bluuebunny/crossref_metadata_2025 huggingface dataset:
Case Report: A case of hepatocellular carcinoma with aberrant right hepatic artery treated with transarterial chemoembolization and infusion chemotherapy separately to bilobar lesion combining with systemic therapies and sequential hepatectomy
Authors: Wei Yong-Guang , Su Hao , Lv Zi-li , Liao Xi-Wen , Zeng Zhi-Ming , Jia Yu-Xuan , Huang Hua-Sheng , Shen Xiao-Qiang , Zhu Guang-Zhi , Han Chuang-Ye , Ye Xin-Ping , Peng Tao
Published: July 2023

Background

Hepatocellular carcinoma (HCC) with a dismal prognosis is the second most deadly malignancy globally. Surgery is believed to be a curative approach. Nevertheless, there is still a considerable probability of postoperative recurrence. Most patients present in advanced stages with a surgically and oncologically unresectable disease. Systemic medicines are increasingly important to downstage the disease and further improve survival.

Case summary

A 67-year-old Chinese man with uncontrolled hepatitis B was discovered to have liver masses with abnormal serum vitamin K absence or antagonist-II (PIVKA-II) level during checkup for upper abdominal discomfort. Abdominal multiphase computerized tomography (CT) and gadoxetate disodium–enhanced magnetic resonance imaging (MRI) showed the bulky bilobar HCCs of Barcelona Clinic Liver Cancer stage B and China Liver Cancer Staging stage IIa. Furthermore, the aberrant right hepatic artery (RHA) originates from the superior mesenteric artery. Due to the location being adjacent to important vasculatures and massive size of the right-sided lesion, curative resection appears to be challenging. To achieve a favorable surgical margin, repeated hepatic arterial infusion chemotherapy (HAIC) was adopted through the variant RHA, while transarterial chemoembolization (TACE) was delivered to the left lobe to arrest tumor growth. Furthermore, sintilimab plus lenvatinib served as the sequential systemic therapy. After 5 months of conversion treatment, the partial response with a decreased serum PIVKA-II level was attained. The R0 hepatectomy was then performed without postoperative complications. The immunohistochemistry and next-generation sequencing results suggested that the two-side HCCs existing tumor heterogeneity were not completely consistent. The patient continues to be without evidence of disease.

Conclusion

Our case highlights a favorable outcome in a man with bilobar bulky HCC after undergoing the comprehensive therapeutic schedule that includes personalized intervention and systemic drug therapy. In terms of conversion therapy, our case provides a secure and practical reference for managing unresectable bilobar HCC coexisting with the aberrant hepatic artery.
Association between physical fitness and musculoskeletal health in firefighters
Authors: Ras Jaron , Soteriades Elpidoforos S. , Smith Denise L. , Kengne Andre P. , Leach Lloyd
Published: July 2023

Introduction: Firefighters are often placed in situations that require high levels of physical exertion, leading to significant strain on firefighters’ musculoskeletal system, predisposing them to musculoskeletal discomfort (MSD) and/or musculoskeletal injury (MSI). Physical fitness programs are often recommended and justified, in part, to prevent injuries. The aim of this study was to determine the association between physical fitness and musculoskeletal health (MSH) in firefighters.

Methods: A total of 308 full-time firefighters took part in the study conducted in Cape Town, South Africa. Physical fitness tests encompassed a non-exercise estimation for cardiorespiratory fitness, grip and leg strength for upper and lower body strength, push-ups and sit-ups for muscular endurance, and sit-and-reach for flexibility. The Nordic Musculoskeletal Questionnaire and Cornell Musculoskeletal Discomfort Questionnaire were used to determine MSIs and MSD, respectively. A p-value <0.05 indicated statistical significance.

Results: Every one-unit increase in AbVO2max, push-ups, sit-ups, and sit-and-reach decreased the odds of firefighters reporting MSIs by 5% (p = 0.005), 3% (p = 0.017), 3% (p = 0.006), and 3% (p = 0.034), respectively. Every one repetition increase in push-up capacity increased the odds of firefighters reporting neck, elbow and forearm, wrist and hand, and thigh discomfort by 3% (p = 0.039), 4% (p = 0.031), 5% (p = 0.002), and 5%` (p = 0.007), respectively. Every one repetition increase in sit-up capacity increased the odds of firefighters reporting upper back discomfort and thigh discomfort by 5% (p = 0.045) and 7% (p = 0.013), respectively.

