algebra of the trapezoidal fuzzy number. In this paper, using the parametric form approach to trapezoidal fuzzy numbers, an alternative to multiplication with only one formula is constructed for various cases. Furthermore, based on the definition of multiplication for any trapezoidal fuzzy number, $\tilde{u}$ is constructed $\frac{1}{\tilde{u}}$ so that $\tilde{u}\otimes \frac{1}{\tilde{u}}=\tilde{i}=(1,1,0,0)$. Based on these conditions, we show that various properties that apply to real numbers also apply to any trapezoidal fuzzy number. Furthermore, we modify the elementary row operational steps for the trapezoidal fuzzy number matrix, which can be used to determine the inverse of a trapezoidal fuzzy number matrix with the order $m\times m$. We also give the steps and examples necessary to determine the general inverse for a trapezoidal fuzzy number matrix of the order $m\times n$ with $m \ne n$. This ability to easily determine the inverse and general inverse of a trapezoidal fuzzy number matrix has a number of applications, such as solving fully trapezoidal fuzzy number linear systems and fuzzy transportation problems, especially in applications in fields outside of mathematics; for example, the application of triangular fuzzy numbers in medical problems is a topic currently receiving a significant amount of attention. Full article