bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Building Extraction Network via Multi-Scale Foreground Modeling and Gated Boundary Refinement
Authors: Liu Junlin , Xia Ying , Feng Jiangfan , Bai Peng
Published: December 2023

Deep learning-based methods for building extraction from remote sensing images have been widely applied in fields such as land management and urban planning. However, extracting buildings from remote sensing images commonly faces challenges due to specific shooting angles. First, there exists a foreground–background imbalance issue, and the model excessively learns features unrelated to buildings, resulting in performance degradation and propagative interference. Second, buildings have complex boundary information, while conventional network architectures fail to capture fine boundaries. In this paper, we designed a multi-task U-shaped network (BFL-Net) to solve these problems. This network enhances the expression of the foreground and boundary features in the prediction results through foreground learning and boundary refinement, respectively. Specifically, the Foreground Mining Module (FMM) utilizes the relationship between buildings and multi-scale scene spaces to explicitly model, extract, and learn foreground features, which can enhance foreground and related contextual features. The Dense Dilated Convolutional Residual Block (DDCResBlock) and the Dual Gate Boundary Refinement Module (DGBRM) individually process the diverted regular stream and boundary stream. The former can effectively expand the receptive field, and the latter utilizes spatial and channel gates to activate boundary features in low-level feature maps, helping the network refine boundaries. The predictions of the network for the building, foreground, and boundary are respectively supervised by ground truth. The experimental results on the WHU Building Aerial Imagery and Massachusetts Buildings Datasets show that the IoU scores of BFL-Net are 91.37% and 74.50%, respectively, surpassing state-of-the-art models.
Indoor Localization System Based on RSSI-APIT Algorithm
Authors: Shen Xiaoyan , Xu Boyang , Shen Hongming
Published: December 2023

An indoor localization system based on the RSSI-APIT algorithm is designed in this study. Integrated RSSI (received signal strength indication) and non-ranging APIT (approximate perfect point-in-triangulation test) localization methods are fused with machine learning in order to improve the accuracy of the indoor localization system. The system focuses on the improvement of preprocessing and localization algorithms. The primary objective of the system is to enhance the preprocessing of the acquired RSSI data and optimize the localization algorithm in order to enhance the precision of the coordinates in the indoor localization system. In order to mitigate the issue of significant fluctuations in RSSI, a technique including the integration of Gaussian filtering and an artificial neural network (ANN) is employed. This approach aims to preprocess the acquired RSSI data, thus reducing the impact of multipath effects. In order to address the issue of low localization accuracy encountered by the conventional APIT localization algorithm during wide-area localization, the RSSI ranging function is incorporated into the APIT localization algorithm. This addition serves to further narrow down the localization area. Consequently, the resulting localization algorithm is referred to as the RSSI-APIT positioning algorithm. Experimental results have demonstrated the successful reduction of inherent localization errors within the system by employing the RSSI-APIT positioning algorithm. The present study aims to investigate the impact of the localization scene and the number of anchors on the RSSI-APIT localization algorithm, with the objective of enhancing the performance of the indoor localization system. The conducted experiments demonstrated that the enhanced system exhibits several advantages. Firstly, it successfully decreased the frequency of anchor calls, resulting in a reduction in the overall operating cost of the system. Additionally, it effectively enhanced the accuracy and stability of the system’s localization capabilities. In a complex environment of 100 m2 in size, compared with the traditional trilateral localization method and the APIT localization algorithm, the RSSI-APIT localization algorithm reduced the localization error by about 2.9 m and 1.8 m, respectively, and the overall error was controlled within 1.55 m.
The Environmental Impacts of Radio Frequency and Power Line Communication for Advanced Metering Infrastructures in Smart Grids
Authors: BenRhouma Ons , Rebai Chiheb , Ben-Romdhane Manel , Di Cara Dario , Artale Giovanni , Panzavecchia Nicola
Published: December 2023

In the neighborhood area network (NAN), the advanced metering infrastructure (AMI) enables a bidirectional connection between the smart meter (SM) and the data concentrator (DC). Sensors, such as smart meter nodes or environmental sensor nodes, play a crucial role in measuring and transmitting data to central units for advanced monitoring, management, and analysis of energy consumption. Wired and wireless communication technologies are used to implement the AMI-NAN. This paper delves into a novel approach for optimizing the choice of communication medium, air for radio frequency (RF) or power lines for power line communication (PLC), between the SM and DC in the context of the AMI-NAN. The authors methodically select the specific technologies, RF and NB-PLC (narrowband power line communication), and meticulously characterize their attributes. Then, a comparative analysis spanning rural, urban, and industrial settings is conducted to evaluate the proposed method. The overall reliability performance of the AMI-NAN system requires a packet error rate (PER) lower than 10%. To this end, an efficient approach is introduced to assess and enhance the reliability of NB-PLC and RF for AMI-NAN applications. Simulation results demonstrate that wireless communication is the optimal choice for the rural scenario, especially for a signal-to-noise ratio (SNR) lower than 25 dB. However, in urban environments characterized by higher SNR values and moderately dense networks, NB-PLC gains prominence. In denser networks, it outperforms wireless communication, exhibiting a remarkable 10 dB gain for a bit error rate (BER) of 10−3. Moreover, in industrial zones characterized by intricate network topologies and non-linear loads, the power line channel emerges as the optimal choice for data transmission.
A Method for Quantifying Back Flexion/Extension from Three Inertial Measurement Units Mounted on a Horse’s Withers, Thoracolumbar Region, and Pelvis
Authors: Hatrisse Chloé , Macaire Claire , Hebert Camille , Hanne-Poujade Sandrine , De Azevedo Emeline , Audigié Fabrice , Ben Mansour Khalil , Marin Frederic , Martin Pauline , Mezghani Neila , Chateau Henry , Chèze Laurence
Published: December 2023

