bluuebunny/crossref_metadata_2025 huggingface dataset:
Implementation of sustainable farming practices by cocoa farmers in Ecuador and Uganda: the influence of value chain factors
Authors: Tennhardt Lina M. , Lambin Eric F. , Curran Michael , Schader Christian
Published: July 2023

A key strategy of chocolate manufacturers is the promotion of sustainable farming practices amongst their supplying cocoa producers. A growing body of micro-economic literature has analysed factors influencing the adoption of such practices, yet broadly disregarded value chain factors. Information on how factors within single value chains increase the adoption of sustainable farming practices can help direct chocolate companies’ investments and increase return of investments in sustainability. The objective of this study was to understand: (a) how important value chain factors are, relative to farmer and farm factors, for cocoa farmers’ implementation of sustainable farming practices and (b) through which mechanisms value chain factors influence sustainable farming practices implementation. By integrating the practice adoption with sustainable supply chain management literature, we contribute to closing an important research gap. We collected data from 394 cocoa farmers in Ecuador and Uganda and analysed the determinants of implementation sustainable farming practices, testing quantitatively whether value chain factors with variation within single value chains are significantly associated with practice implementation. These factors included information factors (farmers’ access to training; advisory service through the value chain) and structural factors (value chain organisation and persistence; farmers’ dependency on this value chain). We selected 11 sustainable farming practices or indicators across three sustainability dimensions, i.e., environmental, social, and economic. We found that value chain factors are comparable to farmer and farm factors in explaining the implementation of sustainable farming practices across dimensions. Both capacity building and stable relationships were significantly related with the implementation of certain sustainable farming practices. Yet these results were weaker than expected, indicating that their potential was not fully exploited within our case study value chains. Through their value chain sustainability initiatives, chocolate companies should disseminate knowledge, address inhibitors to sustainable farming practices implementation beyond knowledge, and align sustainability goals with all value chain actors.
Developing strategies for stabilizing the livelihood of smallholder farmers through non-farm activities: the application of the SWOT-AHP-TOWS analysis
Authors: Baghernejad Jafar , Sabouri Mohammad Sadegh , Shokati Amghani Mohammad , Norozi Abbas
Published: July 2023

Non-farm activities are a means of livelihood stabilization and are regarded as a sustainable approach to bringing balance to the economic, social, cultural, and environmental dimensions of sustainable livelihood. The main purpose of this study was to develop strategies for stabilizing the livelihood of smallholder farmers through non-farm activities using a combined SWOT-AHP-TOWS model. The results of analyzing the strategic space for developing strategies for stabilizing the livelihood of smallholders through non-farm activities revealed that the strengths (0.391) were more than the weaknesses (0.276) in the internal space and that the opportunities (0.195) were more than the threats (0.138) in the external space. Also, it was found that the internal challenges (S + W = 0.667) were more important than the external challenges (O + T = 0.33) in developing livelihood stabilization strategies. Further, the results showed that the beneficial space (O + S = 0.586) dominated the risky space (T + W = 0.414). Eventually, 20 strategies were developed among which the most important ones were “establishing and developing greenhouse cultivation based on the crop patterns considering the relative advantages of the villages” and “establishing microcredit foundations and funds to support the youth in getting involved in rural non-farm businesses.” In general, the results can provide new insights into the stabilization of the livelihood of smallholders through non-farm activities.
Effects of glutamine, glutamate, and aspartate on intestinal barrier integrity and amino acid pool of the small intestine in piglets with normal or low energy diet
Authors: Deng Yuankun , Cheng Hao , Li Junyao , Han Hui , Qi Ming , Wang Nan , Tan Bi'e , Li Jianjun , Wang Jing
Published: July 2023

Aspartate (asp), glutamate (glu), and glutamine (gln) are the major energy fuels for the small intestine, and it had been indicated in our previous study that the mix of these three amino acid supplementations could maintain intestinal energy homeostasis. This study aimed to further investigate whether the treatment of gln, glu, and asp in low energy diet affects the intestinal barrier integrity and amino acid pool in weaning piglets. A total of 198 weaned piglets were assigned to 3 treatments: control (basal diet + 1.59% L-Ala); T1 (basal diet + 1% L-Gln + 0.5% L-Glu + 0.1% L-Asp); and T2 (low energy diet + 1% L-Gln + 0.5% L-Glu + 0.1% L-Asp). The blood, jejunum, and ileum were obtained on day 5 or on day 21 post-weaning, respectively. Our results showed that T1 and T2 treatments increased the abundances of occludin, claudin-1, and claudin-3 in the small intestine while decreasing the serum diamine oxidase (DAO) and D-lactate levels in weaning piglets. Meanwhile, T1 and T2 treatments significantly increased the positive rate of proliferating cell nuclear antigen (PCNA) of the small intestine, promoting intestinal cell proliferation. We also found that supplementation with glu, gln, and asp improved the serum amino acid pool and promoted ileal amino acid transporter gene expression of slc3a2, slc6a14, and slc7a11 in weaned piglets. Additionally, on day 21 post-weaning, T1 and T2 treatments stimulated the phosphorylation of the mTOR-S6K1-4EBP1 signaling pathway in the small intestine, which may implicate the enhanced protein synthesis rate. In summary, dietary supplementation of gln, glu, and asp was beneficial to the intestinal barrier function and amino acid pool regulation, while the benefits of gln, glu, and asp treatment might be diminished by the low-energy diet. The results demonstrated that the supplementation of gln, glu, and asp under low energy levels was preferentially supplied as the energy fuel to restore the gut barrier function in piglets on day 5 post-weaning. With the increase in age and intestinal maturation (on day 21 post-weaning), gln, glu, and asp supplementation could also show an effect on the regulation of the amino acid pool and protein synthesis.
Artificial Intelligence in the Tourism Industry: An Overview of Reviews
Authors: García-Madurga Miguel-Ángel , Grilló-Méndez Ana-Julia
Published: July 2023

