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Processing plastic waste into fuel oil with pyrolysis technology. Research on plastic oil as an alternative fuel has been widely conducted. One of them is research on processing plastic bag waste into fuel oil with pyrolysis technology. In this study, a set of pyrolysis equipment was made by Lecturers and Students of UMNU Kebumen. The plastic waste used in this study was 1 kg of plastic bag. Meanwhile, the pyrolysis process used a temperature of 250oC-300oC. The fuel oil produced in the pyrolysis process of this study was approximately 400 ml. The average density of fuel oil from plastic bag waste was 0.733 gr/ml. The results of this density calculation are in the possibility of the density of gasoline, namely 0.710 gr/ml to 0.770 gr/ml. This pyrolysis process shows great potential for converting plastic waste into an environmentally friendly alternative energy source. Furthermore, the efficiency of this pyrolysis technology can be improved by adjusting the temperature and processing time, as well as by selecting a wider variety of plastic types. This technology has the potential to be applied more widely in plastic waste management within the community as a solution to reduce environmental pollution while generating renewable energy.

The discharge of surfactant-laden wastewater from the rapidly expanding laundry industry poses significant environmental risks, especially in densely populated urban areas. While constructed wetlands (CWs) and Eco-Enzyme technology have shown promise for surfactant remediation, their standalone application requires long hydraulic retention times (HRTs), limiting practical implementation. This study evaluated the efficacy of a novel integrated system combining a subsurface constructed wetland (SSFCW) with fruit peel-derived Eco-Enzyme to treat synthetic laundry wastewater. Over a 6-day treatment period, the combined system achieved a remarkable surfactant removal efficiency of 99.63%, reducing the concentration from 225 mg/L to 0.835 mg/L—well below the regulatory threshold of 3 mg/L. The synergistic degradation mechanism involves enzymatic hydrolysis via Eco-Enzyme lipase and protease activity, complemented by microbial mineralization in the wetland rhizosphere. This system maintains optimal environmental conditions, with a stable pH of 6.85-7.32 and a temperature of 30.9-35.2°C, supporting robust biological activity. These findings demonstrate that the integrated Eco-Enzyme/SSFCW system overcomes the limitations of conventional HRT approaches, offering a highly efficient, sustainable, and practical decentralized wastewater treatment solution for the laundry industry.  

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.

Objective– This article aims to comprehensively examine the main types of food crop pests and their attack patterns through a systematic literature review approach. The research focuses on the dynamics of pest attacks, changes in ecological patterns due to climate change, and advances in modern identification technology that enable more accurate early detection. This study also highlights the significance of new paradigms of pest identification based on artificial intelligence (AI), genomics, and landscape mapping in supporting food security at the regional and national levels. Design/methodology/approach– This study used the Systematic Literature Review (SLR) method for scientific publications from 2015–2025 from reputable sources such as Scopus, Web of Science, PubMed, ScienceDirect, SpringerLink, Taylor & Francis, Wiley, AGRIS, and Google Scholar. Of the 326 articles identified in the initial stage, 30 articles in English and Indonesian were selected through a screening process based on strict inclusion–exclusion criteria. All articles were then analyzed using thematic coding techniques to produce an in-depth, evidence-based synthesis. Findings– The study produced four key findings: (1) there are five dominant pests in global food crops, namely Thrips tabaci, Spodoptera exigua/frugiperda, Helicoverpa armigera, Nilaparvata lugens and Sitophilus oryzae; (2) attack patterns are strongly influenced by temperature, humidity, pesticide resistance, and monoculture; (3) modern identification technology AI, drone imagery, multispectral sensors, and DNA Barcoding have increased detection accuracy to 94–98%; and (4) community-based early warning systems accelerate field response and reduce the risk of crop failure. Practical implications– These findings provide a scientific basis for local governments, agricultural extension workers, and farmers to gradually adopt pest identification technology and strengthen integrated monitoring systems at a regional scale. Authenticity/value– This article offers a new conceptual model of “Pest Identification Pyramid – Attack Pattern – Early Warning System” that integrates pest biology, digital technology, and community response to improve national food security.

