bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on visual servo strategy for the uncalibrated non-standard manipulator
Authors: Wang Wei
Published: August 2024

Aiming at the problem that the hand-eye calibration of the uncalibrated non-standard manipulator cannot be addressed by the general calibration method, i.e., the eye-in-hand and eye-to-hand calibration method of the visual servo system was proposed to guide the manipulator without calibration. First, a camera was installed on the manipulator to complete the eye-in-hand calibration and achieve transformation from the end coordinate system to the camera coordinate system. Second, a second camera was used to achieve eye-to-hand calibration and obtain the transformation relationship from the end coordinate system to the calibration board. The entire calibration process only requires the collection of images from two scenarios, i.e., the docking and the completion of the docking processes. The implementation results show that the rotation error between groups is less than 1.5°, and the translation error between groups is less than 8 mm. Therefore, the proposed visual servo strategy can accurately guide the uncalibrated non-standard manipulator.
Employment of conditional random fields for monocular depth estimation
Authors: Zhang Xudong , Zhu Guosong
Published: August 2024

Estimating depth from a solitary RGB image, known as monocular depth estimation, presents a significant challenge. Currently, most methods for this task involve designing increasingly complex networks to regress the depth map straightforwardly. However, we have adopted a more interpretable approach by using Conditional Random Fields from optimization methods. Additionally, to facilitate better information transfer between nodes, a multi-head attention mechanism is employed to calculate multiple energy functions, which are then optimized by the network into an accurate depth map. Experiments demonstrate that our method can accurately estimate the depth of landscapes.
Design of embedded real-time human detection system based on Zynq platform
Authors: Duan Hongliang , Xu Yang , Lu Zhenlin , Yang Ruoling , Qin Xiansheng
Published: August 2024

With the continuous breakthroughs in intelligent algorithm models, the computational complexity of target detection algorithms has continued to increase. At the same time, the performance and power consumption requirements of edge systems for hardware devices have become increasingly stringent. To better weigh the detection speed and accuracy of the artificial intelligence algorithm deployed on the embedded Zynq platform, we streamline and optimize the YOLO-v4 network model and then retrain the model based on the COCO dataset. Finally, the quantification and compilation processes are carried out in sequence to realize model migration and deployment from the server side to the Zynq device. Through experimental verification, the detection time of the modified YOLO-v4 algorithm model in this paper is 31.42 ms, and the detection error is only 4.0 pixels.
Design and application of portable eddy current inspection device for thimble tubes of nuclear power plant
Authors: Yu Yongcheng , Wang Baige , Yang Hongbo , He Yi , Li Xiang
Published: August 2024

The Thimble tube is an important piece of equipment in the nuclear power plant; its role is to provide a neutron flux detector with a channel for the reactor fuel assembly. Eddy current detection is the main way to evaluate and track the wear of the thimble tube. In order to conduct more efficient detection of the thimble tube, this paper has carried out a series of studies on the mechanical device of the eddy current detection of the thimble tube and the software control system. The inspection system is compact, portable, and does not require an external power supply or gas source, making it easy to carry and operate. It is well-suited for promotion in various nuclear power plants.
Bogie-YOLO: A key component detection model for high-speed train bogies
Authors: Wang Jiagan , Li Jinglong
Published: August 2024

The performance of high-speed trains heavily relies on the bogie, a critical component supporting the body and steering. Traditional methods for inspecting bogie components, which rely on manual identification, are not just time-consuming but also expensive. Furthermore, deep learning models often face difficulties in handling dimly lit, intricate backgrounds and detecting numerous small objects. To solve these challenges, we propose Bogie-YOLO, a targeted detection model tailored for bogie photography scenes. Bogie-YOLO utilizes the YOLOv5 framework and introduces the Coordinate and Squeeze-and-Excitation Attention Module (CSAM), improving positional information and channel relationships. Additionally, to expedite convergence, we adopt EIoU as the bounding box loss. Experimental results demonstrate an average Precision of 87.9% and an average mAP0.5 of 83.2% on the dataset of key components of train bogies.
Study on the influence of the blade inlet area-to-impeller inlet area ratio on hydraulic performance under various specific speed conditions
Authors: Chen Biwen , Chen Changsheng , Ma Zhenlai , Li Guoping , Zhang Yi , Zhang Ning
Published: August 2024

To investigate the unknown effects of the relationship between the impeller inlet area and blade design on the hydraulic performance of centrifugal pumps at various specific speeds within typical rotating fluid equipment, this paper aims to conduct experiments on three types of pumps with different specific speeds (ns
=155, ns
=125, and ns
=88). The study analyzes how the S1/S0
ratio, which represents the blade inlet area to impeller inlet area ratio, affects the hydraulic performance of different centrifugal impellers. Furthermore, it explores the changing patterns of how the S1/S0
ratio influences the pump’s head and efficiency. The results indicate a recommended range of S1/S0
ratios for centrifugal pumps optimized for efficiency at different speeds. When considering the impact on cavitation performance, it is suggested that the S1/S0
ratio should not exceed the upper limit of this recommended range.
A study on air quality prediction with multiple features based on GCN-LSTM
Authors: Jin Yongming , Ren Ge , Hu Yuxin , Wang Wennan , Zhang Jintao
Published: August 2024