Back mobility is a criterion of well-being in a horse. Veterinarians visually assess the mobility of a horse’s back during a locomotor examination. Quantifying it with on-board technology could be a major breakthrough to help them. The aim of this study was to evaluate the accuracy of a method of quantifying the back mobility of horses from inertial measurement units (IMUs) compared to motion capture (MOCAP) as a gold standard. Reflective markers and IMUs were positioned on the withers, eighteenth thoracic vertebra, and pelvis of four sound horses. The horses performed a walk and trot in straight lines and performed a gallop in circles on a soft surface. The developed method, based on the three IMUs, consists of calculating the flexion/extension angle of the thoracolumbar region. The IMU method showed a mean bias of 0.8° (±1.5°) (mean (±SD)) and 0.8° (±1.4°), respectively, for the flexion and extension movements, all gaits combined, compared to the MOCAP method. The results of this study suggest that the developed method has a similar accuracy to that of MOCAP, opening up possibilities for easy measurements under field conditions. Future studies will need to examine the correlations between these biomechanical measures and clinicians’ visual assessment of back mobility defects.
A Novel Smart Shoe Instrumented with Sensors for Quantifying Foot Placement and Clearance during Stair Negotiation
Authors: Ram Malarvizhi , Baltzopoulos Vasilios , Shaw Andy , Maganaris Constantinos N. , Cullen Jeff , O’Brien Thomas , Kot Patryk
Published: December 2023

Trips and slips are significant causal perturbations leading to falls on stairs, especially in older people. The risk of a trip caused by a toe or heel catch on the step edge increases when clearance is small and variable between steps. The risk of a slip increases if the proportion of the foot area in contact with the step is reduced and variable between steps. To assess fall risk, these measurements are typically taken in a gait lab using motion-capture optoelectronic systems. The aim of this work was to develop a novel smart shoe equipped with sensors to measure foot placement and foot clearance on stairs in real homes. To validate the smart shoe as a tool for estimating stair fall risk, twenty-five older adults’ sensor-based measurements were compared against foot placement and clearance measurements taken in an experimental staircase in the lab using correlations and Bland–Altman agreement techniques. The results showed that there was a good agreement and a strong positive linear correlation for foot placement (r = 0.878, p < 0.000) and foot clearance (r = 0.967, p < 0.000) between sensor and motion analysis, offering promise for advancing the current prototype into a measurement tool for fall risk in real-life staircases.
Multi-Dimensional Liquid Chromatography of Pulse Triacylglycerols with Triple Parallel Mass Spectrometry
Authors: Byrdwell William C. , Kotapati Hari Kiran
Published: December 2023

We analyzed ten pulses (the dried seeds of legumes), i.e., baby lima beans, black beans, black-eyed peas, butter beans, cranberry beans, garbanzo beans, green split peas, lentils, navy beans, and pinto beans, using three-dimensional liquid chromatography (3D-LC) with parallel second dimensions, LC × (LC + LC). We combined non-aqueous reversed-phase (NARP) chromatography as the first dimension separation, 1D, with argentation UHPLC for separation based on degree and location of unsaturation in the first second dimension, 2D(1), and multi-cycle NARP-UHPLC in the second second dimension, 2D(2). Pulses contained 1.9% to 2.7% lipids, except garbanzo beans, which contained 6.2% lipids. High-resolution, accurate-mass (HRAM) orbitrap mass spectrometry (MS) was used to perform lipidomic analysis of the 2D(2) and percent relative quantification, showing that the most abundant average triacylglycerol (TAG) molecular species across all pulses were PLL at 10.67% and PLLn at 10.45%. Common beans (Phaseolus vulgaris) were clustered together using principal component analysis (PCA), showing the highest levels of linolenic acid, C18:3, in molecular species such as PLnLn, LLnLn, and OLLn, with palmitic (P), C16:0, linoleic (L), 18:2, linolenic (Ln), 18:3, and oleic (O), 18:1, FAs. Calibration curves derived from interweaved sets of regioisomer standards allowed the absolute quantification of 1,2- and 1,3-regioisomers for a subset of TAGs.
Motivations to Collect: How Consumers Are Socialized to Build Product Collections
Authors: Johnson Jorgensen Jennifer , Sorensen Katelyn , Spilinek Melisa
Published: December 2023