Increasing in energy demand and limited fossil fuel reserves have driven the use of environmentally friendly alternative energy sources. This study aims to analyze the effect of pyrolysis temperature variations on the quality of biopellets made from bagasse and coffee husks. The materials were prepared in a 50:50 ratio with the addition of 15% tapioca flour as a binder. The pyrolysis process was carried out at temperatures of 450°C, 500°C, and 550°C for 120 minutes in oxygen-free conditions. The biochar resulting from pyrolysis was formed into biopellets, which were then tested for proximate composition, calorific value, and combustion rate. The results showed that an increase in pyrolysis temperature had a significant effect on the characteristics of the biopellets. A temperature of 550°C produced the lowest moisture content (8.436%), the highest fixed carbon content (62.191%), the highest calorific value (6293 cal/g), and the highest combustion rate (0.05789 g/sec). Conversely, ash content increased with rising temperature, while volatile matter content decreased. Thus, the best biopellets were obtained at a temperature of 550°C. This study confirms the potential of bagasse and coffee husks as raw materials for biopellets through pyrolysis temperature optimization to support the development of sustainable biomass energy.

The instability of the climate is becoming increasingly prominent across Southeast Asia, creating uncertainty in agricultural systems that are highly dependent on seasonal weather patterns. Indonesia, where rice remains the primary staple food, is particularly vulnerable to the effects of rising temperatures and rainfall deficits. This study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict rice production while incorporating indicators of extreme climate anomalies. Using publicly available datasets, including FAOSTAT production statistics, NOAA rainfall and temperature anomalies, and climate indices from the World Bank, this model was developed following the Box-Jenkins procedure. Among the configurations tested, the SARIMA model (1,1,1)(0,1,1)₁₂ showed the strongest performance, reflected in a MAPE of 4.62% and low RMSE values. The model indicates that significant El Niño events can reduce annual rice production by 3–7%, while wetter La Niña conditions may support production recovery. These findings highlight the importance of integrating climate-sensitive data into agricultural forecasting. The model presented here could support early warning systems, adaptive farming strategies, and long-term food security planning in Indonesia.

The normal flora of chickens refers to the microbial communities that naturally inhabit the gastrointestinal tract and body surfaces of chickens, playing a crucial role in health, digestion, and immunity. This community consists of beneficial bacteria such as Lactobacillus, Bifidobacterium, and Bacillus, which assist in feed fermentation, vitamin synthesis, and inhibition of pathogenic growth. Additionally, the normal flora may include potential pathogenic bacteria such as Escherichia coli, Salmonella spp., and Campylobacter, which can cause diseases if microbial balance is disrupted. The diversity and balance of the microbiota are influenced by various abiotic factors, including feed quality, access to clean water, ambient temperature, humidity, and environmental hygiene. Biotic factors, such as microbial interactions, rearing systems, and contact with other animals, also play a significant role in determining microbial composition. Several studies have shown that the use of feed additives, such as probiotics and phytogenics, can enhance populations of beneficial bacteria while suppressing pathogenic bacteria. Extensive rearing systems, which provide chickens with more space and exposure to natural environments, tend to increase microbiota diversity compared to semi-intensive systems with more restricted conditions. Understanding these factors is essential for developing effective health management strategies and optimizing safe and sustainable poultry production.

Activated carbon has been developed for supercapacitor electrode material due to their high degree of micro porosity and large surface area. The carbon source, preparation conditions such as temperature and atmosphere, and preparation method strongly influence the crystalline structure and the properties of carbon materials. This article is focused on the crystalline structure analysis of bamboo and coconut coir activated carbon The bamboo and coconut coir carbon were fabricated by using pyrolysis method. The activated bamboo carbon and activated coconut coir carbon were produced using a chemical activation method where H3PO4 solution as activator agent. Characterization of the physical/crystalline structure of the bamboo carbon (BC), and coconut coir carbon (CCC) and bamboo activated carbon (BAC), coconut coir activated carbon (CCAC) was determined using XRD measurement. The XRD spectra of BC and BAC indicate that the percentage crystallinity are 29.1%, and 18.4% respectively. For CCC and CCAC the percentage crystallinity are 11.3% and 13.2%, respectively. The interlayer spacing (dhkl) for BC is 4.05 Angstrom, and for BAC is 3,79 Angstrom. The crystallite height (Lc) for BAC is 6.64 Angstrom and for BC is 21.56 Angstrom. The interlayer spacing (dhkl) for CCC and CCAC are the same 4.05 Angstrom. The crystallite height (Lc) for CCAC is 4.96 and for CCC is 2.83 Angstrom.

