bluuebunny/crossref_metadata_2025 huggingface dataset:
The Microbial Genetic Diversity and Succession Associated with Processing Waters at Different Broiler Processing Stages in an Abattoir in Australia
Authors: Gichure Josphat Njenga , Coorey Ranil , Njage Patrick Murigu Kamau , Dykes Gary A. , Muema Esther K. , Buys Elna M.
Published: March 2023

The high organic content of abattoir-associated process water provides an alternative for low-cost and non-invasive sample collection. This study investigated the association of microbial diversity from an abattoir processing environment with that of chicken meat. Water samples from scalders, defeathering, evisceration, carcass-washer, chillers, and post-chill carcass rinsate were collected from a large-scale abattoir in Australia. DNA was extracted using the Wizard® Genomic DNA Purification Kit, and the 16S rRNA v3-v4 gene region was sequenced using Illumina MiSeq. The results revealed that the Firmicutes decreased from scalding to evisceration (72.55%) and increased with chilling (23.47%), with the Proteobacteria and Bacteroidota changing inversely. A diverse bacterial community with 24 phyla and 392 genera was recovered from the post-chill chicken, with Anoxybacillus (71.84%), Megamonas (4.18%), Gallibacterium (2.14%), Unclassified Lachnospiraceae (1.87%), and Lactobacillus (1.80%) being the abundant genera. The alpha diversity increased from scalding to chilling, while the beta diversity revealed a significant separation of clusters at different processing points (p = 0.01). The alpha- and beta-diversity revealed significant contamination during the defeathering, with a redistribution of the bacteria during the chilling. This study concluded that the genetic diversity during the defeathering is strongly associated with the extent of the post-chill contamination, and may be used to indicate the microbial quality of the chicken meat.
A Novel Measurement Method for Spin Polarization Three Axis Spatial Distribution in Spin-Exchange Relaxation Free Atomic Magnetometer
Authors: Fang Xiujie , Li Jin , Ma Yanning , Wei Kai , Fan Wenfeng , Zhai Yueyang , Quan Wei , Xiao Zhisong
Published: March 2023

The measurement of atomic spin polarization distribution in spin-exchange relaxation free (SERF) magnetometer is an important topic for improving the sensitivity and consistency of multi-channel magnetic field measurement applications. A novel spin polarization spatial distribution measurement method is presented based on the transient response of the magnetometer after modulating the pumped light with a chopper. Polarization is obtained by a slow-down factor based on the fast spin-exchange interaction effects. Longitudinal and transverse polarization distributions are measured simultaneously without interrupting the operation of the SERF status. Under different oscillating magnetic fields, the spin polarization is measured at the cell centroid. Residual magnetic field inside the magnetometer is obtained from the linear relationship between the precession frequency and the oscillating magnetic field. The one-dimensional polarization distributions in the x, y, and z axes are measured using a digital micromirror device with a resolution of 0.25 cm. The measurement results conform to the Lambert-Bier absorption law and the Gaussian distribution law. Furthermore, 7 × 7 two-dimensional spatial distribution measurements of polarization on the xy and yz planes are performed. Nonuniformity of 1.04 in the xy plane and 1.82 in the yz plane in the built magnetometer. Compared with other measurement methods, the distribution measurement method proposed is independent of optical depth and suitable for low polarization and high polarization applications. Based on the results of the proposed measurement method of spin polarization spatial distribution, further compensation can improve the application consistency of multi-channel magnetic field measurements and improve the sensitivity of single-channel differential measurements.
Transmission Properties in Plasma Photonic Crystal Controlled by Magnetic Fields
Authors: Wang Hailu , Li Jianfei , Guo Liang , Ma Dongliang , Yao Jingfeng , Li He-Ping
Published: March 2023

The transmission properties in two-dimensional plasma photonic crystal composed of plasma and yttrium–iron–garnet rods with square lattices are demonstrated under different electron densities and external magnetic fields. The TE and TM modes respond to the permittivity tensor and the permeability tensor produced by the magnetic field. For TM polarization, two distinct attenuation peaks appear in the ranges of 3.4–3.62 GHz and 3.78–4 GHz, induced by the external magnetic fields, and the location of these attenuation peaks can be modulated by modifying the electron densities. For TE polarization, a flat transmission spectrum was obtained in the range of 4–4.6 dB by increasing the electron density to 3×1012 cm−3. Then, a Y-shaped plasma photonic crystal waveguide is designed. The transmission path can be modulated by changing the direction of the external magnetic field. By regulating the electron density, switching the Y-shaped waveguide on and off can be achieved.
Finite-Size Effects of Casimir–van der Waals Forces in the Self-Assembly of Nanoparticles
Authors: Esquivel-Sirvent Raul
Published: March 2023

Casimir–van der Waals forces are important in the self-assembly processes of nanoparticles. In this paper, using a hybrid approach based on Lifshitz theory of Casimir–van der Waals interactions and corrections due to the shape of the nanoparticles, it is shown that for non-spherical nanoparticles, the usual Hamaker approach overestimates the magnitude of the interaction. In particular, the study considers nanoplates of different thicknesses, nanocubes assembled with their faces parallel to each other, and tilted nanocubes, where the main interaction is between edges.
The Chromatin Remodeling Factor BrCHR39 Targets DNA Methylation to Positively Regulate Apical Dominance in Brassica rapa
Authors: Zhu Wei , Xie Zhengqing , Chu Zhenni , Ding Yakun , Shi Gongyao , Chen Weiwei , Wei Xiaochun , Yuan Yuxiang , Wei Fang , Tian Baoming
Published: March 2023

