bluuebunny/crossref_metadata_2025 huggingface dataset:
Mesopotamian Synchronistic Chronography and the Book of Kings
Authors: Weingart Kristin
Published: March 2023

The Book of Kings uses a particular synchronistic framework to present the parallel histories of the kingdoms of Israel and Judah in 1 Kings 14–2 Kings 17. Some Ancient Near Eastern chronographic compositions (synchronistic king lists, the Neo-Babylonian chronicle, the so-called Synchronistic History) also record chronological relationships between ruler sequences in neighboring kingdoms. This paper distinguishes between synchronized dating and synchronistic compositions, offers a comparison between these compositions and the Book of Kings, and discusses aspects of the latter’s characteristics and pragmatics. The extant Mesopotamian synchronistic compositions presuppose and express a special connection between Assyria and Babylonia. It seems that a similar idea—applied to Israel and Judah—also stands behind the synchronistic composition in 1 Kings 14–2 Kings 17.
Economic Uncertainty and Firms’ Capital Structure: Evidence from China
Authors: Gao Chenglin , Tsusaka Takuji W.
Published: March 2023

This article assesses the effects of economic uncertainty on the corporate capital structure of Chinese-listed firms using a panel dataset of 1138 firms with A-shares traded on the Shanghai Stock Exchange and Shenzhen Stock Exchange for the period 2006–2020 and fixed-effect regression analysis. Economic uncertainty had a negative influence on Chinese firms’ debt ratios, especially for non-state-owned enterprises. Furthermore, firms’ leverage decreased on average during the 2008 Great Recession, whereas it increased during the 2018–2019 US–China Trade War and the 2020 COVID-19 pandemic. The findings provide quantitative evidence of the effects of economic uncertainty on the capital structure of firms in a transition economy.
DRFM-Based Repeater Jamming Reconstruction and Cancellation Method with Accurate Edge Detection
Authors: Han Bowen , Qu Xiaodong , Yang Xiaopeng , Li Wolin , Zhang Zhengyan
Published: March 2023

Digital radio frequency memory (DRFM) based repeater jamming can create false targets, which can lead to a loss of situational awareness, misidentification of targets, and decreased overall performance of the radar system. Traditional jamming suppression methods do not give due importance to the accurate estimation of the jamming edge, resulting in jamming residual and poor anti-jamming performance. To tackle this issue, this paper explores the reason and impact of inaccurate jamming edge estimation and proposes a DRFM-based repeater jamming reconstruction and cancellation method with accurate edge detection. In the proposed method, firstly, multiple jamming parameters are obtained by computing the short-time fractional Fourier transformation (STFRFT) spectrogram of the received signal. To avoid jamming residue, the proposed method estimates the accurate jamming edges by the joint use of the difference of box (DOB) filters and time domain deconvolution (TDDC) curves. Numerical simulations and experiments are conducted to evaluate the algorithm’s effectiveness in countering smeared spectrum (SMSP) and interrupted sampling repeater jamming (ISRJ). The results demonstrate its superior jamming reconstruction and suppression performance than other methods.
Radar Emitter Identification with Multi-View Adaptive Fusion Network (MAFN)
Authors: Yang Shuyuan , Peng Tongqing , Liu Huiling , Yang Chen , Feng Zhixi , Wang Min
Published: March 2023

Radar emitter identification (REI) aims to extract the fingerprint of an emitter and determine the individual to which it belongs. Although many methods have used deep neural networks (DNNs) for an end-to-end REI, most of them only focus on a single view of signals, such as spectrogram, bi-spectrum, signal waveforms, and so on. When the electromagnetic environment varies, the performance of DNN will be significantly degraded. In this paper, a multi-view adaptive fusion network (MAFN) is proposed by simultaneously exploring the signal waveform and ambiguity function (AF). First, the original waveform and ambiguity function of the radar signals are used separately for feature extraction. Then, a multi-scale feature-level fusion module is constructed for the fusion of multi-view features from waveforms and AF, via the Atrous Spatial Pyramid Pooling (ASPP) structure. Next, the class probability is modeled as Dirichlet distribution to perform adaptive decision-level fusion via evidence theory. Extensive experiments are conducted on two datasets, and the results show that the proposed MAFN can achieve accurate classification of radar emitters and is more robust than its counterparts.
Unsupervised Change Detection for VHR Remote Sensing Images Based on Temporal-Spatial-Structural Graphs
Authors: Wu Junzheng , Ni Weiping , Bian Hui , Cheng Kenan , Liu Qiang , Kong Xue , Li Biao
Published: March 2023