This study aims to comprehensively synthesize existing literature on AI in tourism, highlighting key themes, strengths, and limitations, and illuminating pathways for future research, including the identification of emerging areas within this context. AI technology is rapidly transforming various sectors, including tourism, by augmenting customer service, improving operational efficiency, personalizing travel experiences, and supporting sustainability initiatives. Despite the growing body of research on this topic, there is a need for a comprehensive and systematic summary of the existing literature to illuminate the most effective uses of AI in this context and to identify gaps for future research. We employed the ‘overview of reviews’ methodology, which involved systematically locating, appraising, and synthesizing the results of previous review articles. Using Web of Science, we identified and analyzed 31 previous review articles on the application of AI in tourism. Their synthesis presents a comprehensive perspective on the present status of AI technology in the tourism sector. It provides valuable insights, not only for practitioners contemplating the implementation of AI solutions but also for academics aspiring to advance this study area. Moreover, the academic implications of this study reaffirm its objective to shape future inquiries into AI applications within tourism, advocate for the efficacious and sustainable employment of AI, and provoke scholarly exploration at the nexus of technology and sustainability in this sector.
VLDNet: An Ultra-Lightweight Crop Disease Identification Network
Authors: Li Xiaopeng , Zhang Yichi , Peng Yuhan , Li Shuqin
Published: July 2023

Existing deep learning methods usually adopt deeper and wider network structures to achieve better performance. However, we found that this rule does not apply well to crop disease identification tasks, which inspired us to rethink the design paradigm of disease identification models. Crop diseases belong to fine-grained features and lack obvious patterns. Deeper and wider network structures will cause information loss of features, which will damage identification efficiency. Based on this, this paper designs a very lightweight disease identification network called VLDNet. The basic module VLDBlock of VLDNet extracts intrinsic features through 1 × 1 convolution, and uses cheap linear operations to supplement redundant features to improve feature extraction efficiency. In inference, reparameterization technology is used to further reduce the model size and improve inference speed. VLDNet achieves state-of-the-art model (SOTA) latency-accuracy trade-offs on self-built and public datasets, such as equivalent performance to Swin-Tiny with a parameter size of 0.097 MB and 0.04 G floating point operations (FLOPs), while reducing parameter size and FLOPs by 297 times and 111 times, respectively. In actual testing, VLDNet can recognize 221 images per second, which is far superior to similar accuracy models. This work is expected to further promote the application of deep learning-based crop disease identification methods in practical production.
Chernozem Land Use Differentiation by Temperature-Dependent IR Spectra
Authors: Proskurnin Mikhail A. , Volkov Dmitry S. , Timofeev Yaroslav V. , Fomin Dmitry S. , Rogova Olga B.
Published: July 2023

Granulometric aggregate fractions (20 µm–2 mm) of chernozem soils with different agriculture-use histories (native steppe, permanent bare fallow, arable land, and shelterbelt) were investigated in mid-IR and far IR regions (4000–100 cm−1) by heating in the air from 25 to 215 °C, using ATR FTIR and linear discriminant analysis to differentiate the land-use samples without chemical perturbation. The temperature dependences of the band maxima significantly differed for bands of silicate matrix and bands with the contribution of soil organic matter and were more stable to experimental conditions compared to the absolute positions. The thermal behavior of the integral intensities of the IR bands at 790–750 cm−1 and 440–420 cm−1 that was different compared to pure quartz, may be attributed to –C–H bending of alkanes and (poly)aromatic structures and skeletal bending, and could be used to distinguish intact soils from agriculturally used samples. The different temperature behaviors of the bands for fractions of soils with different land use are shown, with the maximum difference in fractions below 20–50 µm and medium fractions (50–200 µm). Changes in the band-maximum frequencies and the integral intensities of the bands were reversible for a heating–cooling cycle. The linear discriminant analysis of the spectra obtained for granulometric fractions of chernozem soils made it possible to separate the samples of native steppe, arable land, bare fallow, and shelterbelt with a high probability based on the type of vegetation and agrogenic load, mainly on the basis of the spectral ranges associated with biogenic forms of quartz and phytoliths.
Maize Nitrogen Grading Estimation Method Based on UAV Images and an Improved Shufflenet Network
Authors: Sun Weizhong , Fu Bohan , Zhang Zhao
Published: July 2023

