bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Propagation Characteristics of Temperature Anomaly in the Intermediate and Deep Ocean Layers
Authors: Lan Yunjie , Song Junqiang , Sun Difu , Leng Hongze , Zhou Yong , Tan Jiaming
Published: June 2023

Due to the lack of observational data, the understanding of propagation characteristics of temperature anomaly is mainly concentrated in the sea surface and the waters above thermocline. Little is known about the propagation characteristics of temperature anomaly in the middle and deep oceans. Based on the gridded Argo data, we analyze the propagation characteristics of temperature anomaly along the isodensity. We found that the temperature anomalies in the middle and deep ocean layers propagate westward at most latitudes, which is the same direction as the propagation of the surface temperature anomaly caused by oceanic Rossby waves. Through the Radon transform, we determined the propagation speeds on different isodensities and explored the relationship of propagation speeds and latitudes and water depth. To the best of our knowledge, this study is the first to explore the propagation characteristics of mid-deep temperature anomalies at the ocean basin scale.
Seamount Clutter Modeling for Long Range Seabed Reverberation in Deep Sea
Authors: Zou Chenglu , Mo Dongpeng , Gao Bo
Published: June 2023

Reverberation clutter induced by discrete strong scatterers is an important reason which will increase the false alarm probability of active detection, which seriously affects the performance of active sonar detection. In this paper, taking seamount as an example, the theoretical modeling and simulation of the clutter in the deep-sea induced by seamount are carried out by using the ray-mode theory. Through ray-mode approximation, the acoustic rays in surface and reverse channels are ignored, a theoretical model is proposed to calculate the seabed reverberation clutter induced by seamounts, and the physical mechanism of how the seabed reverberation clutter is induced by seamounts is analyzed. The results show that reverberation intensity will increase in the location of seamount and induce discrete reverberation clutter. The greater the fluctuation of seamount, the greater the reverberation clutter intensity.
Geoacoustic Inversion Using Modal Dispersion Curves
Authors: Ding Jiaqi , Zhao Xiaofeng
Published: June 2023

This paper presents a broadband geoacoustic inversion method based on matching the estimated dispersion curves extracted from low frequency pulse signal by warping transform and the simulated dispersion curves. The single receiver broadband inversion of the geoacoustic parameters is implemented using the first three orders of the dispersion curves, and the results are assessed using the parameter distribution and propagation loss deviation. The simulation outcomes demonstrate the method’s broad applicability for inverting geoacoustic parameters and the warping transform’s relative accuracy for dispersion curves estimation in waveguide environments with significant vertical sound speed modification.
The Preliminary Study on Backscattering Strength of High Frequency Sonar in Extremely Shallow Water
Authors: Xu Gaofeng , Wang Mouye , Liu Chenshen , Cao Kang , Yang Shenghui , Chen Siyuan
Published: June 2023

The nearshore water area is a very important part of maritime safety and application water area. Due to the complexity of sound propagation, and the influence of terrain, geology, tidal range and experiment’s difficulty, there has few researches in this area. Based on an existing engineering works, this article attempts to give some related calculations of the bistatic backscattered acoustic strength received and launched by a high frequency sonar in the extremely shallow water area with the depth less than 10 meters, and followed by in situ experiments test. It shows that the calculated results are in good agreement with the experimental tests. This work is a preliminary attempt in the study of extremely shallow water areas, and will provide a certain reference for the later sonar work in extremely shallow water areas.
Study of Thin Barrier GaN Biosensor with Improved Sensitivity
Authors: Zhang Peng , Liu Can , Zhang Jihua
Published: June 2023

The sensitivity of GaN biosensor device is directly related to transconductance (gm) of the HEMT device. In this paper, by applying with AlGaN/GaN material of thin AlGaN barrier and appropriately Al composition, the maximum transconductance (gm,max) is shifted near the zero gate voltage spontaneously. The sensitivity of the biosensor can be improved without gate reference electrode. The sensitivity of 0.35 mA/pH in pH detection and 1pg/ml in protein detection is achieved.
Study on Levodopa Sensor Modified by Ti3C2 MXene
Authors: Zhang Yi , Xia Xiaoyu , Shou Jialong , Liang Bo , Fang Lu
Published: June 2023

The dosage management of levodopa is of great significance in the treatment of Parkinson’s disease. Measuring the patient’s levodopa concentration helps the physician to adjust the dose in time, so as to avoid complications. In this paper, we report an electrochemical sensor based on titanium carbide (Ti3C2) MXene nanomaterials, which can sensitively reflect the change of levodopa concentration. In the study, Ti3C2/GCE electrode was prepared by a simple drop-coating method, and then the Ti3C2/Pt/GCE electrode was prepared by modifying Pt nanoparticles on the electrode surface by impregnation adsorption method. After modification, the response of the GCE electrode to levodopa before and after Ti3C2 decoration was compared by cyclic voltammetry (CV), and the diffusion control process on the electrode surface was also investigated. Then, differential pulse voltammetry (DPV) was used to explore the relationship between the peak current of the oxidation anode and the concentration of levodopa. Finally, the relationship between current and concentration at a certain time point was explored by chronoamperometry (CA), and the time charge curves were plotted to explore the functional relationship between the amount of charge exchanged in the reaction and the concentration of levodopa (Coulomb analysis method). The research shows that the coulomb analysis method has a higher detection sensitivity and linear range, and this technology is expected to be applied to the in vivo monitoring of levodopa in our future research.
Enhanced Energy Density of Polyetherimide Using Low Content Barium Titanate Nanofillers
Authors: Wu Xudong , Tan Daniel Q.
Published: June 2023

