bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of the Vulnerability of Small-Scale Capture Fisheries in Pangpang Bay, Banyuwangi Regency, Indonesia
Authors: Setyaningrum E W , Harahab N , Wiadnya D G R , Sambah A B 
Published: April 2024

Abstract
Pangpang Bay is called a multi-use area because it has the potential not only for capture fisheries and aquaculture, but also for mangrove conservation. However, small-scale fishermen in Pangpang Bay feel the impact of climate change. The impact of climate change on fisheries can lead to vulnerability to the sustainability of fishing communities in particular The benefits of research as a basis for developing small-scale capture fisheries in Pangpang Bay, Banyuwangi Regency, Indonesia. The research was conducted from March to July 2023. The method used in this study is a quantitative descriptive method, with data collection methods using surveys and direct observation. The analytical method used is the vulnerability of capture fisheries, with data analysis carried out on 1) Exposure, the variable is sea surface temperature (SST); 2) Sensitivity, the variables are analyzed being the catch and the number of fishermen; 3) Adaptive capacity, the variables are mangrove area, number of fish landings and number of fishery instructors. The results of the study showed that for small-scale capture fisheries in Pangpang is in the category of moderate vulnerability to climate change. In this case, it has implications for policies that can increase adaptive capacity and reduce sensitivity
Potential Carbon Stock of Seagrass Biomass in Malang Regency
Authors: Dewi C S U , Handayani M , Kurniawan F , Yona D , Rohenda A A , Asadi M A 
Published: April 2024

Abstract
Research on blue carbon content in seagrass beds also has important implications for coastal ecosystem management. The goals of this research are: (1) To determine the composition of seagrass species; (2) to analyze the health of the seagrass ecosystem; and (3) and to analyze the carbon content stored in seagrass biomass on the coast of Malang Regency. This research was conducted in 2018 at five stations, namely: Kondang Merak Beach (St 1), Balekambang Beach (St 2), Gatra Beach (St 3), Sendangbiru Beach (St 4), and Waru-Waru Beach (St 5) of Malang Regency. The method used to collect data refers to Seagrass Watch. The biomass value of seagrass species can be estimated by measuring the wet weight and dry weight of seagrass. Analysis of carbon content in seagrass biomass can be calculated using a method that refers to The First Nation-Wide Assessment Identifies Valuable Blue-carbon. There are five types of seagrasses found on five beaches in Malang Regency, namely: Halodule pinifolia, Halodule uninervis, Halophila ovalis, Syringodium isotifolium, and Thalassia hemprichii. This seagrass meadow ecosystem is in an unhealthy condition, with a small biomass value. Seagrass beds that are formed only in the form of small spots (1.19 Ha) are known to be able to store carbon of 0.0544 Mg C.
Comparative Study of Growth Performance of Three Tilapia Strain in Intensive Culture System
Authors: Nugroho K C , Rizky P N , Harijono T , Halim A M , Nasuki  , Ritonga L BR 
Published: April 2024

Abstract
High stocking density is an important factor in cultivation to increase the eficency if fish farming for nutrition requirements. High stocking density generally impact on fish physiology and growth performance. However, many studies on individual fish have different growth perfromance from different strain. Some technology had been developed to optimize tilapia production in high stocking density. This study aimed to determine the growth performance of three different strain of tilapia (red tilapia, nirwana tilapia, and jatimbulan tilapia) reared in 20 m3 of round tarpaulin with additional of microbubble generator. This study used an intensive system with 100 fish/m3 of stocking density. The results showed that the microbubble generator was able to increase the carrying capacity of water quality such as increasing dissolved oxygen in the waters, temperature and pH. The high content of dissolved oxygen in the water significantly increasing the growth of tilapia. The highest weight was found in red tilapia with a final weight of 199.56 ± 0.05 gr, followed by Nirwana tilapia with 176.59 ± 0.10 gr, and Jatimbulan tilapia with 153.91 ± 0.06 gr. In addition, the high content of dissolved oxygen in the waters also affect the rearing period of tilapia.
Total suspended solid (TSS) pollution load model at the Jagir River Estuary, Surabaya, East Java
Authors: Supriatna  , Putri V E , Maizar A , Anitasari S , Darmawan A 
Published: April 2024