Most people have collected products at some point in their lives; however, little is known about how people are socialized to collect. This mixed methods study recruited 213 participants to explain and explore the influences of family, friends, romantic partners, and online social media on the continued intention to build product collections. Qualitative findings revealed a clear pattern of familial influences when participants shared how their collections started. When starting collections, participants acquired products through either personal interest in the products or receiving gifts from family members. However, quantitative results indicate that friends, romantic partners, and social media have a greater influence after the product collection has started. The results and findings of this study also guide an adaptation of the consumer socialization theory.
Ecological Benefits and Plant Landscape Creation in Urban Parks: A Study of Nanhu Park, Hefei, China
Authors: Wu Shaowei , Yao Xiaojie , Qu Yinqi , Chen Yawen
Published: December 2023

Plant landscape creation in urban parks is an important aspect of urban ecological construction under the goal of “carbon neutrality”. In this study, the plant community of Nanhu Park in Hefei City was considered the research subject, and its tree species’ composition and diameter at breast height (DBH) were analyzed. The ecological benefits of the park’s green space were evaluated using the i-tree Eco model, and the carbon sequestration, runoff retention, air pollution removal, and oxygen production benefits were quantified as economic values and combined with the landscape effect evaluation method. The results show that Nanhu Park is rich in tree species types, with 5871 trees of 41 species in 23 families and 32 native species, among which three species of Sapindus mukorossi, Eucommia ulmoides, and Triadica sebifera accounted for 43.7% of the total number of trees. The dominant tree DBH was intermediate (7.6–15.2 cm). In Nanhu Park, the economic benefits were ordered as follows: carbon sequestration > runoff retention > air pollution removal > oxygen production benefits. The dominant tree species strongly contributed to the total ecological benefit of urban park green space; the ecological benefit of individual trees was not positively correlated with the number of tree species; native tree species had better ecological and landscape effects, while plant communities with growth changes and hierarchical depth of landscape were more popular. The analysis of ecological benefits and landscape evaluation of urban park green space provide a theoretical basis for enhancing the plant landscape, thus providing a case reference for promoting the construction of park green space in Hefei.
Estimating the Impact of COVID-19 on International Trade: Cases of Major Countries Using the SUR Model
Authors: Kim Taeil , Park Sunghwa , Kim Hanna , Kwon Janghan
Published: December 2023

This study examined the intricate effects of the COVID-19 pandemic on international trade complexity. Focusing on major international trading entities such as Hong Kong, Korea, China, Japan, and the U.S., we evaluated how confirmed COVID-19 cases and government responses influenced trade patterns, particularly in exports and imports. We employed a seemingly unrelated regression (SUR) analysis with cyclic regression coefficients to scrutinize changes in trade relationships between 2020 and 2021. Korea serves as the central country for this analysis, and the findings extend to other nations. The results revealed the varied impact of COVID-19 on trade across countries. Exports from Korea to China were correlated with COVID-19 case numbers and government actions in both countries. Additionally, imports from China, Japan, the U.S. and Vietnam—excluding Hong Kong—were significantly influenced by confirmed COVID-19 cases in Korea, reflecting the demand dynamics. Government interventions also played a substantial role in shaping trade patterns. Previous studies have primarily focused on financial markets and supply chains, whereas our study analyzed the changes in trade with Korea’s five largest trading partners: China, the U.S., Japan, Vietnam, and Hong Kong. Notably, we utilized long-term data and changes in major trading partners in Asia over time.
A Bilevel Model for Carbon Pricing in a Green Supply Chain Considering Price and Carbon-Sensitive Demand
Authors: Mesrzade Pegah , Dehghanian Farzad , Ghiami Yousef
Published: December 2023

In today’s industrial landscape, there is a mounting urgency to mitigate the adverse environmental impacts of emissions stemming from supply chain operations. On one front, policy-makers impose increasingly stringent emission reduction targets for supply chains, while on another front, consumers express a heightened preference for products and services with reduced carbon footprints. This study addresses the challenge of determining an optimal carbon pricing strategy by integrating the imperatives of a green supply chain with carbon taxation policies. To this end, we introduce a bi-level mixed-integer linear programming model for supply chain network planning, encompassing considerations of carbon taxation policies and the responsiveness of demand to the final product’s price and associated carbon emissions. Findings from a case study underscore that an escalation in carbon pricing prompts the supply chain to prioritize emissions reduction through the utilization of environmentally conscious approaches. The results reveal the need for a USD 0.9/kg carbon price to achieve a 10% emission reduction, resulting in an 80% profit decline. Notably, a 10% reduction has profound impacts, which leads to the suggestion of a gradual approach. Furthermore, as carbon prices reach higher levels, the supply chain tends toward curtailing production, thereby fostering an environment conducive to emission abatement. Consequently, policy formulators must judiciously calibrate a fitting carbon pricing mechanism to strike a harmonious equilibrium between emission reduction targets and the financial outlays of the supply chain.
Impacts of Strong El Niño–Southern Oscillation Events on Wintertime Northern Hemisphere Storm Tracks in Two Pacific Decadal Oscillation Phases during 1950–2010
Authors: Chu Cuijiao , Yang Xiu-Qun , Sun Xuguang , Ren Xuejuan , Chang Edmund K. M. , Fang Jiabei , Hu Haibo
Published: December 2023