With the aim of automatically extracting fine change information from ground objects, change detection (CD) for very high resolution (VHR) remote sensing images is extremely essential in various applications. However, the increase in spatial resolution, more complicated interactive relationships of ground objects, more evident diversity of spectra, and more severe speckle noise make accurately identifying relevant changes more challenging. To address these issues, an unsupervised temporal-spatial-structural graph is proposed for CD tasks. Treating each superpixel as a node of graph, the structural information of ground objects presented by the parent–offspring relationships with coarse and fine segmented scales is introduced to define the temporal-structural neighborhood, which is then incorporated with the spatial neighborhood to form the temporal-spatial-structural neighborhood. The graphs defined on such neighborhoods extend the interactive range among nodes from two dimensions to three dimensions, which can more perfectly exploit the structural and contextual information of bi-temporal images. Subsequently, a metric function is designed according to the spectral and structural similarity between graphs to measure the level of changes, which is more reasonable due to the comprehensive utilization of temporal-spatial-structural information. The experimental results on both VHR optical and SAR images demonstrate the superiority and effectiveness of the proposed method.
Forward Electromagnetic Induction Modelling in a Multilayered Half-Space: An Open-Source Software Tool
Authors: Deidda Gian Piero , Díaz de Alba Patricia , Pes Federica , Rodriguez Giuseppe
Published: March 2023

Electromagnetic induction (EMI) techniques are widely used in geophysical surveying. Their success is mainly due to their easy and fast data acquisition, but the effectiveness of data inversion is strongly influenced by the quality of sensed data, resulting from suiting the device configuration to the physical features of the survey site. Forward modelling is an essential tool to optimize this aspect and design a successful surveying campaign. In this paper, a new software tool for forward EMI modelling is introduced. It extends and complements an existing open-source package for EMI data inversion, and includes an interactive graphical user interface. Its use is explained by a theoretical introduction and demonstrated through a simulated case study. The nonlinear data inversion issue is briefly discussed and the inversion module of the package is extended by a new regularized minimal-norm algorithm.
Rapid Estimation of Undifferenced Multi-GNSS Real-Time Satellite Clock Offset Using Partial Observations
Authors: Xie Wei , Huang Guanwen , Fu Wenju , Du Shi , Cui Bobin , Li Mengyuan , Tan Yue
Published: March 2023

Real-time satellite clock offset is a crucial element for real-time precise point positioning (RT-PPP). However, the elapsed time for undifferenced (UD) multi-global navigation satellite system (GNSS) real-time satellite clock offset estimation at each epoch is increased with the growth of stations, which may fall short of real-time application requirements. Therefore, a rapid estimation method for UD multi-GNSS real-time satellite clock offset is proposed to improve the computation efficiency, in which both the dimension of the normal equation (NEQ) and the number of redundant observations are calculated before adjustment; if these two values are larger than the predefined thresholds, the elevation mask is gradually increased until they are less than the predefined thresholds. Then, the clock offset estimation is conducted; this method is called clock offset estimation using partial observations. Totals of 50, 60, 70 and 80 stations are applied to perform experiments. Compared to clock offset estimation using all observations, the elapsed times of clock offset estimation using partial observations can be reduced from 6.80 to 3.10 s, 7.93 to 2.97 s, 12.04 to 3.14 s for 60, 70 and 80 stations, respectively. By using the proposed method, the elapsed time of the clock offset estimation at each epoch is less than 5 s. The estimated clock offset accuracy for GPS, BDS-3, Galileo and GLONASS satellites are better than 0.04, 0.05, 0.03 and 0.16 ns when using the partial observations to estimate clock offset with 50, 60, 70 and 80 stations, respectively. For the multi-GNSS kinematic PPP using the estimated clock offset from 50, 60, 70 and 80 stations with partial observations, the positioning accuracy at 95% confidence level in the east, north and up direction are better than 2.70, 2.20 and 5.60 cm, respectively.
Accuracy Assessment and Validation of Multi-Source CHIRPS Precipitation Estimates for Water Resource Management in the Barada Basin, Syria
Authors: Alsilibe Firas , Bene Katalin , Bilal Ghada , Alghafli Khaled , Shi Xiaogang
Published: March 2023

The lack of sufficient precipitation data has been a common problem for water resource planning in many arid and semi-arid regions with sparse and limited weather monitoring networks. Satellite-based precipitation products are often used in these regions to improve data availability. This research presents the first validation study in Syria for Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) estimates using in-situ precipitation data. The validation was performed using accuracy and categorical statistics in the semi-arid Barada Basin, Syria, between 2000 and 2020. Multiple temporal scales (daily, pentad, monthly, seasonally, and annual) were utilized to investigate the accuracy of CHIRPS estimates. The CHIRPS results indicated advantages and disadvantages. The main promising result was achieved at the seasonal scale. Implementing CHIRPS for seasonal drought was proven to be suitable for the Barada Basin. Low bias (PBwinter = 2.1%, PBwet season = 12.7%), high correlation (rwet season = 0.79), and small error (ME = 4.25 mm/winter) support the implementation of CHIRPS in winter and wet seasons for seasonal drought monitoring. However, it was observed that CHIRPS exhibited poor performance (inland pentads) in reproducing precipitation amounts at finer temporal scales (pentad and daily). Underestimation of precipitation event amounts was evident in all accuracy statistics results, and the magnitude of error was higher with more intense events. CHIRPS results better corresponded in wet months than dry months. Additionally, the results showed that CHIRPS had poor detection skill in drylands; on average, only 20% of all in-situ precipitation events were correctly detected by CHIRPS with no effect of topography found on detection skill performance. This research could be valuable for decision-makers in dryland regions (as well as the Barada Basin) for water resource planning and drought early warning systems using CHIRPS.
Improving Consistency of GNSS-IR Reflector Height Estimates between Different Frequencies Using Multichannel Singular Spectrum Analysis
Authors: Lei Jintao , Li Wenhao , Zhang Shengkai
Published: March 2023