Abstract
The research aims to create a TSS spatial distribution map model and a pollution load model at the Jagir River Estuary—a descriptive method for the last six years to describe the TSS pollution load model. Satellite image data processing uses SNAP software with the application of the Laili algorithm (2015). The distribution of TSS pollution values and loads still meets the quality standards for mangrove ecosystems. The average value of TSS distribution in 2018 - 2023, respectively, is 18,966 mg/L, 14,578 mg/L, 17,267 mg/L, 16.921 mg/L, and 15,013 mg/L with the highest TSS pollution load occurring in 2023 and the lowest in 2021 and 2022. The average pollution load from 6 research stations in 2019 – 2023 still does not exceed the capacity limit of the Jagir River to accommodate TSS accumulation. The TSS pollution load model in the Jagir River Estuary, Surabaya, East Java, follows a quadratic pattern with the regression equation y = -173.01X2 + 2339.20X - 2299.80 (R2 = 0.7674) with a peak TSS pollution load reaching 5,607.05 tons m3/month in January and lowest in August
Sediment metals content in upstream Citarum river, West Java Province, Indonesia
Authors: Sugiarti  , Sudarso J , Ibrahim A , Yamin M , Zamroni M , Aisyah S 
Published: April 2024

Abstract
Study of metal content in sediments is important for monitoring condition of Citarum river. This study aims to determine several metal namely Fe, Cu, Mn, Zn, Ni, Cr, Cd, Pb, Hg, and As in upstream Citarum river sediments. Research was conducted in the Cirasea sub-watershed, namely Gunung Wayang, Babakan Village, Cikitu Village, Wangi Sagara, Puncak Cae, Koyod, Andir, and Nanjung. Sediment samples were taken once a month from September to November 2022. Several physico-chemical parameter were carried insitu and laboratory. Sediment metal analysis was carried out using the ICP-OES (Inductively Coupled Plasma - Optical Emission Spectrometry) technique. Metal concentrations were compared with standards and the relationship between metal parameters and physico-chemical parameters was analyzed by PCA (Principal Component Analysis). Metals that were not found in all stations were Hg and Cd. The metal found with the largest concentration was Fe, otherwise metal found with the smallest concentration was As. Based on PCA, there are three groups based on metals and physico-chemical parameters. Advanced metal analysis needs to be carried out in the Citarum river from upstream to downstream to determine water quality of Citarum river.
Embryonic and Larval Development of Hybrid Giant Grouper × Potato Grouper (PGGG) Male and Tiger Grouper (TG) Female
Authors: Fahrudini F. , Yuniartik M. , Muzaki A. , Mahardika K. , Ismi S. , Supii A. 
Published: April 2024

Abstract
This study aims to collect data and information of embryonic and larval development on the cross breeding between hybrid Giant Grouper × Potato Grouper (PGGG) male and Tiger Grouper (TG). The body weight of broodfish above 20 kg for male and 4 kg for female. Broodstock selection of Hybrid Giant Grouper × Potato Grouper (PGGG) male can usually be stripped by applying gentle pressure to the abdomen between the pelvic and the vent fins. Induced spawning by mixing sperm and eggs manually. Signs of a mature gonadal female are characterized by an enlarged abdomen, slow movements and tend to tilt, the genital opening increasingly swollen and red in colour. The results show that spawning at night 23.30 with an average egg diameter of 0.85±0.09 mm. The eggs hatch at 15.45 or 16 hours 15 minutes. Hatching rate being 24%-32%.
Machinability studies of Al–4Mg/in-situ MgAl2O4 nano composites: measurement of cutting forces and machined surface roughness
Authors: Satish Kumar T , Thankachan Titus , Čep Robert , Kalita Kanak 
Published: April 2024

