bluuebunny/crossref_metadata_2025 huggingface dataset:
Toward Exoplanet Transit Spectroscopy Using JWST/MIRI’s Medium Resolution Spectrometer
Authors: Deming Drake , Fu Guangwei , Bouwman Jeroen , Dicken Daniel , Espinoza Nestor , Glasse Alistair , Greene Thomas , Kendrew Sarah , Law David , Lustig-Yaeger Jacob , Garcia Marin Macarena , Schlawin Everett
Published: August 2024

The Mid-Infrared Instrument Medium Resolution Spectrometer (the MRS) on JWST has potentially important advantages for transit and eclipse spectroscopy of exoplanets, including lack of saturation for bright host stars, wavelength span to longward of 20 µm, and JWST’s highest spectral resolving power. We here test the performance of the MRS for time series spectroscopy by observing the secondary eclipse of the bright stellar eclipsing binary R Canis Majoris. Our observations push the MRS into saturation at the shortest wavelength, more than for any currently known exoplanet system. We find strong charge migration between pixels that we mitigate using a custom data analysis pipeline. Our data analysis recovers much of the spatial charge migration by combining detector pixels at the group level, via weighting by the point-spread function. We achieve nearly photon-limited performance in time series data at wavelengths longward of 5.2 µm. In 2017, Snellen et al. suggested that the MRS could be used to detect carbon dioxide absorption from the atmosphere of the temperate planet orbiting Proxima Centauri. We infer that the relative spectral response of the MRS versus wavelength is sufficiently stable to make that detection feasible. As regards the secondary eclipse of this Algol-type binary, we measure the eclipse depth by summing our spectra over the wavelengths in four channels, and also measuring the eclipse depth as observed by TESS. Those eclipse depths require a temperature for the secondary star that is significantly hotter than previous observations in the optical to near-IR, probably due to irradiation by the primary star. At full spectral resolution of the MRS, we find atomic hydrogen recombination emission lines in the secondary star, from principal quantum levels n = 7, 8, 10, and 14.
Wide Post-common Envelope Binaries from Gaia: Orbit Validation and Formation Models
Authors: Yamaguchi Natsuko , El-Badry Kareem , Rees Natalie R. , Shahaf Sahar , Mazeh Tsevi , Andrae Renŕ
Published: August 2024

Astrometry from Gaia DR3 has enabled the discovery of a sample of 3000+ binaries containing white dwarfs (WD) and main-sequence (MS) stars in relatively wide orbits, with orbital periods P
orb = (100–1000) days. This population was not predicted by binary population synthesis models before Gaia and—if the Gaia orbits are robust—likely requires very efficient envelope ejection during common envelope evolution (CEE). To assess the reliability of the Gaia solutions, we measured multi-epoch radial velocities (RVs) of 31 WD+MS binary candidates with P
orb = (40–300) days and AstroSpectroSB1 orbital solutions. We jointly fit the RVs and astrometry, allowing us to validate the Gaia solutions and tighten constraints on component masses. We find a high success rate for the Gaia solutions, with only 2 out of the 31 systems showing significant discrepancies between their Gaia orbital solutions and our RVs. Joint fitting of RVs and astrometry allows us to directly constrain the secondary-to-primary flux ratio

S

, and we find

S
≲
0.02

for most objects, confirming the companions are indeed WDs. We tighten constraints on the binaries’ eccentricities, finding a median e ≈ 0.1. These eccentricities are much lower than those of normal MS+MS binaries at similar periods, but much higher than predicted for binaries formed via stable mass transfer. We present MESA single and binary evolution models to explore how the binaries may have formed. The orbits of most binaries in the sample can be produced through CEE that begins when the WD progenitor is an AGB star, corresponding to initial separations of 2–5 au. Roughly 50% of all post-common envelope binaries are predicted to have first interacted on the AGB, ending up in wide orbits like these systems.
Neural subspaces of imagined movements in parietal cortex remain stable over several years in humans
Authors: Bashford L , Rosenthal I A , Kellis S , Bjånes D , Pejsa K , Brunton B W , Andersen R A
Published: August 2024

Objective. A crucial goal in brain–machine interfacing is the long-term stability of neural decoding performance, ideally without regular retraining. Long-term stability has only been previously demonstrated in non-human primate experiments and only in primary sensorimotor cortices. Here we extend previous methods to determine long-term stability in humans by identifying and aligning low-dimensional structures in neural data. Approach. Over a period of 1106 and 871 d respectively, two participants completed an imagined center-out reaching task. The longitudinal accuracy between all day pairs was assessed by latent subspace alignment using principal components analysis and canonical correlations analysis of multi-unit intracortical recordings in different brain regions (Brodmann Area 5, Anterior Intraparietal Area and the junction of the postcentral and intraparietal sulcus). Main results. We show the long-term stable representation of neural activity in subspaces of intracortical recordings from higher-order association areas in humans. Significance. These results can be practically applied to significantly expand the longevity and generalizability of brain–computer interfaces.

