bluuebunny/crossref_metadata_2025 huggingface dataset:
A multi-objective optimization algorithm-based capacity scheduling method for photovoltaic power hybrid energy storage systems
Authors: Wang Shuaijie , Guan Xin , Liu Shu
Published: December 2024

In this study, the combination of crossover algorithm and particle swarm optimization—crossover algorithm-particle swarm optimization (CS-PSO) algorithm—to optimize photovoltaic hybrid energy storage scheduling, improving global search and convergence speed, is discussed. The new method reduces energy storage costs and energy losses, ensures supply–demand balance and interaction power constraints, and maintains population diversity through cross-search. The CS-PSO algorithm introduces battery state of charge optimization for energy storage scheduling, improving global search and convergence speed, and obtaining accurate multi-objective scheduling results. The experiment shows that the optimal configuration for photovoltaic energy storage is 10 045 batteries + 687 244 supercapacitors, with a cost of 3.452 × 105 yuan and an energy loss of less than 5%. CS-PSO has similar costs but lower losses and faster convergence compared to traditional methods.
Energy Recovery from Municipal Waste using Machine Learning Algorithm to Produce Biogas
Authors: Mojtahed Ali , Massulli Axel Riccardo
Published: December 2024

The application of machine learning in the field of renewable energy witnessed progressive growth in recent years. Historically, its role in improving efficiency in energy systems, optimization of demand and production and renewable generation and/or price forecasting, has been well established in the literature.

This paper aims to apply the AI approach in the field of energy recovery from municipal waste collection to produce biogas. Generally, the correlation between the collective waste and the amount of energy that can be extracted is not straightforward. Several aerobic and anaerobic digestion processes are performed inside the collection. On the other hand, climate condition and the quality of the ground itself has a determining role to accelerate or limit the chemical transformation. As a result, the available models in this field are mostly fit to real data obtained from one specific case study that cannot be applied to other cases.

The methodology conducted in this paper contains municipal waste database in Italy from 2017 to 2022 that is used to train the Machine learning models. After creating the models, the new sets of data are imported to verify model accuracy. In this step, six algorithms are introduced: simple tree, linear regression, support vector regression, gaussian process, kernel and artificial neural networks. The final results are compared with the available models in this field. Three-step optimization is applied to improve the accuracy of the machine learning model including manual filtration, principal component analysis and Auto minimal dependency detection. Eventually, the final results show accuracy in the range of 92-99 % depending on the used algorithm. On the other hand, using the available mathematical mode with the same input, the maximum accuracy of 90% is achieved. The simulation is carried out using MATLAB ML toolbox.
Supercritical CO2 heat pumps for industrial decarbonization: a techno-economic analysis
Authors: Massulli Axel Riccardo , Mojtahed Ali , Ciancio Alessandro , Vespasiano Domiziana , Santoli Livio de
Published: December 2024

The hard to abate sectors produce a quarter of the annual global CO2 emissions. Their decarbonization is, therefore, pivotal in tackling climate change. The main strategies for decarbonizing the hard to abate sectors include synthetic fuels, hydrogen and electrification. The electrification and the direct use of hydrogen strategies would require drastic changes for the end users’ production process. Synthetic fuels, on the other hand, present approximately the same chemical characteristics of fossil fuels, hence, their utilization would not require substantial modifications for the end users. This strategy is, for that reason, the most promising. In low temperature industries, however, the direct electrification of the thermal energy needs could immediately be feasible by substituting natural gas boilers (NGB) with supercritical CO2 heat pumps (SCO2HP). In the present work, the cost-effectiveness of such a strategy is assessed. To do so different indicators such as the levelized cost of heat and the annual CO2 emissions have been calculated. The results show that SCO2HPs could represent a very interesting option in terms of CO2 emissions reduction (up to -67%), due to their intrinsic renewable energy share. From the economic point of view, however, this solution presents serious drawbacks. The cash flow of the investment is, in fact, negative despite very optimistic technical assumptions for the SCO2HP. Moreover, a sensitivity analysis has also been performed. From which, it emerges that the cost-effectiveness of such a solution is strongly dependent on the price of the energy vectors, mainly natural gas and electricity. Specifically, just for the cash flow to be greater than zero, a 30% reduction of the electricity price would be required. Symmetrically, a 30% increase in the natural gas price could catalyse a shift from the NGB to the SCO2HP.
Design and testing of an innovative electro-dynamic filter for gas turbine intake systems
Authors: Pinelli Michele , Piovan Mattia , Suman Alessio , Zanini Nicola , Minotti Stefano , Rossin Stefano
Published: December 2024