Maize is a vital crop in China for both food and industry. The nitrogen content plays a crucial role in its growth and yield. Previous researchers have conducted numerous studies on the issue of the nitrogen content in single maize plants from a regression perspective; however, partition management techniques of precision agriculture require plants to be divided by zones and classes. Therefore, in this study, the focus is shifted to the problems of plot classification and graded nitrogen estimation in maize plots performed based on various machine learning and deep learning methods. Firstly, the panoramic unmanned aerial vehicle (UAV) images of maize farmland are collected by UAV and preprocessed to obtain UAV images of each maize plot to construct the required datasets. The dataset includes three classes—low nitrogen, medium nitrogen, and high nitrogen, with 154, 94, and 46 sets of UAV images, respectively, in each class. The training set accounts for eighty percent of the entire dataset and the test set accounts for the other twenty percent. Then, the dataset is used to train models based on machine learning and convolutional neural network algorithms and subsequently the models are evaluated. Comparisons are made between five machine learning classifiers and four convolutional neural networks to assess their respective performances, followed by a separate assessment of the most optimal machine learning classifier and convolutional neural networks. Finally, the ShuffleNet network is enhanced by incorporating SENet and improving the kernel size of the Depthwise separable convolution. The findings demonstrate that the enhanced ShuffleNet network has the highest performance; its classification accuracy, precision, recall, and F1 scores were 96.8%, 97.0%, 97.1%, and 97.0%, respectively. The RegNet, the optimal model among deep learning models, achieved accuracy, precision, recall, and F1 scores of 96.4%, 96.9%, 96.5%, and 96.6%, respectively. In comparison, logistic regression, the optimal model among the machine learning classifiers, attained accuracy of 77.6%, precision of 79.5%, recall of 77.6%, and an F1 score of 72.6%. Notably, the logistic regression exhibited significant enhancements of 19.2% in accuracy, 17.5% in precision, 19.5% in recall, and 24.4% in the F1 score. In contrast, RegNet demonstrated modest improvements of 0.4% in accuracy, 0.1% in precision, 0.6% in recall, and 0.4% in the F1 score. Moreover, ShuffleNet-improvement boasted a substantially lower loss rate of 0.117, which was 0.039 lower than that of RegNet (0.156). The results indicated the significance of ShuffleNet-improvement in the nitrogen classification of maize plots, providing strong support for agricultural zoning management and precise fertilization.
RepC-MVSNet: A Reparameterized Self-Supervised 3D Reconstruction Algorithm for Wheat 3D Reconstruction
Authors: Liu Hui , Xin Cheng , Lai Mengzhen , He Hangfei , Wang Yongzhao , Wang Mantao , Li Jun
Published: July 2023

The application of 3D digital models to high-throughput plant phenotypic analysis is a research hotspot nowadays. Traditional methods, such as manual measurement and laser scanning, have high costs, and multi-view, unsupervised reconstruction methods are still blank in the field of crop research. It is challenging to obtain a high-quality 3D crop surface feature composition for 3D reconstruction. In this paper, we propose a wheat point cloud generation and 3D reconstruction method based on SfM and MVS using sequential wheat crop images. Firstly, the camera intrinsics and camera extrinsics of wheat were estimated using a structure-from-motion system with feature maps, which effectively solved the problem of camera point location design. Secondly, we proposed the ReC-MVSNet, which integrates the heavy parametric structure into the point cloud 3D reconstruction network, overcoming the difficulty of capturing complex features via the traditional MVS model. Through experiments, it was shown that this research method achieves non-invasive reconstruction of the 3D phenotypic structure of realistic objects, the accuracy of the proposed model was improved by nearly 43.3%, and the overall value was improved by nearly 14.3%, which provided a new idea for the development of virtual 3D digitization.
Development of a Training Strategy Aimed at Increasing Veterinarians’ Awareness of the Proper Use of Antibiotics on Rabbit Farms
Authors: Crovato Stefania , Menegon Francesca , Mascarello Giulia , Pinto Anna , Nadin Anna , Piovan Gloria , Ricaldi Guido , Di Martino Guido , Pozza Giandomenico
Published: July 2023

The rabbit sector faces significant challenges with antimicrobial usage (AMU) and antimicrobial resistance (AMR). A focus group involving nine rabbit sector stakeholders identified key issues contributing to high AMU and the need for veterinarians’ training. Participants emphasized the lack of clear legislation, biosecurity standards, and the importance of training on best practices to reduce AMU. To address these concerns, a blended learning course for rabbit veterinarians was organized, focusing on responsible drug use to lower AMU. The course design promoted participant interaction using group exercises and online discussions. The output was an operational tool, encompassing crucial elements to reduce drug dependence, covering housing, environmental conditions, feed, reproduction, disease prevention, diagnosis, and treatments. Validated in veterinarians’ daily practices, the tool proved valuable. The tool, presented as a checklist, assists veterinarians in supporting field activities regarding proper drug use and AMR issues. It also helps farmers address knowledge gaps in breeding management and adopt biosecurity practices for disease prevention. Participants were encouraged to use the tool during farm visits and collaborate with farmers. The project presented in the manuscript is a crucial step towards the development of effective strategies for responsible AMU and AMR mitigation in the rabbit sector and beyond.
Black Soldier Fly Larva Oil in Diets with Roughage to Concentrate Ratios on Fermentation Characteristics, Degradability, and Methane Generation
Authors: Prachumchai Rittikeard , Cherdthong Anusorn
Published: July 2023