Previous studies of GNSS-IR mainly focused on the legacy L1C signal; the potential of modernized signals (L2C and L5Q) has not yet been fully exploited. In this paper, we applied the Multichannel Singular Spectrum Analysis (M-SSA) method to extract common interference patterns from different frequencies simultaneously. The three-frequency (L1C, L2C, and L5Q) signal-to-noise ratio (SNR) measurements from a total of 840 satellite rising and setting arcs, occurring between day of year 250 to 279 in year 2020 and 2021, were used. By comparing GNSS-IR reflector heights obtained from the original and M-SSA-reconstructed SNR time series, we found that M-SSA significantly improves the between-frequency consistency, as shown by an increase in the values of R-squared of linear regression from (0.69, 0.67, 0.89) to (0.95, 0.96, 0.98), and a decrease in RMSE from (0.10 m, 0.10 m, 0.06 m) to (0.04 m, 0.04 m, 0.02 m) for S1C-S2C, S1C-S5Q, and S2C-S5Q pair, respectively. Our results validate (1) the effectiveness of the M-SSA method in extracting common interference patterns from multi-frequency SNR time series, and (2) the superiority of modernized civil signals L2C and L5Q over the legacy L1C signal in GNSS-IR studies. We also emphasize the important role that the L5 signal will play in future GNSS-IR research because of its compatibility and interoperability among different satellite navigation systems.
Turbulence: A Significant Role in Clear-Air Echoes of CINRAD/SA at Night
Authors: Teng Yupeng , Li Tianyan , Ma Shuqing , Chen Hongbin
Published: March 2023

It is commonly believed that clear-air echoes detected by weather radars are caused by atmobios migration. However, clear-air echoes are sometimes inconsistently related to the activity of living creatures. In some cases, the characteristics of radar products seem to conform to biological scattering, but the movement of echoes cannot be observed. For these reasons, we sought to expand the cause of clear-air echoes from a Chinese Doppler S-band Weather Radar (CINRAD/SA) in Beijing. Some contradictions were discovered in a case which diverged from previous conclusions. It was found that the progression and movement of clear-air echoes do not conform to the rules of biological activities. The frequency distribution of dual-wavelength ratio peaks is 21.5 dB, which is in accordance with Villars–Weisskopf’s turbulence theory. From 1 May to 20 May, the 58% dual-wavelength ratio between the S-band and the X-band was distributed between 18 dB and 24 dB. These results show that more than half of the clear-air echoes of CINRAD/SA at night were caused by turbulence in Beijing. A new model of troposcatter propagation, the reflecting-layers model, was then introduced to explain the radar observations. According to the reflecting-layers model, the echoes’ diurnal variation and reflectivity characteristics are attributed to the effects of turbulent mixing. Excessive turbulent mixing affects the generation of the reflective layer, thereby weakening the echo signal. It is necessary to re-examine the position of turbulence in clear-air echoes.
Enhanced CNN Classification Capability for Small Rice Disease Datasets Using Progressive WGAN-GP: Algorithms and Applications
Authors: Lu Yang , Tao Xianpeng , Zeng Nianyin , Du Jiaojiao , Shang Rou
Published: March 2023

An enhancement generator model with a progressive Wasserstein generative adversarial network and gradient penalized (PWGAN-GP) is proposed to solve the problem of low recognition accuracy caused by the lack of rice disease image samples in training CNNs. First, the generator model uses the progressive training method to improve the resolution of the generated samples step by step to reduce the difficulty of training. Second, to measure the similarity distance accurately between samples, a loss function is added to the discriminator that makes the generated samples more stable and realistic. Finally, the enhanced image datasets of three rice diseases are used for the training and testing of typical CNN models. The experimental results show that the proposed PWGAN-GP has the lowest FID score of 67.12 compared with WGAN, DCGAN, and WGAN-GP. In training VGG-16, GoogLeNet, and ResNet-50 with PWGAN-GP using generated samples, the accuracy increased by 10.44%, 12.38%, and 13.19%, respectively. PWGAN-GP increased by 4.29%, 4.61%, and 3.96%, respectively, for three CNN models over the traditional image data augmentation (TIDA) method. Through comparative analysis, the best model for identifying rice disease is ResNet-50 with PWGAN-GP in X2 enhancement intensity, and the average accuracy achieved was 98.14%. These results proved that the PWGAN-GP method could effectively improve the classification ability of CNNs.
CLIP-Based Adaptive Graph Attention Network for Large-Scale Unsupervised Multi-Modal Hashing Retrieval
Authors: Li Yewen , Ge Mingyuan , Li Mingyong , Li Tiansong , Xiang Sen
Published: March 2023