Gas turbines require filtered air to preserve the efficiency and the life of the system. Different degradation processes can occur depending on the size and composition of the contaminants. The compressor’s performance can be negatively impacted by fouling and erosion, which alter the shape and roughness of the blades. To limit the effects of the aforementioned phenomena, multistage filtration systems are commonly installed to reduce the contamination at the gas turbine inlet. Traditionally, particle separation is performed through mechanical filtration, which means that the particles are trapped inside cartridges of porous materials. This typology of filter guarantees a high filtration efficiency but on the other hand, generates remarkable pressure drop increasing over time. Inertial filters instead separate contaminants from the flow by utilizing the effect of inertial force acting on solid particles. In recent years, electrostatic filters have become widespread. The fibers of the filters are charged with an electrostatic field before installation and this charge is capable of separating and attracting metallic particles from the airflow. However, the electrostatic field’s effect diminishes with the exposure time with this type of filter. This work presents an innovative electro-dynamic filtration system for gas turbine application. The geometry and electrical features of the devices are designed to keep low-pressure drop and high capturing efficiency over time. To achieve these goals, a numerical campaign is performed using an in-house OpenFOAM solver, which takes into account the effect of the electrostatic force of the Lagrangian phase. After the definition of the optimal geometry, experimental tests are performed to validate the numerical results. Finally, a comprehensive laboratory-scale campaign is carried out to evaluate filtration efficiency under various environmental conditions, including different types of contaminants, concentrations, and exposure times.
Optimal configuration of adjustable capacity transformers considering OPF distribution
Authors: Hao Shuqing , Cai Yongxiang , Wang Zhuoyue , He Guanglu
Published: December 2024

The configuration of adjustable capacity transformers will be affected by photovoltaic (PV) penetration. In this paper, we propose a method for optimizing the configuration of adjustable capacity transformers by analyzing the parameters and topology of the low-voltage distribution network, which considers both the operational safety of the grid and economic objectives under optimal power flow (OPF) distribution. We construct the objectives and constraints of the OPF-solving model in different PV penetration scenarios for two types of node topologies and utilize second-order cone (SOC) relaxation to solve the results of OPF distribution. In order to calculate the long-term power loss and comprehensively assess the transformer replacement cost, we analyze the low-voltage side load curves of the adjustable capacity transformers based on the OPF solutions. We design an optimization scheme for the capacity configuration of transformers under different PV penetration rates. Simulation results show that the proposed scheme can effectively reduce power loss and transformer operating costs.
Research on sliding mode active disturbance rejection based on permanent magnet synchronous motor
Authors: Mu Poxuan , Yao Xiaodong
Published: December 2024

The motor is more and more common in our daily life, because the permanent magnet synchronous motor has many advantages such as high efficiency, simple structure and reliability. For the motor speed response time is long, overshoot and load instability. The motor speed loop controller is designed by combining the mathematical model of a permanent magnet synchronous motor with sliding mode control theory and nonlinear active disturbance rejection control theory. By combining with ADRC, this method has the characteristics of small overshoot, small buffeting, very fast response and strong anti-load disturbance ability. The feasibility of the sliding mode active disturbance rejection control algorithm is verified by Matlab/Simulink simulation experiments.
Short-term prediction of offshore wind power based on CNN and LSTM
Authors: Liu Jingang , Wang Jianbao , Yu Lei , Zhang Feng , Li Yu
Published: December 2024

This paper presents an innovative short-term wind power forecasting model based on deep learning, aimed at improving the economic benefits and system stability of wind power projects. The model combines a CNN, known for efficient feature extraction, with an LSTM network, recognized for its exceptional time series processing ability. Additionally, an attention mechanism is applied to probabilistically assign weights, enhancing key feature influence and further improving wind direction prediction accuracy. First, the LSTM predicts the gas properties of the unit state, followed by a convolutional model with attention mechanisms to forecast wind power. Experimental results demonstrate that this model significantly reduces prediction errors and enhances accuracy. Specifically: 1) A comprehensive deep learning model is proposed, integrating CNN and LSTM advantages, with an attention mechanism to enhance performance. 2) Key issues in wind power forecasting are effectively addressed, improving accuracy and stability. 3) Both real and simulated data demonstrate the model’s exceptional performance. 4) Experimental results confirm that the model surpasses traditional methods with lower forecast errors and higher accuracy. Future work will focus on refining the model to handle complex environments and overcoming technical challenges in practical applications.
Mining cap light based on EEG control
Authors: Chen Zhuo
Published: December 2024

A new type of lamp holder based on brain-computer interface control is proposed, which makes the application of new technology in underground mining intelligent, including eye potential and myoelectricity generated when you blink. The extraction of brain wave shapes with different frequencies can reflect the state of the human brain. The current brain wave frequency mainly studied is between 0-100 Hz. The concrete method is to extract the original brainwave data by Fourier transform and wavelet transform. A double-threshold blink detection algorithm is designed, which can effectively avoid pseudo-blink interference and extract the concentration. And the function verification is carried out. The specific implementation method is to use the TAGM module to collect and preprocess brainwaves and transmit them to the Arduino controller through the Bluetooth module to generate control instructions. The results show that the scheme is effective.
Preparation and pressure-bearing performance of polymer-encapsulated optical fiber for high lateral pressure sensing
Authors: Huang Tingyu , Zhu Pingyu , Li Jianlin , Xiong Danli , Wang Mingxing
Published: December 2024