Currently, the scarcity of high-quality, expensive animal feed is a primary factor driving up the cost of animal husbandry. As a result, most researchers have focused on improving the potential of using alternative feed resources derived from the black soldier fly larva. In particular, the utilization of oil from black fly larvae is a byproduct of the industry. The aim of this study was to investigate the influence of black soldier fly larva oils and the proportion of roughage-to-concentrate ratios on gas kinetics, rumen characteristics, degradability, and mitigate CH4 production by using in vitro gas production techniques. The in vitro investigation used a completely randomized design (CRD) with a 2 × 4 factorial arrangement. The level of R:C ratios (60:40 and 40:60) were factor A, while BSFO levels (0, 2, 4, and 6% of DM) were factor B. Under this investigation, the combined impact of R:C ratio and BSFO on the kinetics of gas and accumulative gas production was found to be significant (p < 0.01). After 4 h of incubation, the pH and ammonia-nitrogen (NH3-N) concentration were found to be impacted by the inclusion of BSFO levels at different R:C-ratios (p < 0.01). Moreover, after 4 and 8 h of incubation, supplementing the BSFO at 4% with the level of R:C ratio at 40:60 resulted in a significant reduction in the amount of CH4 in the rumen (p < 0.05). However, the inclusion of BSFO levels at different R:C ratios had no effect on the degradability of DM after 12 and 24 h of incubation (p > 0.05), whereas increasing the concentration of BSFO in concentrate at 6% reduced the DM degradability after 24 h of incubation (p < 0.05). Furthermore, adding BSFO to the diet at various R:C ratios enhanced the propionate (C3) concentration, with the highest level observed with the level of R:C ratio at 40:60 and 4% BSFO inclusion (p < 0.05). To summarize, the addition of BSFO at 4% with a 40:60 of R:C ratio increased C3 levels, decreased CH4 emission, and preserved DM degradability. A R:C ratio of 40:60 could improve the total volatile fatty acids and digestibility. Moreover, the inclusion of 6% BSFO at different R:C ratios lowered the in vitro dry matter digestibility, in vitro organic matter digestibility, NH3-N, and protozoal populations.
A Cadaveric Study Using Computed Tomography for Measuring the Ocular Bulb and Scleral Skeleton of the Atlantic Puffin (Aves, Alcidae, Fratercula arctica)
Authors: Fumero-Hernández Marcos , Encinoso Mario , Ramírez Ana Sofia , Morales Inmaculada , Suárez Pérez Alejandro , Jaber José Raduan
Published: July 2023

Imaging diagnosis plays a fundamental role in avian medicine. However, there are few publications regarding its use in ophthalmology. Seabirds, in particular, present a peculiar ecology since their lives take place in very diverse environments: the aquatic, the terrestrial, and the aerial. This fact implies a series of adaptations at a visual level that are necessary for adequate interaction with the environment. Therefore, knowledge of eye particularities is of great importance for the scientific community since it allows us to deepen our understanding of the ocular anatomy and biology of these animals, which are increasingly present in veterinary and wildlife centers. In our study, we performed a morphometric analysis of the ocular bulb and its internal structures in the puffin (Fratercula arctica) using advanced imaging techniques such as CT.
In Vitro Activity of Ceftaroline and Comparators against Bacterial Isolates Collected Globally from Patients with Skin and Soft Tissue Infections: ATLAS Program 2019–2020
Authors: Kuraieva Alona , Cabezas-Camarero Guillermo , Kiratisin Pattarachai , Utt Eric
Published: July 2023

The objective of this study was to assess the in vitro activity of ceftaroline and a panel of comparator agents against isolates causing skin and soft tissue infections (SSTIs) collected in Africa/Middle East, Asia–Pacific, Europe, and Latin America from 2019–2020. Minimum inhibitory concentrations (MIC) were determined using European Committee on Antimicrobial Susceptibility Testing criteria. All the methicillin-susceptible Staphylococcus aureus (MSSA) isolates were susceptible to ceftaroline. Across all regions, ceftaroline demonstrated potent activity against methicillin-resistant S. aureus (MRSA, susceptibility 89.5–93.7%) isolates. Susceptibility to vancomycin, daptomycin, linezolid, teicoplanin, trimethoprim sulfamethoxazole, and tigecycline was ≥94.1% in MSSA and MRSA isolates. Against β-hemolytic streptococci isolates, ceftaroline demonstrated very potent activity (MIC90 0.008–0.03 mg/L) across all regions. All β-hemolytic streptococci isolates were susceptible to linezolid, penicillin, and vancomycin (MIC90 0.06–2 mg/L). Among the extended-spectrum β-lactamases (ESBL)-negative Enterobacterales tested (E. coli, K. pneumoniae, and K. oxytoca), susceptibility to ceftaroline was high (88.2–98.6%) in all regions. All ESBL-negative Enterobacterales were susceptible to aztreonam. Potent activity was observed for amikacin, cefepime, and meropenem (94.1–100%) against these isolates. Overall, ceftaroline showed potent in vitro activity against isolates of pathogens causing SSTIs. Continuous surveillance of global and regional susceptibility patterns is needed to guide appropriate treatment options against these pathogens.
Evaluation of Antibiotic Prescribing Pattern Using WHO Access, Watch and Reserve Classification in Kinshasa, Democratic Republic of Congo
Authors: Kakumba Jocelyn Mankulu , Kindenge Jérémie Mbinze , Kapepula Paulin Mutwale , Iyamba Jean-Marie Liesse , Mashi Murielle Longokolo , Mulwahali Jose Wambale , Kialengila Didi Mana
Published: July 2023