Conclusion: Maintenance of physical fitness is likely beneficial in reducing MSIs, which, however, may increase the feeling of MSD in firefighters. In addition, it may be noticed that there is an ideal level of physical fitness that is conducive to the reduction of MSIs and should be studied further.
Performance analysis of two typical greenhouse lettuce production systems: commercial hydroponic production and traditional soil cultivation
Authors: Wang Lichun , Ning Songrui , Zheng Wengang , Guo Jingyu , Li Youli , Li Yinkun , Chen Xiaoli , Ben-Gal Alon , Wei Xiaoming
Published: July 2023

Introduction

Due to the shortage of land and water resource, optimization of systems for production in commercial greenhouses is essential for sustainable vegetable supply. The performance of lettuce productivity and the economic benefit in greenhouses using a soil-based system (SBS) and a hydroponic production system (HPS) were compared in this study.

Methods

Experiments were conducted in two identical greenhouses over two growth cycles (G1 and G2). Three treatments of irrigation volumes (S1, S2, and S3) were evaluated for SBS while three treatments of nutrient solution concentration (H1, H2, and H3) were evaluated for HPS; the optimal levels from each system were then compared.

Results and discussion

HPS was more sensitive to the effects of environmental temperature than SBS because of higher soil buffer capacity. Compared with SBS, higher yield (more than 134%) and higher water productivity (more than 50%) were observed in HPS. We detected significant increases in ascorbic acid by 28.31% and 16.67% and in soluble sugar by 57.84% and 32.23% during G1 and G2, respectively, compared with SBS. However, nitrate accumulated in HPS-grown lettuce. When the nutrient solution was replaced with fresh water 3 days before harvest, the excess nitrate content of harvested lettuce in HPS was removed. The initial investment and total operating cost in HPS were 21.76 times and 47.09% higher than those in SBS, respectively. Consideration of agronomic, quality, and economic indicators showed an overall optimal performance of the H2 treatment. These findings indicated that, in spite of its higher initial investment and requirement of advanced technology and management, HPS was more profitable than SBS for commercial lettuce production.
Effect of high salinity and of priming of non-germinated seeds by UV-C light on photosynthesis of lettuce plants grown in a controlled soilless system
Authors: Fgaier Salah , Aarrouf Jawad , Lopez-Lauri Félicie , Lizzi Yves , Poiroux Florine , Urban Laurent
Published: July 2023

High salinity results in a decrease in plant photosynthesis and crop productivity. The aim of the present study was to evaluate the effect of UV-C priming treatments of lettuce seeds on photosynthesis of plants grown at high salinity. Non-primed and primed seeds were grown in an hydroponic system, with a standard nutrient solution, either supplemented with 100 mM NaCl (high salinity), or not (control). Considering that leaf and root K+ concentrations remained constant and that chlorophyll fluorescence parameters and root growth were not affected negatively in the high salinity treatment, we conclude that the latter was at the origin of a moderate stress only. A substantial decrease in leaf net photosynthetic assimilation (Anet) was however observed as a consequence of stomatal and non-stomatal limitations in the high salinity treatment. This decrease in Anet translated into a decrease in growth parameters; it may be attributed partially to the high salinity-associated increase in leaf concentration in abscisic acid and decrease in stomatal conductance. Priming by UV-C light resulted in an increase in total photosynthetic electron transport rate and Anet in the leaves of plants grown at high salinity. The increase of the latter translated into a moderate increase in growth parameters. It is hypothesized that the positive effect of UV-C priming on Anet and growth of the aerial part of lettuce plants grown at high salinity, is mainly due to its stimulating effect on leaf concentration in salicylic acid. Even though leaf cytokinins’ concentration was higher in plants from primed seeds, maintenance of the cytokinins-to-abscisic acid ratio also supports the idea that UV-C priming resulted in protection of plants exposed to high salinity.
Mitochondrial genome annotation with MFannot: a critical analysis of gene identification and gene model prediction
Authors: Lang B. Franz , Beck Natacha , Prince Samuel , Sarrasin Matt , Rioux Pierre , Burger Gertraud
Published: July 2023