With the proliferation of multi-modal data generated by various sensors, unsupervised multi-modal hashing retrieval has been extensively studied due to its advantages in storage, retrieval efficiency, and label independence. However, there are still two obstacles to existing unsupervised methods: (1) As existing methods cannot fully capture the complementary and co-occurrence information of multi-modal data, existing methods suffer from inaccurate similarity measures. (2) Existing methods suffer from unbalanced multi-modal learning and data semantic structure being corrupted in the process of hash codes binarization. To address these obstacles, we devise an effective CLIP-based Adaptive Graph Attention Network (CAGAN) for large-scale unsupervised multi-modal hashing retrieval. Firstly, we use the multi-modal model CLIP to extract fine-grained semantic features, mine similar information from different perspectives of multi-modal data and perform similarity fusion and enhancement. In addition, this paper proposes an adaptive graph attention network to assist the learning of hash codes, which uses an attention mechanism to learn adaptive graph similarity across modalities. It further aggregates the intrinsic neighborhood information of neighboring data nodes through a graph convolutional network to generate more discriminative hash codes. Finally, this paper employs an iterative approximate optimization strategy to mitigate the information loss in the binarization process. Extensive experiments on three benchmark datasets demonstrate that the proposed method significantly outperforms several representative hashing methods in unsupervised multi-modal retrieval tasks.
Simultaneous Sleep Stage and Sleep Disorder Detection from Multimodal Sensors Using Deep Learning
Authors: Cheng Yi-Hsuan , Lech Margaret , Wilkinson Richardt Howard
Published: March 2023

Sleep scoring involves the inspection of multimodal recordings of sleep data to detect potential sleep disorders. Given that symptoms of sleep disorders may be correlated with specific sleep stages, the diagnosis is typically supported by the simultaneous identification of a sleep stage and a sleep disorder. This paper investigates the automatic recognition of sleep stages and disorders from multimodal sensory data (EEG, ECG, and EMG). We propose a new distributed multimodal and multilabel decision-making system (MML-DMS). It comprises several interconnected classifier modules, including deep convolutional neural networks (CNNs) and shallow perceptron neural networks (NNs). Each module works with a different data modality and data label. The flow of information between the MML-DMS modules provides the final identification of the sleep stage and sleep disorder. We show that the fused multilabel and multimodal method improves the diagnostic performance compared to single-label and single-modality approaches. We tested the proposed MML-DMS on the PhysioNet CAP Sleep Database, with VGG16 CNN structures, achieving an average classification accuracy of 94.34% and F1 score of 0.92 for sleep stage detection (six stages) and an average classification accuracy of 99.09% and F1 score of 0.99 for sleep disorder detection (eight disorders). A comparison with related studies indicates that the proposed approach significantly improves upon the existing state-of-the-art approaches.
Challenges in the Analytical Preparation of a Biological Matrix in Analyses of Endocrine-Disrupting Bisphenols
Authors: Milić Nataša , Milanović Maja , Drljača Jovana , Sudji Jan , Milošević Nataša
Published: March 2023

Endocrine-disrupting chemicals (EDCs) are xenobiotics presented in a variety of everyday products that may disrupt the normal activity of hormones. Exposure to bisphenol A as EDC at trace and ultra-trace levels is associated with adverse health effects, and children are recognized as the most vulnerable group to EDCs exposure. In this review, a summary is presented of up-to-date sample preparation methods and instrumental techniques applied for the detection and quantification of bisphenol A and its structural analogues in various biological matrices. Biological matrices such as blood, cell-free blood products, urine, saliva, breast milk, cordial blood, amniotic and semen fluids, as well as sweat and hair, are very complex; therefore, the detection and later quantification of bisphenols at low levels present a real analytical challenge. The most popular analytical approaches include gas and liquid chromatography coupled with mass spectrometry, and their enhanced reliability and sensitivity finally allow the separation and detection of bisphenols in biological samples, even as ultra-traces. Liquid/liquid extraction (LLE) and solid-phase extraction (SPE) are still the most common methods for their extraction from biological matrices. However, many modern and environmentally safe microextraction techniques are currently under development. The complexity of biological matrices and low concentrations of analytes are the main issues for the limited identification, as well as understanding the adverse health effects caused by chronical and ubiquitous exposure to bisphenols and its analogues.
Determination of Ethylene Glycol Dinitrate in Environmental and Forensic Water Samples Using Microextraction by Packed Sorbent Followed by Gas Chromatography
Authors: Hrouzková Svetlana , Pócsová Tamara , Lelkesová Tímea , Ulbrich Pavol
Published: March 2023