Sea cucumbers (Holothuroidea) are marine organisms of high ecological and economic value, yet their populations in many tropical regions have declined due to exploitation pressures. This study aimed to analyze the density and distribution patterns of sea cucumbers in the coastal waters of Tuhaha, Saparua Island, Central Maluku Regency. A quantitative descriptive survey was conducted using 1 × 1 m quadrat transects along eight transect lines perpendicular to the shoreline. Density was calculated based on the number of individuals per unit area, while distribution patterns were determined using Morisita’s index. Four species of sea cucumbers were identified, namely Holothuria scabra, Holothuria atra, Bohadschia vitiensis, and Bohadschia marmorata, with a total of 33 individuals and an overall density of 0.19 ind/m². Species H. scabra exhibited the highest density (0.11 ind/m²), whereas H. atra and B. vitiensis had the lowest (0.01 ind/m²). The population distribution was aggregated (Id = 6.11), indicating a strong association with specific microhabitats, particularly muddy-sand substrates. Environmental parameters (temperature 30°C, salinity 30 ‰, pH 6.90) remained within the optimal tolerance range for sea cucumbers. These findings indicate that Tuhaha waters continue to support Holothuroidea populations; however, the aggregated distribution pattern reflects vulnerability to overexploitation. Therefore, ecosystem-based management through habitat conservation, catch restrictions, and the integration of aquaculture and restocking is essential to ensure the sustainability of sea cucumber resources in Central Maluku.

Background: Sleep disorders in adolescents are a significant health problem, with a global prevalence reaching 57.8% and particularly high rates in several cities in Indonesia. Poor sleep quality negatively impacts physical health, such as the risk of cardiovascular disease and anemia, as well as mental and cognitive health. Sleep behavior is influenced by a dynamic interaction between personal and environmental factors, as explained in Social Cognitive Theory (SCT). Research Objective: To identify the determinants of sleep deprivation in adolescents, specifically individual and environmental factors, based on a Social Cognitive Theory (SCT) perspective through a literature review from 2019 to 2025. Method: This study utilized a literature review. To obtain research data, the authors searched for scientific articles through Google Scholar, PubMed, and ScienceDirect databases, then analyzed 10 articles that met the inclusion criteria, published between 2020 and 2025. Results: Factors significantly associated with adolescent sleep quality were identified, with individual factors being the most dominant determinant (found in 7 studies), including academic stress and smartphone addiction. Furthermore, a positive association was found with environmental factors (found in 4 studies), such as bright lighting, noise, and uncomfortable room temperature. Conclusion: Within the framework of Social Cognitive Theory, adolescent sleep quality is the result of a reciprocal interaction between personal factors (perceived stress and self-control over gadgets), the physical environment, and sleep behavior. Individual factors such as stress and nighttime gadget use reduce self-efficacy for regular sleep, which is exacerbated by an unfavorable environment.

The dynamic changes in weather patterns in Jambi City require an accurate temperature prediction system, thus this study aims to compare the performance of Random Forest and Support Vector Regression (SVR) algorithms in predicting daily maximum temperatures using weather data from 2020–2024 obtained from OpenMeteo with the application of Feature Engineering including lag and rolling window features. The test results indicate that the SVR model with a Radial Basis Function (RBF) kernel optimized using Grid Search (C=10, epsilon=0.2, gamma=0.01) significantly outperforms Random Forest based on a statistical Paired T-test (p-value < 0.05), yielding an R-squared (R²) value of 87.46%, Mean Absolute Error (MAE) of 0.3818 °C, and Root Mean Squared Error (RMSE) of 0.4964 °C compared to Random Forest's R² of 84.05%, where the previous day's temperature (lag) and three-day rolling average were identified as the most dominant predictors, leading to the recommendation of SVR as the more effective method for temperature prediction in the study area.

This study examines Japanese advertising and product-information texts on Shiseido Japan’s official website (www.brand.shiseido.co.jp) that grammatically prevent readers from construing statements as universal claims (“always” or “true for everyone”). It addresses two problems: how universal readings are blocked through grammatical construction in this register, and how the main blocking mechanisms differ in limiting generalisation and managing scope. The data consist of sentence-level usage, precautionary, and quality-related statements that plausibly invite broad general interpretations. Seven analytically representative tokens are used as illustrative evidence, covering wake-negation, baai-based case framing, and event/occasion packaging with V-ru koto ga aru, including rare-event calibration with mare ni and layered conditional framing. The study employs qualitative, theory-driven grammatical analysis focusing on clause structure, embedding, nominalisation, connective relations, and the compositional contribution of key markers. The results identify recurring templates with distinct structural signatures. Wake-negation blocks over-strong uptake by denying a candidate inference (…to iu wake de wa arimasen). Case framing with baai shifts categorical commitments into situation-restricted possibility (…baai ga arimasu), including complex variants that add causal linkage, avoidance marking, and directive closure. Event/occasion packaging with koto plus existential predication (…koto ga arimasu) presents anomalies as contingent occurrences, and it can be triggered by causal conditions (e.g., temperature change) or conditional frames (…to). Rare-event marking with mare ni further calibrates frequency and often co-occurs with contrastive reassurance about quality. Overall, universal-blocking emerges as a set of non-redundant grammatical routes that constrain inference, situational domain, and event profiling in a compact public informational genre.