Air pollution is closely intertwined with public health. However, air quality forecasting is a very challenging project, and the influencing factors are intertwined. This study focuses on analyzing and forecasting urban air quality from a spatiotemporal perspective, based on various factors that affect air quality. In this paper, a diverse dataset impacting air quality was collected, including pollutant concentrations, meteorological conditions, and traffic flow data. A GCN-LSTM model was developed to thoroughly explore the spatial relationships between different sites within the study area. This paper also conducted forecasts over different time lengths. The experiments revealed that weather data and traffic data can help improve air quality prediction results. The forecasting accuracy for RMSE and MAE were improved by an average of 8.7% and 8.5% in PM2.5, and 12.8% and 11.9% in NO2, respectively. Within the model, the GCN-LSTM model, which considers spatiotemporal features, outperformed single models and proved to be more advantageous in long-term forecasting. The findings of this study contribute to the improvement of air quality prediction effectiveness.
Optimization design and simulation of chip-level micro-gas columnar preconcentrator structure based on ANSYS
Authors: Lu Luoping , Ran Yunzheng , Tian Xiangpeng , Zheng Ronghao , Zhong Jianwei , Liao Honghua
Published: August 2024

To achieve the efficient preconcentration of measured sample gas in microfluidic chips, this paper proposes a design and simulation scheme for the chip-level micro-gas columnar pre-concentrated structure based on ANSYS software. Firstly, four kinds of chip-level micro-gas columnar preconcentrators with circular, semi-circular, triangular, and rhombic shapes were designed, and the simulation analysis of gas-phase micro-fluidic dynamics was performed on the chip-level micro-gas columnar preconcentrator by ANSYS software. The influence of different design parameters on the pre-concentrated effect of level chips micro-gas columnar preconcentrator was discussed. The simulation results show that the arrangement is a triangle arranged in reverse parallel, and the preconcentration effect of the chip-level micro gas column preconcentrator is better.
Research on model predictive trajectory tracking control based on particle swarm optimization
Authors: Yu Yu , Shen Xingkui , Wang Weiling
Published: August 2024

This paper focuses on the challenges of low trajectory tracking accuracy in intelligent vehicles caused by different road adhesion coefficients and vehicle speed conditions, aiming to address these challenges with an improved Model Predictive Control (MPC) algorithm. Firstly, a model encompassing vehicle dynamics featuring two degrees of freedom has been formulated, along with a corresponding model to track trajectory errors. Secondly, a Model Predictive Control (MPC) algorithm is designed with trajectory tracking accuracy and control increment as the objective functions. Additionally, incorporating a Particle Swarm Optimization (PSO) algorithm with MPC allows for the dynamic computation of the optimal prediction horizon size. Afterward, a unified simulation model is assembled, employing both CarSim and MATLAB/Simulink, to conduct dual-lane trajectory tracking simulation analysis under diverse road adhesion and vehicle speed conditions. The efficacy of the proposed control algorithms is validated through this process.
Intelligent following car based on Ultra-wideband technology
Authors: Zhao Rujie , Zhang Heng , Qi Fenghua
Published: August 2024

To facilitate convenience and reduce the burden of traveling, this paper introduces an intelligent following car designed with ultra-wideband wireless carrier communication (UWB) technology as the core, combined with a variety of sensor peripherals. The car is controlled by the STM32F407XGT6 microcontroller, and the pressure sensor inside the car box can realize real-time measurements of the goods inside the box. In addition, the intelligent following car is loaded with an ultrasonic automatic obstacle avoidance device around the car, and the bottom of the car is loaded with an automatic trajectory device. The car adopts the function of wireless charging so that the body can achieve better airtightness. The car also has the function of the Internet of Things to transmit data, so that it can realize the diversification of the functions of the transport of goods, which not only greatly facilitates people’s lives but also lays the foundation for the intelligent transportation of goods.
Human action recognition based on smart sensors
Authors: Lin Lanxin , Sun Di
Published: August 2024

This paper focuses on human activity recognition using LSTM (Long Short-Term Memory) for handling time series data issues. Because LSTM can better deal with the correlation and long-term correlation of time-series data, the model is utilized to build a human-computer interaction action recognition system. The experimental steps include the following: first, the bracelet sensor obtains the human body acceleration signal to obtain a data set containing a variety of motion postures and preprocesses the data. Then, LSTM is used to extract the features of the dataset, and the feature vector is utilized as the input to train the model iteratively. Finally, to improve the accuracy of recognition, a variety of machine learning models will be applied to compare the performance with the LSTM model. The experimental results indicate that the proposed model can better capture the acceleration changes of human motion in various life scenes, and can accurately distinguish various sudden action patterns of the human body in daily life. This also makes the human movement behavior recognition system proposed in this study more comprehensive.
Dual-mode object detection algorithm based on feature enhancement and feature fusion
Authors: Ji Qingbo , Qi Yufei
Published: August 2024