Clinical Trials

NCT01849822, NCT01958086, NCT01964261
Analysis and structural optimization of tree-type dynamic pressure groove mechanical seal performance
Authors: Wu Zechen
Published: August 2024

This study investigates the influence of the order and groove ratio of a tree-shaped groove on the sealing performance of a mechanical seal system using numerical simulations. A three-dimensional geometric model of the tree-shaped groove is established using SOLIDWORKS, and the mesh is divided in ANSYS Meshing before being imported into Fluent software for analysis. The single-factor experimental method is employed to evaluate the impact of different parameters on the opening force and leakage rate by varying one parameter while keeping others constant. The simulation results reveal that in the low-speed range, the tree-shaped groove with a groove ratio of 3:4:5 exhibits a larger opening force. Conversely, in the high-speed range, the three-stage tree-shaped groove with a groove ratio of 1:1:1 demonstrates a lower leakage rate and a larger opening force. The findings highlight the significant impact of the order and groove ratio of the tree-shaped groove on the sealing performance of the non-contact mechanical seal. The three-stage tree-shaped groove outperforms the second and fourth stages in terms of mechanical sealing performance, particularly in regard to the opening force. This study provides valuable insights into optimizing the design of tree-shaped grooves in mechanical seal systems to enhance their overall performance and efficiency.
Rolling bearing fault detection method based on teager energy operator
Authors: Zhou Jie
Published: August 2024

This study presents a method for detecting subtle faults in rolling bearings by integrating singular value decomposition (SVD) and local modal decomposition (LMD). Initially, the original vibration signals undergo reconstruction in phase space and subsequent denoising using SVD. Following this, LMD is employed to generate multiple local modal functions (IMFs), with the most sensitive IMF components selected based on kurtosis-mean square deviation criteria. The Teager energy operator (TEO) is then computed for these selected IMFs. Ultimately, Fourier transform analysis of TEO is utilized to identify faults in the inner and outer races of rolling bearings. Test results conducted on publicly available data from Case Western Reserve University validate the accurate identification of inner and outer race faults using this approach.
Valve positioner control based on active disturbance rejection control technology
Authors: Ding Yike , Wang Na , Sun Zhibo , Shang Xiaolei , Rui Shuwang , Wang Zhongdi
Published: August 2024

The pneumatic regulating valve is a commonly used actuator in industrial settings. The smart valve positioner, acting as the “brain” of the pneumatic regulating valve, dictates the control performance of the regulating valve, primarily relying on control algorithms. However, the valve positioner is a typical nonlinear system, making it difficult to establish an accurate mathematical model that can reflect the system’s nonlinear characteristics. Furthermore, due to variations in valves, installations, and processes, the model parameters differ significantly, making it challenging to self-adjust the parameters. Additionally, various disturbances in process control will affect the positioner’s control performance. Currently, commonly used control algorithms rely on precise mathematical models, resulting in poor control performance and robustness. Therefore, this article proposes a smart valve positioner control algorithm based on self-disturbance control. Initially, a steady-state model and system identification of the valve positioner system are obtained through open-loop testing, resulting in a simplified second-order system model. Based on the simplified second-order model, a second-order self-disturbance control algorithm is designed. Finally, a test platform based on the rapid control prototype (RCP) controller is established to verify that the introduction of the self-disturbance control algorithm can effectively enhance the control performance and robustness of the smart valve positioner.
Thermostatic interaction analysis of electric wheel brakes
Authors: Yan Yan , Wang Wenzhe , Cao Liangqiang , Hang Peng , Fu Xianlan , Huang Tao
Published: August 2024

The electric wheel needs to integrate the hub motor and brake in the wheel hub, so the integration scheme and structural parameters of each component in the electric wheel determine whether the electric wheel can coordinate the operation and the quality of the running performance. In this paper, the thermosolid interaction analysis of the brakes of electric wheels is carried out through ABUQUS software, so as to judge that the performance of the brakes meets the requirements, which will play a decisive role in the safety performance and life limit of the automobile, so as to effectively improve the working stability of electric wheels.
Research on agricultural monitoring and progressive map creation in the context of ground-air coordination
Authors: Li Xiaoyong
Published: August 2024

By combining network RTK technology, 2D LiDAR, and UAV, this study investigates how to achieve agricultural monitoring effectively. A collaborative agricultural monitoring scheme was designed to accurately and comprehensively gather agricultural information in response to the demand for targeted sequential monitoring at specified agricultural locations. To establish a low-cost and low computational power-dependent 3D map of obstacle point clouds, a progressive 3D mapping scheme based on 2D LiDAR was designed and implemented. The effectiveness of the design was validated through simulation experiments and partly through real-world testing.
GNSS location error reduction method for microtremor survey system based on EMD-CNN-LSTM
Authors: Wu Wei , Mou Xingang
Published: August 2024