High pressure resin transfer molding technology is used for the mass production of lightweight composite parts. The external pressure exerted has an impact on the actual pressure value of the preform in the mold cavity, which affects product quality. A polymer-encapsulated optical fiber pressure sensor is proposed to measure the lateral pressure in a closed and severely confined mold cavity. The quartz sand particles were mixed with water-borne polyurethane in a semi-cured state, and then made into a thin ribbon, and single-mode bare optical fiber was embedded between the two layers for overall curing and molding to obtain an ultra-thin polymer optical fiber transverse pressure sensor. Several sensor samples with two specifications of length×width×thickness of 150 mm×1.3 mm×0.45 mm and 150 mm×4.0 mm×1.0 mm were prepared, and both samples were placed simultaneously in the middle layer of 30 layers of uniaxial non-crimped glass fiber, namely 15 layers, for pressure experiments. The experimental results show that the optical loss of polymer-encapsulated optical fibers is proportional to pressure, and optical fiber samples with a width of 4 mm can withstand lateral pressures up to 40 MPa, providing an important monitoring method for pressure measurement in high-pressure resin transfer molding chambers.
Analysis of the impact of IGS station spatial distribution on GNSS data processing
Authors: Xiong Hongwei , Ai Qingsong
Published: December 2024

The selection of IGS stations has been a hot topic in GPS control network processing in recent years. But so far, the problem has not been resolved. In order to improve the accuracy of GNSS control networks and get some conclusions for engineering applications. Thus, the impacts of the spatial distribution and number of the introduced IGS stations are analyzed with GAMIT software. The results show that the GAMIT software can obtain ideal results in both processing short baselines and long baselines. The root mean square error has been significantly improved after IGS stations are introduced. The spatial distribution of IGS stations has a significant impact on the GNSS control network solution. The unreasonable distribution of stations can cause errors of more than 10 mm in three-dimensional components and baseline length. Meanwhile, the accuracy of the solutions can be improved to a certain mm with the reasonable 4~8 IGS stations evenly distributed around the measured station.
A design of a data acquisition unit for an annular fuel rod gap detection system
Authors: Zhang Yanzhen , Liu Chong , Wu Longxiong , Li Kaijia
Published: December 2024

A design scheme of the data acquisition unit for the annular fuel rod gap detection system is proposed in response to the particularity and importance of the annular fuel rod in application. The working principle and hardware composition of the data acquisition unit are introduced. Functional module design, software design, and the test of the data acquisition unit are achieved. A dual CPU approach is adopted, one for parameter settings and control and the other for data collection to ensure that the data are not lost. The experimental results show that the proposed data acquisition unit has good adaptive ability, high stability, complete and accurate data recording. All performance indicators can meet the system detection requirements.
An evaluation method for multi-energy complementary configuration of urban energy consumption terminals
Authors: Gao Yuqun , Dong Hong , Hu Liujun , Gao Yanna , Zeng Fanhong , Lei Yi , Xu Yunxia
Published: December 2024

The paper established a quality evaluation method for the quality of multi-energy complementary configurations of urban energy consumption terminals. Firstly, an index System Based on unit area evaluation indicators is established based on economic cost, energy consumption, and pollutant emission. Secondly, a calculation method for the comprehensive evaluation index based on the entropy weight method is devised. The weight can be modified considering expert preferences. Finally, the scientific nature of the method is proved through the actual evaluation of three multi-energy complementary systems. The conclusions showed that distributed PV power generation has the most prominent contributions to reducing fuel costs, improving environmental performance, and improving primary energy utilization. Distributed gas co-generation can also improve the comprehensive benefit index of multi-energy systems.
A probability prediction model for flood disasters based on Multi-layer Perceptron
Authors: Wang Yiquan , Zhang Jialin , Chang Yuhan
Published: December 2024

Flood disasters are characterized by high frequency, severe destructive power, and extensive impact. The prediction of flood disasters holds great significance. This paper proposes a flood disaster prediction model based on a multi-layer perceptron (MLP). Firstly, the model employs the Spearman correlation coefficient and random forest feature importance algorithm to identify the most influential feature indicators. Subsequently, an MLP neural network is established, trained, and optimized. Experimental findings demonstrate that the model accurately forecasts the likelihood of flood disasters through sample processing, achieving a coefficient of determination of approximately 85.27%.
A proximal bundle algorithm for solving generalized variational inequalities with inexact data
Authors: Zhang Siqi , Huang Ming , Feng Yongxiu , Lin Sida , Zhang Yajing
Published: December 2024

This paper proposes a proximal bundle algorithm based on the proximal point method for solving generalized variational inequalities with inexact data. First, the explanation of subgradient values and function inexactness is given. The algorithm’s fundamental steps are then presented. Ultimately, the algorithm’s convergence is confirmed under specific circumstances.
Relations between general sombor index and other general indices
Authors: Shi Zhanfei
Published: December 2024

The Sombor Index is a topological index in graph theory used to measure the complexity of a graph, particularly in describing the connectivity and structural diversity of a graph. We utilize the Sombor index as a bridge to explore the relationships between the Sombor index and other indices (General Zeroth-Order Randić Index, General Randić Index, General Sum-connectivity Index, General Redefined First Zagreb Index) in a more general multiparameter setting, thereby establishing new upper and lower bounds. Through a detailed analysis of data visualized from the Erdős-Rényi model, we’ve pinpointed limitations in our estimation methods. Nevertheless, the majority of our estimates are praiseworthy.
Modeling and analysis transmission and crosstalk of through-silicon via
Authors: Pan Jingjing , Wang Weimin , Wu Yongle , Liu Yuanan
Published: December 2024