Ethylene glycol dinitrate (EGDN) is a liquid nitrate ester, a secondary explosive. In the past, it was used as an explosive ingredient in dynamite along with nitroglycerine. Due to its various applications, the reliable detection of EGDN in the environment is a key issue for both forensic and environmental applications. In these areas, sensitive and reliable methods for determining the concentration of nitro compounds are needed. Microextraction by packed sorbent (MEPS) is an innovative approach to green technology in the sample preparation field. Compared to conventional solid-phase extraction (SPE), MEPS uses a smaller sample volume and can be easily combined with various chromatographic techniques. An important benefit is the reduction of sorbent amount and up to 100-times repeatable use compared to disposable SPE columns, thus reducing the costs of analysis as well as waste production. Optimal extraction parameters for isolating EGDN from water, e.g., 30 µL of toluene as extraction agent, working in one cycle and in draw/discard mode, were selected. Method validation was performed, obtaining a limit of detection and a limit of quantification of 0.45 pg/μL and 1.34 pg/μL, respectively. Accuracy in terms of recovery rates was evaluated over a wide concentration range, obtaining values from 83.7 to 90.0%. The satisfactory linearity expressed by the coefficient of determination was 0.9914. A matrix factor of −9.3% indicates a weak matrix effect. The application to real environmental water samples and a forensic post-blast wash water sample was realized. EGDN detection in the post-blast samples provides valuable information for forensic technicians.
Online Membrane Sampling for the Mass Spectrometric Analysis of Oil Sands Process Affected Water-Derived Naphthenic Acids in Real-World Samples
Authors: Monaghan Joseph , Steenis Dylan , Vander Meulen Ian J. , Peru Kerry M. , Headley John V. , Gill Chris G. , Krogh Erik T.
Published: March 2023

Large volumes of oil sands process-affected waters (OSPW) result from heavy oil extraction in Alberta, Canada. Currently, a toxic legacy of ca. 500 Mm3 is stored in tailings ponds under a zero-discharge policy. OSPW is a complex mixture of suspended and dissolved materials including a wide range of inorganic and organic contaminants. Classically defined naphthenic acids (NAs; CnH2n+ZO2) are one of the primary toxic fractions in OSPW and have therefore been the subject of considerable research interest. Most studies employ considerable sample cleanup followed by liquid chromatography and/or high-resolution mass spectrometry (HRMS) for the characterization of these complex mixtures. However, these strategies can be time- and cost-intensive, limiting the scope of research and adoption for regulatory purposes. Condensed phase membrane introduction mass spectrometry (CP-MIMS) is emerging as a “fit-for-purpose” approach for the analysis of NAs. This technique directly interfaces the mass spectrometer with an aqueous sample using a hydrophobic semi-permeable membrane, requiring only pH adjustment to convert NAs to a membrane-permeable form. Here, we examine the perm-selectivity of classical NAs (O2) relative to their more oxidized counterparts (O3–O7) and heteroatomic (N, S) species collectively termed naphthenic acid fraction compounds (NAFCs). The investigation of 14 model compounds revealed that classically defined NAs are greater than 50-fold more membrane permeable than their oxidized/heteroatomic analogs. HRMS analysis of real OSPW extracts with and without membrane clean-up further supported selectivity towards the toxic O2 class of NAs, with >85% of the overall signal intensity attributable to O2 NAs in the membrane permeate despite as little as 34.7 ± 0.6% O2 NAs observed in the directly infused mixture. The information collected with HRMS is leveraged to refine our method for analysis of NAs at unit mass resolution. This new method is applied to 28 archived real-world samples containing NAs/NAFCs from constructed wetlands, OSPW, and environmental monitoring campaigns. Concentrations ranged from 0–25 mg/L O2 NAs and the results measured by CP-MIMS (unit mass) and SPE-HRMS (Orbitrap) showed good agreement (slope = 0.80; R2 = 0.76).
Sewage Water Treatment Using Chlorella Vulgaris Microalgae for Simultaneous Nutrient Separation and Biomass Production
Authors: Alazaiza Motasem Y. D. , He Shan , Su Dongxiao , Abu Amr Salem S. , Toh Pey Yi , Bashir Mohammed J. K.
Published: March 2023

Recovery of wastewater is essential for better management of water resources and can aid in reducing regional or seasonal water shortages. When algae were used to clean wastewater, amazing benefits were guaranteed, such as a decrease in the formation of dangerous solid sludge and the creation of valuable algal biomass through recycling of the nutrients in the wastewater. The trace elements nitrogen, phosphorus, and others that microalgae need for cell development are frequently present in contaminated wastewater. Hence, microalgal bioremediation is used in this study as an effective technique for the simultaneous treatment of COD, NH3-N, and orthophosphate from domestic wastewater and biomass production. Different concentrations of wastewaters were used. The maximum removals attained were: 84% of COD on the fifth day using the lowest mixing ratio of 50%, 95% of ammoniacal nitrogen, and 97% of phosphorus. The highest biomass production was achieved at day 12, except for the mixing ratio of 80% where the growth rate increased until day 14 at 400 mg/L.
Keeping the Knives Sharp: Socioeconomic Innovation in the Artisan Sector of Butchery in Italy
Authors: Fontefrancesco Michele Filippo , Costa Andrea
Published: March 2023