In complex conditions, visible light cannot meet the challenge of high-precision target detection requirements, so we propose a dual-modal target detection algorithm, named Dual-Mode Object Detection Based on Feature Enhancement and Feature Fusion (DEF). The algorithm in this paper is improved based on YOLOv5. First, we build a dual-branch feature extraction network for visible light and infrared images, separately extracting features of the images. Subsequently, the extracted feature maps are input separately into a feature enhancement module, which is constituted by a multi-branch convolutional architecture and the ECA (Efficient Channel Attention) mechanism, to acquire an augmented set of salient features. Finally, a module for feature fusion is devised to merge the modality-specific features that are extracted by the two branches. The integrated feature maps are then sent into the subsequent module to achieve dual-mode image target detection. The effectiveness of the proposed algorithm is confirmed through a series of experiments conducted using the FLIR and LLVIP datasets, showcasing its superior performance in practical applications.
Hand-object pose estimation based on HOPE-net and attention mechanism
Authors: Li Pangshan
Published: August 2024

Hand-object pose estimation is an important part of studying hand pose, focusing on the joint detection of hand and object poses. Although CNN has shown advantages in various hand pose estimation, due to various problems such as the complex structure of the hand pose and self-occlusion, there are limitations in the extraction of image features and the amount of calculation. Existing studies have shown that GCNs have a good performance in dealing with such problems. In this paper, we propose to combine the attention module with two adaptive GCNs, and perform feature enhancement operations on hand features. While ensuring the accuracy of feature extraction, it can effectively improve the network calculation speed and avoid some questions such as self-occlusion. Experimental results show that, through end-to-end training, the algorithm has a great performance on the problem of hand-object pose estimation.
Argo data anomaly detection algorithm based on selective ensemble of fuzzy clustering
Authors: Zhu Wanlu , Jiang Yongguo , Zhang Meng
Published: August 2024

Argo profiling float data is a crucial data source for fundamental research and predictive forecasting operations in oceanography and environmental science. However, compiling and organizing such datasets demands considerable time and human resources. Therefore, the quest for effective methods of detecting anomalies in Argo data is of paramount importance. In this regard, we propose three improvement strategies within the stacking ensemble framework: preserving the original training set, weighting base model outputs, and combining the two former methods. The aim is to explore implicit relationships within the data, enhance model prediction diversity, and improve Accuracy. Additionally, in the selection of base models, to address the challenge of conventional clustering-based ensemble algorithms in achieving high levels of both diversity and accuracy among base learners, we introduce a selective ensemble method based on C-means clustering. This method selects base learners for the ensemble based on weighted scores derived from membership and performance evaluation metrics. Both of these enhancement approaches demonstrate effective application and improved detection performance when applied to Argo data.
Orthogonalized layer features with wavelet for multi-object tracking
Authors: Ban Yandeng , Yang Jinlong
Published: August 2024

Multi-object tracking (MOT) in crowded scenes presents challenges such as target occlusion and interference from similar objects. The detection models that rely on the target center as positive samples often struggle with noise introduced by ambiguous data annotations. To address these issues, we propose to segregate the target features of distinct frequency tiers by leveraging wavelet decomposition. Features obtained from two-dimensional wavelet decomposition can exhibit orthogonality and complementarity along the horizontal and vertical directions. Low-frequency components are usually related to visible targets, while high-frequency energy often emanates from targets undergoing occlusion, enabling discrimination between multi-layered objectives. Experimental evaluations are conducted on the MOT17 and MOT20 benchmarks (motchallenge.net), which demonstrate that our approach has better competitive performance compared to the current state-of-the-art methods.
Research and application of rough set attribute reduction algorithm based on genetic algorithm in pipeline network
Authors: Li Hong , Zhou kai , Zhuang Zhao
Published: August 2024

Explosion accidents often occur in urban underground pipeline network systems, which has caused safety issues in public life and industrial production. GIS technology can visually manage pipeline equipment and solve the problem of valve closure analysis in the event of current accidents, avoiding the escalation of the situation, but it cannot make predictions. Attribute reduction is one of the core contents of rough set theory and is an NP-hard problem. This article proposes a custom attribute reduction algorithm based on an algorithm and uses the algorithm in the historical burst pipeline dataset of the water supply pipeline industry. The results have shown that this method combined with a decision tree can make predictions before water pipe leakage. The algorithm provides the best result of attribute reduction for decision tree methods, which will improve the speed and accuracy of decision-making.
Enhancing Robot Learning with Transformer-based Morphology Modeling
Authors: Wang Yanying
Published: August 2024