Based on through-silicon-vias (TSVs) technology, a circuit model of ground-signal-signal-ground (GSSG)-type TSVs structure has been proposed. The lumped parameters are derived based on the size of the structure considering the adjacent structure. The results of the equivalent-circuit model match well with the results of the electro-magnetic simulation. The model is also placed on a two-layer PCB board for simulation. Signal integrity, near-end and far-end crosstalk are researched in this model. Finally, based on GSSG-type TSVs, the transmission performance and crosstalk behaviors are analyzed with different structure parameters.
Performance evaluation of Hard-Isolated GPONs using regular perturbation methods
Authors: Zeng Ying , Kang Zhongmiao , Shi Zhan , Zhang Peiming
Published: December 2024

In this study, we conduct a detailed performance evaluation of hard-isolated Gigabit Passive Optical Networks (GPONs) using Regular Perturbation (RP) methods. The Nonlinear Schrodinger Equation (NLSE) is utilized to model signal propagation in optical fibers, capturing the effects of nonlinearities and dispersion. By applying RP techniques, we derive approximate solutions to the NLSE, offering a more manageable approach for performance analysis in highly nonlinear regimes. Key performance metrics such as Normalized Squared Deviation (NSD), Bit-Error Rate (BER), and Achievable Information Rates (AIR) are analyzed to validate the accuracy and efficiency of the RP models. Simulation results demonstrate that the RP method provides good accuracy in modeling nonlinear effects, particularly at lower input power levels. Additionally, the study investigates the impact of hard isolation on mitigating nonlinearities, showing significant benefits in maintaining signal integrity and reducing BER. The findings highlight the potential of RP methods as robust tools for performance evaluation and optimization in advanced optical communication systems.
Deep learning-based Doppler-spread target detection with attention mechanism
Authors: Bu Linsheng , Chang Wanyu , Chen Defeng , Fu Tuo
Published: December 2024

In radar systems, Doppler spread issues commonly arise in space small target detections when integration is adopted during a long coherent processing interval (CPI). Even with precise compensation for translational effects, phase variations introduced by changes in target observation attitudes (TOA) can still cause the target’s echo energy to spread across multiple Doppler cells. As the TOA undergoes several periods over a CPI, the echo energy disperses into equidistant Doppler cells within a range cell in the range-Doppler (RD) map, which poses challenges for traditional methods and results in degraded detection performance. Leveraging the convolutional neural network’s ability to autonomously extract data features, we propose a neural network based on the Doppler auto-correlation attention mechanism (DAAM) to enhance detection accuracy and precisely estimate target positions by exploiting the sparse characteristics of the target Doppler spectrum. Training and testing are conducted using an RD map dataset, and experimental results demonstrate the network’s advanced performance in both target detection and position estimation.
Satellite imagery reveals widespread coconut plantations on Pacific atolls
Authors: Burnett Michael W , French Rory , Jones Breonna , Fischer Alexander , Holland Alexandra , Roybal Irina , White Timothy , Steibl Sebastian , Anderegg Leander D L , Young Hillary , Holmes Nick D , Wegmann Alexander
Published: December 2024

Efforts to mitigate tropical deforestation overlook coconut palm (Cocos nucifera L.) plantations on atolls—low island ecosystems that represent the most common landforms in the Pacific basin. Coconut palms have a deep history in the Pacific and were planted extensively over the last two centuries to meet the surging demand for coconut oil exports. But despite wide interest in the global footprint of palm crops, the distribution of coconut palms on Pacific atolls has remained unknown. We applied a supervised machine learning classifier to satellite imagery to produce 2 m resolution vegetation maps of 235 of 266 Pacific atolls. Despite the abandonment of many plantations in recent decades, we find that coconut palms surpass native broadleaf trees in terms of canopy area: coconut presently covers 58.3% of the mapped atolls’ total forested area and 24.1% of their total land area. 51.2% of these coconut canopies occur in monocultures indicative of plantation agriculture and drastic ecological changes. Even among atolls with climates equally suitable for coconut palms, coconut canopy coverage is 32.1 percentage points greater on those that historically exported coconut products, demonstrating the significant and persistent effects of plantations on forest compositions in the tropical Pacific. Coconut palms are most dominant on large, wet islands, reflecting their high rates of water use and thus their potential to deplete critical groundwater resources. The spread of coconut plantations also came at the expense of native vegetation critical for wildlife habitat, nutrient cycling, and soil formation. The severe environmental impacts of coconut plantations urge ecosystem management in a region uniquely exposed to climate change.
Utilization of natural and chemical activation corncob as adsorbent for continuous purification of used cooking oil using adsorption column
Authors: Haryanto B , Tambun R , Alexander V , Winarta J , Tarigan G I B B , Siadari J T C
Published: December 2024

Corncob waste can be utilized for various purposes. The purpose of this research is to determine corncob adsorption ability and adsorption kinetics model of purified used cooking oil using natural and activated corncobs which is effective and economical method due to its low cost, simple, and regenerative. The research method used in this research is experimental. This study consisted of three stages, namely the manufacture adsorbent of natural and activated corncobs, purification, determination turbidy of used cooking oil. The natural corncob adsorbent turbidity is 13.2 NTU and the activated corncob adsorbent turbidity is 10.8 NTU. Percentage removal efficiency of natural corncob adsorbent is 97.238% and activated corncob adsorbent is 97.741%. It is shown from the pseudo-second-order graph that a chemical interaction occurs, the value of the coefficient of determination (R2) obtained is 1, using natural and activated corncob adsorbent.
Effect of watering interval on growth and production of 2 maize (Zea mays L.) varieties in drought stressed media treated with chitosan
Authors: Lubis K , Satria R
Published: December 2024