Dielectric polymer composites containing low content inorganic fillers exhibit superior dielectric properties and endurance for simultaneous enhancement of dielectric constant and breakdown strength. Polyetherimide (PEI) composites incorporated with nanosized BaTiO3 of 0.1vol.% loading fraction was developed in this work and their breakdown strength reached a maximum value along with the highest dielectric constant. The discharged energy density increased to 8.33 J/cm3, which is 2.33 times that of pure PEI. The composite film also maintained an excellent endurance after 104 cycles of fatigue test. The low-level filling for enhanced composite property may reduce the challenge in film scale-up manufacturing.
Multiclass Support Vector Data Description in Extreme Learning Machine Feature Space
Authors: Lei Yaolin , Ding Wenrui , Wang Yufeng , Zhang Wenbo , Chai Xinghua
Published: May 2023

Support vector data description (SVDD) method aims to address the one-class classification (OCC) problem to find a hypersphere-shaped description of target data set. For extending SVDD to multiclass classification while remaining the ability of detecting outliers, we propose a novel multiclass SVDD scheme which can be used in effective feature mapping and meta-class separation based on the extreme learning machine algorithm (ELM-MSVDD). Accordingly, the imprecise data difficult to distinguish in specific classes is classified to a meta-class,the meta-class is defined by the disjunction of these specific classes, this operation can reduce the error rate effectively. Experimental results of our ELM-MSVDD method show well performance on the datasets from the UCI machine learning library and radar signal source recognition. Meanwhile, our proposed method provide a theoretical and practical support for other relevant pattern recognition field.
Research on Att-NFM Recommendation Algorithm Based on Attention Mechanism
Authors: Huang Zhen , Zhao Suling , Ye Renzhen
Published: May 2023

Attentional mechanisms play an important role in artificial intelligence, and inspired by other work, we apply them in the field of recommendation algorithms. By combining factorization machine and neural network together through parallel structure, the linear model of factorization machine is used for binary feature interaction, and the nonlinear relationship between features is explored in using neural network. We propose the Att-NFM model to learn attention coefficients in neural networks to characterize the importance between features. Through experiments, we use AUC and LogLoss as evaluation metrics, and the results show that the evaluation metrics are improved by 3% and 2%, respectively.
Simple and Effective Complementary Label Learning Based on Mean Square Error Loss
Authors: Wang Chenggang , Xu Xiong , Liu Defu , Niu Xinyu , Han Shijiao
Published: May 2023

In this paper, we propose a simple and effective complementary label learning approach to address the label noise problem for deep learning model. Different surrogate losses have been proposed for complementary label learning, however, are often sophisticated designed, as the losses are required to satisfy the classifier consistency property. We propose an effective square loss for complementary label learning under unbiased and biased assumptions. We also show theoretically that our method assurances that the optimal classifier under complementary labels is also the optimal classifier under ordinary labels. Finally, we test our method on three different benchmark datasets with biased and unbiased assumptions to verify the effectiveness of our method.
Att GGO-Net:A Semantic Segmentation Method of Lung CT Images with Self and Cross Attention Mechanism
Authors: Yang Xiao Ling , Jin Zhe Yi
Published: May 2023

Chest computed tomography imaging (CT) has become a diagnostic method for many lung diseases due to its high sensitivity and low missed rate of infection. Convolutional neural network based semantic segmentation model that can assist physicians in auxiliary diagnosis. The existing semantic segmentation models do not work well for lesion segmentation of lung CT images, small lesions are not sensitive, and the contour segmentation is coarse, for which this paper designs a network named Att GGO-Net specifically for lung CT images ground glass opacity(GGO) and Lung consolidation segmentation.The basic encoder-decoder network structure used in this study is the U-Net++ model. After feature mapping is finished at each level of U-Net++, cross attention mechanism is introduced to optimize the semantic information in the decoder layer. Finally, self attention mechanism is added to complete the overall network structure.The extensive attention mechanism will cause the network to have many parameters. To solve the issue of large network parameters, the research substitutes depthwise separable convolution for traditional convolution. The data collection of lung CT scans with ground glass opacity and lung consolidation was used for the experiments. The segmentation performance was compared between Att GGO-Net and three classical medical image segmentation networks, namely, U-Net, U-Net++, and Attention U-Net (Att U-Net). The experimental findings demonstrate that the strategy used in this research has a superior segmentation effect than the comparison method.
Application of Deep Reinforcement Learning in Quantum Control
Authors: Cheng Yiming , Yin Yixi , Lin Jiaying , Wang Yaobin
Published: May 2023