Northern Hemisphere storm track activities (NHSTs) not only influence the weather and climate along their paths, but they also play a crucial role in climate systems by systematically transporting heat, momentum, and moisture. Distinguish from previous studies focusing on atmospheric circulation anomalies, this study provides further evidence of wintertime NHSTs variation under the influence of strong El Niño–Southern Oscillation (ENSO) events and Pacific Decadal Oscillation (PDO) variation with ERA-20C reanalysis data, from two mathematical aspects of linear superposition and nonlinear modulation. While ENSO warm/cold events lead the entire NHSTs to a general south/north shift, the ENSO impact in two PDO phases exhibits a remarkable difference with the PDO phase. The Pacific storm track (PST) anomalies largely strengthen equatorward and downstream when both ENSO and PDO are in phase, but its anomalies tend to be weakened when ENSO and PDO are out of phase. Generally, the opposite situation occurs with Atlantic storm track (AST) anomalies, which display a strengthening dipole pattern when ENSO and PDO are out of phase. Apparently, the result is roughly a linear superposition of ENSO and PDO-only impacts. Nevertheless, further analyses indicate that the nonlinear modulation of PDO on the ENSO impact on NHSTs exists. With respect to the PST, it exhibits approximately parallel bands of south-north dipole anomalies when ENSO is in the PDO positive phase, but only the south branch remains when ENSO is in the PDO negative phase. Generally, a similar situation occurs to AST anomalies over the Atlantic Ocean. The modulation may be mainly associated with the atmospheric mean flow anomalies and the midlatitude sea surface temperature anomalies to ENSO in different PDO phases. To some extent, the results may be beneficial for understanding the variation of extreme weather events brought by NHSTs.
Determinants of the Capital Structure of the Oil and Gas Industry in Malaysia: The Moderating Role of Earnings Volatility
Authors: Marimuthu Maran , Hamzah Hana Halini , Bangash Romana
Published: December 2023

This paper examines the relationship between firm-specific factors and the capital structure of the oil and gas (O&G) industry in Malaysia.. In addition, this paper adds to the literature by investigating the moderating effect of earnings volatility on the relationship between firm-specific factors and capital structure. Random effect models with cluster-robust standard errors were used to analyze this relationship. Using the secondary data from 30 O&G firms listed on the main market of Bursa Malaysia collected between 2010 and 2019 (10 years), the results show that profitability, asset tangibility, liquidity, and firm size significantly impact the capital structure of the O&G industry in Malaysia. However, growth opportunities, non-debt tax shields, and firm age had no significant impact. In addition to this, earnings volatility significantly moderated the relationship between asset tangibility and leverage. In short, when earnings volatility acts as a moderating variable, the relationship between asset tangibility, which is otherwise positive without moderation, turns negative. This study is useful for policymakers in the O&G industry in Malaysia and will help their managers to decide on capital structure for sustainable growth.
Coupling Coordination Development of the Ecological–Economic System in Hangzhou, China
Authors: Ni Jialiang , Zheng Xiaodong , Zheng Yuman , Zhang Yunhe , Li Huan
Published: December 2023

The contradiction between China’s urban economic development and the ecological environment has become increasingly prominent. Promoting the coordinated development of the ecological–economic system is an effective way to achieve sustainable development. Based on the analysis of the coupling mechanism of the ecological environment and economic development, the county unit is taken as the research unit to evaluate the coupling coordination degree (CCD) of Hangzhou’s ecological–economic system and analyze the evolution of coupling coordination characteristics and landscape ecological patterns in Hangzhou from 2010 to 2020. The results show that (1) the ecological protection and economic development status of Hangzhou is generally good, but the ecological environment index cannot maintain stable growth; (2) the coupling coordination degree of ecological–economic systems in various regions shows an overall upward trend, and ecological protection and economic development show positive interaction characteristics; (3) the development of CCD between regions is uneven, and the development level of a single subsystem in each region hinders coupling coordination between the two parties; and (4) changes in morphological spatial patterns further prove the coupling relationship between the two systems. Although the regional ecological connectivity has been optimized, the increase in perforations is crowding out the core area, and the regional ecological carrying capacity is facing challenges.
Energy and Business Synergy: Leveraging Biogenic Resources from Agriculture, Waste, and Wastewater in German Rural Areas
Authors: Pollack Moritz , Lück Andrea , Wolf Mario , Kraft Eckhard , Völker Conrad
Published: December 2023

The imperative to transform current energy provisions is widely acknowledged. However, scant attention has hitherto been directed toward rural municipalities and their innate resources, notably biogenic resources. In this paper, a methodological framework is developed to interconnect resources from waste, wastewater, and agricultural domains for energy utilization. This entails cataloging existing resources, delineating their potential via quantitative assessments utilizing diverse technologies, and encapsulating them in a conceptual model. The formulated models underwent iterative evaluation with engagement from diverse stakeholders. Consequently, 3 main concepts, complemented by 72 sub-concepts, were delineated, all fostering positive contributions to climate protection and providing heat supply in the rural study area. The outcomes’ replicability is underscored by the study area’s generic structure and the employed methodology. Through these inquiries, a framework for the requisite energy transition, with a pronounced emphasis on the coupling of waste, wastewater, and agriculture sectors in rural environments, is robustly analyzed.
Investigation of Buckling Behavior of Cracked FG Cylindrical Panels Reinforced by Graphene Platelets
Authors: Cho Jin-Rae
Published: December 2023