The SHPRH (SNF2, histone linker, PHD, RING, helicase) subfamily belonging to ATP-dependent chromatin remodeling factor is the effective tumor-suppressor, which can polyubiquitinate PCNA (proliferating cell nuclear antigen) and participate in post-replication repair in human. However, little is known about the functions of SHPRH proteins in plants. In this study, we identified a novel SHPRH member BrCHR39 and obtained BrCHR39-silenced transgenic Brassica rapa. In contrast to wild-type plants, transgenic Brassica plants exhibited a released apical dominance phenotype with semi-dwarfism and multiple lateral branches. Furthermore, a global alteration of DNA methylation in the main stem and bud appeared after silencing of BrCHR39. Based on the GO (gene ontology) functional annotation and KEGG (Kyoto encyclopedia of genes and genomes) pathway analysis, the plant hormone signal transduction pathway was clearly enriched. In particular, we found a significant increase in the methylation level of auxin-related genes in the stem, whereas auxin- and cytokinin-related genes were hypomethylated in the bud of transgenic plants. In addition, further qRT-PCR (quantitative real-time PCR) analysis revealed that DNA methylation level always had an opposite trend with gene expression level. Considered together, our findings indicated that suppression of BrCHR39 expression triggered the methylation divergence of hormone-related genes and subsequently affected transcription levels to regulate the apical dominance in Brassica rapa.
Production of Secondary Metabolites from Cell Cultures of Sageretia thea (Osbeck) M.C. Johnst. Using Balloon-Type Bubble Bioreactors
Authors: Kim Ji-Hye , Han Jong-Eun , Murthy Hosakatte Niranjana , Kim Ja-Young , Kim Mi-Jin , Jeong Taek-Kyu , Park So-Young
Published: March 2023

Sageretia thea is used in the preparation of herbal medicine in China and Korea; this plant is rich in various bioactive compounds, including phenolics and flavonoids. The objective of the current study was to enhance the production of phenolic compounds in plant cell suspension cultures of Sageretia thea. Optimum callus was induced from cotyledon explants on MS medium containing 2,4-dichlorophenoxyacetic acid (2,4-D; 0.5 mg L−1), naphthalene acetic acid (NAA, 0.5 mg L−1), kinetin (KN; 0.1 mg L−1) and sucrose (30 g L−1). Browning of callus was successfully avoided by using 200 mg L−1 ascorbic acid in the callus cultures. The elicitor effect of methyl jasmonate (MeJA), salicylic acid (SA), and sodium nitroprusside (SNP) was studied in cell suspension cultures, and the addition of 200 µM MeJA was found suitable for elicitation of phenolic accumulation in the cultured cells. Phenolic and flavonoid content and antioxidant activity were determined using 2,2 Diphenyl 1 picrylhydrazyl (DPPH), 2,2′-azino-bis (3-ethybenzothiazoline-6-sulphonic acid (ABTS), ferric reducing antioxidant power (FRAP) assays and results showed that cell cultures possessed highest phenolic and flavonoid content as well as highest DPPH, ABTS, and FRAP activities. Cell suspension cultures were established using 5 L capacity balloon-type bubble bioreactors using 2 L of MS medium 30 g L−1 sucrose and 0.5 mg L−1 2,4-D, 0.5 mg L−1 NAA, and 0.1 mg L−1 KN. The optimum yield of 230.81 g of fresh biomass and 16.48 g of dry biomass was evident after four weeks of cultures. High-pressure liquid chromatography (HPLC) analysis showed the cell biomass produced in bioreactors possessed higher concentrations of catechin hydrate, chlorogenic acid, naringenin, and other phenolic compounds.
Development and the Effect of Weather and Mineral Fertilization on Grain Yield and Stability of Winter Wheat following Alfalfa—Analysis of Long-Term Field Trial
Authors: Hlisnikovský Lukáš , Menšík Ladislav , Kunzová Eva
Published: March 2023

Within the framework of a long-term experiment, established in 1955, we evaluated the development and effects of weather and mineral fertilization (Control, NPK1, NPK2, NPK3, NPK4) on the yield and stability of winter wheat following alfalfa. In total, 19 seasons were analysed. The weather changed considerably at the experimental site. Significant increases in minimal, mean, and maximal temperatures were dated to the period 1987–1988, while precipitation remained the same to this day (insignificantly increasing trend by 0.5 mm per annum). Rising temperatures in November, May, and July positively affected wheat grain yield, especially in treatments with higher N doses. No relationship between yield and precipitation was recorded. Highest inter-annual yield variability was recorded in Control and NPK4 treatments. Although minerally fertilized treatments provided slightly higher yields, the difference between Control and NPK treatments was insignificant. According to the linear-plateau response model, the recommended dose of 44 kg ha−1 N corresponds with yield of 7.4 t ha−1, while Control provides an average yield of 6.8 t ha−1. The application of higher doses did not lead to significant grain yield increase. Alfalfa as a preceding crop reduces the need of N fertilization and contributes to sustainable conventional agriculture, however, its share in crop rotations is decreasing both in the Czech Republic and in Europe.
Spirometric Profile among Detergents Factory Workers in the North West Bank of Palestine: A Cross-Sectional Study
Authors: Rahhal Belal , Nazzal Zaher , Jamal Abrar , Quqa Ola , Makharze Tasneem , Aqel Noora
Published: March 2023