Machine learning technology based on artificial neural network has been successfully applied to solve many scientific problems. One of the most interesting areas of machine learning is reinforcement learning, which has natural applicability to optimization problems in physics. In the quantum control task, it is necessary to find a set of optimal control functions to transfer a quantum system from the initial state to the target state with the highest fidelity possible, which is essentially an optimization task. In this paper, we use Deep Deterministic Policy Gradient algorithm (DDPG) to study the quantum control tasks. We use the algorithm to control the transfer of several quantum systems from one state to another. The results show that DDPG algorithm can find a control strategy to make the fidelity of the final state and the target state of the quantum system be maximum value 1. The results show the potential of DDPG in quantum control.
Relation-enhanced Diffusion Model with LightGCN for Information Diffusion Prediction
Authors: Shang Yingdan , Zhou Bin , Zeng Xiang , Chen Kai , Yu Han
Published: May 2023

Modeling and predicting the information diffusion process on social platforms is a critical task in many real-world applications. Recent studies generally model the diffusion graph using graph neural networks to capture the implicit dependencies among users. However, existing studies construct the diffusion graph in a way which cannot fully describe the global dependencies of users due to their narrow definition of user relationship. Meanwhile, graph neural networks in these methods are not suitable for the social network scenario which has scarce node attributes. Therefore, we propose a novel diffusion graph construction method which can enhance relations among users and adopt a simplified graph convolutional operation which is suitable for diffusion prediction scenario. The learned user embedding in our model can effectively preserve the microscopic structure and the high-order proximities between users lies in both the social graph and diffusion graph. Experimental results on four real-world datasets show that the proposed model is superior to the most advanced information diffusion prediction methods.
Design of a Multi-modal Sensor Fusion Unmanned Vehicle System based on Computer Vision
Authors: Li Yaxin , Chen Yan , Yang Zhen , Tong Chao , Yang Xinxing , Zhong Weiliang
Published: May 2023

With the development of artificial intelligence and Internet of Things technology, more and more intelligent products are appearing around us, such as sweeping robots, library service robots and drug delivery robots, etc. In the practical application of these intelligent robots, the core technology of spatial positioning is inseparable, and considering the cost, signal interference and positioning accuracy, it is necessary to study the positioning technology with low cost, small size and strong anti-interference factors. To solve the problem of poor performance of positioning accuracy when using low-cost sensors in the physical environment, we use a four-wheel drive vehicle model as a carrier to build an unmanned vehicle system based on multi-modal sensor fusion, binocular vision localization and other technologies. The core of the system is the MM32F3277G9P chip from MindMotion and the Raspberry Pi embedded development board. The proposed vision information is based on the Intel Realsense T265 camera, which is fused with the data from the nine-axis inertial measurement unit (IMU) and the dual-frequency global positioning system (GPS), so that the positioning algorithm can continuously provide robust and accurate state estimation results in the physical environment through the complementary advantages.
A New Data Dimension Reduction Method Based On Convolution In The Application Of Authenticity Identification Of Traditional Chinese Medicine LongGu
Authors: Wu Qiang , Zhu YuTing , Shi Wei , Wang TianYan , Huang YaWei , Jiang DongJing , Liu Xun
Published: May 2023

As a commonly used Chinese herbal medicine in clinic, LongGu is often used as a powder, which is difficult to identify manually. In this study, unsupervised learning method was used to model and identify LongGu, calcining LongGu and LongGu counterfeits by infrared spectroscopy. After preprocessing the original data, the current commonly used principal component analysis method, which was used to reduce the data dimension, and then the KMeans algorithm was used to realize the classification and identification of the samples. The classification results were better in the theoretical samples, but not in the actual samples. Based on this problem, this paper proposes a convolution-based machine learning feature dimension reduction method for spectral data. Comparing with the data method of principal component analysis, the partial classification effect is obviously better on the theoretical data. The effect of identifying calcining LongGu and LongGu counterfeits in actual data has been significantly improved, and it also provides a reference for the application of machine learning technology in the field of spectral identification of traditional Chinese medicine.
A CAPTCHA Recognition Algorithm Based on Character Segmentation and Random Forest
Authors: Kong Suke , Chang Yu-Chung
Published: May 2023

In this paper, the CAPTCHA code is recognized based on character segmentation and random forest algorithm. In the recognition process, the gray conversion method, binarization method, and Gaussian filtering method are used to process the image, and then the projection segmentation method is used to segment the characters. Using the random forest algorithm to recognize a single character, the recognition accuracy reaches 94%. In character recognition, it is found that 5 and S, 8 and B, 4 and A, and T and F have the lowest resolution accuracy ratio.
Multi-factor network modeling and simulation analysis of time-series dynamic and static climate
Authors: Luo Guilan , Hu Anshun , Wang Caikui , Ma Xin , Fang Lianbiao , Liu Xuan
Published: May 2023