Compared to nuclear genomes, mitochondrial genomes (mitogenomes) are small and usually code for only a few dozen genes. Still, identifying genes and their structure can be challenging and time-consuming. Even automated tools for mitochondrial genome annotation often require manual analysis and curation by skilled experts. The most difficult steps are (i) the structural modelling of intron-containing genes; (ii) the identification and delineation of Group I and II introns; and (iii) the identification of moderately conserved, non-coding RNA (ncRNA) genes specifying 5S rRNAs, tmRNAs and RNase P RNAs. Additional challenges arise through genetic code evolution which can redefine the translational identity of both start and stop codons, thus obscuring protein-coding genes. Further, RNA editing can render gene identification difficult, if not impossible, without additional RNA sequence data. Current automated mito- and plastid-genome annotators are limited as they are typically tailored to specific eukaryotic groups. The MFannot annotator we developed is unique in its applicability to a broad taxonomic scope, its accuracy in gene model inference, and its capabilities in intron identification and classification. The pipeline leverages curated profile Hidden Markov Models (HMMs), covariance (CMs) and ERPIN models to better capture evolutionarily conserved signatures in the primary sequence (HMMs and CMs) as well as secondary structure (CMs and ERPIN). Here we formally describe MFannot, which has been available as a web-accessible service (https://megasun.bch.umontreal.ca/apps/mfannot/) to the research community for nearly 16 years. Further, we report its performance on particularly intron-rich mitogenomes and describe ongoing and future developments.
Place-based sustainability—act or wait-and-see?
Authors: Zemite Ieva , Kunda Ilona
Published: July 2023

There has been a “territorial turn” in exploring sustainable development in the past two decades. Sustainability is increasingly seen as rooted in local resources, relationships, and values. Cultural and creative industries (CCIs) are a substantial local resource and participants of local development. One of the most important relationships of CCIs is with local municipalities, which usually have an overview of local resources, and a role of redefining national level policy ideas, including those pertaining to cultural and art activities. The paper examines place-based development in Valmiera County as a case of national cultural policy translation, grounded in interpretations of the availability of local resources for change. The paper argues that the trajectories of local cultural development are indeed strongly related to local development agents' interpretations of available local resources, including active CCIs. In negotiating the preferred development paths, the stakeholders tend to respond to one of two strategies carried out by the local municipality: Act (taking upon themselves the role of a cultural operator) or Wait-and-see (enabling other cultural operators, the private and non-governmental CCI sector). The paper also highlights the role of creative intermediaries in negotiation processes. The Wait-and-see approach is seen as preferable and more sustainable in the long run.
Procedural fairness in ethnic-cultural decision-making: fostering social cohesion by incorporating minority and majority perspectives
Authors: Dierckx Kim , Van Hiel Alain , Valcke Barbara , van den Bos Kees
Published: July 2023

Recent research describes how procedural fairness can be used to resolve issues related to ethnic-cultural matters. The central finding in this strand of literature is that when minority members experience procedurally fair treatment by societal actors regarding ethnic-cultural issues, this will lead to a range of outcomes that are beneficial for social cohesion. Although these results are promising, it remains yet to be shown that such group-specific treatment fairness does not hamper social cohesion by inciting misapprehension among members of non-recipient groups. Therefore, the present study set out to examine two central questions. First, how would minority group members respond to treatment fairness of citizens belonging to another minority group? Second, how would majority group members respond to treatment fairness of citizens belonging to minority groups? Two experimental studies (total N = 908) examined these questions. In Study 1, we compared ethnic-cultural minorities’ reactions to procedurally (un)fair treatment of their own versus a different minority group. In Study 2, we compared minority and majority group members’ responses to procedurally (un)fair treatment of minority group members. Results show that minority group member reactions to ethnic-cultural procedural fairness emanate from a shared bond with the fairness recipient(s) of the other minority group. Conversely, majority group members’ reactions are driven primarily by a perceived moral obligation to act rightfully toward members of disadvantaged groups. Taken together, our results suggest that ethnic-cultural procedural fairness enactment fosters societal unity among different groups, possibly strengthening social cohesion for well-being and prosperity among members of these groups.
Can PTSD be prevented? A novel approach to increasing physiological resilience: a pilot study
Authors: Dust Mark
Published: July 2023