Abstract
The present research aims to study the dry turning machinability characteristics of in situ Al-4Mg/MgAl2O4 nanocomposite by High-Speed Steel tool. The influence of various machining process parameters, such as feed rate, depth of cut and cutting speed on the surface roughness and cutting force of the nanocomposites was measured while performing dry turning. From the turning operation results, it is noticed that up to 100 m min−1, the cutting force increased and with further increases in cutting speed, the cutting force starts decreasing up to 150 m min−1. The type of chips and built-up edge (BUE) development were studied using a scanning electron microscope. BUE formations were higher at low cutting speeds (50 m min−1) and lower at high cutting speeds (150 m min−1). At a given depth of cut and feed rate, with an increase in cutting speed, the length of the chip and chip curls increased. Further, higher 2 wt% of in situ MgAl2O4 addition changes long-curled chips to segmental-type chips. With a feed rate of 0.14 mm/rev, the Al-4Mg/1 wt% MgAl2O4 nanocomposite showed the lowest surface roughness value of 2.4 μm proving usage of high speed steel can provide a better surface finish while turning Al-4Mg/MgAl2O4 nanocomposite.
Efficient quantum algorithm for all quantum wavelet transforms
Authors: Bagherimehrab Mohsen , Aspuru-Guzik Alán 
Published: April 2024

Abstract
Wavelet transforms are widely used in various fields of science and engineering as a mathematical tool with features that reveal information ignored by the Fourier transform. Unlike the Fourier transform, which is unique, a wavelet transform is specified by a sequence of numbers associated with the type of wavelet used and an order parameter specifying the length of the sequence. While the quantum Fourier transform, a quantum analog of the classical Fourier transform, has been pivotal in quantum computing, prior works on quantum wavelet transforms (QWTs) were limited to the second and fourth order of a particular wavelet, the Daubechies wavelet. Here we develop a simple yet efficient quantum algorithm for executing any wavelet transform on a quantum computer. Our approach is to decompose the kernel matrix of a wavelet transform as a linear combination of unitaries (LCU) that are compilable by easy-to-implement modular quantum arithmetic operations and use the LCU technique to construct a probabilistic procedure to implement a QWT with a known success probability. We then use properties of wavelets to make this approach deterministic by a few executions of the amplitude amplification strategy. We extend our approach to a multilevel wavelet transform and a generalized version, the packet wavelet transform, establishing computational complexities in terms of three parameters: the wavelet order M, the dimension N of the transformation matrix, and the transformation level d. We show the cost is logarithmic in N, linear in d and superlinear in M. Moreover, we show the cost is independent of M for practical applications. Our proposed QWTs could be used in quantum computing algorithms in a similar manner to their well-established counterpart, the quantum Fourier transform.
Pansharpening and spatiotemporal image fusion method for remote sensing
Authors: Anand Sakshi , Sharma Rakesh 
Published: April 2024

Abstract
In last decades, remote sensing technology has rapidly progressed, leading to the development of numerous earth satellites such as Landsat 7, QuickBird, SPOT, Sentinel-2, and IKONOS. These satellites provide multispectral images with a lower spatial resolution and panchromatic images with a higher spatial resolution. However, satellite sensors are unable to capture images with high spatial and spectral resolutions simultaneously due to storage and bandwidth constraints, among other things. Image fusion in remote sensing has emerged as a powerful tool for improving image quality and integrating important features from multiple source images into one, all while maintaining the integrity of critical features. It is especially useful for high-resolution remote sensing applications that need to integrate features from multiple sources and hence a vital pre-processing step for various applications, including medical, computer vision, and satellite imaging. This review initially gives a basic framework for image fusion, followed by statistical analysis and a comprehensive review of various state-of-the-art image fusion methods, where they are classified based on the number of sensors used, processing levels, and type of information being fused. Subsequently, a thorough analysis of STF and pansharpening techniques for remote sensing applications has been covered, where the dataset of the DEIMOS-2 satellite is employed for evaluating various pansharpening methods while MODIS and Landsat images are employed in the spatiotemporal fusion method. A comparative evaluation of several approaches has been carried out to assess the merits and drawbacks of the current approaches. Several real-time applications of remote sensing image fusion have been explored, and current and future directions in fusion research for remote sensing have been discussed, along with the obstacles they present.
Enhancing robotic manipulator fault detection with advanced machine learning techniques
Authors: Khan Faiq Ahmad , Jamil Akhtar , Khan Shaiq Ahmad , Hameed Alaa Ali 
Published: April 2024