Maize is an important food source after rice, but its production is often hampered by drought stress. This study aimed to assess the effect of watering intervals on two maize varieties by applying chitosan from black soldier fly (Hermetia illucens) cocoon shell waste. The study used a Randomized Block Design (RBD) with two treatment factors. The first factor was watering interval consisting of five levels: daily watering, no watering for 4, 8, 12, and 16 days. The second factor was maize variety, Bonanza F1 and Bisi-18. The results showed that the watering interval had a significant effect on plant height, number of leaves, stem diameter at 7 weeks of age, cob weight, 100 seed weight, root volume and length, and time of female flower emergence. The treatment of Bonanza F1 and Bisi-18 varieties also had a significant effect on plant height at 7 weeks of planting, stem diameter at 6-7 weeks of planting, root volume, root length, female flowering time, cob weight and length. In addition, the interaction between watering interval and maize varieties significantly affected plant height, 100 seed weight, cob length, root volume, root length, and time to female flower emergence.
Physicochemical Properties of Indonesian Native Chickens Meats and by-products
Authors: Susanto E , Fadlilah A , Amin M F , Aliyya W L N , Atma A A , Handayani I M , Sholiha R , Ni’amah M , Masro’i  , Novianti P A A , Nafi’iyah N , Rahayu P P
Published: December 2024

This study explores the unique qualities of local meats, focusing on Indonesia’s native Java chickens to provide a comparative reference for global meat varieties. The research involved 11 Java chickens from Lamongan Regency, analysed using laboratory exploratory and descriptive analytical methods. Findings revealed that the carcass weight distribution consisted of 21.93% breast, 34.95% thigh, and varying percentages for red, white, and dark offal. The water holding capacity (WHC) was recorded as 42.35% for breast and 42.11% for thigh, with notable variation in offal. Cooking loss results showed 27.07% for breast and thigh, with higher variability for offal. The meat texture averaged 6.51-6.58 kg/cm2 and pH values ranged from 5.81 to 6.45. Proximate analysis of Java chicken meat revealed moisture content of 73.63-73.74%, protein between 21.86-23.88%, fat content of 1.23-1.32%, energy values of 174.34-176.25 Kcal/100g, ash at 1.00-1.05%, and soluble protein measured at 1.14 mg/ml. These results highlight the diversity in the physical and chemical properties of Java chicken meat and its by-products, demonstrating its unique characteristics compared to other meat strains worldwide.
Effect of NaCl and CaCl2 addition on physicochemical characteristics of beef rarit
Authors: Patriani P , Pulungan L H , Regita F T , Sari T V
Published: December 2024

Rarit is a jerky speciality from Lombok, West Nusa Tenggara, Indonesia. This product is made from a mixture of meat, tamarind, and NaCl through a marination process before drying. This research focuses on examining the effects of adding CaCl2 on the physicochemical properties of beef rarit. The study employed a Completely Randomised Design approach consisting of 4 different treatment variations, each replicated 5 times. Data were analysed using ANOVA to assess the effects of the treatments. Further analysis was performed using Duncan’s test. The treatments evaluated were as follows: N0 = 0 M CaCl2, N1 = 0.1 M CaCl2, N2 = 0.2 M CaCl2, and N3 = 0.3 M CaCl2. The findings indicated that the addition of CaCl2 had a significant effect (P<0.05) on pH, cooking shrinkage, and tenderness but did not have a significant influence (P>0.05) on the colour of rarit meat. The addition of CaCl2 was shown to reduce pH, cooking shrinkage, and moisture content while increasing tenderness. The best sample in terms of physicochemical properties was rarit meat with 0.1 M CaCl2 added (N1). Beef rarit with the addition of 0.1 M CaCl2 to the marinade can support the sustainable development of livestock products.
The preference mapping and the drivers of liking for ready-to-drink tea beverages
Authors: Tenadi A , Ervina E
Published: December 2024

This study aims to investigate the preference mapping and drivers of liking for ready-to-drink tea (RTD) tea beverages. The RTD tea samples were selected based on the type (black, green, oolong) and different sugar additions (sweetened, unsweetened, less sweet). In addition, various flavored (jasmine, milk, fruity) teas were also included. A total of 12 RTD tea samples were evaluated using descriptive analysis by 14 trained panelists. In addition, the preferences of the RTD teas were investigated by 131 participants (mean age 24.0 ± 9.2, 45% men, 55% women). The results show that the sensory profiles of RTD tea were very diverse based on the ingredients such as type of tea, flavors, and sweetness levels. The acceptability indicated that sweetened and flavored RTD teas have a higher liking (p< 0.05) compared to unsweetened and plain samples. The preference mapping shows that the overall liking for RTD tea was associated with sweet taste, sweet aroma, and sweet aftertaste while sensorial attributes of bitter taste, bitter aroma, astringent, burnt aroma, bitter aftertaste, and leafy aroma may influence consumers disliking. This study identifies factors that drive consumer preferences for RTD tea and provides insight for the development of RTD tea by maximizing sensorial properties.
Exploration of plant milk potentials in moringa leaf ice cream: Physicochemical and sensory evaluations
Authors: Sadek N F , Usman M Z K
Published: December 2024