Large errors exist when the microtremor survey system uses the global navigation satellite system (GNSS) for static localization. Aiming at the problem that the existing methods cannot effectively weaken the random error and multipath error, an error weakening method based on Empirical Mode Decomposition (EMD), Convolutional Neural Networks (CNN), and Long Short-term Memory Networks (LSTM) is proposed. The model first uses EMD to decompose the high-frequency random error, then reconstructs the low-frequency component and extracts the local features using CNN, and finally learns the change rule of multipath error using LSTM and weakens it. The model can remove random errors in the early stage while reducing the interference of noise on the neural network in the later stage and then improve the accuracy of localization. The experimental results show that the model can effectively improve the localization accuracy in the case of short-time measurements so that the localization accuracy in the E, N, and U directions can be improved by 74.57%, 74.76%, and 71.86%, respectively, which is more than 10% higher than the localization accuracy improvement rate of the existing CNN-LSTM model.
A damping method of grid strapdown inertial navigation system for polar region
Authors: Yuan Kefei , Yang Xiaohui , Tian Guopeng , Ben Yueyang
Published: August 2024

The conventional north-pointing inertial navigation system (INS) always points to the geographic north. However, when the system crosses the pole in the polar region, the geographic north quickly changes with 180 degrees, and the rate of change of the geographic north direction will be infinite. Secondly, when the latitude approaches to 90 degrees, which will lead to singularities in math for the angular velocity caused by vehicle motion. With its special definition and mechanization, the grid coordinate system can solve the above problems. Nevertheless, the error analysis of the grid strapdown INS shows that there are Schuler periodic and Foucault periodic oscillations, which badly degrade the accuracy of the strap-down INS. To address the problem, the external horizontal damping technology is adopted in this paper, wherein the external velocity is provided by the Doppler velocity log (DVL). Finally, the feasibility and the effectiveness of the external damping technology were verified by simulation.
Lidar pose estimation method based on factor graph optimization
Authors: Zhou Lin , Qiu Jian , Han Peng , Liu Dongmei , Luo Kaiqing
Published: August 2024

In the realm of computer vision and robotics, lidar SLAM is an important technical means. Lidar SLAM frequently serves to ascertain the spatial coordinates and orientation of mobile robots amidst their surroundings. In this paper, an adaptive factor graph optimization of the lidar pose estimation method using only point-to-line constraints is proposed. The algorithm only employs the measure of distance between a point and a line to construct the constraint problem and solves the pose through point-to-line ICP and the pose with higher accuracy by optimizing the factor graph. The robustness of mobile robots in indoor scenes is improved. According to different application scenarios, including street and gate, comparative experiments are carried out on the open-source data set M2DGR containing lidar data. Empirical findings demonstrate that the algorithm put forth enhances the precision of the lidar odometer when confronted with outdoor scenarios, thereby bolstering the resilience of mobile robots traversing urban thoroughfares.
A top-view indoor localization based on discrete distillation of CLIP
Authors: Zhao Aojie , Liu Yifan , Cheng Kun , Ma Aiping , Yu Jianguo
Published: August 2024

Indoor robot localization is a challenging problem in computer vision due to sensor obstacles in a crowded environment. Pure vision localization is increasingly popular since it does not require sensors other than low-cost cameras. We adopt a top-view camera setup, effectively avoiding the problem of positioning failure due to potential occlusion of front-view cameras. We use the distilling of a pre-trained large-scale vision language CLIP model to improve the performance degradation caused by the small data set size. Our solution achieved promising performance in our customized classification-based localization test data.
Digital twin for subcritical extraction driven by data-mechanism fusion
Authors: Yan Yuhang , Zhang Huibing , Pan Fang , Jai Fei
Published: August 2024

Addressing the key issue of low automation in the production process due to the complex associations involved in sub-critical extraction, this study focuses on sub-critical extraction as the subject matter. It integrates data models with mechanism models to construct a multidimensional digital twin model and conduct systematic research. By combining simulation methods with real-time experimental data, the study establishes multilevel machine characteristics of data, behavior, and mechanisms, and identifies data patterns. From the perspectives of the physical production line, virtual production line, and data transmission, it builds an interactive mapping platform for multi-source data and carries out corresponding application research.
Risk early warning model of tunnel engineering based on computer vision and CNN
Authors: Zhong Ming
Published: August 2024