Abstract
The optimization of rotating machinery processes is crucial for enhanced industrial productivity. Automatic machine health monitoring systems play a vital role in ensuring smooth operations. This study introduces a novel approach for fault diagnosis in robotic manipulators through motor sound analysis to enhance industrial efficiency and prevent machinery downtime. A unique dataset is generated using a custom robotic manipulator to examine the effectiveness of both deep learning and traditional machine learning in identifying motor anomalies. The investigation includes a two-stage analysis, initially leveraging 2D spectrogram features with neural network architectures, followed by an evaluation of 1D MFCC features using various conventional machine learning algorithms. The results reveal that the proposed custom CNN and 1D-CNN models significantly surpass traditional methods, achieving an F1-score exceeding 92%, highlighting the potential of sound analysis for automated fault detection in robotic systems. Additional experiments were carried out to investigate 1D MFCC features with various machine learning algorithms, including KNN, DT, LR, RF, SVM, MLP, and 1D-CNN. Augmented with additional data collected from the locally designed manipulator, our experimental setup significantly enhances model performance. Particularly, the 1D-CNN stands out as the top-performing model on the augmented dataset.
Characteristics study of heterojunction III-nitride/β-Ga2O3 nano-HEMT for THz applications
Authors: Rao G Purnachandra , Lenka Trupti Ranjan , Vadalà Valeria , Nguyen Hieu Pham Trung 
Published: April 2024

Abstract
In this research study, a recessed gate III-Nitride high electron mobility transistor (HEMT) grown on a lattice matched β-Ga2O3 substrate is designed. This research investigation aims to enhance DC and RF performance of AlGaN/GaN HEMT, and minimize the short-channel effects by incorporating an AlGaN back layer and field plate technique, which can enhances electron confinement in two-dimensional electron gas (2DEG). A precise comparison analysis is done on the proposed HEMT’s input characteristics, output characteristics, leakage current characteristics, breakdown voltage properties, and RF behaviour in presence and absence of AlGaN back layer in regards to field plate configuration. The inclusion of back barrier aids in raising the level of conduction band, which reduces leakage loss beneath the buffer, and aids in keeping the 2DEG to be confined to narrow channel. Furthermore, the field plate design offers an essential electric field drift between gate and drain, resulting to enhanced breakdown voltage characteristics.
β-Ga2O3 nanostructures for photocatalytic degradation of red amaranth toxic dye
Authors: Castillo-Saenz J R , Domínguez D , Arias A , Arce L A , Farías M H , Pérez-Landeros O M , Valdez B , Nedev N , López - Medina J. 
Published: April 2024

Abstract
Beta gallium oxide (β-Ga2O3) microstructures composed of ∼50 nm nanoparticles were synthesized by the hydrothermal method. Using the Tauc plot method a value of ∼4.9 eV was obtained for the optical band gap of β-Ga2O3. TEM and XRD analyses revealed high crystallinity of the β-phase of gallium oxide nanostructures. Since there are few publications for the photocatalytic properties of β-Ga2O3 the obtained results contribute to better understanding of the photocatalytic effect of this material on toxic dye red amaranth. Moreover, it is shown that β-Ga2O3 is a very efficient photocatalyst leading to high percentage degradation of dyes for relatively short periods. For example, the degradation of red amaranth and rhodamine B toxic dyes under UV light irradiation reached 97% and 100% after 165 and 120 min, respectively.
The association between cannabis use and neuroimaging measures in older adults: findings from the UK biobank
Authors: Vered Shiraz , Sznitman Sharon , Weinstein Galit 
Published: April 2024

Abstract

Background
Cannabis use has increased in recent years. However, the long-term implications of cannabis use on brain health remain unknown. We explored the associations of cannabis use with volumetric brain magnetic resonance imaging (MRI) measures in dementia-free older adults.

Methods
This cross-sectional and longitudinal study included dementia-free participants of the UK Biobank aged ≥60 years. Linear regression models were used to evaluate the association of cannabis use and patterns of use with volumetric brain MRI measures. The association between cannabis use and change in brain MRI measures over time was also tested. All models were adjusted for potential confounders.