This research aimed to develop and evaluate moringa leaf ice cream (MLIC) utilizing various plant milks (almond, soybean, mung bean, and coconut) as cow milk alternatives. This study found that replacing cow milk with plant milk in MLIC formulation had no impact on overrun but significantly affected the melting resistance, total dissolved solids (TDS), color, and overall sensory acceptance. Based on the physicochemical and sensory evaluations, soy milk was found to have the most potential to replace cow milk in MLIC formulation. The MLIC made with soy milk has an overrun of 12%, melting resistance of 42%, TDS of 22°Brix, and a hedonic score of 6.9 out of 7 (tend to “like it very much”). This sample was associated with bright green, fatty, milky, creamy, and soft, where the last three attributes had a strong positive correlation with MLIC overall liking.
Sustainable extraction of Amber rice bran oil (Oryza sativa L.): a comparative study between conventional and ultrasound-assisted extraction using ethanol as solvent
Authors: Turki S S , Mohammed N K , Muhialdin B J , Hussin A S M
Published: December 2024

Rice bran oil (RBO) is highly valued due to its unique properties. Due to environmental obstacles such as climate variation and global warming, there has been a growing interest in green extraction approaches recently, like ultrasound-assisted extraction (UAE), which offer several advantages in terms of sustainability and environmental impact such as reduced extraction period, solvent usage, and significantly increased extracted amount compared to conventional methods. This work compared oil extraction from Iraqi Amber rice bran employing ethanol as an eco-friendly solvent and two methods, traditional ethanol soaking (SEE) and ultrasound-assisted extraction (UAEE). According to the results, the oil yield by UAEE was higher than SEE, by 19.22% and 17.98%, respectively. A minor increase in the refractive index was recorded in the oil extracted using SEE, and a slight increase in the acidity and free fatty acid values in UAEE. The γ-Oryzanol in the oil extracted using UAEE was higher, reaching 5.88 mg/g. Fatty acid analysis showed that the oil extracted using SEE contained mostly linoleic acid, and UAEE had the highest value of palmitoleic acid. Using ethanol as a solvent, both techniques were generally successful in extracting oil from amber rice bran, with the UAEE method outperforming SEE.
Potential of Medan Barangan Banana (Musa acuminate L .) Extract as Antiphotooxidative in Linoleic Acid Systems
Authors: Sibuea P , Nauli A P , Siahaan R Y , Pandiangan M , Sihombing D R
Published: December 2024

The fat oxidation is a major cause of quality degradation in various processed food products. Oxidation can occur through reactions with diradical triplet oxygen or nonradical singlet oxygen, which may be formed from triplet oxygen in the presence of sensitizers and light. Erythrosine, a common food colorant, is a photosensitizer due to its conjugated double bonds. This study investigates the antioxidant and antiphotooxidant activities of the Medan Barangan banana fruit and its potential to enhance the oxidative stability of food products, particularly in the linoleic acid system. Key methods include extraction and activity testing of various banana extracts: hexanes (HE), hexanes acetone sequential (HASE), and hexanes acetone ethanol sequential (HAESE). Results show all extracts possess reducing power and DPPH radical scavenging activities, with HAESE demonstrating the highest efficacy. Additionally, HAESE exhibits significant singlet oxygen quenching, reducing linoleic acid peroxide formation in the presence of erythrosine. These findings suggest potential applications of Medan Barangan banana in food preservation, contributing to more sustainable agricultural practices by utilizing local agricultural commodities.
Utilization of Kano Model and Quality Function Deployment to Identify Key Attributes for Developing Sorghum Functional Beverages
Authors: Setiawan L , Romulo A
Published: December 2024

Sorghum, a nutrient-rich grain, is underutilized in Indonesia, particularly in beverage products. Currently, there is a lack of studies on sorghum in Indonesia, specifically on creating beverage products and gathering data on customer satisfaction levels and expectations for drinks made from sorghum. This study aimed to ascertain the specific characteristics that customers desire in sorghum functional beverages. This study addresses the lack of research on sorghum-based beverages by identifying the specific characteristics consumer’s desire. Using the Kano model and Quality Function Deployment (QFD), the study reveals eight key factors: natural sugar use, BPOM certification, nutritional content listing, weight information, antioxidants and vitamin inclusion, healthy ingredients, sterilization, and aseptic packaging. Seven technical solutions are proposed to meet consumer demands: storage, presentation, temperature management, product control, processing, packaging, and development. The QFD analysis highlights three critical attributes: aseptic packaging, sterilization methods, and antioxidants and vitamins in the beverages. These findings are crucial for developing sorghum beverages that align with consumer expectations, potentially increasing sorghum consumption in Indonesia. Furthermore, this research supports sustainable food practices by promoting the use of underutilized, nutrient-dense crops like sorghum, thereby enhancing food diversity and security.
Alpha-glucosidase inhibitory activity of Balakka (Phyllanthus emblica) bark and fruit
Authors: Addina S , Harahap E S
Published: December 2024