Background: The AWaRe tool was set up by the World Health Organization (WHO) to promote the rational use of antimicrobials. Indeed, this tool classifies antibiotics into four groups: access, watch, reserve and not-recommended antibiotics. In The Democratic Republic of Congo, data on antibiotic dispensing (prescribing) by health professionals according to the AWaRe classification are scarce. In this research work, we aimed to explore antibiotic dispensing pattern from health professionals according to the WHO AWaRe classification to strengthen the national antimicrobial resistance plan. Methods: For this purpose, a survey was conducted from July to December 2022 in the district of Tshangu in Kinshasa. From randomly selected drugstores, drug-sellers were interviewed and randomly selected customers attending those drugstores were included in the study for medical prescriptions collection. The prescribed antibiotics were classified into the access, watch, reserve and not-recommended antibiotics group and by antibiotics number by prescription among pharmacies surveyed. Results: 400 medical prescriptions were collected from 80 drugstores and among which, 301 (75.25%) contained antibiotics. Out of 301 prescriptions, we noticed 164 (54.5%) containing one antibiotic, 117 (38.9%) containing two antibiotics, 15 (5%) containing three antibiotics and 5 (1.6%) containing four antibiotics. A total of 463 antibiotics were prescribed and distributed as 169 (36.5%) were from the access group, 200 (43.2%) from the watch group and 94 (20.3%) from not-recommended antibiotics group, respectively. This can explain the fact of emerging bacterial strains, as, according to the WHO recommendations, the access group should be prioritized because of its activity against a wide range of commonly encountered pathogens and its showing low resistance susceptibility compared to antibiotics from other groups. Based on the anatomical, therapeutic and chemical (ATC) classifications, we observed that third generation cephalosporins represented 34.33% of the prescribed antibiotics, followed by penicillins (17.17%), macrolides (7.63%), aminoglycosides (7.36%) and Imidazole (7.36%), thus accounting approximately for 74% of the classes of antibiotics prescribed. Additionally, among them, the most frequently prescribed antibiotics were Ceftriaxone (21.38%), Amoxicillin (11.01%), Gentamycin (5.61%), Amoxicillin-clavulanic acid (5.61%), Azithromycin (4.97%) and Metronidazole (4.75%), thus accounting for approximately 54% of all the prescribed antibiotics. Conclusion: These results highlight the importance of strict implementation of the national plan to combat antimicrobial resistance and the need to train health workers in the correct application of the WHO AWaRe classification.
Pullout Behavior of a Polymeric Strap in Compacted Dry Granular Material
Authors: Herceg Karolina , Minažek Krunoslav , Domitrović Dubravko , Horvat Ivan
Published: July 2023

Strap reinforcement is very commonly used as reinforcement material in mechanically stabilized earth walls (MSEW). Metal straps are mostly used as reinforcement material. However, in humid climates, where the risk of damage to metal straps due to corrosion is high, the use of geosynthetic straps is quite justified. In the Croatian coastal region, geosynthetic straps were used as reinforcement for two very high MSEWs. In both cases, the backfill material crushed stone aggregate from the neighboring site was used. According to the relevant standards, it is recommended that the backfill material should have a uniformity coefficient of Cu ≥ 4.0. To meet these requirements, it is usually necessary to sieve and crush the backfill material. To evaluate the influence of the uniformity coefficient on the friction interaction coefficient between a geosynthetic strap and a crushed stone aggregate, a series of pullout tests with different confining stresses and aggregate grain size distributions were conducted. The pullout tests were performed for three different uniformity coefficients of the crushed stone aggregate. The results confirmed the justification to use backfill material with a uniformity coefficient higher than 4.0. The pullout tests were performed with one strap, two closely spaced straps, and two separated straps. The results showed that lateral friction contributes to the pullout force in the amount of 16.1% of the total force.
Novel Methodology for Scaling and Simulating Structural Behaviour for Soil–Structure Systems Subjected to Extreme Loading Conditions
Authors: Alisawi Alaa T. , Collins Philip E. F. , Cashell Katherine A.
Published: July 2023

This paper is concerned with the calibration and validation of a numerical procedure for the analysis of pile performance in soft clays during seismic soil–pile–superstructure interaction (SSPSI) scenarios. Currently, there are no widely accepted methods or guidelines. Centrifuge and shaking table model tests are often used to supplement the available field case histories with the data obtained under controlled conditions. This paper presents a new calibration method for establishing a reliable and accurate relationship between full-scale numerical analysis and scaled laboratory tests in a 1g environment. A sophisticated approach to scaling and validating full-scale seismic soil–structure interaction problems is proposed that considers the scaling concept of implied prototypes as well as “modelling of models” techniques that can ensure an excellent level of accuracy. In this study, a new methodology was developed that can provide an accurate, practical, and scientific calibration for the relationship between full-scale numerical analysis and scaled laboratory tests in the 1g environment. The framework can be followed by researchers who intend to validate their seismic soil–structure interaction findings.
DialogCIN: Contextual Inference Networks for Emotional Dialogue Generation
Authors: Lou Wenzhe , Yang Wenzhong , Wei Fuyuan
Published: July 2023