The transformer model has made significant progress in various areas through large-scale training. In contrast, the traditional robot performs a single task, and there is an issue with migrating the strategic model. In this study, a Robot Morphology Learning (RML) method is proposed to enhance efficiency and generalization performance by learning multiple tasks in a transformer model. RML constructs the robot’s morphology as a graph and utilizes a graph neural network to handle graphs of arbitrary connections and sizes, addressing the disparity in state and action space dimensions. RML breaks through the limitation of non-migration of models, realizes efficient training, and improves the generalization performance of models, enabling quick adaptation to new tasks. Experimental results show that the proposed method outperforms previous methods in both multi-task learning and transfer learning experiments.
Compliance control of a rehabilitation massage robot in dynamic scenes
Authors: Wang Fan , Chen Chao , Di Chong , Liu Zhaoyang , Wang Zengfa
Published: August 2024

Medical robots encounter challenges when interacting with people or operating in complex and dynamic environments due to the variability of human morphology and the unpredictability of environmental changes. Compliance of human-robot interaction is the primary goal of medical robots when in contact with the human body. Therefore, robots must be able to adaptively adjust their forces and actions to ensure safety and comfort during the contact process. This paper focuses on the compliance control of rehabilitation massage robots in dynamic scenes. We propose a mechanical arm compliance control method based on the Soft Actor-Critic (SAC) algorithm. We construct a simulated massage environment in a dynamic scene according to the task requirements and design a massage path covering the entire back. Under the framework of deep reinforcement learning, the optimal reward function is designed to achieve constant force control under dynamic scenes. Through numerous simulation experiments, we have verified that the robotic arm can move along the predetermined path under the massage while maintaining a constant contact force with the body simulation module. The actual contact force and target contact force control are realized within 0.1 N.
Probiotics and Vegetable Oil Association: A Review
Authors: Hussain Abrar , Parveen Sara , Riaz Mahwish , Zia Amina , Abid Ali Syed
Published: August 2024

Probiotics are live microorganisms that confer a health benefit on the host when administered in adequate amounts. Currently, seven genera are used for their probiotic’s potential. Strains having the desired characteristics are associated with plenty of health benefits. Recent literature shows that different plant and vegetable oils are associated with probiotics. Other reports indicated their various aspects. For instance, the effect of probiotics on plant-based oil, edible oil, probiotics, the use of oils for probiotics encapsulation, etc., are documented. It is also reported that probiotics from vegetable sources and plant oil have some advancements, such as vegetarian and lactose-intolerant people using them. The multidimensional association between probiotics and vegetable oils attracts researchers to explore it. This research area is growing fast, but more limited research data is needed. Hence, this literature study was performed to identify the relationship, pros, and cons and provide recent insight into the literature for the researchers. Materials were collected by searching the related terminologies on different websites, finding the downloaded literature, and analyzing it critically. The results were presented as described by the authors, and a correlation was found that helps to enhance the field. Collectively, it was found that probiotics and plant and vegetable oils have a multifactorial relationship, and both can increase the effects of one another.
Detection of new oil palm fruit bunches based on deep learning
Authors: Harmiansyah  , Sembiring E P , Oviana E T , Supriyanto
Published: August 2024

The Indonesian oil palm plantation experiences a production rate of fresh fruit bunches and crude palm oil (CPO) influenced by the quality of fruits with excellent and uniform maturity accepted by the palm oil processing factory. The uniformity of fruit quality produced from the harvesting process through fruit maturity criteria, namely fractions 00, fraction 1, fraction 3, and fraction 4, necessitates the need for maturity detection based on deep learning with a pre-trained YOLOv5 model capable of detecting the maturity of new oil palm fruit bunches. The aim is to develop a deep learning-based maturity detection model based on visual images and analyse the deep learning algorithm’s performance by classifying the maturity level of new oil palm fruit bunches. The method collects a dataset of new oil palm fruit bunches with 4180 images. The collected dataset will be annotated with Roboflow, so the annotation results will undergo training, validation, and testing processes using the deep learning script. The result obtained for the data found to detect the ripeness levels of Fresh Fruit Bunches (FFB), shows that the YOLOv5 model demonstrates strong performance with an accuracy reaching 72.5% in testing, which falls into the category of fair annotation. These results indicate that the system created and used is functioning well.
The effect of mycorrhiza on the growth of Ylang-Ylang seedlings
Authors: Anita Nugraheni Y M M , Mansur Irdika , Sulistiani Erina
Published: August 2024

Ylang-ylang (Cananga odorata forma genuina Steenis) is a plant with high economic value and many benefits. Mycorrhizal application is necessary to obtain a quality mother plant. Therefore, a study on the mycorrhiza effect on ylang-ylang growth was needed, especially in the seedling stage. This study aimed to analyse the impact of mycorrhiza infection on the development of ylang-ylang seedlings. The method used in this study was the isolation of Claroideoglomus etunicatum spores. Ten of each spore was inoculated onto the sterilised roots (using 5% Natrium hypochlorite) of the three-month-old ylang-ylang seedlings and then planted on zeolite media and soil in a 6 x 10 cm pottery, which was then transferred to each pot 13 cm in diameter and 16 cm in height. For comparison, zeolite and soil media were used without the addition of mycorrhiza. The result of this study showed that the application of mycorrhizae using soil media had the highest increase in height (4.19 cm), diameter (0.72 mm), number of leaves (2.6 strands), and number of buds (0.8 leaf buds) compared to other treatments.
Evaluation of environmental impact of small-scale oil palm cultivation in Ghana: A life cycle assessment of Juaben Municipal District
Authors: Biney M , Yudha Y S , Agbolosoo J A
Published: August 2024