Background: The exposure to chemicals in detergent factories, as some studies have shown, has a negative impact the health of workers’ respiratory systems. The aim of the study was to assess the lung function parameters of detergent factory workers and compare it with those of a group of non-chemical workers using spirometry. Methods: A comparative cross-sectional study was performed involving male detergent factory workers and a comparison group of non-chemical workers. An interviewer-administered questionnaire was used to record workers’ characteristics, and the data were entered to the spirometry device. The spirometry test was performed to assess workers’ lung functions; we recorded the forced expiratory volume in the first second (FEV1), forced vital capacity (FVC), FEV1/FVC ratio, peak expiratory flow (PEF), forced expiratory flow between 25 and 75% of FVC (FEF25–75%), and lung age. Results: A total of 94 participants (28 detergent factory workers and 66 non-chemical workers) were included in the study. Detergent factory workers showed significant decreasing in the values of FEV1, FEV1/FVC ratio, PEF, FEF25–75%, and increased lung age compared to non-workers group (p < 0.05 to p < 0.001). Conclusions: This study showed that detergent factory workers have significantly lower pulmonary functions compared to workers in other non-chemical occupations. Further studies are needed to find the cause of this difference.
Resonance-Enhanced Pulsing Water Injection for Improved Oil Recovery: Micromodel Experiments and Analysis
Authors: Tan Yawen , Zhang Yiqun , Hui Chengyu , Yu Chao , Tian Shouceng , Wang Tianyu , Wang Fei
Published: March 2023

Enhanced oil recovery (EOR) is a crucial technology in the petroleum industry, influenced by several factors, including flooding fluids and methods. The adjustment of injection strategies and the application of vibration stimulation can significantly impact oil recovery, especially residual oil. In this study, we conducted experiments using a glass micromodel to investigate the effect of pulsing water injection on oil recovery. Our results show that when the pulse frequency matches the natural frequency of the micromodel, resonance occurs during the two-phase flow of pulse driving, which causes an increase in the amplitude of oscillation, enhances the mobility of oil, and improves recovery. The efficiency of the kinetic energy of displacement is also improved. However, when the frequency is 3 Hz, the absence of resonance leads to the opposite effect. In addition, we found that a greater amplitude increases the fluidity of oil. These findings have significant implications for the design of EOR strategies and methods. Our experimental results provide insight into the effect of pulse water injection on oil recovery and offer a potential strategy for the optimization of EOR techniques.
Highly Efficient CO2 Capture and Utilization of Coal and Coke-Oven Gas Coupling for Urea Synthesis Process Integrated with Chemical Looping Technology: Modeling, Parameter Optimization, and Performance Analysis
Authors: Wang Qiang , Yang Yong , Zhou Huairong
Published: March 2023

The resource endowment structure of being coal-rich and oil-poor makes China’s production of coal-based ammonia and urea, with a low production cost and a good market, a competitive advantage. However, the process suffers from high CO2 emissions and low energy efficiency and carbon utilization efficiency due to the mismatch of hydrogen-to-carbon ratio between raw coal and chemicals. Based on the coal-to-urea (CTU) process and coal-based chemical looping technology for urea production processes (CTUCLAS&H), a novel urea synthesis process from a coal and coke-oven gas-based co-feed chemical looping system (COG-CTUCLAS&H) is proposed in this paper. By integrating chemical looping air separation and chemical looping hydrogen production technologies and the synergies between coal gasification, low-energy consumption CO2 capture and CO2 utilization are realized; the excess carbon emissions of the CTU process are avoided through coupling the pressure swing adsorption of COG, and the low carbon emissions of the proposed system are obtained. In this work, the novel process is studied from three aspects: key unit modeling, parameter optimization, and technical-economic evaluation. The results show that COG-CTUCLAS&H achieves the highest system energy efficiency (77.10%), which is much higher than that of the CTU and CTUCLAS&H processes by 40.03% and 32.80%, respectively, when the optimized ratio of COG to coal gasified gas is 1.2. The carbon utilization efficiency increases from 35.67% to 78.94%. The product cost of COG-CTUCLAS&H is increased compared to CTU and CTUCLAS&H, mainly because of the introduction of COG, but the technical performance advantages of COG-CTUCLAS&H make its economic benefits obvious, and the internal rate of return of COG-CTUCLAS&H is 26%, which is larger than the 14% and 16% of CTU and CTUCLAS&H, respectively. This analysis will enable a newly promising direction of coal and COG-based co-feed integrated chemical looping technology for urea production.
Asymptotic Quantization of a Particle on a Sphere
Authors: Romero José L. , Klimov Andrei B.
Published: March 2023