In recent years, emotional dialogue generation garnered widespread attention and made significant progress in the English-speaking domain. However, research on emotional dialogue generation in Chinese still faces two critical issues: firstly, the lack of high-quality datasets with emotional characteristics makes it difficult for models to fully utilize emotional information for emotional intervention; secondly, there is a lack of effective neural network models for extracting and integrating inherent logical information in the context to fully understand dialogues. To address these issues, this paper presented a Chinese dialogue dataset called LifeDialog, which was annotated with sentiment features. Additionally, it proposed DialogCIN, a contextual inference network that aims to understand dialogues based on a cognitive perspective. Firstly, the proposed model acquired contextual representations at both the global and speaker levels. Secondly, different levels of contextual vectors were separately inputted into the understanding unit, which consists of multiple inference modules. These modules iteratively performed reasoning and retrieval to delve into the inherent logical information of the dialogue context. Subsequently, appropriate emotions were predicted for feedback. Finally, an emotion-aware decoder was employed to generate a response. Experimental results on our manually annotated dataset, LifeDialog, demonstrated that DialogCIN can effectively simulate human cognitive inference processes, enabling a better understanding of dialogue context and improving the quality of generated dialogues.
A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
Authors: Sajid Asher , Sonbul Omar S. , Rashid Muhammad , Jafri Atif Raza , Arif Muhammad , Zia Muhammad Yousuf Irfan
Published: July 2023

This research presents a novel binary Edwards curve (BEC) accelerator designed specifically for resource-constrained embedded systems. The proposed accelerator incorporates the fixed window algorithm, a two-stage pipelined architecture, and the Montgomery radix-4 multiplier. As a result, it achieves remarkable performance improvements in throughput and resource utilization. Experimental results, conducted on various Xilinx Field Programmable Gate Arrays (FPGAs), demonstrate impressive throughput/area ratios observed for GF(2233). The achieved ratios for Virtex-4, Virtex-5, Virtex-6, and Virtex-7 are 12.2, 19.07, 36.01, and 38.39, respectively. Furthermore, the processing time for one-point multiplication on a Virtex-7 platform is 15.87 µs. These findings highlight the effectiveness of the proposed accelerator for improved throughput and optimal resource utilization.
Tree Rings as Proxies of Historical Runoff in a National Park in Northern Mexico: A Major Ecosystem Service Provider
Authors: Villanueva-Díaz José , Correa-Díaz Arian , Castruita-Esparza Luis Ubaldo , Gutiérrez-García Jesús Valentín , Martínez-Sifuentes Aldo Rafael , Reyes-Camarillo Fátima del Rocío
Published: July 2023

A dendrochronological network of conifers (Pinus leiophylla, Pinus cembroides, Pinus engelmannii) was developed in the Cumbres de Majalca National Park (CMNP) in Chihuahua, Mexico, to reconstruct historical runoff patterns and examine the impact of ocean–atmosphere phenomena. The CMNP plays a vital role as a runoff source for Conchos River tributaries and groundwater recharge for Chihuahua City and nearby populations. The ring-width chronologies displayed a common signal from 1859 to 2021, with the highest association found between P. engelmannii and P. leiophylla (r = 0.65) and the lowest between P. cembroides and P. engelmannii (r = 0.55). The first principal component explained 75.7% of the variance, and among the species, P. leiophylla exhibited the highest correlation (0.624, p < 0.05) with the accumulated streamflow records from the previous November to July, allowing the construction of a bootstrapped model for runoff reconstruction. The reconstructed streamflow spanned from 1859 to 2014, with an average of 2.732 × 108 m3. Periods of low runoff occurred in 1860–1880, 1940–1960, and 1994–2014, while extreme wet years with high runoff occurred in 1865, 1884, and 1987. The interannual streamflow variability correlated significantly with ENSO indices (SOI, MEI, TRI, and sea surface temperature anomalies), particularly during the winter–spring seasons, indicating that warm phases of the ENSO increased precipitation and runoff. The analysis of return periods revealed probabilities for specific runoff volumes, enabling stakeholders to use the information to develop effective strategies for sustainable water allocation and utilization in the region.
Assessments of Use of Blended Radar–Numerical Weather Prediction Product in Short-Range Warning of Intense Rainstorms in Localized Systems (SWIRLS) for Quantitative Precipitation Forecast of Tropical Cyclone Landfall on Vietnam’s Coast
Authors: Hung Mai Khanh , Tien Du Duc , Quan Dang Dinh , Duc Tran Anh , Dung Pham Thi Phuong , Hole Lars R. , Nam Hoang Gia
Published: July 2023