Safety management in tunnel construction is important to ensure the safety of construction personnel and equipment. This paper proposes a tunnel engineering risk early warning model based on computer vision and convolutional neural network (CNN), aiming at realizing real-time monitoring and risk early warning of the construction site using modern image processing technology and deep learning algorithm. Many image data of tunnel engineering construction sites were collected, and the data were preprocessed and marked. Then, the paper designs a depth CNN model for feature extraction and risk identification of construction site images. Through the performance evaluation of the model on the training set and test set, the effectiveness and feasibility of the model in risk early warning are verified. The experimental results show that the proposed model achieves high accuracy, recall, and F1 score on the test set, which is superior to the traditional method based on image features and artificial rules. At the same time, we analyzed the prediction results of the model on the actual construction site images. We found that the model can effectively identify different risk factors and has strong generalization ability and practicability. To sum up, this study provides a new technical means and solution for the safety management of tunnel engineering, which has important theoretical and practical significance.
Precipitation nowcasting method based on Spatial-Temporal Dual Discriminators
Authors: Li Wenfeng , Zhou Xiao
Published: August 2024

The prediction of radar echo sequence images is a spatiotemporal sequence forecasting problem, which is one of the main challenges in precipitation nowcasting. Addressing issues such as poor extraction of spatiotemporal features by previous models and blurry image sequence predictions, this study proposes a Spatial-Temporal dual Discriminator Precipitation Nowcasting Model (STD-SNGAN) based on spectral normalization generative adversarial networks (SNGAN). The model utilizes multi-scale convolution modules (Inception) to extract spatial features from radar echo images and convolutional gated recurrent units (ConvGRU) to extract temporal features between feature maps. By introducing a hidden feature sampler to enhance the extraction capability of the convolutional gated recurrent units and designing spatial-temporal dual discriminators to constrain the generator’s predicted samples, the spatiotemporal forecasting ability is enhanced. Experimental results demonstrate that the proposed STD-SNGAN model outperforms other algorithms in critical success index (CSI) and probability of detection (POD) for high echo intensity and long-term regions.
Productivity evaluation method of offshore fractured reservoir based on artificial intelligence ChatGPT
Authors: Li Tingli , Yuan Zhiqian , Yang Wei
Published: August 2024

At present, the analogy method is usually used to determine the production capacity. Due to the strong heterogeneity of fractured reservoirs in the Bohai Sea, the prediction error is large and cannot meet the needs of scheme deployment. Therefore, based on the test productivity, the characteristic parameter λ was introduced to characterize fractured reservoir productivity, and the relevant data affecting productivity were preprocessed by the K-nearest algorithm and Z-Score standardization method. Then, the feature vector of parameter λ was selected by the average impurity reduction MDI feature selection method. Finally, in the GPT Building module of ChatGPT, the reservoir parameters and productivity test data are used to train GPT and the deep forest prediction model with parameter λ is built intelligently by GPT. This method is applied to the CFD oilfield productivity evaluation in the Bohai Sea, and the prediction error caused by the difference in reservoir physical properties is reduced. The coincidence rate between prediction and actual is more than 85%, which provides a new idea and method for the productivity evaluation of offshore fractured reservoirs.
Detection of rice pests based on RepMob-Yolo
Authors: Feng Jiale , Li Youxin , Li Zhizhong
Published: August 2024

Aiming at the disadvantages of high labor intensity, poor real-time performance, and low efficiency of traditional methods for identifying rice pests, a rice pest identification model based on RepMob-Yolo is proposed. MobileNetV3 is used as the backbone network, and the RepMob structure is used to replace the conventional downsampling structure in MobileNetV3, the GELU is used to replace the ReLU and the last four convolutional layers of MobileNetV3, the global average pooling layer and the fully connected layer are pruned to realize the lightweight design of the model. The DANet module is embedded to realize the extraction of effective information by the model, extract the semantic information of higher key features, and improve the recognition accuracy. The FPN structure is combined with the RepMob structure for feature extraction and prediction, which improves the operation speed of the model. The experimental results show that RepMob-Yolo achieves 97.68% accuracy, 96.00% recall, 96.82% prediction rate, and 146.37 f/s frame rate in the identification of public pest datasets, which provides technical support for accurate and real-time identification of rice pests.
Evaluation decision on green degree combination of typical welding processes for large-diameter thick plates
Authors: Di Fei , Zhang Kexu , Wu Jinjun , Li Jingying , Wang Ying , Qu Haoyuan
Published: August 2024

Aiming at the environmental impact of large-diameter thick plate manufacturing scenarios in the welding process, a comprehensive evaluation method combining AHP and TOPSIS is proposed to analyze and make decisions on the green welding process suitable for large-diameter thick plates. The optimal green welding process evaluation and decision-making index system for large-diameter thick plates is constructed. The system can be flexibly applied to different large-diameter plate welding scenarios by focusing on different optimization objectives, providing welders with a more suitable green welding process. The constructed model is applied to the seashore factory welding scenarios and general welding scenarios for comparative decision-making, and the effectiveness and feasibility of the proposed comprehensive decision-making evaluation method are verified by the actual data calculation results.
Design and implementation of power socket soldering robot arm system
Authors: Liu Jingqiao , Wu Shuqin , Lin Zicheng , Zhang Huimin , Fan Yong , Xie Wu
Published: August 2024