The buckling behavior of a functionally graded graphene-platelet-reinforced composite (FG-GPLRC) was traditionally investigated, mostly with respect to its undamaged structures. In this context, the current study investigated the buckling behavior of an FG-GPLRC cylindrical panel with an anti-symmetric central crack by introducing a 2-D extended natural element method (XNEM). The displacement was basically expressed with the first-order shear deformation theory (FSDT) and approximated using Laplace interpolation functions (for the non-singular displacement part) and crack-tip singular functions (for the singular displacement part) without grid refinement around the crack tips. The complex numerical manipulation on the curved shell surface was resolved by geometrically transforming the curved shell surface to a 2-D planar rectangular NEM grid. The painstaking numerical locking was suppressed by adopting the concept of a stabilized MITC3+ shell element. The validity of the developed numerical method was examined through a benchmark test, and the fundamental buckling loads of cracked FG-GPLRC cylindrical panels were investigated in depth by changing the major parameters. The numerical results also included a comparison with the FG-CNTRC. The numerical results indicated that the developed numerical method effectively predicts the buckling loads with reasonable accuracy, and that the fundamental buckling load of cracked FG-GPLRC cylindrical panels are remarkably influenced by the inclination angle and length of the crack as well as the other associated parameters.
A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction
Authors: Rahman Hameedur , Khan Abdur Rehman , Sadiq Touseef , Farooqi Ashfaq Hussain , Khan Inam Ullah , Lim Wei Hong
Published: December 2023

Computed tomography (CT) is used in a wide range of medical imaging diagnoses. However, the reconstruction of CT images from raw projection data is inherently complex and is subject to artifacts and noise, which compromises image quality and accuracy. In order to address these challenges, deep learning developments have the potential to improve the reconstruction of computed tomography images. In this regard, our research aim is to determine the techniques that are used for 3D deep learning in CT reconstruction and to identify the training and validation datasets that are accessible. This research was performed on five databases. After a careful assessment of each record based on the objective and scope of the study, we selected 60 research articles for this review. This systematic literature review revealed that convolutional neural networks (CNNs), 3D convolutional neural networks (3D CNNs), and deep learning reconstruction (DLR) were the most suitable deep learning algorithms for CT reconstruction. Additionally, two major datasets appropriate for training and developing deep learning systems were identified: 2016 NIH-AAPM-Mayo and MSCT. These datasets are important resources for the creation and assessment of CT reconstruction models. According to the results, 3D deep learning may increase the effectiveness of CT image reconstruction, boost image quality, and lower radiation exposure. By using these deep learning approaches, CT image reconstruction may be made more precise and effective, improving patient outcomes, diagnostic accuracy, and healthcare system productivity.
Titanium Dioxide (TiO2) Nanoparticle Toxicity in a Caenorhabditis elegans Model
Authors: Huang Sen-Ting , Lu Jian-He , Jualo Sherwin M. , Tayo Lemmuel L. , Mansor Wan-Nurdiyana-Wan , Lai Yi-Chieh , Wang Chih-Lung , Chao How-Ran
Published: December 2023

Titanium dioxide is a compound that is used in the food, cosmetic, and paint industries; however, it is still toxic to humans and the environment. This study determined the toxicities of titanium dioxide nanoparticles (TiO2 NPs) in a Caenorhabditis elegans (C. elegans) model. The effects of commercially available (C-TiO2) and synthetically (S-TiO2) prepared TiO2 NP solutions on lethality, lifespan, growth, reproduction, locomotion, and gene expression were studied in C. elegans. Exposure to TiO2 NPs (0.0, 0.01, 0.1, 1.0, and 10 mg/L) did not result in any change to the survival rate or body length of the nematodes, regardless of the concentration. However, there was a decrease in the reproduction (brood size) and locomotion (body bending and head thrashing) of the nematodes as the TiO2 NP concentration increased. The longevity of the nematodes was shortened following TiO2 NP exposure. The gene expression of sod-1, sod-3, ctl-1, ctl-2, cyp35A2, mlt-1, and mlt-2 in the nematodes showed that there was an overexpression of all genes when the worms were exposed to 1 mg/L C-TiO2 or 10 mg/L S-TiO2. It was therefore concluded that compared with S-TiO2, C-TiO2 possibly causes more toxicity or genotoxicity in the C. elegans model.
Biochemical Stability and Microbial Control of Reconstituted DaxibotulinumtoxinA-lanm for Injection
Authors: Ellis Kimberlee , Thach Thai , Gallagher Conor J.
Published: December 2023