With the development of complex networks and big data disciplines, complex networks and big data are widely used in various fields to reveal the changing characteristics of things. This paper applies the method of complex network to climatology, explores the complex interaction of various climatic factors in China’s regional climate system with the method of complex network, selects the meteorological big data of 288 meteorological stations in China from 1984 to 2016, defines the meteorological stations and connecting network under various climatic factors as a single factor climate network, and adds the connecting edge between meteorological stations with different climatic factors on this basis, Then the multi-factor climate network is constructed by the network characteristic parameters. The analysis of correlation and stability shows that the correlation of climate network from strong to weak is spring, autumn, winter and summer; In the process of 33 years of time series change, the correlation of climate network shows a weak downward trend, with strong stability and good viability.
Curriculum Learning for Debiased Recommendation with Explicit and Implicit Feedback
Authors: Fu Tianren , Chen Jiawei
Published: May 2023

The recommender system (RS) has played an increasingly important role in Internet applications. Recent literature on RS mainly focused on better fitting the user behavior data. However, user behavior data is observational, not experimental. This makes for a wide range of biases in the data. In this paper, we introduce a novel framework to combine the advantages of both multi-task and curriculum learning for the debiased recommendation. Unlike existing methods that are limited to specific feedback, our method follows multi-task learning to unify both explicit and implicit feedback. And these two feedbacks are learned in a curriculum learning manner by shifting from explicit to implicit. In this way, our method not only makes better use of the available information in user behavior data but also overcomes the task-balancing problem in multi-task learning. Extensive experiments have been conducted on two real-world datasets and prove that our method delivers state-of-the-art performance and significantly improves the debiasing ability of the recommendation model.
Research on Military Logistics Data Warehouse
Authors: Zhou Zhaoguo , Yang Yang , Yan Bowen , Wen Hongwu , Gao Dongdong , Guo Hongbiao
Published: May 2023

According to military logistics data warehouse dealing with large-scale data effect is poor. This paper puts forward the construction scheme of dual computing engine military logistics data warehouse based on hive in order to improve the military logistics support capability. The scheme adopts Hadoop and Spark dual search architecture to support efficient storage of heterogeneous data. In addition, a data block placement strategy based on correlation analysis was used. This article use the intersection relationship matrix and the concurrency relationship matrix to calculate the correlation between the data block placed on the node and the data block to be placed. The access frequency of the target data block was integrated to select the appropriate node storage. The scheme can effectively improve hive’s query efficiency and provide more effective information aided decision support for commanders.
Intelligent Table Lamp Based on Single Chip Microcomputer
Authors: Long Tao
Published: May 2023

At present, modern smart home is increasingly favored by people, and the functions of intelligent desk lamps become diversified with the demand updating. In the process of using desk lamps, poor sitting posture or overuse of eyes may cause vision loss and physical pain. Therefore, it is of great theoretical significance and practical value to actively explore and develop an intelligent desk lamp with low cost and functions of automatic dimming, sitting posture correction, reminder of rest, energy conservation and environmental protection. In the intelligent desk lamp designed in this paper, STC89C51 microcontroller is used as the core controller, BH1750 light sensor module is used to detect the current ambient light intensity, and the pulse width modulation (PWM) technology is used to control the brightness of the desk lamp for TB6612 motor drive. The infrared probe module is used for personnel detection. When the infrared probe module detects that someone is approaching, the desk lamp will be automatically turned on through the relay module. When no one is detected, the microcontroller controls the relay to automatically turn off the desk lamp and power off. The intelligent desk lamp is very convenient to use. It can not only save electric energy effectively, but also solve the problem of touching the lamp in the dark and the potential safety hazard for people by automatically switching on and off the lamp. It has good application value and market prospects.
Prediction of Liaoning Maritime Traffic Accidents Based on Improved SCGM(1,1)c Model
Authors: Ma Xushi , Zhou Zhaoxin , Wang Shipeng , Qin Qi , Yang Siyuan , Zhu Junjie , Zhu Zhaoxin , Li Xin
Published: May 2023

In order to ensure the safety of maritime traffic in Liaoning sea area and improve the prediction accuracy of maritime traffic accidents. In this paper, the number of ship collision accidents in Liaoning sea area from 2012 to 2021 have been statistically analyzed, and the number of accidents have predicted by using the modified SCGM(1,1)C model modified by Markov chain, and have solved by Matlab programming. The results show that Markov theory can effectively overcome the shortage of volatility prediction of cloud grey system. Compared with SCGM(1,1)C model only, the average relative error of the improved model is reduced by 6.349 %, which provides a strong reference for the establishment of ship collision traffic warning mechanism in Liaoning sea area.
Characterization of the TruSense S310 laser range system for contact-less measurement of liquid levels in large-volume neutrino detectors
Authors: Steiger H.Th.J. , Theisen E. , Oberauer L. , Pilarczyk O. , Wurm M.
Published: June 2023

Neutrino experiments often use large volumes of water, organic scintillators or noble
liquids as active detection material. Due to the large hydrostatic and buoyancy forces involved,
precise knowledge of the liquid levels inside the detector tank are mandatory. Here we present the
main characteristics of the TruSense S310 Laser Range System. Level measurements can be performed
without direct contact to the liquid and through a gas-proof acrylic window, thus preserving the
strict radiopurity and chemical requirements of the target liquid. We report the results of a suit
of laboratory experiments for short-term precision tests (±5 mm) and long-term stability
studies. Moreover, we demonstrate that the infrared laser can be used while standard bi-alkali
PMTs are operational. We discuss the mechanical layout and integration of the system in the OSIRIS
pre-detector that will monitor the radiopurity of the liquid scintillator for the large-volume
neutrino experiment JUNO.
Divergent response of crops and natural vegetation to the record-breaking extreme precipitation event in 2020 modulated by topography
Authors: Chen Jiuyi , Qiu Bo , Guo Weidong , Li Lingfeng , Miao Xin
Published: June 2023