Quantum systems whose states are tightly distributed among several invariant subspaces (variable spin systems) can be described in terms of distributions in a four-dimensional phase-space T∗S2 in the limit of large average angular momentum. The cotangent bundle T∗S2 is also the classical manifold for systems with E(3) symmetry group with appropriately fixed Casimir operators. This allows us to employ the asymptotic form of the star-product proper for variable (integer) spin systems to develop a deformation quantization scheme for a particle moving on the two-dimensional sphere, whose observables are elements of e(3) algebra and the corresponding phase-space is T∗S2. We show that the standard commutation relations of the e(3) algebra are recovered from the corresponding classical Poisson brackets and the explicit expressions for the eigenvalues and eigenfunctions of some quantized classical observables (such as the angular momentum operators and their squares) are obtained.
Comparing Water Indices for Landsat Data for Automated Surface Water Body Extraction under Complex Ground Background: A Case Study in Jilin Province
Authors: Liu Shu , Wu Yanfeng , Zhang Guangxin , Lin Nan , Liu Zihao
Published: March 2023

Derived from Landsat imagery and capable of enhancing the contrast between surface water bodies and the background, water indices are widely used in surface water body extraction. Whether one index and its optimal threshold can maintain the best all year round is a question. At present, however, little research has considered the effect of time or conducted experiments with data from different months. To identify the best index for surface water body extraction, two regions in Jilin Province were selected for the case study and a comprehensive comparative analysis considering the imagery acquisition time was conducted. Ten water indices were selected and calculated based on the 30 m spatial resolution Landsat TM/OLI imagery acquired in 1999 and 2001 and 2019 and 2021 from May to October. The indices included the Modified Normalized Difference Water Index (NDWI3 and MNDWI), Automated Water Extraction Index (AWEI) for images with and without shadow, Multi-Band Water Index (MBWI), New Water Index (NWI), Water Ratio Index (WRI), Sentinel-2 Water Index (SWI) originally calculated based on the Sentinel-2 imagery, New Comprehensive Water Index (NCIWI), Index of Water Surfaces (IWS), and Enhanced Water Index (EWI). The OTSU algorism was adopted to adaptively determine the optimal segmentation threshold for each index and the indices were compared in terms of inter-class separability, threshold sensitivity, optimal threshold robustness, and water extraction accuracy. The result showed that NWI and EWI performed the best in different months and years, with the best water enhancement effect that could suppress background information, especially for the water-related land use types and cloud pollution. Their optimal segmentation thresholds throughout the study period were more stable than others, with the ranges of −0.342 to −0.038 and −0.539 to −0.223, respectively. Based on the optimal thresholds, they achieved overall accuracies of 0.952 to 0.981 and 0.964 to 0.981, commission errors of 0 to 28.2% and 0 to 7.7%, and omission errors of 0 to 15% and 0 to 8%, with a kappa coefficient above 0.8 indicating good extraction results. The results demonstrated the effectiveness of NWI and EWI combined with the OTSU algorithm in better monitoring surface water during different water periods and offering reliable results. Even though this study only focuses on the lakes within two regions, the indices have the potential for accurately monitoring the surface water over other regions.
Dynamic Assessment of Drought Risk of Sugarcane in Guangxi, China Using Coupled Multi-Source Data
Authors: Guga Suri , Riao Dao , Zhi Feng , Sudu Bilige , Zhang Jiquan , Wang Chunyi
Published: March 2023

Globally, drought is an increasing threat to agricultural ecosystems, resulting in impaired crop yields, high food prices, and low incomes for farmers. Fluctuations in crop production and prices can exert a negative transboundary effect on food exporting and importing countries across the world through international trade. Therefore, it is important to regionally assess agricultural drought risk to reduce crop yield reduction by adapting existing systems. In this study, from the perspective of Chinese sugar security, a comprehensive assessment index of drought risk of sugarcane was constructed by considering the atmosphere–soil–crop continuum. Based on disaster-causing factors (hazards) and exposure, vulnerability, and mitigation capabilities of disaster subjects (disaster bearers), a risk assessment model of drought disaster of sugarcane in the growing season was established. Results of this study were three-fold. First, the maximum entropy model accurately reflected the reliability and relative importance of the disaster-causing factors of vegetation condition index (VCI), soil moisture condition index (SMCI), and standardized precipitation evapotranspiration index (SPEI), with the area under the curve value of the comprehensive drought risk of sugarcane being greater than 0.75. Second, the drought frequency and impact range in four growth stages of sugarcane significantly declined with the increasing drought severity. Light drought was prevalent in each growth stage, and the occurrence frequency of severe drought was relatively low. The drought frequency was significantly higher in the seedling and maturity stages than in the tillering and stem elongation stages, and the drought distribution was mainly concentrated in the southwest and central regions. Finally, the spatial distribution characteristics of drought risk significantly differed among the four growth stages of sugarcane. The risk level in the seedling stage declined from the southwest to the northeast. The high risk in the tillering stage was mainly concentrated in the southwest and northeast of the study region. In the stem elongation stage, the southwest became a low- risk area. In the maturity stage, the risk level was higher in the southeast than in the other areas. As sugarcane is majorly planted on dry slopes with uneven rainfall, a lack of good infrastructure, and the further intensification of global warming, sugarcane areas that were highly exposed to drought stress were highly vulnerable to drought risk, which in turn weakens farmers’ willingness to plant, thus threatening the security of sugar and biofuel production.
Spatiotemporal Evolution and Hysteresis Analysis of Drought Based on Rainfed-Irrigated Arable Land
Authors: Du Enyu , Chen Fang , Jia Huicong , Wang Lei , Yang Aqiang
Published: March 2023