DaxibotulinumtoxinA-lanm for injection (DAXI) is a unique US Food and Drug Administration-approved product comprising daxibotulinumtoxinA and a stabilizing excipient peptide (RTP004). DAXI has a longer-labeled shelf life (72 h) following reconstitution than other botulinum toxin type A products. Here, we report the stability and microbial control of reconstituted DAXI when stored at 2 °C–8 °C over a period of 36 days (Study 1) and 7 days (Study 2) following reconstitution with unpreserved or preserved saline. The pH and biological activity of reconstituted DAXI in the 50 U/vial and 100 U/vial formats remained stable at the final assessed time point in both preserved and unpreserved saline when refrigerated (2 °C–8 °C). No changes in recoverable 150 kDa neurotoxin (measured by enzyme-linked immunosorbent assay) were observed over 6 days of refrigeration. Bacterial growth or pathogen proliferation was not observed in DAXI reconstituted in preserved or unpreserved saline in both studies.
State-of-the-Art Virology Research in Norway
Authors: Rinaldo Christine Hanssen , Tryland Morten
Published: December 2023

Norway is situated in a remote and sparsely inhabited part of the world with about 5 [...]
Maternal COVID-19 Serological Changes—Comparison between Seroconversion Rate in First and Third Trimesters of Pregnancy and Subsequent Obstetric Complications: A Cohort Study
Authors: Rayo Maria N. , Aquise Adriana , Fernandez-Buhigas Irene , Gonzalez-Gea Lorena , Garcia-Gonzalez Coral , Sanchez-Tudela Mirian , Rodriguez-Fernandez Miguel , Tuñon-Le Poultel Diego , Santacruz Belen , Gil Maria M.
Published: December 2023

Pregnant women are especially vulnerable to respiratory diseases. We aimed to study seroconversion rates during pregnancy in a cohort of consecutive pregnancies tested in the first and third trimesters and to compare the maternal and obstetric complications in the women who seroconverted in the first trimester and those who did so in the third. This was an observational cohort study carried out at the Hospital Universitario de Torrejón, in Madrid, Spain, during the first peak of the COVID-19 pandemic. All consecutive singleton pregnancies with a viable fetus attending their 11–13-week scan between 1 January and 15 May 2020 were included and seropositive women for SARS-CoV2 were monthly follow up until delivery. Antibodies against SARS-CoV-2 (IgA and IgG) were analyzed on stored serum samples obtained from first- and third-trimester routine antenatal bloods in 470 pregnant women. Antibodies against SARS-CoV-2 were detected in 31 (6.6%) women in the first trimester and in 66 (14.0%) in the third trimester, including 48 (10.2%) that were negative in the first trimester (seroconversion during pregnancy). Although the rate of infection was significantly higher in the third versus the first trimester (p = 0.003), no significant differences in maternal or obstetric complications were observed in women testing positive in the first versus the third trimester.
Machine Learning Techniques in Dosing Coagulants and Biopolymers for Treating Leachate Generated in Landfills
Authors: Matovelle Carlos , Quinteros María , Heras Diego
Published: December 2023

The leachate discharges generated in sanitary landfills contain many pollutants that are harmful to the environment; treatments are scarce and should be carried out better. The use of coagulation–flocculation processes has been one of the most widely used, but due to the complexity of the characterization of the leachate, the dosing strategy of coagulants and biopolymers needs to be clarified. Therefore, the present study was carried out to determine the doses of coagulants and biopolymers suitable for coagulation–flocculation processes in the treatment of leachates using computational models of machine learning techniques such as artificial neural networks (ANNs); these allow for decreasing the operations of the tests of jars in the laboratory, optimizing resources. Through laboratory experimentation, there are real results of the effectiveness of applying biopolymers in leachate treatments at different concentration levels. The laboratory results were taken as input variables for the algorithms used; after the validation and calibration process, we proceeded to estimate predicted data with the computational model, obtaining predictions of optimal doses for treatment with high statistical adjustment indicators. It is verified that the applied coagulation–flocculation treatments reduce the turbidity values in the leachate and contaminants associated with suspended solids. In this way, the jar tests are optimized so that the operational costs decrease without affecting the results of adequate dosing.
Flood Estimation and Control in a Micro-Watershed Using GIS-Based Integrated Approach
Authors: Shuaibu Abdulrahman , Mujahid Muhammad Muhammad , Bello Al-Amin Danladi , Sulaiman Khalid , Kalin Robert M.
Published: December 2023

Flood analyses when using a GIS-based integrated approach have been successfully applied around the world in large-sized watersheds. This study employed hydrological-hydraulic modeling to analyze flash floods by integrating HEC-HMS, HEC-RAS, and ArcGIS software for flood evaluation and control in a micro-watershed in the Samaru River, Nigeria. The watershed boundaries, its characteristics (soil and land use), the topographical survey, and the intensity duration frequency curve (IDF) of the study area were produced using data-driven techniques. The HEC-HMS model was used to derive the peak discharges for 2-, 5-, 10-, 25-, 50-, 100-, and 200-year return periods with the frequency storm method. Afterward, the water surface profiles for the respective return periods were estimated using the HEC-RAS hydrodynamic model. The simulated design flood for the 2-, 5-, 10-, 25-, 50-, 100-, and 200-year return periods at the reference location (the NUGA gate culvert) were 3.5, 6.8, 9.1, 12.1, 14.3, 16.6, and 19.0 m3/s, respectively, while those at the watershed outlet for the respective return periods were 7.5, 14.9, 20.3, 27.3, 32.6, 38.0, and 43.5 m3/s, respectively (with a water height of 0.9 m, 1.1 m, 1.3 m, 1.33 m, 1.38 m, 1.5 3m, and 1.8 m, respectively), at the NUGA gate culvert cross-section. The maximum water depths of about 0.9 m and 1.0 m were recorded in the right and left overbanks, which were similar to the simulated water depth for the 2- and 5-year return periods. Hence, for the smart control of floods passing through the river and major hydraulic structures, a minimum design height of 1.50 m is recommended. For the most economic trapezoidal channel section, a normal depth of 1.50 m, a bottom width of 1.73 m, a top width of 3.50 m, and a free board of 0.30 m is proposed to curb the overtopping of floods along the channel sub-sections. The findings of this study could help hydraulic engineers minimize flooding in streams and rivers overbanks in a micro-watershed.
Flood Detection in Polarimetric SAR Data Using Deformable Convolutional Vision Model
Authors: Yu Haiyang , Wang Ruili , Li Pengao , Zhang Ping
Published: December 2023