Palm oil has become increasingly popular in recent years due to its various uses in the food, cosmetics, and biofuel industries, resulting in a high global demand. In the 2021/22 marketing year, palm oil production reached approximately 73.8 million metric tons, up from 73 million in 2020/21. Palm oil yields more per area than other oil crops such as sunflower, soybean, and rapeseed. It is also economically feasible to grow in areas where other crops typically do not thrive, providing a source of income for many people. Palm oil cultivation has also contributed to economic growth, rural community development, and poverty reduction in disadvantaged regions, especially in Indonesia, Ghana, and Malaysia. The study’s findings indicate that Fresh Fruit Bunches (FFB) production significantly contributes to the global warming index. The study also shows that the eutrophication effect resulting from fertiliser usage was lower, with a total value of 1.8 kg PO4 equivalent, which is lower than the values reported in previous studies. This is attributed to the common usage of fertilisers in the study area due to financial constraints. An in-depth analysis of small-scale farmers’ local vegetable oil production was also studied, focusing on the challenges encountered during oil palm cultivation and processing.
The position of Indonesia’s palm oil in the vegetable oil international market
Authors: Nugrahapsari R A , Harianto  , Nurmalina R , Fariyanti A
Published: August 2024

The increase in global crude oil prices and awareness of energy sustainability are driving the development of the biofuel industry. Palm oil has high productivity and competitive prices compared to other vegetable oils, encouraging competition among major palm oil exporters and significant vegetable oils. The research examines factors influencing Indonesia’s position in the global vegetable oil competition and assesses how economic shocks impact Indonesian palm oil competitiveness. This study utilized the Almost Ideal Demand System model on secondary data in the time series period of 1990-2022. The research results showed that the increase in global income is the most influential factor in the rise in the market share of Indonesian palm oil. The increase in crude oil prices favored the position of Indonesian palm oil compared to Malaysia. Fluctuations in Malaysian palm oil prices could affect the competitiveness of Indonesian palm oil. Malaysia’s palm oil, soybean oil, and sunflower oil are more complementary than Indonesia’s. Indonesia needs to collaborate with significant vegetable oil exporters, to create a palm oil commodity exchange, and to synchronize policies from upstream to downstream to ensure the fulfillment of domestic and export palm oil needs.
Structure of Social Forestry Service Area in Cenrana District, Maros Regency
Authors: Ali A , Supratman  , Syamsu A
Published: August 2024

Damaged road access, absence of markets in some villages, and lack of access to technology are challenges for forest communities in the Cenrana District. Optimising service centres in social forestry (SF) locations can support social forestry activities. Based on scalogram and gravity analysis, the structure of social forestry areas in the Cenrana Sub-district shows Limampoccoe Village as the sub-district level service centre, providing warehousing, commodity distribution, quality control, and packaging and branding services. Labuaja Sub-Village (Laiya Village), Malaka Sub-Village (Cenrana Baru Village), Bululohe Sub-Village (Rompegading Village), and Nahung Sub-Village (Labuaja Village) serve as commodity auction market locations, local trials, counselling, honey cultivation facilities, and processing technology for derivative products such as aren palm, candlenut, porang, and coffee. Road infrastructure and tourist attraction signposts are in Malaka Hamlet (Cenrana Baru Village). Conflicts in Labuaja Village require conflict resolution in social forestry management.
Current status of Indonesia’s palm oil products and their competitiveness in the global market
Authors: Setiajiati F , Nurrochmat D R , van Assen B W. , Purwawangsa H
Published: August 2024

Oil palm is recognised as contributing significantly to crucial economic growth in Indonesia. Still, it is often considered a severe threat to tropical forest ecosystems due to the large amount of forest conversion for oil palm cultivation. Also, oil palm business and marketing processes have many opportunities and challenges. In response, this research examines and reveals in depth the current state of Indonesia’s palm oil business and marketing in the global market. By interviewing, reviewing the literature, and conducting a stakeholder analysis, we found that Indonesia has high competition for global palm oil, with processed products such as Refined Palm Oil and Oleochemical Mix dominating the world market and high export value growth. However, the export value for crude oil, such as palm oil and palm kernel oil, in 2020 - 2021 is low due to government policies for downstream and industrialisation programs. The government, as policymakers, the oil palm funding agency (BPDPKS), and the oil palm companies are key players with high interest and influence in the sustainable development of the oil palm business. European Union and Press can be context setters with low interest but high influence on business and marketing processes in Indonesia.
The strategic architecture of “BPDPKS” – Oil Palm Plantation Fund Management Agency
Authors: Fitriyah  , Suroso A I , Baga L M
Published: August 2024