The increased blood glucose level as a consequence of insufficient insulin synthesis or ineffective insulin use is a degenerative disease known as diabetes mellitus. Prevention involves inhibiting the α-glucosidase enzyme, which hydrolyzes carbohydrates into glucose. The Balakka plant (Phyllanthus emblica Linn.), growing in South Tapanuli, potentially inhibits α-glucosidase due to its bioactive compounds. This study examined the sustainability of functional food potential by analyzing the inhibitory effect of Palakkad bark and fruit extracted using N-hexane and ethanol on the α-glucosidase enzyme. Ethanol and N-hexane were utilized for extraction, and the results were analyzed for α-glucosidase inhibitory activity and qualitative phytochemicals. Results showed that n-hexane extracts of Balakka bark and fruit were positive for steroids. Ethanol extracts of bark contained tannins and saponins, while fruit extracts contained flavonoids, tannins, and saponins. Ethanol extracts of bark and fruit demonstrated the best α-glucosidase enzyme inhibitory activity at 62.08 ± 1.05% and 64.48 ± 0.38% respectively, at 200 ppm. Thus, the Balakka plant, especially its bark and fruit, shows potential as an antihyperglycemic agent.
The influence of economic environment on the entrepreneurial behaviour of millennial farmers to increase food security
Authors: Qonita R R A , Masyhuri  , Jamhari  , Perwitasari H
Published: December 2024

Millennial farmers’ entrepreneurial behaviour cannot be separated from the influence of the economic environment because the environment will affect business sustainability. This analysis examines the influence of the economic environment (access to capital and credit, access to market and access to business networks) on the entrepreneurial behaviour of millennial farmers in the Special Region of Yogyakarta Province, Indonesia. This research utilizes a quantitative approach with survey methods. The sample consists of 219 millennial farmers in the Special Region of Yogyakarta who are Komisariat Daerah Petani Millennial members. Sampling was done by simple random sampling. Data millennial farmers were processed using multiple linear analyses. The research findings show that the variable of access to markets and access to business networks as a business economic environment could improve the entrepreneurial behaviour of millennial farmers. Variables of access to capital and credit do not affect entrepreneurial behavior. Access to markets and access to business networks have a positive impact on entrepreneurial behaviour. The entrepreneurial behaviour of millennial farmers can be improved by entrepreneurship training with digital marketing, empowering millennial farmers and business mentoring.
Palm oil mill effluent variation stage treatment methods to reduce organic matter in biogas production
Authors: Sigalingging R , Tampubolon E G
Published: December 2024

Palm Oil Mill Effluent (POME) is a promising resource for biogas production. Despite its acidic nature (pH), which can hinder optimal biogas output, this study has shown that with the right treatments, POME can be a primary material for biogas production. This discovery offers hope for the future of waste management. This study conducted a comprehensive study with 4 treatments using cow dung as a starter and NaOH as a pH controller, with as many as four repetitions to investigate variation POME volume (5 and 3 L) on different stage methods of fermentation (single stage and double stage of bioreactor). The purpose of this study was to obtain the percentage decrease in organic matter, pressure, and volume of biogas, as well as flame and flame duration. The methods used in this study were single-stage anaerobic bioreactors and two-stage anaerobic bioreactors. The results showed the highest percentage of organic matter reduction found in treatment using a Double-stage anaerobic bioreactor with 3 L of POME was 1.26%, biogas volume was 1,900.38 ml, biogas pressure was 1.0151 bar, and the duration of the flame was 68.50 seconds with a blue flame.
Loop-shaped electrode triboelectric nanogenerator for onboard wind-flow energy harvesting
Authors: Mudgal Trapti , Tiwari Manas , Bharti Deepak
Published: December 2024

Wind-flow experienced aboard a moving vehicle is one of the few energy resources which is observed very regularly around humans everyday but remains underexplored and largely untapped so far. To harvest this form of wind energy conveniently, a triboelectric nanogenerator (TENG) with a novel loop-shaped electrode (LSE) developed using polyimide film wrapped with aluminum foil has been proposed in this study. The unique construction and arrangement style of the electrode not only provides simultaneous strength and flexibility to endure the high-speed wind but also augments the levels of air flow-induced fluttering. At a wind speed of 5.5 m s−1, LSE-TENG achieves a power density of 70 mW m−2 and drives several electronic utilities. Further, with its placement on a moving vehicle and the benefit of specific design and performance, LSE-TENG scavenges the energy from the wind-flow experienced aboard the moving vehicle and powers external electronics onboard. This investigation suggests that the simplicity of TENGs and their efficient design procedures together can be highly conducive for effective energy conversion from unconventional and small-scale energy sources.
Enhanced thermal and mechanical properties of sapodilla/PLA biocomposites using filament extrusion 3D printing
Authors: R Nalaeram Sivaram , Thiagamani Senthil Muthu Kumar , Ebrahimnezhad-Khaljiri Hossein , Subramaniam Jeyanthi , Krishnasamy Senthilkumar , Muthukumar Chandrasekar , Thanh Mai Nguyen Tran , Khan Anish
Published: December 2024