Floods represent a significant natural hazard with the potential to inflict substantial damage on human society. The swift and precise delineation of flood extents is of paramount importance for effectively supporting flood response and disaster relief efforts. In comparison to optical sensors, Synthetic Aperture Radar (SAR) sensor data acquisition exhibits superior capabilities, finding extensive application in flood detection research. Nonetheless, current methodologies exhibit limited accuracy in flood boundary detection, leading to elevated instances of both false positives and false negatives, particularly in the detection of smaller-scale features. In this study, we proposed an advanced flood detection method called FWSARNet, which leveraged a deformable convolutional visual model with Sentinel-1 SAR images as its primary data source. This model centered around deformable convolutions as its fundamental operation and took inspiration from the structural merits of the Vision Transformer. Through the introduction of a modest number of supplementary parameters, it significantly extended the effective receptive field, enabling the comprehensive capture of intricate local details and spatial fluctuations within flood boundaries. Moreover, our model employed a multi-level feature map fusion strategy that amalgamated feature information from diverse hierarchical levels. This enhancement substantially augmented the model’s capability to encompass various scales and boost its discriminative power. To validate the effectiveness of the proposed model, experiments were conducted using the ETCI2021 dataset. The results demonstrated that the Intersection over Union (IoU) and mean Intersection over Union (mIoU) metrics for flood detection achieved impressive values of 80.10% and 88.47%, respectively. These results surpassed the performance of state-of-the-art (SOTA) models. Notably, in comparison to the best results documented on the official ETCI2021 dataset competition website, our proposed model in this paper exhibited a remarkable 3.29% improvement in flood prediction IoU. The experimental outcomes underscore the capability of the FWSARNet method outlined in this paper for flood detection using Synthetic Aperture Radar (SAR) data. This method notably enhances the accuracy of flood detection, providing essential technical and data support for real-world flood monitoring, prevention, and response efforts.
Marine Copepods as a Microbiome Hotspot: Revealing Their Interactions and Biotechnological Applications
Authors: Feng Jiantong , Mazzei Maurizio , Di Gregorio Simona , Niccolini Luca , Vitiello Valentina , Ye Yingying , Guo Baoying , Yan Xiaojun , Buttino Isabella
Published: December 2023

Copepods are the most abundant organisms in marine zooplankton and the primary components of the food chain. They are hotspots for highly adaptable microorganisms, which are pivotal in biogeochemical cycles. The microbiome, encompassing microorganisms within and surrounding marine planktonic organisms, holds considerable potential for biotechnological advancements. Despite marine microbiome research interests expanding, our understanding of the ecological interactions between microbiome and copepods remains limited. This review intends to give an overview of the recent studies regarding the microbiome associated with marine copepods, with particular focus on the diversity of bacteria and fungi. The significance of copepod-associated microbiomes in different contexts, such as aquaculture and biodegradation processes, was evaluated. The ability of the microbiome to mitigate harmful bacterial growth in cultured organisms was also explored. The microbiome associated with copepods has demonstrated efficacy in reducing the proliferation of detrimental bacteria in aquaculture, paving the way for the commercial utilization of natural zooplankton in fish rearing. Additionally, copepod-associated microbiomes may play a role in addressing marine environmental challenges, such as the bioremediation of polluted marine matrices. Overall, this review represents a basis for investigating intricate copepod-associated microbiomes and their diverse applications, enhancing our comprehension of the ecological and evolutionary significance of marine microbiomes.
Analysis of the demand for nutrients, ingredients, and food products of animal origin for the purpose of import substitution
Authors:   Plekhanov Russian University of Economics, Sokolov A.Yu.
Published: December 2023

Management of current scientifi c research and development is based on the life cycle of a product (goods), which begins with a marketing justifi cation for a development project and an analysis of the relevance of the problems. Therefore, the results of the author’s study of the potential demand for such items as raw materials, ingredients, food products, etc., which need to be developed in the context of industrial import substitution in our country, may be useful.
The methodology relevance of land use planning expertise
Authors:   Real Estate Cadastre and Land Use Department, State University of Land Use Planning, Borodina O.B. , Zdanova R.V. ,   Real Estate Cadastre and Land Use Department, State University of Land Use Planning, Sinitsa Y.S. ,   Real Estate Cadastre and Land Use Department, State University of Land Use Planning
Published: December 2023