The Oil Palm Plantation Fund Management Agency, Badan Pengelola Dana Perkebunan Kelapa Sawit, is a government institution that manages funds for oil palm plantations using export levies from crude palm oil (CPO) products and their derivatives. However, there is a dichotomy between supporting conglomerates and smallholders. To address this issue, an analysis is required to determine the agency’s position as a strategic organization capable of implementing sustainable oil palm development programs. The strategic architecture outlines the future and business outlook of the agency. Hence, this study uses IPA analysis to analyze the current planning needs and what is required in the agency’s strategic architecture. The core competencies were identified using VRIO frameworks and external analysis using PESTEL. Result from that analysis as a parameter to create a strategic architecture for 2024-2028. The strategic architecture has been developed based on internal and external evaluation matrices, employing strengths, weaknesses, opportunities, and threats (SWOT) analysis. Additionally, this research was followed by preparing an action plan as a guideline for implementation programs.
Strategy for Increasing Exports of Indonesian Palm Oil
Authors: Azahari D H , Dwi Laksani Dian , Amalia Rizki , Kusumawardhana Ramiaji
Published: August 2024

Palm oil and its derivatives contribute to 13% of Indonesia’s total exports. Indonesia has a significant opportunity to become a major player in the global palm oil trade but faces threats in expanding market access and even maintaining existing markets. This research aims to analyse the strengths, weaknesses, opportunities, and threats in the plantation sector, especially palm oil, in Indonesia, using competitiveness analysis and Export Product Dynamic to formulate strategies for enhancing the export of palm products and derivatives. From this analysis, it is concluded that Indonesia has strength in its geographic conditions, but needs to strengthen human resources, especially in the agricultural sector, and add value to export commodities beyond raw materials. With the growth of the international market and the increase in the number of free trade agreements held by Indonesia, the potential for developing market access for palm and its derivatives is likely to expand. However, Indonesia also faces non-tariff barriers and environmental unfriendliness stigma, resulting in rejection or obstacles for Indonesian products to enter export destination countries. Strategies to be explored further include expanding into emerging markets while maintaining traditional markets, optimizing research and development for upstream efficiency and downstream derivative product diversification, improving sustainable palm oil industry governance, and enhancing domestic policies to support the palm oil industry.
Evaluating the market positions of soybean oil-producing countries in the world market
Authors: Qanitha M , Majiidu M , Puwawangsa H , Ekayani M
Published: August 2024

Soybeans have an essential position as the main legume crop in the world food system. This research focuses on four high-producer countries of soybean, i.e., India, the Netherlands, Brazil, and the USA. The subject of soybeans as a vegetable oil includes soybean seeds, soybean oil, soybean oil cake, and refined soybeans. This research aims to identify opportunities for the industrial development of soybeans and their derivatives from socio-economic aspects in the world. The analytical method used in this soybean research is the BCG matrix. The data collection used is secondary data from several countries that export soybeans from 2020 to 2021, as well as a literature review of several kinds of literature. Soybean exporters in 2021 are in first place, namely Brazil (USD 39 billion), the United States (USD 27.8 billion) is in second place, Paraguay (USD 2.99 billion) is in third place, The Netherlands (USD 462 million) is in 7th place, and India (USD 22.9 million) is in 33rd place. The analysis results from soybean research show that Argentina took the cash cow position in the BCG matrix and has excellent potential for development in the soybean industry and its derivative products to meet market trends worldwide.
Exploring sustainable cooking oil consumption patterns in Punjab, Pakistan: A comprehensive scientific study
Authors: Arshad A , Nurrochmat NA , Arshad U , Sudarsono S , Bashir U , Ayyaz M
Published: August 2024

Palm oil is the world’s most widely traded vegetable oil, with total trade amounting to 77.22 million metric tons of total vegetable oil production in 2023. Palm oil is usually used as the material of cooking oil and food products, biofuel, cosmetics, and other derivates. Pakistan imported $3.36B in Palm Oil, becoming the third largest importer of Palm Oil in the world. However, palm oil has faced deforestation in the last few years. To solve that, the palm oil plantation must be sustainably certified (RSPO), and the impact will increase the palm oil price. Therefore, this research aims to explore Pakistan’s sustainable cooking oil consumption patterns. This research uses purposive sampling from diverse socio-economic backgrounds and geographical locations within Pakistan. The total respondents are 52 respondents from Punjab, Pakistan. The data analysis uses descriptive statistical analysis. The results show that palm oil was the most used cooking oil (68%), followed by sunflower (22%). Over half of the participants (56%) were willing to pay a premium for environmentally friendly cooking oils. Most participants (84%) were familiar with sustainable certification labels like RSPO, and most participants (82%) believed that adopting sustainable palm oil production practices could alleviate environmental and ethical concerns.
Bioethanol derived from rice straw agricultural waste as alternative energy towards a pollution-free era
Authors: Pratama I L , Baharrudin R , Rahman M F , Siregar J J
Published: August 2024