In order to solve the problem of high cost and low efficiency in manual welding of power socket welding components, a soldering robot arm system was developed. The gripper, 5-DOF manipulator, clamp, support, and control system were designed. The coordinate system of the robot arm was constructed, and the working space of the robot arm was analyzed by using the MATLAB robot toolbox. It was concluded that the x0 coordinate range of the welding working area was [175mm, 215mm], the y0 coordinate range was [55mm, -55mm], and the z0 coordinate was 90mm. At the same time, the motion trajectory of the robot arm was determined. Using Raspberry PI as a controller, the control system is developed by programming Python language. The prototype is made, and a welding test is carried out. The test shows that the system has completed the welding operation, and the welding effect is good.
Prediction of melting characteristics of sludge coal blending based on Multi-task Learning
Authors: Yu Zhiao , Zhang Kaibing , Li Bing , Tong Zhenbo
Published: August 2024

In response to the urgent need for sustainable energy solutions, this study pioneers the use of a Multi-task Learning (MTL) framework to predict the melting characteristics of sludge-coal mixtures, a key aspect in optimizing co-firing processes. The integration of sludge, a byproduct of wastewater treatment, with coal in co-firing processes, presents a promising avenue to enhance energy efficiency while reducing greenhouse gas emissions. Addressing the complexities of predicting combustion behavior due to variable chemical compositions and temperatures, the MTL approach enables learning from diverse datasets, ensuring accurate and generalizable predictions. Outperforming conventional machine learning models, our framework signifies a breakthrough in predictive accuracy. Despite facing challenges such as dataset limitations, the research underscores the framework’s potential for broad industrial application, aiming for future improvements that include dataset expansion and real-time data integration. This study not only advances machine learning applications in energy and environmental domains but also contributes to the global pursuit of sustainability.
Image style migration method based on transformer generative adversarial network combined with contrast learning strategy
Authors: Chen Xing
Published: August 2024

Aiming at the problems of large loss of image patch embedding information and lack of stylized features in the generated image during transformer style migration, a style migration model combining contrast learning with transformer backbone generative adversarial network is established. Firstly, overlapping patch embedding is used to serialize the input images to get richer features; then the style and content contrast loss of contrast learning is used to mine the non-significant information between stylized images to reduce the style and content loss; finally, a multi-scale discriminator discriminates the true and false of stylized images to optimize the generator to get more stylistic features. The results on MS-COCO and WikiArt datasets show that the model improves the effectiveness and transformation efficacy of stylized images in style migration.
Indicator and sign recognition in real electricity scenario
Authors: Wang Baigen , Dong Lanfang , Ma Tao , Huang Kexuan
Published: August 2024

A multitude of instruments and equipment are contained within the electricity scenario, and their operation leads to fluctuations in a wide range of parameters. These changes are typically represented through indicators and signs. This study introduces an efficient and robust method for recognizing these indicators and signs, utilizing a variety of image processing techniques, including the computation of image mean and variance, as well as template matching. Given the plethora of indicators and signs present on substation equipment, it is crucial to discern the illumination status of indicators at different locations and relay this information to the system based on the indicator’s significance. Considering the indoor setting, which is relatively uncomplicated, this study employs the variance and mean of the indicator area image for judgment. As for identifiers, which often vary in shape and color, this study uses template matching for identification, subsequently reporting the operational status of the equipment marked by this area. Experimental results demonstrate that compared to deep learning methods, traditional image processing algorithms yield superior outcomes while significantly reducing computational costs and time.
Predictive algorithm of instrument health condition based on linear regression and radial basis function neural network
Authors: Jiang Wenrui , Wang Yi , Lin Yiyong , Zhao Lifen , Luo Pingtao
Published: August 2024

Enterprises and institutions engaged in social production activities need to apply for mandatory verification of general instruments and meters on production equipment according to law, and its verification period is determined by national or local verification and calibration procedures, but the health condition in the calibration intervals is difficult to know. To improve equipment reliability, this paper relies on the linear regression model to research the health condition of general instruments and meters, uses MATLAB tools to analyze the data samples of typical equipment pressure gauge, and combines radial basis function neural network to form a complete algorithm, and finally provides a complete implementation scheme for the health condition prediction of general instruments and meters in service.
Potential for developing an intercropping system on oil palm fields in peatlands
Authors: Arifin Y F , Noor I , Budiman A , Wibowo A D
Published: August 2024