Drought poses a serious threat to agricultural production and food security in the context of global climate change. Few studies have explored the response mechanism and lag time of agricultural drought to meteorological drought from the perspective of cultivated land types. This paper analyzes the spatiotemporal evolution patterns and hysteresis relationship of meteorological and agricultural droughts in the middle and lower reaches of the Yangtze River in China. Here, the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index products and surface temperature products were selected to calculate the Temperature Vegetation Dryness Index (TVDI) from 2010 to 2015. Furthermore, we obtained the Standardized Precipitation Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI) for the same period. Based on these indices, we analyzed the correlation and the hysteresis relationship between agricultural and meteorological drought in rainfed and irrigated arable land. The results showed that, (1) compared with SPEI, the high spatial resolution PDSI data were deemed more suitable for the subsequent accurate and scientific analysis of the relationship between meteorological and agricultural droughts. (2) When meteorological drought occurs, irrigated arable land is the first to experience agricultural drought, and then alleviates when the drought is most severe in rainfed arable land, indicating that irrigated arable land is more sensitive to drought events when exposed to the same degree of drought risk. However, rainfed arable land is actually more susceptible to agricultural drought due to the intervention of irrigation measures. (3) According to the cross-wavelet transform analysis, agricultural droughts significantly lag behind meteorological droughts by about 33 days during the development process of drought events. (4) The spatial distribution of the correlation coefficient between the PDSI and TVDI shows that the area with negative correlations of rainfed croplands and the area with positive correlations of irrigated croplands account for 77.55% and 68.04% of cropland areas, respectively. This study clarifies and distinguishes the details of the meteorological-to-agricultural drought relationship in rainfed and irrigated arable land, noting that an accurate lag time can provide useful guidance for drought monitoring management and irrigation project planning in the middle and lower reaches of the Yangtze River.
Defense against Adversarial Patch Attacks for Aerial Image Semantic Segmentation by Robust Feature Extraction
Authors: Wang Zhen , Wang Buhong , Zhang Chuanlei , Liu Yaohui
Published: March 2023

Deep learning (DL) models have recently been widely used in UAV aerial image semantic segmentation tasks and have achieved excellent performance. However, DL models are vulnerable to adversarial examples, which bring significant security risks to safety-critical systems. Existing research mainly focuses on solving digital attacks for aerial image semantic segmentation, but adversarial patches with physical attack attributes are more threatening than digital attacks. In this article, we systematically evaluate the threat of adversarial patches on the aerial image semantic segmentation task for the first time. To defend against adversarial patch attacks and obtain accurate semantic segmentation results, we construct a novel robust feature extraction network (RFENet). Based on the characteristics of aerial images and adversarial patches, RFENet designs a limited receptive field mechanism (LRFM), a spatial semantic enhancement module (SSEM), a boundary feature perception module (BFPM) and a global correlation encoder module (GCEM), respectively, to solve adversarial patch attacks from the DL model architecture design level. We discover that semantic features, shape features and global features contained in aerial images can significantly enhance the robustness of the DL model against patch attacks. Extensive experiments on three aerial image benchmark datasets demonstrate that the proposed RFENet has strong resistance to adversarial patch attacks compared with the existing state-of-the-art methods.
Robust and Fast Normal Mollification via Consistent Neighborhood Reconstruction for Unorganized Point Clouds
Authors: Liu Guangshuai , Li Xurui , Sun Si , Yi Wenyu
Published: March 2023

This paper introduces a robust normal estimation method for point cloud data that can handle both smooth and sharp features. Our method is based on the inclusion of neighborhood recognition into the normal mollification process in the neighborhood of the current point: First, the point cloud surfaces are assigned normals via a normal estimator of robust location (NERL), which guarantees the reliability of the smooth region normals, and then a robust feature point recognition method is proposed to identify points around sharp features accurately. Furthermore, Gaussian maps and clustering are adopted for feature points to seek a rough isotropic neighborhood for the first-stage normal mollification. In order to further deal with non-uniform sampling or various complex scenes efficiently, the second-stage normal mollification based on residual is proposed. The proposed method was experimentally validated on synthetic and real-world datasets and compared to state-of-the-art methods.
A Deep-Learning Based Pipeline for Estimating the Abundance and Size of Aquatic Organisms in an Unconstrained Underwater Environment from Continuously Captured Stereo Video
Authors: Böer Gordon , Gröger Joachim Paul , Badri-Höher Sabah , Cisewski Boris , Renkewitz Helge , Mittermayer Felix , Strickmann Tobias , Schramm Hauke
Published: March 2023