Extreme precipitation events have posed a threat to global terrestrial ecosystems in recent decades. However, the response of terrestrial ecosystems to extreme precipitation in areas with various vegetation types and complex topography remains unclear. Here, we used satellite-based solar-induced chlorophyll fluorescence measurements, a direct proxy of photosynthetic activity, to assess the response of vegetation to the record-breaking extreme precipitation event during the East Asia monsoon season in eastern China in 2020. Our results demonstrate that vegetation was adaptable to moderate increases in precipitation, but photosynthetic activity was significantly inhibited by exposure to extreme precipitation because of insufficient photosynthetically active radiation and waterlogging. The responses of vegetation photosynthesis to extreme precipitation were regulated by both vegetation type and topography. Crops in the lowland areas in eastern China were severely damaged due to their higher vulnerability and exposure to extreme precipitation. The topography-induced redistribution of precipitation accounts for the modulation of vegetation response to extreme precipitation. Our research highlights the urgent need for effective management and adaptive measures of croplands under the elevated risk of extreme precipitation in the future.
Equipment for Testing the Tensile Strength of Natural Fibers: Design and Implementation
Authors: Arifin Y F , Siswanto R , Arsyad M , Shintia Novi , Ridha M H , Ramadhani M R
Published: June 2023

The use of fibers, especially natural fibers, as construction materials has begun to develop. Natural fibers are also used for soil stabilization, where there are two mechanisms for increasing soil strength: tensile strength and fiber-soil friction. The purpose of this research is to design and build simple, inexpensive tools that can be used to test the tensile strength of a natural fiber. The tool consists of three main parts, namely the pull tool, loadcell, and display, and a supporting component, namely the computer. Prior to use, two calibrations were performed with respect to load cell accuracy and the vertical deformation of the tool. The equipment was used directly to measure the tensile strength of one of the natural fibers from oil palm empty fruit bunches. The relationship between stress and strain, fiber diameter, and fiber strength is discussed and compared with behaviors reported from the existing literature. The tensile strength of oil palm empty fruit bunches was found to be 107 MPa on average, with a strain at break of 6%. Fiber behavior, independent of diameter, was discovered to include elastic and plastic regions in the stress-strain relationship.
Gelam Wood (Melaleuca cajuputi Powell) as a Substructure Construction Material
Authors: Ma’ruf Muhammad Afief , Arifin Yudi Firmanul , Asy’ari Mufidah , Rusdiansyah
Published: June 2023

Gelam wood (Melaleuca cajuputi Powell) which is widely used for substructure construction materials in South Kalimantan is classified into strong class II and durable wood class III. This study aims to provide more detailed information regarding what forms of use of gelam wood as substructure construction material and why gelam wood was chosen for it. This information will later be able to support the preparation of future utilization plans for the use of gelam wood considering that currently gelam wood is starting to decrease in availability. The method to identified the use of gelam wood as a substructure construction material is field survey method combine with other references. Gelam wood with a large diameter of 10-12 cm and a length of 4 m is generally used as a construction foundation for 1-storey building with an ultimate bearing capacity of 729,25 kg for single pile foundation. In terms of cost, the foundation with gelam wood material is cheaper than the mini-pile concrete foundation for the construction of the same building. The large size of gelam wood is also used as sheet pile or retaining wall with an average flexural strength of 100,13 MPa. The small size of gelam wood is generally used as wooden scaffolding where the cost of gelam wood scaffolding is 26.85% cheaper than iron scaffolding in the rental price, and 15 times the cost of the iron scaffold in the purchase price.
Roof Runoff Analysis: A Case for Cebu Technological University - Main Campus
Authors: Oraya A F A , Alburo B V , Cauban M K S , Canene J B
Published: June 2023

The volume of runoff varies depending on the surface on which it flows. Cebu Technological University - Main Campus, which is situated in Cebu City at the intersection of M.J. Cuenco Avenue and R. Palma Street, has been selected as location for the investigation of the effect of roofing properties on roof runoff. The average runoff coefficient was calculated under various rainfall scenarios. These were also related to the roofing structure, design patterns and materials whether these are sloping roofs or flat roofs. This investigation aims to enhance efficiency in rainwater harvesting systems. The calculated values of runoff coefficient in various roofs in the campus range from 0.78 to 0.92. A comparative analysis was used to differentiate the gap between the calculated runoff coefficient and the established values from literature. The recommended size for the rainwater tanks were calculated by taking into consideration the amount of volume that can be collected in the roofs in the campus.
Study of Potential Development of Rainfed Land in Maluka Baulin Swamp Irrigation Area
Authors: Rahmadi Abdul Holis , Novitasari Novitasari , Fitriati Ulfa
Published: June 2023