Planting using a monoculture method is believed to impact the diversity of plant species on oil palm plantations, especially on peatlands, so the discourse on cultivating oil palm plantations using a polyculture system is to enrich species and avoid fears of environmental damage. It is necessary to conduct a study to determine the potential impact of an intercropping system between oil palm and other crops on the Land Equivalent Ratio (LER) value, intra and interspecific competition between plants, and reducing soil fertility. Data was obtained through a literature review and Forum Group Discussion (FGD) in a quadruple helix system comprising academia, farmer groups, district plantation services, and companies. Various literature studies showed that polyculture planting with an intercropping system reduces or inhibits palm oil production in peatland, but it can increase LER > 1.5. This is in line with the results of the FGD, which shows that planting using an intercropping system causes delays in palm oil production; consequently, the farmers and palm oil companies prefer monoculture planting. However, from the results of the FGD, a solution was obtained to overcome the above problems by arranging the planting distance when planting was planned, namely with a broader distance of 10 x 10 m or with a population of 100-110 plants per hectare, so that there is enough space to carry out intercropping and reduce competition. The results showed that polyculture planting with an intercropping system could also enrich plant species, apart from having the potential to increase LER > 1.5.
Life cycle assessment of oil palm plantation in Riau province: evaluating environmental impacts
Authors: Yudha Y S , Biney M , Tarigan S D
Published: August 2024

Indonesia possesses palm oil as one of its strategic commodities, and it plays a crucial role in its economic growth. Over the years, the expansion of land and the production of Fresh Fruit Bunches (FFB) of palm oil has consistently increased, accompanied by a rise in land area and crude palm oil (CPO) production. The pivotal role of Indonesia as the world’s largest palm oil producer must be highlighted through sustainability studies, especially in environmental aspects. Life cycle assessment is a technique utilized to evaluate the environmental implications of products, processes, and services, spanning their complete life cycle from the extraction of raw materials to waste management. Life Cycle Assessment (LCA) encompasses various environmental impact categories, providing a comprehensive understanding of the ecological contribution of a product or service. This research assesses the environmental impact of oil palm plantation activities in Riau province, Indonesia. The research’s boundary is set to a cradle-to-gate analysis within the oil palm plantation subsystem. The Life Cycle Assessment was conducted utilizing the SimaPro 9.5.0.0 software employing the ReCiPe 2016 method. The total emissions generated during the production process of 5 tons of Fresh Fruit Bunches (FFB) amount to 1634.22 kg CO2eq/5 tons of FFB. The major contributors to emissions are the production and use of inorganic fertilizers. Specifically, the types of fertilizers responsible for the high greenhouse gas emissions are KCl, followed by phosphorus and nitrogen fertilizers. Besides the potential for global warming, human carcinogenic toxicity is another potential impact, primarily from fertilization. The findings in this study can serve as scientific evidence in formulating solutions for sustainable oil palm plantations, particularly in reducing greenhouse gas emissions. An environmentally conscious approach to lowering emissions requires meticulous scrutiny and implementation of precision farming. It chiefly involves keeping a close watch on the quantity of fertilizer used in plantation settings and promoting using fertilizers with lower carbon footprints.
Does consumer scepticism impact purchase intention toward palm oil as a food product?
Authors: Haro A , Setiawan M , Sunaryo  , Hussein AS
Published: August 2024

In recent years, the production of vegetable oil crops has expanded more quickly than other food or industrial crops. With a negative impact on the environment and the viability of producing communities, palm oil primarily grown in tropical regions has emerged as the most traded and produced oil globally. Palm oil is increasingly used in many products despite being linked to several troubling ecological, social, and health-related issues. Customers are given a wrong impression of the industry by popular opinion. To examine the reasons behind this disparity in the instance of palm oil, the current study uses an online survey to investigate the influence of consumer attitude and scepticism toward Indonesian consumers’ purchase intention (N = 200). The result showed that consumers’ intention to purchase palm oil as a food product has been influenced by increasing consumer attitude and decreasing consumer scepticism. An increase in consumer intentions to buy palm oil as a food product is driven by this alignment, which gets stronger. The findings of this study provide important new information for developing production and marketing strategies that will improve consumers’ behavioural intention to use vegetable oils.
Characteristics of Arabica and Robusta Spent Coffee Grounds Oil Extract with Different Solvents
Authors: Kresna M S , Windy R , Suci A P , Bovi W H
Published: August 2024

High coffee consumption contributes to waste, such as spent coffee grounds (SCG). SCG contains up to 20 % oil, with the main oil content being linoleic and palmitic fatty acids. So, SCG has the potential to be used in valuable products. This study aims to characterise Arabica and Robusta SCG oil using maceration methods with different solvents (hexane, ethanol, and petroleum benzene) to obtain maximum coffee oil as an alternative. Arabica and Robusta SCG characterisation comprises the composition content, oil yield, fatty acid profile, and viscosity. The results showed that Arabica SCG oil yield was higher than Robusta, with hexane becoming the most efficient solvent to extract the oil, with 28,87% and 25,57%, respectively. The lowest free fatty acid (FFA) found in Arabica SCG oil extract using hexane is 1,95%, which consists of palmitic acid (9.41%), linoleic acid (5.37%), and stearic acid (4.7%). The highest viscosity is Arabica SCG oil extract using hexane, which is caused by the large amount of long-chain fatty acids extracted by the solvent, such as palmitic acid and linoleic acid. The FFA content of Arabica SCG oil extract using hexane has the potential to be edible oil (cooking oil) and biodiesel.
Exploring malapari (Pongamia pinnata (L.) Pierre) from the Eastern part of Indonesia
Authors: Aminah A , Cahyaningsih R , Putri A I , Cahyono D D N , Danu  , Djam’an D , Irmayanti L , Wibisono H S , Syamsuwida D , Suita E , Nuroniah H S , Siregar N , Lukman A H , Hendrato K A , Utami E R B , Bahanan F
Published: August 2024