This paper investigates the socioeconomic and cultural transformation in food artisan entrepreneurship due to the complexification of the food sector and ongoing globalisation through a case study conducted among the artisan butchers of Genoa, Italy. The butcher’s trade has enjoyed centrality and social and cultural promotion that made butcher shops key places in the urban foodscape. However, this centrality is challenged by both new consumption trends and the imposition of large-scale organised distribution as the fulcrum of mass food trade. These changes raise the question about the future of the profession and its knowledge. This paper addresses this question by investigating the structure of the butcher’s business and the practices involved in knowledge creation and transmission, exploring the factor of change and its effects on the butcher’s profession, as well as the very foundational aspects of the artisanship. The research suggests that artisanship should be read as a form of entrepreneurship capable of placing and moving the craftsman within the global social hierarchy of a community. Therefore, the preservation of artisanship and its sociocultural complexity cannot be limited to the mere preservation of gastronomic forms and techniques.
Nomadic Bodies: From Their Intermittent Invisibility to Their Permanent Persistence—The Story of a Nomadic Cigano/Roma Family in Its Transit through Montemor-o-Novo, Portugal in (Post)Pandemic Times
Authors: Nievas Agostina Del Valle
Published: March 2023

The present study entails an ethnographic investigation of nomadic Cigano/Roma families in Portugal, examining their living conditions and corporeal experiences. The study centers on families that are forcibly moving throughout the Alentejo region of Portugal, with a particular focus on the relationship between geopolitical/urban space and corporeality. The movement of these families is motivated by a search for a safe place to camp and earn enough money to sustain their family group. The research aims to provide an in-depth understanding of the struggles and challenges that these families face in their daily lives, drawing on ethnographic and visual data to explore their experiences. Through an analysis of their voices and experiences, the study highlights the difficulties of finding better living conditions and the everyday struggles of life under tarps, without access to basic necessities such as water, energy, and sanitation. Ultimately, this research offers insights into the complex interplay between geography, social and economic structures, and the corporeal experiences of marginalized communities.
The Confidence of and Concern about Using Mobile Banking among Generation Z: A Case of the Post COVID-19 Situation in Thailand
Authors: Silanoi Wischaya , Naruetharadhol Phaninee , Ponsree Khwanjira
Published: March 2023

This research paper focuses on mobile banking acceptance among Generation Z users by combining the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) theory, as well as extending the theories with perceived trust and risk. During the COVID-19 pandemic, the online questionnaire survey was distributed using Google Forms, and the sample group was Thai Generation Z who were aged between 18 and 25 years old. The research objectives aim to (i) investigate the crucial effects that potentially affect user intention and actual usage of mobile banking, (ii) identify the most influential factor impacting users’ intention and behavior, (iii) further study the trust and risk perception of Generation Z users on mobile-banking intention and actual usage, (iv) discuss the findings with the antecedent studies, and (v) contribute the research findings both theoretically and practically. The proposed constructs include perceived usefulness, perceived ease of use, social influence, facilitating conditions, perceived trust, perceived risk, behavioral intention, and actual usage. There are fourteen proposed hypotheses to be tested. Based on the outcomes and the standardized coefficient beta, perceived usefulness (β = 0.518) was the strongest factor determining Generation Z’s behavioral intention, while perceived ease of use (β = 0.809) impacting perceived usefulness demonstrated the strongest relationship among all of the hypotheses.
Architectural and Mechanical Changes after Five Weeks of Intermittent Static Stretch Training on the Medial Gastrocnemius Muscle of Active Adults
Authors: Lévenéz Morgan , Moeremans Matthieu , Booghs Cédric , Vigouroux Florent , Leveque Clément , Hemelryck Walter , Balestra Costantino
Published: March 2023

We investigated the effects of intermittent long-term stretch training (5 weeks) on the architectural and mechanical properties of the muscle–tendon unit (MTU) in healthy humans. MTU’s viscoelastic and architectural properties in the human medial gastrocnemius (MG) muscle and the contribution of muscle and tendon structures to the MTU lengthening were analyzed. Ten healthy volunteers participated in the study (four females and six males). The passive stretch of the plantar flexor muscles was achieved from 0° (neutral ankle position) to 25° of dorsiflexion. Measurements were obtained during a single passive stretch before and after the completion of the stretching protocol. During the stretch, the architectural parameters of the MG muscle were measured via ultrasonography, and the passive torque was recorded by means of a strain-gauge transducer. Repeated-measure ANOVA was applied for all parameters. When expressed as a percentage for all dorsiflexion angles, the relative torque values decreased (p < 0.001). In the same way, architectural parameters (pennation angle and fascicle length) were compared for covariance and showed a significant difference between the slopes (ANCOVA p < 0.0001 and p < 0.001, respectively) suggesting a modification in the mechanical behavior after stretch training. Furthermore, the values for passive stiffness decreased (p < 0.05). The maximum ankle range of motion (ROM) (p < 0.01) and the maximum passive torque (p < 0.05) increased. Lastly, the contribution of the free tendon increased more than fascicle elongation to the total lengthening of the MTU (ANCOVA p < 0.001). Our results suggest that five weeks of intermittent static stretch training significantly change the behavior of the MTU. Specifically, it can increase flexibility and increase tendon contribution during MTU lengthening.
Innovative Technology Strategies for the Sustainable Development of Self-Produced Energy in the Colombian Industry
Authors: Orjuela-Abril Sofia , Torregroza-Espinosa Ana , Duarte-Forero Jorge
Published: March 2023