This research presents a blended system implemented by the Vietnam National Center for Hydro-Meteorological Forecasting to enhance the nowcasting and forecasting services of quantitative precipitation forecasts (QPFs) of tropical cyclone (TC) landfalls on Vietnam’s coast. Firstly, the extrapolations of rain/convective systems from multiple radars in Vietnam in ranges up to 6 h were carried out using Short-Range Warning of Intense Rainstorms in Localized Systems (SWIRLS) developed by the Hong Kong Observatory. Secondly, the forecast from the numerical weather prediction (NWP) system, based on the WRF-ARW model running at 3 km horizontal resolution, was blended with radar-based quantitative precipitation estimates and nowcasts of SWIRLS. The analysis showed that the application of the nowcast system to TC-related cloud forms is complicated, which is related to the TC’s evolution and the different types and multiple layers of storm clouds that can affect the accuracy of the derived motion fields in nowcast systems. With hourly accumulated rainfall observation, skill score validation conducted for several TCs that landed in the center of Vietnam demonstrated that the blending of nowcasting and NWP improve the quality of the QPFs of TCs in forecast ranges up to 3 h compared to the pure NWP forecasts.
The Importance of the “Time Factor” for the Evaluation of Inhibition Mechanisms: The Case of Selected HDAC6 Inhibitors
Authors: Cellupica Edoardo , Caprini Gianluca , Fossati Gianluca , Mirdita Doris , Cordella Paola , Marchini Mattia , Rocchio Ilaria , Sandrone Giovanni , Stevenazzi Andrea , Vergani Barbara , Steinkühler Christian , Vanoni Maria Antonietta
Published: July 2023

Histone deacetylases (HDACs) participate with histone acetyltransferases in the modulation of the biological activity of a broad array of proteins, besides histones. Histone deacetylase 6 is unique among HDAC as it contains two catalytic domains, an N-terminal microtubule binding region and a C-terminal ubiquitin binding domain. Most of its known biological roles are related to its protein lysine deacetylase activity in the cytoplasm. The design of specific inhibitors is the focus of a large number of medicinal chemistry programs in the academy and industry because lowering HDAC6 activity has been demonstrated to be beneficial for the treatment of several diseases, including cancer, and neurological and immunological disorders. Here, we show how re-evaluation of the mechanism of action of selected HDAC6 inhibitors, by monitoring the time-dependence of the onset and relief of the inhibition, revealed instances of slow-binding/slow-release inhibition. The same approach, in conjunction with X-ray crystallography, in silico modeling and mass spectrometry, helped to propose a model of inhibition of HDAC6 by a novel difluoromethyloxadiazole-based compound that was found to be a slow-binding substrate analog of HDAC6, giving rise to a tightly bound, long-lived inhibitory derivative.
Assessing and Modelling of Post-Traumatic Stress Disorder Using Molecular and Functional Biomarkers
Authors: Skolariki Konstantina , Vrahatis Aristidis G. , Krokidis Marios G. , Exarchos Themis P. , Vlamos Panagiotis
Published: July 2023

Post-traumatic stress disorder (PTSD) is a complex psychological disorder that develops following exposure to traumatic events. PTSD is influenced by catalytic factors such as dysregulated hypothalamic-pituitary-adrenal (HPA) axis, neurotransmitter imbalances, and oxidative stress. Genetic variations may act as important catalysts, impacting neurochemical signaling, synaptic plasticity, and stress response systems. Understanding the intricate gene networks and their interactions is vital for comprehending the underlying mechanisms of PTSD. Focusing on the catalytic factors of PTSD is essential because they provide valuable insights into the underlying mechanisms of the disorder. By understanding these factors and their interplay, researchers may uncover potential targets for interventions and therapies, leading to more effective and personalized treatments for individuals with PTSD. The aforementioned gene networks, composed of specific genes associated with the disorder, provide a comprehensive view of the molecular pathways and regulatory mechanisms involved in PTSD. Through this study valuable insights into the disorder’s underlying mechanisms and opening avenues for effective treatments, personalized interventions, and the development of biomarkers for early detection and monitoring are provided.
Mass Spectrometric Analysis of Cucurbitacins and Dihydrocucurbitacins from the Tuber of Citrullus naudinianus
Authors: Benka Moritz , Görlitz Kristof , Schöttgen Michael C. , Lagies Simon , Mohl Daniel A. , Kather Michel , Du Preez-Bruwer Iwanette , Mumbengegwi Davis , Teufel Robin , Kowarschik Stefanie , Huber Roman , Plattner Dietmar A. , Kammerer Bernd
Published: July 2023