Agricultural land in South Borneo has high rice productivity. The supporting factors for rice productivity are irrigation areas, one of which is the Maluka Baulin Swamp Irrigation Area (DIR). It is located in Kurau, Tanah Laut Regency. It has an area of 421 ha (Decree of the Minister PUPR of the Republic Indonesia) now to 286 ha (land function and administrative other villages) with use as agricultural land. It so far has a low average rice production and cropping index of one crop in one year. The purpose of this study was to analyze the availability and demand of water well as the provision of irrigation water based on the water balance for the development of cropping patterns and schedules. This study collects primary and secondary data. Preliminary data are location survey, instantaneous discharge, and questionnaire. Secondary data are climatological data, maps of land types, and data on the discharge Tabanio-Bajuin River. The water requirement is obtained from the calculation of land preparation and consumptive use of water for plants. Water availability is obtained by analyzing rainfall, calculating evapotranspiration, calibrating F.J. Mock Model discharge, and calculating mainstay discharge. The temporary result obtained the water balance is land area is obtained with cropping pattern and schedule that increases to paddy-paddy-palawija, which was previously only paddy-paddy. Then, agricultural production, especially rice and secondary crops, will directly grow.
Assessment of physical and chemical characteristics of bio-organic compost from seafood sludge
Authors: Phuong Nguyen Thi , Nguyen Ho
Published: June 2023

Seafood sludge (SS) was one of the solid wastes generated during the wastewater treatment processes of seafood processing factories, and it increasingly discharged to the environment. This study aimed to reuse sludge from seafood wastewater treatment plants for producing microbial-organic fertilizer to handle environmental pollution. SS samples were collected at seafood factories in Dong Thap province. Other materials, including sugarcane filter cake (SC) and bagasse (B) were collected in Hau Giang province. Five treatments with three replications for each consisted of a mixture ratio of SS, SC and B at (1) 10-60-30, respectively; (2) 10 + 70 + 20; (3) 20 + 60 + 20; (4) 10 + 70 + 20 and (5) 20 + 80 + 0. All composting mixtures were analyzed for temperature, moisture content, pH, electrical conductivity, volume reduction, organic carbon (OC), total nitrogen (TN), total phosphorus (TP) and total potassium (TK) at stages of 0 and 49 days after incubation. Results after 49 incubation days showed that the pH value of both kinds of sludge was slightly acid to neutral (6-7). Electrical conductivity values were suitable from 0.74 to 1.57 mS/cm. The weight loss of incubated samples was 35.87-77.56% after a 49-day incubation. Organic carbon contents were at a high level (over 30% C). After 49 incubation days, the TN, TP, and TK content were high at 2.01-3.03%N; 6.71-9.95%P2O5; and 2.62-3.15%K2O, respectively. The C/N ratio ranged from 12.15 to 19.1. The optimal ratio was mixed with 20% seafood sludge and 80% sugarcane filter cake. The period of 49 days was seen as the suitable incubation time for composting maturity and stability.
Effect of Nutrition on Microalgal Growth and Pollutant Decrease in Contaminated River
Authors: Nisa C , Ramadani A P , Dewangi D K , Nugroho A , Fauzana N A , Mardina P , Putra M D
Published: June 2023

Microalgae have the ability to remove nutrients, heavy metals, and pollutants in the form of organic substances. Thus, it is expected that the use of microalgae can reduce pollutant levels in contaminated rivers. This study aims to reduce the concentration of pollutants in the Martapura river contaminated with industrial waste by utilizing microalgae. The maximum microalgal growth was obtained on day 4 with a nutritional variation of 5.25 mL as the concentration was 0.0115 g/L. The decrease in the level of pollutants occurred during the treatment with a decrease in sulfate levels from 18.9 mg/L to 11 mg/L. The amount of cadmium decreased from 0.0014 mg/L to an undetectable point. On the other hand, the iron content dropped quite drastically from 1.8 mg /L to 0.8 mg/L.
Risk Analysis of Coal Hauling Road Upgrade Project at PT. Borneo Indobara to Support Sustainable Development
Authors: Koirin  , Prasetia Irfan
Published: June 2023

This study aims to analyze the risks during the construction implementation phase and to propose mitigation strategies. This study was framed by case study design with the project for improving the coal haul road of PT. Borneo Indobara as the chosen case. This project still adheres to support sustainable development concept where the new design can minimize dust pollution and air pollution. Data were collected through interviews, brainstorming, similar previous studies, and questionnaires. All risk variables in the questionnaire were tested for validity and reliability, and then descriptive analysis was carried out. The Godfrey method analysis matrix was used to determine the dominant risks. Identification of valid risk variables found as many as 41 risks divided into 9 sources. Based on the analysis results, 14 dominant risks regarding the impact of time delays and 8 dominant risks regarding the impact of cost losses were found. Based on the results of the study 8 risk mitigation strategies are recommended: 1) Implementation of Management of change; 2) Determination of the implementation method that best suits the needs and conditions in the field; 3) Strengthened supply chain management; 4) Application of construction technology; 5) Strengthened maintenance management of materials and equipment; 6) Tightened supervision and monitoring of implementation activities; 7) Improved coordination between project owners and implementation contractors including stakeholders; 8) Strengthened human resources management and its implementation.
Black Spot Identification and Road Accident Prediction Model on Cebu South Coastal Road (CSCR)
Authors: Sisbreño A P , Sumilhig J A , Venci T J C , Canseco-Tuñacao H A R , Cavero D B M
Published: June 2023