The large-scale use of non-biodegradable materials, mainly comprising plastics, has raised serious environmental concerns for their viable alternatives. However, most of the biocomposites, including PLA-based matrix material, exhibit shortcomings in mechanical and thermal properties, thus posing serious barriers to their applications. Dealing with such challenges, the present work is related to the additive manufacture of biocomposites using poly(lactic) acid (PLA) reinforced with sapodilla seed shell (SSS) particulates through a fused deposition method technique. PLA was mixed with different concentrations of SSS fillers such as 5 wt.%, 10 wt.%, 15 wt.%, 20 wt.%, and 25 wt.%. PLA and SSS were extruded into filaments used for 3D printing. The experimental results reported an improvement in tensile and flexural strength; in particular, the composites showed tensile and flexural strengths around 25.5 and 49.46 MPa, respectively, which is an increase of about 51.25% and 27.6% as compared to the PLA matrix. However, the addition of SSS fillers did not have any significant influence on impact energy absorption. Thermal stability was checked using thermogravimetric analysis, while its char residue increased from 1.15% to 2.59% in the composites, compared to pure PLA at 0.64%. These results clearly indicate that SSS fillers can overcome the inherent weaknesses of PLA, offering a promising solution toward lightweight and environmentally sustainable applications in additive manufacturing, such as biodegradable packaging materials and lightweight automotive interior components.
Extraction of lignin from Duabanga grandiflora and its valorization for nanocomposite development with enhanced mechanical and UV shielding properties
Authors: Sarmah Jayanta K , Deka Rishikesh , Khound Sagarika , Gogoi Parikshit , Ali Asadulla Asraf
Published: December 2024

In this study, we report on extracting lignin from Duabanga grandiflora (Khukon), an untapped wood source, and its transformation into nano-lignin (NL) to create durable and thermally stable composites. Utilizing ultrasonication, we synthesized spherical lignin nanoparticles with an average size of 8 nm, as verified by high-resolution transmission electron microscopy. These nanoparticles were integrated into a polyvinyl alcohol (PVA) and guar gum (GG) matrix, resulting in PVA-GG-NL (PGNL) composites. PVA-GG nanocomposite films containing various contents of lignin nano-particles (1, 2 and 3 wt%) were formulated by a simple solvent cast method and cross-linked by adding borax. Addition of 1 wt% lignin nanoparticles brought in the composite films with 29.8 MPa tensile strength and 139.3% elongation at break. Compared to PVA-GG-lignin composites, the PGNL composites exhibited a 59.4% increase in tensile strength and enhanced elongation at break and good thermal degradation properties. Notably, the PGNL composite films were transparent but could shield 99.9% of the UV-A (320–400 nm) and UV-B (280–320 nm) radiations, marking a significant advancement in UV protective materials. Our innovative use of D. grandiflora-derived NL in a dual-polymer network not only underscores the potential of renewable resources in high-performance applications but also aligns with the principles of a circular bioeconomy by offering a sustainable solution for effective UV protection.
The Primacy of Adipose Tissue Gene Expression and Plasma Lipidome in Cardiometabolic Disease in Persons With HIV
Authors: Bailin Samuel S , Ma Siyuan , Perry Andrew S , Terry James G , Carr John Jeffrey , Nair Sangeeta , Silver Heidi J , Shi Mingjian , Mashayekhi Mona , Kropski Jonathan A , Ferguson Jane F , Wanjalla Celestine N , Das Suman R , Shah Ravi , Koethe John R , Gabriel Curtis L
Published: December 2024

Background

Persons with HIV (PWH) on contemporary antiretroviral therapy (ART) are at elevated risk for developing age-related cardiometabolic diseases. We hypothesized that integrative analysis of cross-tissue, multimodal data from PWH could provide insight into molecular programming that defines cardiometabolic phenotypes in this high-risk group.

Methods

We enrolled 93 PWH without diabetes who were virologically suppressed on contemporary ART and obtained measures of insulin resistance, glucose intolerance, and adiposity. We performed circulating lipidomics, proteomics, and metabolomics, as well as subcutaneous adipose tissue (SAT) bulk transcriptomics, and used multiomics factor analysis (MOFA) to perform integrative analyses of these datasets.

Results

The median age was 43 years, median body mass index 30.8 kg/m2, 81% were male, and 56% were self-identified non-Hispanic White. We identified a specific MOFA factor associated with visceral adipose tissue volume (ρ = −0.43), homeostasis model assessment 2 insulin resistance score (ρ = −0.52), liver density (ρ = 0.43), and other cardiometabolic risk factors, which explained more variance in the SAT transcriptome and circulating lipidome compared with the circulating proteome and metabolome. Gene set enrichment analysis of this factor showed extracellular matrix and inflammatory pathways that primarily mapped to SAT myeloid cells and adipose progenitor cells using single-cell deconvolution. Lipidomic analysis showed that this factor was significantly enriched for triacylglycerol and diacylglycerol species.

Conclusions

Our multiomic analysis demonstrated coordinated, multitissue molecular reprogramming in virologically suppressed PWH with elevated cardiometabolic disease risk. Longitudinal studies of PWH with assessments of adipose tissue and lipid handling are necessary to understand mechanisms of cardiometabolic disease in PWH.

Clinical Trials Registration. NCT04451980.