The utilization of stationary underwater cameras is a modern and well-adapted approach to provide a continuous and cost-effective long-term solution to monitor underwater habitats of particular interest. A common goal of such monitoring systems is to gain better insight into the dynamics and condition of populations of various marine organisms, such as migratory or commercially relevant fish taxa. This paper describes a complete processing pipeline to automatically determine the abundance, type and estimate the size of biological taxa from stereoscopic video data captured by the stereo camera of a stationary Underwater Fish Observatory (UFO). A calibration of the recording system was carried out in situ and, afterward, validated using the synchronously recorded sonar data. The video data were recorded continuously for nearly one year in the Kiel Fjord, an inlet of the Baltic Sea in northern Germany. It shows underwater organisms in their natural behavior, as passive low-light cameras were used instead of active lighting to dampen attraction effects and allow for the least invasive recording possible. The recorded raw data are pre-filtered by an adaptive background estimation to extract sequences with activity, which are then processed by a deep detection network, i.e., Yolov5. This provides the location and type of organisms detected in each video frame of both cameras, which are used to calculate stereo correspondences following a basic matching scheme. In a subsequent step, the size and distance of the depicted organisms are approximated using the corner coordinates of the matched bounding boxes. The Yolov5 model employed in this study was trained on a novel dataset comprising 73,144 images and 92,899 bounding box annotations for 10 categories of marine animals. The model achieved a mean detection accuracy of 92.4%, a mean average precision (mAP) of 94.8% and an F1 score of 93%.
Comparison of Low-Cost 5G Electromagnetic Field Sensors
Authors: Deprez Kenneth , Colussi Loek , Korkmaz Erdal , Aerts Sam , Land Derek , Littel Stephan , Verloock Leen , Plets David , Joseph Wout , Bolte John
Published: March 2023

This paper compares different low-cost sensors that can measure (5G) RF-EMF exposure. The sensors are either commercially available (off-the-shelf Software Defined Radio (SDR) Adalm Pluto) or constructed by a research institution (i.e., imec-WAVES, Ghent University and Smart Sensor Systems research group (S³R), The Hague University of Applied Sciences). Both in-lab (GTEM cell) and in-situ measurements have been performed for this comparison. The in-lab measurements tested the linearity and sensitivity, which can then be used to calibrate the sensors. The in-situ testing confirmed that the low-cost hardware sensors and SDR can be used to assess the RF-EMF radiation. The variability between the sensors was 1.78 dB on average, with a maximum deviation of 5.26 dB. Values between 0.09 V/m and 2.44 V/m were obtained at a distance of about 50 m from the base station. These devices can be used to provide the general public and governments with temporal and spatial 5G electromagnetic field values.
Isolation and In Vitro Stability Studies of Edible Plant-Seed Derived (Raphani Semen) Nanoparticles
Authors: An Jiahui , Zhu Yi
Published: March 2023

(1) Background: Edible plant-derived nanoparticles contain large amounts of endogenous substances and can be used as carriers for disease treatment. However, the extraction rate and purity are not high. Raphani Semen, the dried seed of the Raphanus sativus L., has been used as food and medicine for clinical treatment and health care for many years. (2) Methods: This study began with the extraction of edible Raphani Semen-derived nanoparticles (ERDNs) by ultracentrifugation (UC). Then a new method was proposed for ERDNs to be extracted by polyethylene glycol (PEG) and purified by size exclusion chromatography (SEC), followed by SDS-PAGE for identified proteins. The stability of ERDNs was also verified in three digestion simulations. (3) Results: The PEG offered higher yields of 1.14–2.31 mg/g than UC. Transmission electron microscopy showed both UC and PEG with cup-shaped nanoparticles of around 200 nm. The SEC-purified ERDNs contained a range of proteins smaller than 70 kDa. The size stability of ERDNs in digestion solutions demonstrated their ability to withstand extreme conditions, confirming their potential as a nano drug delivery platform. (4) Conclusions: In conclusion, the data suggest that PEG+SEC could isolate ERDNs with high efficiency, providing a reference for the separation of other seed nanoparticles.
Indulgence, Self-Control, and Annuity Preferences: Annuity Choices by Members of the Slovak-Funded Private Pension Pillar
Authors: Baláž Vladimír
Published: March 2023

This paper employs data from an original survey to analyse the annuity preferences of members of the Slovak-funded private pension pillar. The focus is on the impact of socioeconomic variables as well as various behavioural attitudes upon annuitisation decisions. The research first considers some well-known behavioural factors (framing, mental accounting) and then turns to some underresearched traits (indulgence, self-control) and behaviours (compulsive shopping, saving habits) and links them to annuity choices. Moreover, the research studies alternative allocations of a lump sum and relates them to annuitisation decisions. Indulgence emerged as a significant negative predictor of annuitisation. This result likely refers to preferences towards hedonic lifestyles in early retirement. Interestingly, low self-discipline was also significant but positively related to an intention towards annuity purchases. People who are aware of their low financial discipline may introduce internal self-protection mechanisms and resort to tried and tested financial products to avoid anticipated feelings of regret and desperation. Annuitisation, in this case, is a regret avoidance behaviour.
Mapping Impacts of Climate Change on the Distributions of Two Endemic Tree Species under Socioeconomic Pathway Scenarios (SSP)
Authors: HamadAmin Barham A. , Khwarahm Nabaz R.
Published: March 2023