The oil and gas industry plays a crucial role in meeting global energy needs, with crude oil from production wells being the primary product of upstream operations. Prior to further processing, crude oil requires pretreatment at the production site, one of the key stages being phase separation using a flash separator. This study examines the effect of variations in cooling temperature on the performance of liquid phase separation and energy requirements in the flash separation process of light hydrocarbons. The analysis was conducted through process simulation using Aspen HYSYS version 14.2 with the Peng Robinson property package. The feed stream had a mass rate of 10,000 kg per hour, a temperature of 50°F, and atmospheric pressure, with compositions of ethane, propane, isobutane, and normal butane. The process configuration included compression, cooling, and phase separation in a flash separator at a constant pressure of 50 psia. Variations in cooling temperature were applied at 20, 10, and 0°C. The simulation results indicated a thermodynamic critical point at 10°C. At 20°C, no liquid phase was formed, while at 10°C, significant liquid yield was obtained with moderate energy consumption. Lowering the temperature to 0°C dramatically increases liquid recovery, but the cooling energy requirement also increases sharply. Sensitivity analysis confirms a strong inverse relationship between temperature and condensation yield, as well as a surge in energy consumption at low temperatures. The optimal operating condition is set at 10°C, providing a balance between separation efficiency and energy efficiency in accordance with sustainable manufacturing principles.

Biodiversity is essentially a reflection of the number of species and individuals inhabiting a community, as seen in the Pteridophyta group. These plants, which have evolved to have differentiated roots, stems, and leaves, play an important role in forest systems, primarily as protectors of the soil surface from the effects of erosion, in addition to contributing to the decomposition of organic matter that produces nutrients, and being a major part of the producer chain in the trophic structure. The Jaboi hot spring area, located in the Sukajaya District of Sabang City, exhibits unique ecological characteristics, influenced by the presence of the Jaboi volcano, which gives rise to geothermal phenomena such as fumarole activity, hot steam emissions, and the emergence of high-temperature water flows. This geothermal dynamic directly causes alterations in the physical and chemical conditions of the local soil, thereby shaping habitat characteristics and influencing the existence of vegetation, including ferns. To date, there is little scientific information available on the diversity of ferns in this area. Therefore, this study was conducted to examine and measure the diversity of ferns in the Jaboi hot spring area. The study was conducted in October 2025 using an exploratory survey approach to determine plots and purposive sampling techniques for field data collection. Diversity analysis was based on the Shannon-Wiener index (Ĥ) formula. Based on the identification results, 15 species of ferns from a total of 6 families and 433 individuals were found. The diversity index obtained (Ĥ=2.490171) indicates a moderate level.

This research examines the implementation of green building principles in the design of the Multipurpose Building at SMP–SMA Islam Hidayatullah Semarang, focusing on energy-efficient strategies and spatial comfort based on the GREENSHIP GBCI certification criteria. The study employs a qualitative descriptive method through interviews with the architect, analysis of architectural drawings, interpretation of interior design visualizations produced by Falana Studio, and literature review on sustainable building design. The findings indicate that the building consistently applies passive design strategies, including the optimization of natural lighting through large openings and a central void, the application of cross-ventilation on each floor, and the integration of façade vegetation that reduces surface temperature and improves microclimate performance. Material selection such as GRC panels, HPL, and modular plywood supports long-term durability, while the interior design demonstrates strong visual comfort and ergonomic quality through indirect lighting, neutral color schemes, and activity-based furniture layout. According to the GREENSHIP assessment categories, the building fulfills Energy Efficiency and Conservation (EEC), Indoor Health and Comfort (IHC), Material Resources and Cycle (MRC), and Appropriate Site Development (ASD) criteria. In conclusion, the Multipurpose Building successfully integrates green building principles as an effective approach to energy efficiency and the enhancement of the educational environment.