Malapari (Pongamia pinnata (L) Pierre) is a plant species widely distributed throughout Indonesia and can be both a renewable energy and medicinal source. Malapari-related research, such as exploration, characterization, seed production capacity, and propagation (both generative and vegetative) has been done in the western part of Indonesia but not in the eastern part. As a result, exploration was carried out in the east part of Indonesia to locate new malapari population. The population’s morphological characters were also examined, and a dendrogram was used to analyze the characters. Plant samples from Bali, the Maluku Islands, and Nusa Tenggara Islands were investigated, characterized, and gathered. The fruits and cuttings (as plant materials) were collected for ex-situ conservation purposes. The natural distribution of malapari in eastern Indonesia are presented, along with their morphological characters and the dendrogram indicating no apparent distinction between them. The conservation and sustainable usage of this plant might benefit from these findings.
Contribution of palm oil and soybean oil to sustainable development goals
Authors: Bharti V , Obaideen K , Azahari D H , Amalia Rizki
Published: August 2024

Palm oil is the most consumed vegetable oil in the world, followed by soybean oil. The environmental, social, and economic impacts of vegetable oils have always been a matter of much debate despite their importance in food security and use in a wide array of products. Regardless of the criticism, vegetable oils play a central role in global trade, economic stability, food security, employment, and livelihoods of smallholder farmers globally. Recent disruptions in the worldwide supply caused by weather issues, armed conflict, supply chain disruptions, and labour shortages, which resulted in shortages and higher prices of vegetable oils, have reemphasized their importance. This paper attempts to quantify the contribution of the two most consumed vegetable oils to Sustainable Development Goals (SDGs) using 12 selected indicators. These selected indicators cover the three pillars of sustainable development, i.e., economic, environmental, and social. These are namely – Protein supply, saturated and polyunsaturated fatty acids, land use, GHG emissions, contribution to GDP, jobs creation, productivity, water footprint, use of pesticides, fertilizers and food supply. These indicators are associated with 9 SDG goals. The study aims to analyze the complex web of interactions and effects of two primary vegetable oil crops on the Sustainable Development Goals (SDGs). It highlights significant data deficiencies related to their impacts on specific SDGs, notably 13 and 15 (Climate Action & Life on Land), indicating that both the environmental and social consequences of the vegetable oils sector are still inadequately comprehended.
Hybrid deep learning for design of nanophotonic quantum emitter lenses
Authors: Acharige Didulani , Johlin Eric
Published: August 2024

Inverse design of nanophotonic structures has allowed unprecedented control over light. These design processes however are accompanied with challenges, such as their high sensitivity to initial conditions, computational expense, and complexity in integrating multiple design constraints. Machine learning approaches, however, show complementary strengths, allowing huge sample sets to be generated nearly instantaneously, and with transfer learning, allowing modifications in design parameters to be integrated with limited retraining. Herein we investigate a hybrid deep learning approach, leveraging the accuracy and performance of adjoint-based topology optimization to produce a high-quality training set for a convolutional generative network. We specifically explore this in the context of 3D nanophotonic lenses, used for focusing light between plane-waves and single-point, single-wavelength sources such as quantum emitters. We demonstrate that this combined approach allows higher performance than adjoint optimization alone when additional design constraints are applied; can generate large datasets (which further allows faster iterative training to be performed); and can utilize transfer learning to be retrained on new design parameters with very few new training samples. This process can be used for general nanophotonic design, and is particularly beneficial when a range of design parameters and constraints would need to be applied.
Between The Sjambok and the Bantu World
Authors: Fossati Marta
Published: August 2024

This chapter explores the English short stories that the Zulu-language writer Rolfes Dhlomo published in the two South African magazines The Sjambok and the Bantu World between the late 1920s and the 1930s. It discusses how these publishing venues, directed at a ‘European’ and ‘African’ readership respectively, shaped Dhlomo’s short fiction in very different ways at the level of form and content. It also highlights the pivotal role played by the press and the influence it exerted on definitions of the ‘literary’ for creative writing by Black writers in the 1930s. Ultimately, this chapter raises some questions about the ambivalent stance of the writer—as a mission-educated New African intellectual—towards modernity and the role of the English language.
Interlude
Authors: Fossati Marta
Published: August 2024