The vast pool of structurally and functionally distinct secondary metabolites (i.e., natural products (NPs)) is constantly being expanded, a process also driven by the rapid progress in the development of analytical techniques. Such NPs often show potent biological activities and are therefore prime candidates for drug development and medical applications. The ethyl acetate extract of the tuber of Citrullus naudinianus (C. naudinianus), an African melon with edible fruits and seeds, shows in vitro immunomodulatory activity presumably elicited by cucurbitacins that are known major constituents of this plant. Further potentially immunomodulatory cucurbitacins or cucurbitacin derivatives were assumed to be in the tuber. Given the typically high content of cucurbitacins with similar physicochemical features but often distinct bioactivities, an efficient and reliable separation process is a prerequisite for their detailed characterization and assessment in terms of bioactivity. We therefore developed a detection method to screen and differentiate cucurbitacins via high-performance liquid chromatography/quadrupole-time-of-flight tandem mass spectrometry (HPLC-QTOF-MS/MS). In order to confirm the identification, the fragmentation patterns of two cucurbitacins and one 23,24-dihydrocucurbitacin were also investigated. Six characteristic fragments were identified and three of them were employed for the identification of cucurbitacins and 23,24-dihydrocucurbitacins in the extract. As a result, in addition to eight previously reported cucurbitacins from this plant four distinct 23,24-dihydrocucurbitacins (B, D, E, and I) were putatively identified and newly found in the ethyl acetate extract of the tuber of C. naudinianus. The established methodology enables rapid and efficient LC-MS-based analysis and identification of cucurbitacins and 23,24-dihydrocucurbitacins in plant extracts.
Between Shifting and Feedback Processing in the Wisconsin Card Sorting Test in Children with Developmental Language Disorder
Authors: Giandomenico Kristina , Baron Lauren S. , Gul Asiya , Arbel Yael
Published: July 2023

Children with developmental language disorder (DLD) demonstrate deficits in executive functioning; however, the specific components of executive functioning that are affected in this population are not well understood. This study evaluated set shifting and feedback processing in a Wisconsin Card Sorting Test (WCST) performed by 24 children with and without DLD. The behavioral results revealed poorer performance by the DLD group in measures of accuracy, proportion of correct rule shifts, perseverative errors on shift trials, and proportion of effective responses to feedback. Electrophysiological measures (event-related potentials, or ERPs) indicated different patterns of response to negative feedback that signaled the need for rule shifting, with the DLD group showing a trend toward processing shift cues as negative feedback. Group differences were found in the processing of the first and last positive feedback, with overall stronger responses to positive feedback by children with DLD. However, both groups showed a similar pattern of diminished attention to positive feedback when rule learning was established. Taken together, children with DLD demonstrated the inefficient processing of negative feedback in the context of rule-shifting and difficulty in establishing and maintaining a rule.
Development of Intelligent Technologies in SHM on the Innovative Diagnosis in Civil Engineering—A Comprehensive Review
Authors: Vijayan Dhanasingh Sivalinga , Sivasuriyan Arvindan , Devarajan Parthiban , Krejsa Martin , Chalecki Marek , Żółtowski Mariusz , Kozarzewska Alicja , Koda Eugeniusz
Published: July 2023

This comprehensive review focuses on the integration of intelligent technologies, such as the Internet of Things (IoT), Artificial intelligence (AI), and Nondestructive Testing (NDT), in the Structural Health Monitoring (SHM) field of civil engineering. The article discusses intelligent technologies in SHM for residential, commercial, industrial, historical, and special buildings, such as nuclear power plants (NPPs). With the incorporation of intelligent technologies, there have been remarkable advancements in SHM, a crucial aspect of infrastructure safety, reliability, and durability. The combination of SHM and intelligent technologies provides a cost-effective and efficient building monitoring approach, significantly contributing to energy and resource conservation. This article explores using electronic instruments, such as sensors, microcontrollers, and embedded systems, to measure displacement, force, strain, and temperature, which are crucial for detecting structural damage. Implementing intelligent technologies in SHM reduces the reliance on manual and hazardous inspection practices, simplifying and reducing the cost of building monitoring. The article highlights the social, economic, and environmental benefits of adopting intelligent technologies in SHM by presenting key findings from existing research. This review aims to increase the reader’s understanding of the significance of these technologies in enhancing the efficiency of SHM in civil engineering by illuminating their advancements and applications.
Seismic Performance Assessment of the 18th Century Jesuit College in Dubrovnik’s Old City
Authors: Uzair Aanis , Abrahamczyk Lars , Vrban Ante , Penava Davorin
Published: July 2023

The seismic performance assessment of heritage architecture presents many challenges due to the restrictions set forth by the conservation principles to protect the associated social and cultural values. These buildings are typically characterized by unreinforced masonry walls connected by tie-rods, vaults, and wooden floors. The era of construction dates to the time when seismic design regulations were largely unknown, making heritage structures potentially vulnerable to earthquake damage. This study presents the seismic performance assessment of the Jesuit College located in the southern part of the Old City of Dubrovnik. A series of field surveys were conducted to qualitatively examine the material composition and obtain geometrical details in part of the Croatian Science Foundation research project IP-2020-02-3531 entitled “Seismic Risk Assessment of Cultural Heritage in Croatia—SeisRICHerCRO”. The structural response is thoroughly investigated by means of a complex finite element model calibrated using the frequencies determined from ambient vibration measurements and material characteristics obtained from the literature review of representative cultural heritage buildings. The seismic performance is evaluated using linear static and response spectrum analysis in accordance with Eurocode 8 guidelines for the demand seismic action level. The numerical analysis indicates several structural components in the building exhibiting high shear stress concentration and exceeding the elastic tensile limit under the demand ground acceleration level. The assessment further reveals substantial out-of-plane bending of vulnerable wall components (identified by local mode shapes) at low peak ground acceleration levels. The stress concentration in numerous structural components leads to the identification of vulnerable zones where retrofitting measures are essentially required.