The article considers the methodology of land use planning, cadastral and construction-technical expertise. It analyzes the need to create an independent type of expertise – land use planning-cadastral one. The article provides arguments for the definition of this type of expertise by distinguishing the subjects of expert research and objectives. The authors underline the necessity of developing theoretical issues of the methodology of expert research and consider the methods, techniques and means of examination. In conclusion, the authors propose to create a special type of expertise – land use planning-cadastral one – and develop a standard methodology for its implementation.
PERBAIKAN PREVENTIVE MAINTENANCE DENGAN MENGHILANGKAN SIX BIG LOSSES PADA MESIN TEST BENCH LINE POWERTRAIN SECTION DI PT SAPTAINDRA SEJATI
Authors: Lemadi Gayuh , Khoiromi Muhmmad Subkhanul , Prasetio Dian eko Adi
Published: December 2023

Saptaindra Sejati merupakan sebuah perusahaan yang bergerak dibidang jasa pertambangan batu bara. Plant Rebuild Center yang didirikan PT. Saptaindra Sejati juga  tidak  terlepas  dari masalah  yang berkaitan  dengan  efektivitas  mesin/peralatan.  Hal  ini  dapat  terlihat  melalui frekuensi kerusakan yang terjadi pada mesin/peralatan. Akibat lain yang ditimbulkan kerusakan mesin/peralatan yaitu dalam hal kualitas produk yang dihasilkan dimana produk yang tidak sesuai dengan standar kualitas akan diolah kembali. Oleh karena itulah diperlukan langkah-langkah yang efektif dan efisien dalam pemeliharaan mesin/peralatan untuk dapat menanggulangi dan mencegah masalah tersebut. Total Productive Maintenance (TPM) adalah salah satu metode yang dapat digunakan untuk meningkatkan produktivitas dan efisiensi produksi perusahaan. Tidak tepatnya penanganan dan pemeliharaan mesin/peralatan tidak hanya menyebabkan masalah kerusakan saja tetapi juga kerugian lain yang disebut dengan six big losses. Salah satu tujuan TPM adalah untuk meningkatkan efektivitas dengan cara meningkatkan fungsi dan kinerja mesin/peralatan yang digunakan dan mengeliminasi six big losses yang terdapat pada mesin/peralatan. Objek yang diteliti pada penelitian ini adalah mesin test bench yang berada di line Powertrain Section. Tahapan pertama dalam usaha peningkatan efisiensi produksi pada perusahaan ini adalah dengan melakukan pengukuran six big losses dan dari faktor six big losses tersebut dicari faktor terbesar yang mengakibatkan rendahnya efisiensi mesin test bench. Pencapaian nilai OEE mesin test bench selama periode tahun 2021-2022 hanya sebesar 38.12%. Dengan nilai faktor availability sebesar 63.6%, performance sebesar 63.7%, dan quality sebesar 94.1%.
SEMIOTIKA RIFFATERRE DALAM PUISI “LAGU SEORANG GERILYA” KARYA W.S. RENDRA
Authors: Zahrani Fairuz Annisa , Susilo Rini Febrianti , Ratih Rina
Published: December 2023

The poem entitled "Song of a Guerrilla" is a great literary work that is able to express the contents of his thoughts into beautiful poetic verses that make the author interested in researching poetry from W.S Rendra. The purpose of this study is to describe a guerrilla song poem using the study of riffatere semiotics. This research uses a qualitative descriptive approach by describing the facts that are then analyzed. Research data in the form of words, phrases, clauses, sentences contained in poetry texts. Riffatere semiotic reading steps: (1) Heuristic reading and hermeneutic reading, (2) Indirectness of expression, (3) Determining matrices, models, and variants, (4) Hypogram of W.S. Rendra's song of a guerrilla poem, and (5) Summing up data and writing reports. The results of this study describe the meaning that poets want to convey through poetry is about their lovers until the end of their lives on the battlefield. The matrix of a guerrilla's song poem is struggle, resilience, oppression. The model on a guerrilla's song poem uses symbolic and metaphorical meanings.
KETERAMPILAN GURU DALAM PENGELOLAAN KELAS UNTUK MENINGKATKAN KEAKTIFAN BELAJAR SISWA MELALUI PENDIDIKAN AGAMA KATOLIK DI SMA KATOLIK 2 KABANJAHE
Authors: Simbolon Erikson , Katarina Sevita Monica
Published: December 2023

This thesis aims to find out the teacher's skills in class management to increase the activeness of student learning in Catholic Religious education subjects. The research method used by researchers is qualitative. Researchers used the results of observations, interviews, and documentation to observe and find out how the skills of teachers in class management increase students' active learning in Catholic Religion education at Kabanjahe 2 Catholic Senior High School. Informants in this research were Catholic religious education teachers as chritical informants, students as supporting informants, and peer teachers as additional informants. Catholic religious education teachers apply skills in classroom management where students are more active than before, starting from students who dare to ask questions, express opinions, and have group discussions. This can be seen from the average value of students' daily tests before the teacher applies skills in class management, namely 72. After the skills that the teacher makes in class management, the average value of students increases to 81 in cycle 1 and then increases again to 84 in cycle 2.