This chapter considers Dark Testament (1942) by Peter Abrahams, an ‘outsider’ figure in South Africa’s literary history. It thus functions as a link—an interlude—between the first and second parts of this book. The chapter offers a close reading that foregrounds the tensions within Dark Testament between ethical imperatives, the need to document, and a desire to overcome the particular situation of South Africa and to enter the international (mainly Western) literary market. On the level of form, this tension translates into a late modernist aesthetics coupled with autobiographical claims. This chapter also traces the contours of the author’s personal and literary connections with the US, and particularly Langston Hughes, thus positioning Dark Testament in a wider transnational context.
Beyond South Africa and the Politics of Realism
Authors: Fossati Marta
Published: August 2024

This chapter uses Zoë Wicomb’s theoretical essays as a starting point to comment on her two short-story cycles You Can’t Get Lost in Cape Town (1987) and The One that Got Away (2008). It reads the author’s short fiction through the notions of setting and intertextuality, or rather setting as intertextuality. This final chapter argues that Wicomb’s aesthetic practice—intertextuality, metafiction, retellings, uncanniness—stages (ethically) infinite gestures of repetition that resist any essentialist and monolithic interpretations, giving space to the narratives of minor, marginalized voices. She thus discards both the construction of ethnic identities (inherent in the rhetoric of apartheid and the new Rainbow Nation) and acts of generic classification.
Beyond the Law's Reach?
Authors: Nili Shmuel
Published: August 2024

This book argues that fundamental assumptions in contemporary political philosophy need to be rethought in the face of pervasive political violence. At an applied level, the book develops this broad claim by delving into a series of specific controversies, all revolving around affluent democracies’ policy responses to the threat of pervasive violence abroad. Examples include the ethics of giving refuge to beleaguered autocrats to avert civil war in their country; the ethics of prosecuting foreign officials who have colluded with drug cartels; and the admission of oligarchs who acquired their riches by distorting their country’s rule of law. At a more theoretical level, the book aims to show that the moral principles needed to adjudicate these particular controversies can illuminate broader issues in normative political theory. These range from the philosophy of criminal punishment, through the relationship between the law’s letter and its spirit, to the general plausibility of certain moral theories (and meta-theories) as public policy guides.
The Law, Unsealed
Authors: Nili Shmuel
Published: August 2024

Building on the conceptual foundations laid out in Chapter 1, this chapter proceeds in two parts. The opening part examines influential social-scientific research showing why the shadow of violence, once it comes to pervade a given jurisdiction, is extraordinarily hard to remove. This social-scientific work further motivates the book’s focus on how affluent democracies might respond to the shadow of violence abroad within their own jurisdictions. The chapter’s latter part elaborates on why the book not only focuses on democracies’ jurisdictional dilemmas, but also foregrounds democracies’ own entanglement in foreign crime occurring under the shadow of violence. The chapter develops the ideas of ‘relational priority’ and ‘normative path-dependence’ sketched in the introduction and expands on why the thorny third-best justice dilemmas taken up in later chapters have analytical priority over much more abstract, first-best visions of perfect justice, which have long been the mainstay of analytical political philosophy.
Triple network resting-state functional connectivity patterns of alcohol heavy drinking
Authors: Elsayed Mahmoud , Marsden Emma , Hargreaves Tegan , Syan Sabrina K , MacKillop James , Amlung Michael
Published: August 2024

Aims

Previous neuroimaging research in alcohol use disorder (AUD) has found altered functional connectivity in the brain’s salience, default mode, and central executive (CEN) networks (i.e. the triple network model), though their specific associations with AUD severity and heavy drinking remains unclear. This study utilized resting-state fMRI to examine functional connectivity in these networks and measures of alcohol misuse.

Methods

Seventy-six adult heavy drinkers completed a 7-min resting-state functional MRI scan during visual fixation. Linear regression models tested if connectivity in the three target networks was associated with past 12-month AUD symptoms and number of heavy drinking days in the past 30 days. Exploratory analyses examined correlations between connectivity clusters and impulsivity and psychopathology measures.

Results

Functional connectivity within the CEN network (right and left lateral prefrontal cortex [LPFC] seeds co-activating with 13 and 15 clusters, respectively) was significantly associated with AUD symptoms (right LPFC: β = .337, p-FDR = .016; left LPFC: β = .291, p-FDR = .028) but not heavy drinking (p-FDR > .749). Post-hoc tests revealed six clusters co-activating with the CEN network were associated with AUD symptoms—right middle frontal gyrus, right inferior parietal gyrus, left middle temporal gyrus, and left and right cerebellum. Neither the default mode nor the salience network was significantly associated with alcohol variables. Connectivity in the left LPFC was correlated with monetary delay discounting (r = .25, p = .03).

Conclusions

These findings support previous associations between connectivity within the CEN network and AUD severity, providing additional specificity to the relevance of the triple network model to AUD.