Millions of people die in road accidents yearly, causing significant damage to the economy and humanity. The main focus of the study is to analyze road accidents through Black Spot Identification and to establish a Prediction Model on the Cebu South Coastal Road (CSCR)-N840 Highway. The data gathered from government agencies were digitized using SPSS and MS Excel. The study revealed that the year 2018 has the highest number of accident cases with 555 (46.52%) and the lowest severity ratio of 0.41. In contrast, 2019 has the lowest frequency of accident cases with 159 (13.33%) and a severity ratio of 0.72. Through the APW system, the CSCR-N840 Highway’s top black spot locations were at 2.6 km, 1.2 km, 2.3 km, 0.9 km,2.2 km, 5.3 km, 4.6 km, 4.7 km, 3.7 km, 5.9 km, 7.6 km, 7.7 km, 8.2 km, 8.5 km, 6.5 km, 10.2km, 11.4 km, 9.4 km, 12.1 km, and 12.0 km. Also, the researchers presented a heat mapping through QGIS for visual analysis, where severe road traffic accidents usually occur on the Talisay side of CSCR N840 Highway due to 13 junctions, 7 intersections, and 6 curvatures. Lastly, in the developed regression model the accident rate decreases gradually with an increase in the traffic volume. Also, Poisson prediction model, as the accident probability increases, the accident rate may either increase or slowly decrease.
Viability of Napier Grass Ash as Partial Substitute of Cement in CHB
Authors: Canseco-Tuñacao H A R , Nogra J M A , Rosell M F M , Alquilita J C L
Published: June 2023

The effects on the physical and mechanical properties of Napier grass ash (NGA) as substitute for ordinary Portland cement (OPC) on concrete hollow block (CHB) were investigated. Mix ratio of 1:2:4 with water-cement ratio of 0.60, and 10%, 15%, and 20% NGA replacements were adapted. Experimental procedures and testing conformed to American Society of Testing and Materials (ASTM) standards. Slump values, in comparison to control, showed a decline of 16.67%, 25%, and 58.33% for 10%, 15%, and 20% NGA replacements, respectively, which correlates to water absorption result. The denser the CHB, the lower the moisture content. Compressive strength results revealed that all NGA substitutes performed better than the control sample. NGA-20% had the highest compressive strength of 4.2 MPa (individual unit) and 3.9 MPa (average of 3 units) after 28 days of curing. Overall, NGA replacement was not viable for load-bearing but ideal for nonload-bearing CHB using the mix design.
The Grow Impact of Electrical Stimulation and Grow Lights on Lettuce Plant (Lactuca Sativa) in Hydroponics System
Authors: F. Batayola Ferdinand , Melsedec O. Narvios Wilen , Mendez Jasper C. , Casinto Ralph M. , Villegas Carl Jade
Published: June 2023

In today’s agricultural production, hydroponics has become one of the most popular approaches to producing vegetables in a limited space. With its technological advancement, the concept of farming is well adapted. In this study, the researchers develop a process to improve the growth yield production of lettuce through electrical stimulation and the application of grow lights through an aquarium-type hydroponics system. Three comparative set-ups were made during the experiment, a lettuce plant in the soil for the outdoor set-up, and two sets of lettuce planting in an indoor set-up provided with pink and blue LED lights. Each aquarium set-up undergoes an electrical stimulation with different settings of voltage. Based on the results, it was observed that the plant grew quicker in the aquarium set-up with the pink LED lights and a voltage setting of 20V. Based on observation, it showed a substantial impact on the growth as well as their quality, weight, and appearance. Results showed that electricity boosted the yield of lettuce plants where it only takes 24 days for the lettuce plant to be ready for harvest from a normal of 65 days to mature. On the other hand, the outdoor lettuce plant also developed and produced a result of its quality, height of the plant, and appearance. However, the plant takes a long time to grow and per inspection, the leaves eventually dry out. This study manifested that the electrical stimulation and growth lights could trigger biological activities that could enhance the efficient growth yield of hydroponically grown plants.
Surface tribological and electrochemical properties of TiAlSiN coating in seawater
Authors: Liu Aihua , Deng Jianxin , Tian Runzhou
Published: May 2023