This research studies the current state of the Colombian industrial sector, which is focused on self-generation processes. The study’s objective is to search for viable technological strategies that strengthen this particular sector’s competitiveness and sustainable development. The analysis shows that internal combustion engines represent 49% of the technologies used for self-generation. The main fuel used in the sector is natural gas, with a percentage of 56%. The lack of strategies for the use of residual heat and technological inefficiencies caused a loss of 36% in the energy used in the Colombian industrial sector. Thermoelectric generators are a feasible way to recover energy from exhaust gases in engines used for self-generation. Additionally, they allow a 4% reduction in fuel consumption and an improvement in the engine’s energy efficiency. The use of hydrogen as fuel allows a 30% reduction in polluting emissions, such as CO2, CO, HC, and particulate matter. Hydrogen production processes, such as water electrolysis, allow the participation of Colombia’s solar energy potential, leading to sustainable hydrogen production, efficiency (60–80%), and a lower economic cost. In general, the application of thermoelectric generators and the use of hydrogen gas allow the improvement of the Colombian industrial sector’s environmental, social, and economic aspects due to greater competitiveness and the reduction in emissions and operating costs.
An Improved U-Net Model Based on Multi-Scale Input and Attention Mechanism: Application for Recognition of Chinese Cabbage and Weed
Authors: Ma Zhongyang , Wang Gang , Yao Jurong , Huang Dongyan , Tan Hewen , Jia Honglei , Zou Zhaobo
Published: March 2023

The accurate spraying of herbicides and intelligent mechanical weeding operations are the main ways to reduce the use of chemical pesticides in fields and achieve sustainable agricultural development, and an important prerequisite for achieving these is to identify field crops and weeds accurately and quickly. To this end, a semantic segmentation model based on an improved U-Net is proposed in this paper to address the issue of efficient and accurate identification of vegetable crops and weeds. First, the simplified visual group geometry 16 (VGG16) network is used as the coding network of the improved model, and then, the input images are continuously and naturally down-sampled using the average pooling layer to create feature maps of various sizes, and these feature maps are laterally integrated from the network into the coding network of the improved model. Then, the number of convolutional layers of the decoding network of the model is cut and the efficient channel attention (ECA) is introduced before the feature fusion of the decoding network, so that the feature maps from the jump connection in the encoding network and the up-sampled feature maps in the decoding network pass through the ECA module together before feature fusion. Finally, the study uses the obtained Chinese cabbage and weed images as a dataset to compare the improved model with the original U-Net model and the current commonly used semantic segmentation models PSPNet and DeepLab V3+. The results show that the mean intersection over union and mean pixel accuracy of the improved model increased in comparison to the original U-Net model by 1.41 and 0.72 percentage points, respectively, to 88.96% and 93.05%, and the processing time of a single image increased by 9.36 percentage points to 64.85 ms. In addition, the improved model in this paper has a more accurate segmentation effect on weeds that are close to and overlap with crops compared to the other three comparison models, which is a necessary condition for accurate spraying and accurate weeding. As a result, the improved model in this paper can offer strong technical support for the development of intelligent spraying robots and intelligent weeding robots.
Pollution and Risk Assessment of Polycyclic Aromatic Hydrocarbons in Urban Rivers in a Northeastern Chinese City: Implications for Continuous Rainfall Events
Authors: Mu Guangyi , Bian Dejun , Zou Min , Wang Xuege , Chen Fangfang
Published: March 2023

Most studies have concentrated on the distribution of polycyclic aromatic hydrocarbons (PAHs) in air, water, and soil; however, little is known about their behavior during urban wet deposition. During frequent urban rainstorms, surface runoff carries large amounts of organic pollutants into water, which has a significant impact on the quality of the water environment. “Poseidon” made landfall in Jilin Province on 27 August, 3 September, and 7 September 2020, respectively, causing some impact on the surface water. Gas chromatography–mass spectrometry (GC–MS) was used to measure the concentrations of 16 major PAHs in stormwater and water samples from the Yitong River. The PAH concentrations in rainwater runoff gradually decreased with increasing rainfall duration. The PAHs in the Yitong River water samples were mainly composed of two to three rings, with total concentrations ranging 279.19–756.37 ng/L. Ratio characterization and principal component analysis of the sources of PAHs in the water samples revealed that some water bodies had also been contaminated by oil spills in addition to combustion emissions from coal and motor vehicle oil. Health and environmental risks were assessed for PAHs in water bodies from the Yitong River, and health risk assessment showed that exposure to PAHs in the water is not a health risk for human beings; however, the risk for children is higher than that for adults and requires attention. Although the environmental risk is moderate, the risk from Benzo(a)anthracene(BaA) alone is high and requires ongoing attention. In terms of the sustainability of drinking water sources, consideration should be given to taking necessary regulatory and protective measures to prevent further contamination. These results serve as a guide for managing PAHs in urban water bodies and managing water pollution.
Can Environmental Regulations Promote Regional Industrial Transfer?
Authors: Tu Zhengge , Cao Yu , Liu Botao
Published: March 2023