Pistacia eurycarpa Yalt and Pistacia khinjuk Stocks are two important endemic tree species inhabiting mountainous regions in Iraq. Their cultural, medical, and ecological benefits have captured the interest of this study. Numerous researchers have revealed how and to what extent global climate change alters species’ habitats and distribution. This approach aims to quantify the current and future distribution of these tree species in the region and to provide baseline data on how Pistacia respond to the changing environment. Three socioeconomic pathway scenarios (SSP 126, 245, and 585) in two general circulating models (GCMs), MIROC-ES2L and BCC-CSM2-MR, have been utilized to examine the probable future geographical shift of these species during different time periods (2041–2060, 2061–2080, and 2081–2100). This study used the MaxEnt model and geospatial techniques for: (i) anticipating the present level of distributions and assessing the impact of climate change on these species’ possible future distributions; (ii) estimating the areas of species overlap; and (iii) finding the most significant environmental variables shaping their distributions, according to 11 environmental variables and 161 known localities. The findings revealed that 30 out of 36 modeling results showed range expansion in both the MIROC-ES2L and BCC-CSM2-MR models with 16/18 for P. eurycarpa and 14/18 for P. khinjuk. The overall species range expansions and increase in habitat suitability (mainly in the north and northeast) were related to precipitation during the wettest months, topography, and soil type structure (i.e., Chromic Vertisols, Lithosols, and Calcic Xerosols). These recent discoveries provide priceless new information for forestry management efforts and the conservation plan in Iraq, particularly in the overlapping areas in the mountainous regions and highlands. Geospatial approaches and correlation-based modeling are effective tools for predicting the spatial pattern of tree species in the mountain environment.
Capacity Allocation Method Based on Historical Data-Driven Search Algorithm for Integrated PV and Energy Storage Charging Station
Authors: Pan Xiaogang , Liu Kangli , Wang Jianhua , Hu Yutao , Zhao Jianfeng
Published: March 2023

The promotion of electric vehicles (EVs) is an important measure for dealing with climate change and reducing carbon emissions, which are widely agreed goals worldwide. Being an important operating mode for electric vehicle charging stations in the future, the integrated photovoltaic and energy storage charging station (PES-CS) is receiving a fair amount of attention and discussion. However, how to optimally configure photovoltaic and energy storage capacity to achieve the best economy is essential and a huge challenge to overcome. In this paper, based on the historical data-driven search algorithm, the photovoltaic and energy storage capacity allocation method for PES-CS is proposed, which determines the capacity ratio of photovoltaic and energy storage by analyzing the actual operation data, which is performed while considering the target of maximizing economic benefits. In order to achieve the proposed capacity allocation, the method is as follows: First, the economic benefit model of the charging stations is established, taking the net present value and investment payback period as evaluation indicators; then, by analyzing the operation data of the existing charging station with the target of maximizing economic benefits, the initial configuration capacity is obtained; finally, the capacity configuration is verified through a comprehensive case analysis for the actual operation data. The results show that the capacity configuration obtained through the data analysis features an optimized economic efficiency and photovoltaic utilization. The proposed method can provide a theoretical and practical basis for newly planned or improved large-scale charging stations.
Access to Nature Fosters Well-Being in Solitude
Authors: Samangooei Mina , Saull Ralph , Weinstein Netta
Published: March 2023

There has been growing interest in the ways that individuals connected with nature during the COVID-19 pandemic, particularly when they were alone in solitude. This study explored key themes describing individuals’ relationships with nature during this period and, more specifically, when individuals were relating to nature during time spent alone. Sixty participants (aged 19–80 years) discussed solitude during in-depth interviews. Participants were from different backgrounds and 20 different countries of origin. Thematic analysis was conducted by two architects (who may have been sensitive to the functional interaction of spaces in connecting people and nature) and identified descriptions of nature from broader narratives of solitude and time spent alone. Extracts from interview transcripts were coded using hierarchical thematic analysis and a pragmatist approach. The results showed that natural spaces were integral to experiencing positive solitude and increased the chance that solitude time could be used for rest, rejuvenation, stress relief, and reflective thought. Being in their local natural spaces also allowed participants to more spontaneously shift from solitude to social connection, supporting a sense of balance between these two states of being. Finally, solitude in nature, in part because of attention to shifting weather, gave a new perspective. As a result, participants reported increased species solidarity—the awareness that humans are part of an ecosystem shared with other species. We interpret the results in terms of the implications for built environments and the importance of accessing nature for well-being.
Study on the Effectiveness of Sulfate Reducing Bacteria to Remove Heavy Metals (Fe, Mn, Cu, Cr) in Acid Mine Drainage
Authors: Dong Yanrong , Gao Ziqing , Di Junzhen , Wang Dong , Yang Zhenhua , Wang Yunfeng , Xie Zhoufei
Published: March 2023