To investigate the effects of friction surface and electrochemical properties on the tribological behavior of TiAlSiN coatings in seawater, the tribological properties of TiAlSiN coatings in air and seawater are comparatively studied using a ball-on-disk tribometer with Si3N4 ceramic balls as the counter material. The results show that under both friction conditions, the wear rate of TiAlSiN coating is decreased compared with 316 l steel substrate, and the coating presents a good protective effect. When friction occurs in air, the abrasive debris in the wear area cannot be discharged in time, resulting in the accumulation of abrasive debris and accelerating the wear of the coating. When friction occurs in seawater, although seawater is corrosive, the lubricating barrier surface formed by the friction oxide during the friction process significantly reduces the wear rate of the coating. In addition, the excellent electrochemical properties and the dense coating microstructure improve the tribocorrosion resistance of the TiAlSiN coating in seawater. It proves that friction surface has a great impact on the tribological performance of TiAlSiN coatings, and how to form a more favorable friction layer in the tribochemical environment is worthy of further research in the field of marine equipment.
Link between interlayer hybridization and ultrafast charge transfer in WS2-graphene heterostructures
Authors: Hofmann Niklas , Weigl Leonard , Gradl Johannes , Mishra Neeraj , Orlandini Giorgio , Forti Stiven , Coletti Camilla , Latini Simone , Xian Lede , Rubio Angel , Paredes Dilan Perez , Causin Raul Perea , Brem Samuel , Malic Ermin , Gierz Isabella
Published: June 2023

Ultrafast charge separation after photoexcitation is a common phenomenon in various van-der-Waals (vdW) heterostructures with great relevance for future applications in light harvesting and detection. Theoretical understanding of this phenomenon converges towards a coherent mechanism through charge transfer states accompanied by energy dissipation into strongly coupled phonons. The detailed microscopic pathways are material specific as they sensitively depend on the band structures of the individual layers, the relative band alignment in the heterostructure, the twist angle between the layers, and interlayer interactions resulting in hybridization. We used time- and angle-resolved photoemission spectroscopy combined with tight binding and density functional theory electronic structure calculations to investigate ultrafast charge separation and recombination in WS2-graphene vdW heterostructures. We identify several avoided crossings in the band structure and discuss their relevance for ultrafast charge transfer. We relate our own observations to existing theoretical models and propose a unified picture for ultrafast charge transfer in vdW heterostructures where band alignment and twist angle emerge as the most important control parameters.
MMSMAPlus: a multi-view multi-scale multi-attention embedding model for protein function prediction
Authors: Wang Zhongyu , Deng Zhaohong , Zhang Wei , Lou Qiongdan , Choi Kup-Sze , Wei Zhisheng , Wang Lei , Wu Jing
Published: June 2023

Protein is the most important component in organisms and plays an indispensable role in life activities. In recent years, a large number of intelligent methods have been proposed to predict protein function. These methods obtain different types of protein information, including sequence, structure and interaction network. Among them, protein sequences have gained significant attention where methods are investigated to extract the information from different views of features. However, how to fully exploit the views for effective protein sequence analysis remains a challenge. In this regard, we propose a multi-view, multi-scale and multi-attention deep neural model (MMSMA) for protein function prediction. First, MMSMA extracts multi-view features from protein sequences, including one-hot encoding features, evolutionary information features, deep semantic features and overlapping property features based on physiochemistry. Second, a specific multi-scale multi-attention deep network model (MSMA) is built for each view to realize the deep feature learning and preliminary classification. In MSMA, both multi-scale local patterns and long-range dependence from protein sequences can be captured. Third, a multi-view adaptive decision mechanism is developed to make a comprehensive decision based on the classification results of all the views. To further improve the prediction performance, an extended version of MMSMA, MMSMAPlus, is proposed to integrate homology-based protein prediction under the framework of multi-view deep neural model. Experimental results show that the MMSMAPlus has promising performance and is significantly superior to the state-of-the-art methods. The source code can be found at https://github.com/wzy-2020/MMSMAPlus.
A complete graph-based approach with multi-task learning for predicting synergistic drug combinations
Authors: Wang Xiaowen , Zhu Hongming , Chen Danyi , Yu Yongsheng , Liu Qi , Liu Qin
Published: June 2023

Motivation

Drug combination therapy shows significant advantages over monotherapy in cancer treatment. Since the combinational space is difficult to be traversed experimentally, identifying novel synergistic drug combinations based on computational methods has become a powerful tool for pre-screening. Among them, methods based on deep learning have far outperformed other methods. However, most deep learning-based methods are unstable and will give inconsistent predictions even by simply changing the input order of drugs. In addition, the insufficient experimental data of drug combination screening limits the generalization ability of existing models. These problems prevent the deep learning-based models from being in service.

Results

In this article, we propose CGMS to address the above problems. CGMS models a drug combination and a cell line as a heterogeneous complete graph, and generates the whole-graph embedding to characterize their interaction by leveraging the heterogeneous graph attention network. Based on the whole-graph embedding, CGMS can make a stable, order-independent prediction. To enhance the generalization ability of CGMS, we apply the multi-task learning technique to train the model on drug synergy prediction task and drug sensitivity prediction task simultaneously. We compare CGMS’s generalization ability with six state-of-the-art methods on a public dataset, and CGMS significantly outperforms other methods in the leave-drug combination-out scenario, as well as in the leave-cell line-out and leave-drug-out scenarios. We further present the benefit of eliminating the order dependency and the discrimination power of whole-graph embeddings, interpret the rationality of the attention mechanism, and verify the contribution of multi-task learning.

Availability and implementation

The code of CGMS is available via https://github.com/TOJSSE-iData/CGMS.