In the context of building a “Beautiful China”, it is imperative to strengthen environmental regulations to restrict industrial pollution emissions. However, there are significant differences of regulations intensity among different regions, which will lead to an increase in the cost of compliance with regulations for polluting industries, so these industries tend to transfer from areas with strong environmental regulations to areas with weak environmental regulations. Based on the panel data of 282 prefecture-level cities and national patent data from 1994 to 2010, this paper constructs a difference in difference model (DID) to empirically study the impact of environmental regulations on regional industrial transfer and its mechanism. We find that, firstly, the “Two-Control Zones” policy has significantly promoted regional industrial transfer, and its effect has gradually increased in the long run. Then, the promotion effect of the “Two-Control Zones” policy on regional industrial transfer is heterogeneous among different regions due to the regional market environment and resource endowment; that is, the promotion effect is the greatest in Central China, then in Eastern China, and finally in Western China. At the same time, the frequency of industrial transfer in areas with high resource dependence is significantly lower than that in areas with low resource dependence. Finally, mechanism studies find that environmental regulation enhances inter-regional industrial liquidity and promotes regional technological innovation, and the role of environmental regulation on technological innovation is more obvious in regions with weak industrial liquidity. This proves that the “Pollution Heaven Hypothesis” and the “Porter Hypothesis” can be established at the same time in the Chinese context, which provides more reliable empirical evidence for the government to formulate environmental regulations, restrict pollution emissions, and balance environmental governance and sustainable economic development.
Ecological River Water Quality Based on Macroinvertebrates Present in the Ecuadorian Amazon
Authors: Vargas-Tierras Tannia , Suárez-Cedillo Sandra , Morales-León Vanessa , Vargas-Tierras Yadira , Tinoco-Jaramillo Leider , Viera-Arroyo William , Vásquez-Castillo Wilson
Published: March 2023

The Yanaquincha River is one of the tributaries that crosses the canton of La Joya de los Sachas from north to south, where the increase in human activities is affecting the quality of water used for agricultural activities and in tourist resorts. The purpose of this research was to determine the biological quality of the water through the BMWP-Col index and physicochemical parameters. Five sampling sites were selected along the length of the river for this assessment. Relatively intact sites were classified as reference sites (BR and FR), moderately impacted sites (EPC and EFPC), and severely impacted sites (PC). Biological and physicochemical data were collected to examine the quality of the water. The Biological Monitoring Working Party Colombia (BMWP-Col) biotic index and Functional Feeding Group (FFG) grouping were used to evaluate river quality. The results showed that water temperature, depth, width, and turbidity are important parameters in the composition of families. In the sampling sites, the BMWP-Col index was a determinant for river water quality (moderate, poor, or bad). Good quality water was not found in any of the sampling sites. The composition of the macroinvertebrate community changes from the source of the river until it ends its journey in the city. In addition, the family abundance and the composition of the feeding group were observed in the sites that showed similar or the same quality of water. The diversity of families and FFGs was generally higher at sites of moderate water quality. Chironomidae and Annelida were the most abundant families while Leptoceridae was the least abundant. The diversity of families was higher in BR while it was lower in PC and EFPC, grouped in six and four (both PC and EFPC) FFGs, respectively. It is important to carry out this type of study in the Ecuadorian Amazon because there is a lack of knowledge about taxonomic and functional diversity and the physicochemical variables with water quality.
Assessing the Effects of Treated Wastewater Irrigation on Soil Physico-Chemical Properties
Authors: Sdiri Wiem , AlSalem Huda S. , Al-Goul Soha T. , Binkadem Mona S. , Ben Mansour Hedi
Published: March 2023

For assessing the effects of wastewater on soil physical and chemical properties, manual irrigation (MI) and surface drip irrigation (SDI) systems were investigated. The experiment was conducted over 12 months. Before and after the experiment, soil samples were collected from three depths (0–20 cm, 20–40 cm and 40–60 cm) for analysis. The obtained results indicated that wastewater application probably preserves soil quality by maintaining its pH-water values whatever the irrigation system used. This study suggested that nutrient input from wastewater promotes soil microbial activity and organic matter (OM) mineralization. In fact, at the soil depths of 0–20 cm and 20–40 cm, MI using treated wastewater (TWW) leads to decrease OM content. P input may justify treated wastewater fertilizing effect in the topsoil. Moreover, TWW fertilizing effect was demonstrated by increased potassium (K) amount in the two upper soil layers (0–20 cm and 20–40 cm) following SDI system. This last system may block metals (iron (Fe), copper (Cu), cobalt (Co) and selenium (Se)) translocation to plants and their accumulation in soil. In contrast, metal translocation was maintained by the MI system. The present data is encouraging to reuse TWW for agricultural purposes, especially for orchard irrigation.