Aiming at the problem of environmental pollution caused by heavy metals such as Fe, Mn, Cu, and Cr, Sulfate Reducing Bacteria (SRB) were enriched in mining soil. SRB was added to AMD containing different concentrations of Fe, Mn, Cu, and Cr by batch experiments to explore the potential of SRB for treating heavy metals in AMD. Testing combining Scanning Electron Microscopy–Energy Dispersive Spectroscopy (SEM-EDS), X-ray Diffraction (XRD), and X-ray Photoelectron Spectroscopy (XPS) revealed the mechanism by which heavy metal ions were removed by SRB. The results showed that SRB had a strong ability to remove total Fe in the concentration range of 0–300 mg/L, and the removal percentage of total Fe reached to 92.16–93.17%. SRB had a good removal effect on total Cu, in the concentration range of 0–20 mg/L, and the removal percentage of total Cu reached to 79.79–81.80%. SRB had a good removal effect on total Cr as well; SRB activity would probably not be inhibited for total Cr concentrations below 500 mg/L. The ability to solidify different heavy metal ions using SRB was as follows: total Cr > total Fe > total Mn > total Cu. When SRB removed Fe2+ there was precipitation at the same time, appearing as black granules. This precipitation consisted mainly of FeS, and contained small amounts of Fe3(PO4)2-8H2O, FeCO3, Fe(OH)2, Fe(OH)3, and Fe2S as well.
Identifying Territorial Values for Tourism Development: The Case Study of Calabrian Greek Area
Authors: Scorza Francesco , Gatto Rachele Vanessa
Published: March 2023

Specialized tourism based on investments for the construction of main attractors does not represent an effective generalized regeneration strategy for the recovery of European inland rural areas. Remarking on the expected results of national policies and the relative effectiveness of the investment in tourist carriers through the evaluation of case studies, this research argues that the hypothesis to invert abandonment trends through generalized tourism development strategies may not be considered a suitable option in the decision-making process. Instead, the paper proposes a territorial analysis structure that explores socio-ecological dimensions to build knowledge for sustainable strategic plans. The study identifies territorial values in abandoned settlements in Calabrian Greek Area (southern Italy), and presents a new scenario for tourism development that prioritizes investment in supporting local informal tourism welcoming systems as an alternative to large infrastructural investment. Such investment is expected to produce long-term benefits for resident communities in abandoned inland rural areas. The study concludes with general recommendations to improve tourism development policies in a place-based approach.
A Conjoint Analysis Approach, Implications, and Mitigation Plans in Analyzing Students’ Preferences for Online Learning Delivery Types during the COVID-19 Pandemic for Engineering Students: A Case Study in the Philippines
Authors: Yandug Jenalyn Shigella G. , Costales Erika Mae D. , Ong Ardvin Kester S.
Published: March 2023

Traditional face-to-face classes were replaced with online learning when the COVID-19 pandemic happened. Students were unfamiliar with the virtual setup, as well as the technological challenges, which led to dissatisfaction and affected their academic performance. This research study surveyed 230 engineering students and applied conjoint analysis to analyze their preferences in online learning delivery. Using SPSS Statistics software, we revealed the top three attributes for delivery type: interaction, flexibility, and proctored exam. In addition, the most favored combination from the generated stimuli was a form of blended mode delivery. The synchronous meetings and asynchronous activities allowed students to manage their time efficiently. It could be deduced from the findings that students highly preferred the combination considering learner–instructor, live chats, real-time lecture meetings, and automatic availability, with a focus on synchronous online learning modes. Higher education institutions may reflect on the results of this study to reassess the online learning environment they implement among students. Incorporating a student’s learning style to determine the relationship between their preference for online learning delivery type will be helpful for further research through application and study extension. It is suggested that the perceptions of instructors and students enrolled under different learning modalities and their effectiveness should also be addressed. This is a recommendation for future research to consider. Especially near the end of the COVID-19 pandemic, traditional face-to-face learning was being implemented. Thus, future studies may want to consider an analysis of the behavioral intentions of students. On the other hand, analyses such as student behavioral intentions for the future applications of the different modalities and comparisons thereof may be conducted to enhance the implications for, policies of, and strategies of universities.
Software Defect Prediction Analysis Using Machine Learning Techniques
Authors: Khalid Aimen , Badshah Gran , Ayub Nasir , Shiraz Muhammad , Ghouse Mohamed
Published: March 2023

There is always a desire for defect-free software in order to maintain software quality for customer satisfaction and to save testing expenses. As a result, we examined various known ML techniques and optimized ML techniques on a freely available data set. The purpose of the research was to improve the model performance in terms of accuracy and precision of the dataset compared to previous research. As previous investigations show, the accuracy can be further improved. For this purpose, we employed K-means clustering for the categorization of class labels. Further, we applied classification models to selected features. Particle Swarm Optimization is utilized to optimize ML models. We evaluated the performance of models through precision, accuracy, recall, f-measure, performance error metrics, and a confusion matrix. The results indicate that all the ML and optimized ML models achieve the maximum results; however, the SVM and optimized SVM models outperformed with the highest achieved accuracy, 99% and 99.80%, respectively. The accuracy of NB, Optimized NB, RF, Optimized RF and ensemble approaches are 93.90%, 93.80%, 98.70%, 99.50%, 98.80% and 97.60, respectively. In this way, we achieve maximum accuracy compared to previous studies, which was our goal.