Gross motor development is an important aspect of an infant’s growth. Infants aged 6–12 months are in a golden period where physical stimulation plays a vital role in supporting coordination, balance, and muscle strength. Hydrotherapy provides an experience of free movement in water with light resistance, which can strengthen muscles and improve body control. This study aimed to determine the effect of hydrotherapy on the gross motor development of infants aged 6–12 months at Posyandu Dahlia, Palembang City. This research used a quasi-experimental design with a pretest–posttest control group involving 30 infants (15 intervention and 15 control). The intervention was conducted twice a week for four weeks in water with temperatures of 36–37°C. The Denver II instrument was used for assessment. Data were analyzed using paired t-tests and independent t-tests with a significance level of 0.05The average gross motor development score increased significantly in the intervention group from 45.2 to 60.4 (p = 0.001), while the control group showed no significant change (p = 0.094). Hydrotherapy has a significant effect on improving the gross motor development of infants aged 6–12 months. Health workers are encouraged to use this therapy as an alternative stimulation for early childhood development in community health centers.

This research is motivated by the high risk of fatigue experienced by ship crews during voyages, which directly affects occupational safety and mental well-being. Fatigue arises from long working hours, inadequate rest time, heavy workloads, and extreme environmental conditions such as high temperatures, engine noise, and vessel vibrations. On the MT. Sultan Mahmud Badaruddin II, the problem becomes more complex due to the tight work rhythm, short berthing periods, and fast, repetitive loading–unloading activities. Harsh weather conditions, short but intensive sailing distances, and limited relaxation facilities make the crew increasingly vulnerable to both physical and mental fatigue. In addition, a work culture that tends to be authoritarian and lacks communication exacerbates psychological pressure, especially when crew members find it difficult to report their fatigue to superiors. This study uses a qualitative method through direct observation and interviews with all crew members in the deck and engine departments. The aim is to analyze the influence of the work environment and work culture on fatigue levels onboard. The results show that environmental factors such as high temperatures, narrow workspaces, and vessel instability significantly affect physical fatigue. Meanwhile, mental fatigue is triggered by ineffective communication, hierarchical pressure, and an unsupportive work culture. These findings align with the perspectives of Mathis and Jackson and comply with the provisions of the STCW 2010 and MLC 2006, which emphasize the importance of regulating working hours and fatigue management. Overall, optimizing rest hours, improving the work environment, and reforming organizational culture are required to reduce fatigue risks.

Negative Binomial Regression is a statistical modeling approach used to analyze count data with overdispersion, where the variance exceeds the mean. This study applies the method to examine the influence of weather factors on the daily number of cyclists crossing the Williamsburg Bridge in New York City. The independent variables used in the analysis include maximum temperature, minimum temperature, and precipitation. The dataset was obtained from the NYC Department of Transportation through the Kaggle platform and covers the period from April 1 to April 30, 2016. The analysis began with a Poisson Regression model; however, the presence of overdispersion was identified, indicated by a high AIC value of 8598.19, suggesting that the model was not suitable. The alternative Negative Binomial Regression model was then employed and produced a significantly lower AIC value of 518.77, demonstrating a superior fit. The findings indicate that maximum temperature has a positive effect on the number of cyclists, while precipitation shows a significant negative effect. Conversely, minimum temperature does not exhibit a meaningful influence. These results highlight the importance of considering weather conditions when planning bicycle-based transportation systems and support the development of sustainable mobility strategies in urban environments.

This study aims to compare the thermal efficiency of two aluminum and iron-based pans in the water heating process. This research method uses a mixed approach that includes direct observation (qualitative) and quantitative analysis based on changes in water temperature after heating at two volume variations, namely 0.25 L and 0.5 L. Heating was carried out with two time differences, the total of each experiment was four experiments, with two experiments for five minutes and also two experiments for ten minutes. The results showed that iron pans produced heat of 66,150 J at a volume of 0.25 L and 151,200 J at a volume of 0.5 L. Meanwhile, an aluminum pan could produce heat of 53,550 J at a volume of 0.25 L and 67,200 J at a volume of 0.5 L. The difference in heat value was influenced by the thermal conductivity and physical characteristics of each material. This study provides an understanding of the thermal performance of both pot materials and can be considered in the selection of efficient cooking utensils in the household environment.