Rice production occupies the first position as raw material for bioethanol production, with a total output throughout Indonesia of 56.63 million tons in 2023 (BPS, 2023). The purpose of this innovation is to create bioethanol derived from new raw materials, namely rice straw, by applying the simultaneous saccharification and fermentation (SSF) method for the conversion of lignocellulose into alcohol, which will produce bioethanol with a purity level of about 99% with a more efficient process. This method goes through several processes, including breaking the size of the rice straw to 0.1 mm and mixing it with several bacteria for fermentation and hydrolysis. The saccharification process is also carried out until the distillation process to make high levels of bioethanol. Bioethanol from the simultaneous saccharification and fermentation (SSF) method is tested using an Octane Number Analyzer to determine the octane number content. The test included mixing bioethanol with Pertamax 92 to prove the effect of adding bioethanol to Pertamax, and the result was 93.1. Therefore, the innovation of making bioethanol from rice straw with the SSF method is expected to be a solution for Indonesia towards a pollution-free era.
Productivity and Yield of Patchouli (Pogostemon cablin Benth) in Community Forest using Agroforestry Patterns in Tommo District, Mamuju Regency, West Sulawesi Province
Authors: Daud M , Hikmah H , Astuti S , Samrin S , Baharuddin B
Published: August 2024

The productivity of patchouli plants in Indonesia still needs to improve. This is because patchouli cultivation is generally carried out using a monoculture and shifting cropping pattern, which causes land degradation. This study aims to determine the productivity and yield of patchouli (Pogostemon cablin Benth) in community forest using agroforestry patterns in Tommo District, Mamuju Regency, West Sulawesi Province. The study used observation, survey, interview, and questionnaire methods. Respondents were selected using a purposive sampling method of 30 respondents who cultivated patchouli plants using an agroforestry system in community forests. In-depth interviews were conducted with these respondents, followed by a survey of their patchouli cultivation area. The findings revealed that the average annual production of patchouli plants in community forests employing an agroforestry approach amounted to 8,604 kg/year through two harvests on an average land area of 0.74 hectares. Consequently, the average patchouli plant productivity is 10,697.26 kg/ha per year. Patchouli production consists of leaves and twigs that the community has dried and then distilled using conventional distillation equipment. The average yield from the distillation of patchouli leaves and twigs into patchouli oil is 1.96%. Consequently, the productivity of patchouli oil that can be obtained from cultivating patchouli plants is around 209.67 kg/ha per year, equivalent to 246.67 litres/ha per year. The productivity and yield of patchouli plants in the agroforestry pattern are generally lower than in the monoculture pattern, with an average productivity of 13,279 kg/ha per year and a yield of 2.83%. Still, the agroforestry pattern provides added value by producing forestry and agricultural plants combined with the patchouli plant.
Flexible printed circuit-based triboelectric film sensor for integrity monitoring of tensile bolted joints
Authors: Wang Chu , Miura Nanako , Hiraga Motoaki , Masuda Arata
Published: August 2024

This study presents an improved design of a triboelectric film sensor for integrity monitoring of tensile bolted joints, which is designed to capture the micro-scale relative motion due to the bolt’s looseness by utilizing the triboelectric effect of the polymer layer of the sensor in contact with the metal surface of the fastened objects. The key idea is twofold: First, we use the triboelectric effect between the polymer layer and the fastened object itself, instead of the triboelectric effect between two polymer layers. This allows the sensor to be a single sheet configuration instead of two-piece. The second idea is to make the sensor design fabricable as a standard flexible printed circuit. This makes it possible to produce sensors accurately and inexpensively. Experimental tests incorporating the proposed sensor into a tensile bolted joint have demonstrated that the sensor’s voltage output is inversely related to the bolt’s tightness. Additionally, a modeling study adopting Persson’s contact theory has been conducted to refine the understanding of the real contact area, triboelectric charging, and separation dynamics between the polymer and metal layers, which is crucial for the accurate modeling of sensor outputs under dynamic loading conditions. It has been concluded that the integrated mechanical and triboelectric model successfully aligns with the experimental findings, indicating the sensor’s potential for practical applications in bolt integrity monitoring.
Performance prediction of dielectric resonator based mm-wave MIMO antenna using machine learning algorithm
Authors: Sikandar  , Katiyar Himanshu , Gangwar Som Pal , Sharma Anand
Published: August 2024

The alumina ceramic-based dual port radiator is structured and analyzed in this communication. Cylindrical-shaped ceramic is fed by using modified circular aperture. The designed aperture creates dual orthogonal modes i.e.