Results
The sample included 19,932 participants (mean age 68 ± 5 years, 48% men), 3,800 (19%) reported lifetime use of cannabis. Cannabis use was associated with smaller total, white, grey and peripheral cortical grey matter volumes (B = −6,690 ± 1,157; P &lt; 0.001, B = −4,396 ± 766; P &lt; 0.001, B = −2,140 ± 690; P = 0.002 and B = −2,451 ± 606; P &lt; 0.001, respectively). Among cannabis users, longer duration of use was associated with smaller total brain, grey and cortical grey matter volumes (B = −7,878 ± 2,396; P = 0.001, B = −5,411 ± 1,430; P &lt; 0.001, B = −5,396 ± 1,254; P &lt; 0.001, respectively), and with increased white matter hyperintensity volume (B = 0.09 ± 0.03; P = 0.008). Additionally, current vs. former users (B = −10,432 ± 4,395; P = 0.020) and frequent versus non-frequent users (B = −2,274 ± 1,125; P = 0.043) had smaller grey and cortical grey matter volumes, respectively. No significant associations were observed between cannabis use and change in brain MRI measures.

Discussion
Our findings suggest that cannabis use, particularly longer duration and frequent use, may be related to smaller grey and white matter volumes in older ages, but not to late-life changes in these measures over time.
sOCP: a framework predicting smORF coding potential based on TIS and in-frame features and effectively applied in the human genome
Authors: Peng Zhao , Li Jiaqiang , Jiang Xingpeng , Wan Cuihong 
Published: April 2024

Abstract
Small open reading frames (smORFs) have been acknowledged to play various roles on essential biological pathways and affect human beings from diabetes to tumorigenesis. Predicting smORFs in silico is quite a prerequisite for processing the omics data. Here, we proposed the smORF-coding-potential-predicting framework, sOCP, which provides functions to construct a model for predicting novel smORFs in some species. The sOCP model constructed in human was based on in-frame features and the nucleotide bias around the start codon, and the small feature subset was proved to be competent enough and avoid overfitting problems for complicated models. It showed more advanced prediction metrics than previous methods and could correlate closely with experimental evidence in a heterogeneous dataset. The model was applied to Rattus norvegicus and exhibited satisfactory performance. We then scanned smORFs with ATG and non-ATG start codons from the human genome and generated a database containing about a million novel smORFs with coding potential. Around 72 000 smORFs are located on the lncRNA regions of the genome. The smORF-encoded peptides may be involved in biological pathways rare for canonical proteins, including glucocorticoid catabolic process and the prokaryotic defense system. Our work provides a model and database for human smORF investigation and a convenient tool for further smORF prediction in other species.
FlexStat: combinatory differentially expressed protein extraction
Authors: De Silva Senuri , Alli-Shaik Asfa , Gunaratne Jayantha 
Published: April 2024

Abstract

Motivation
Mass spectrometry-based system proteomics allows identification of dysregulated protein hubs and associated disease-related features. Obtaining differentially expressed proteins (DEPs) is the most important step of downstream bioinformatics analysis. However, the extraction of statistically significant DEPs from datasets with multiple experimental conditions or disease types through currently available tools remains a laborious task. More often such an analysis requires considerable bioinformatics expertise, making it inaccessible to researchers with limited computational analytics experience.

Results
To uncover the differences among the many conditions within the data in a user-friendly manner, here we introduce FlexStat, a web-based interface that extracts DEPs through combinatory analysis. This tool accepts a protein expression matrix as input and systematically generates DEP results for every conceivable combination of various experimental conditions or disease types. FlexStat includes a suite of robust statistical tools for data preprocessing, in addition to DEP extraction, and publication-ready visualization, which are built on established R scientific libraries in an automated manner. This analytics suite was validated in diverse public proteomic datasets to showcase its high performance of rapid and simultaneous pairwise comparisons of comprehensive datasets.

Availability and implementation
FlexStat is implemented in R and is freely available at https://jglab.shinyapps.io/flexstatv1-pipeline-only/. The source code is accessible at https://github.com/kts-desilva/FlexStat/tree/main.