H
E

M

11
δ

x

and

H
E

M

11
δ

y

mode inside the ceramic with 90° phase shifts. This property generates circularly polarized waves from 27.6 GHz–28.4 GHz. Anti-parallel placements of antenna ports introduce the concept of space diversity and improve the isolation level by more than 25 dB. To reduce the computational time, two machine learning algorithms i.e. Random Forest and XGBoost are utilized to predict the reflection coefficient variation. Testing of a fabricated prototype of the proposed radiating structure confirms that the designed radiator works well between 26.8 GHz–28.7 GHz. Asymmetrical placement of ports concerning substrate tilts the radiation pattern obtained from different ports in different directions i.e. ±30°. Stable radiation features and good diversity performance make the designed antenna suitable for mm-wave 5G communication system.
Introduction
Authors: Nili Shmuel
Published: August 2024

This introduction presents the book’s central distinction among three categories of normative debates about law and policy. The opening category—‘first-best’ justice debates—covers those normative debates that are most familiar in contemporary legal and political philosophy. These debates presuppose a consolidated liberal-democratic government, whose coercive capacities are unmatched within its jurisdiction. The next category—‘second-best’ justice debates—revolves around the effort to bring about such a government. In ‘third-best’ justice debates, by contrast, the optimism that is constitutive of second-best justice largely or even entirely disappears. This may be because the only way to transition into the circumstances of first-best justice in a given jurisdiction is to pursue policies that endanger the prospects for such transition elsewhere. Or it may be because all plausible scenarios still feature a threat of severe violence pervading the jurisdiction. The book’s overarching question is how to deal with such ultra-tragic circumstances.
Conceptualizing the Law’s Reach: Violence, the Rule of Law, and the Fragility of Democracy
Authors: Nili Shmuel
Published: August 2024

Even within supposed bastions of liberal democracy, basic liberal-democratic values are increasingly under attack. This widely discussed ‘crisis of democracy’ immediately raises question marks around any firm conceptual distinction between consolidated democracies on the one hand, and, on the other hand, jurisdictions pervaded by the shadow of violence. The central goal of this chapter is to address such questions. In the process, the chapter further motivates and elaborates the introduction’s core distinction among first-, second-, and third-best justice dilemmas. The chapter also lays out a broader conceptual apparatus that informs later chapters.
Legal Kleptocracy and Third-Best Justice: Pre-Empting Entanglement (I)
Authors: Nili Shmuel
Published: August 2024

Crime committed under the shadow of violence is often given a legal veneer by those with the power to write the laws. One key result is ‘legal’ privatization of state assets, evident anywhere from corrupt regimes at the heart of the Arab Spring, to the privatizations that dominated post-Soviet countries. This chapter focuses on the jurisdictional dilemmas that affluent democracies are forced to confront, when the winners of such privatizations—from sudden oligarchs to long-established regime cronies—predictably seek to send the spoils of their victory abroad. Should affluent democracies prioritize the letter of the law, and accordingly welcome the relevant funds into their jurisdictions? Or should affluent democracies instead prioritize the more demanding—and elusive—standard of the ‘spirit’ of the law, even at the significant cost of giving up massive sums that can improve the lives of their own citizens if invested in their economies?
Pre-Empting Entanglement (II): Hostages and Ransoms Across Borders
Authors: Nili Shmuel
Published: August 2024

This chapter examines how far affluent democracies can permissibly go in trying to avoid entanglement in crime occurring against the shadow of violence overseas. More specifically, the chapter focuses on criminal threats triggered by international migration, and particularly by knowledge, on the part of ascendant violent actors, that extremely vulnerable locals have (usually remittance-sending) relatives who have emigrated to affluent countries. Such circumstances are ripe for criminal exploitation, in the shape of ubiquitous cross-border ransom demands: extortionists regularly contact the migrants and threaten to harm their families back home, unless they are paid significant sums. The resulting jurisdictional dilemma for affluent democracies is how the very real risk of such extortion should affect their admission decisions regarding separated families. The chapter’s main goal is to develop normative principles, as well as policy proposals, for handling this dilemma.
Relational Responses to Trauma in Twenty-First-Century French and Spanish Women's Writing
Authors: Lawlor Hannie
Published: August 2024

Relational Responses to Trauma in Twenty-First-Century French and Spanish Women’s Writing offers new insight into what it means to write relational lives. It broadens the parameters of existing discussions in terms of geography as well as genre, drawing together two literatures whose prominence in life-writing theory to date could hardly be more different: while French women’s writing has long been at the centre of international discussions of autobiography, the relative invisibility of Spanish women’s writing remains striking. The dialogue that thus underpins this study, between diverse twenty-first-century case studies and broader approaches to life writing, shines a light on what is gained from inviting different voices into the discussion. These narrative projects challenge long-standing critical assumptions in autobiography studies and trauma theory about how writers can and should represent the multiple perspectives that are at the heart of intergenerational stories. In exploring the narrative solutions that these texts propose in response to the ethical questions they navigate, Relational Responses shows that writing relational lives rests on far more than the mere recounting of a shared history. ‘Relating’ in these texts, it proposes, is an act embedded in the telling of the story. It is a mode of testifying together to traumatic experience, one that reveals a powerful preoccupation in contemporary women’s life-writing practice with making space for the many voices and versions that otherwise go unheard.