Much of the U.S. adult population will experience a traumatic event at some point in their lives, resulting in about 20 million people developing posttraumatic stress disorder (PTSD) and costing over $143 billion for healthcare. The Community Resilience Model (CRM) and Mental and Emotional Self-Management (MESM) are potential novel solutions for stemming the tide of PTSD diagnoses resulting from a traumatic event. This pilot study was conducted to examine the phasic and tonic changes in cardiac vagal tone in a non-traumatized sample population (N = 83) after a 1-week intervention. Group comparisons were conducted between the CRM (n = 26), MESM (n = 34), and Control (n = 23) conditions. Participants ranged in age from 18 to 30. A phasic effect on cardiac vagal tone was found for the MESM condition within subjects but not for the CRM or control conditions. A tonic effect on cardiac vagal tone was not found within subjects amongst the three conditions. The phasic effect in the MESM condition was significantly different between itself and the CRM and control groups. No tonic effects on cardiac vagal tone were found between conditions either. These results suggest cardiac vagal tone responds to focused breathing in the moment only, but more research with larger sample sizes, longer intervention duration, and better methods to track home practice compliance are needed before one accepts the insignificant results as valid. This pilot study can serve as an introduction to the study of physiological processes that might be trainable to increase resilience in non-traumatized populations and serve as a springboard for future studies of physiological resilience to traumatic stress.
How household chaos affects social withdrawal of rural children: the indirect role of executive function and receptive language ability
Authors: Zhan Shuwei , Guo Jinna
Published: July 2023

Executive function (EF) and receptive language ability play an important role in the relationship between household chaos and social withdrawal. Young children are neglected in household chaos research and suffer from the negative outcomes of households in China. However, few studies have focused on the relationship between household chaos and social withdrawal in young Chinese children and the chain mediating effect of EF and receptive language ability. This study included 922 preschool-age children (62.58 ± 8.03 months) and their primary caregivers and their teachers were recruited from 21 rural preschools in Guangdong Province in China. Our results show a positive direct effect of household chaos and social withdrawal. Furthermore, an indirect sequential effect of household chaos and social withdrawal on EF and receptive language ability was found. Our findings (a) highlight the significance of paying more attention to household chaos and revealing a better understanding of the effect of EF and receptive language ability on households at an early age and (b) indicate that interventions should be provided to improve the home environment when children are young.
The truth project- paper two- using staff training and consultation to inculcate a testimonial sensibility in non-specialist staff teams working with survivors of child sexual abuse
Authors: Barker Claire , Taggart Daniel , Gonzalez Marta , Quail Sally , Eglinton Rebekah , Ford Stephanie , Tantam William
Published: July 2023

This paper explores how trauma informed training and consultation for non-specialist staff at the Independent Inquiry into Child Sexual Abuse in England and Wales enabled them to work with survivors of non-recent child sexual abuse in the Truth Project and other areas of the Inquiry. The paper draws on data gathered from 32 semi-structured interviews with a range of Inquiry staff, including civil servants, legal professionals, senior operational managers, and researchers. The interview questions mapped on to the trauma informed principles embedded in the Inquiry and considered the efficacy and implementation of this training for engaging with survivors’ voices, working with challenging testimonies and materials, and contributing to epistemic change. Findings included all staff having an awareness of what it meant to be trauma informed in an Inquiry context, talking about the principles in terms of value-based positions. Staff described an awareness of needing to attend to the idiosyncratic experiences of the individual survivor, and there was recognition that previous damage to survivor trust, through institutional failure, meant that demonstrating trustworthiness was a central task. Staff talked about the impacts of participation on some survivors, and the impacts it had on them to be exposed to trauma-related materials. There was acknowledgment of the limitations of the trauma informed approach but also recognition of the wider applications of this learning for other areas of their personal and professional lives. There is some support for the therapeutic culture developed at the Inquiry leading to what Fricker refers to as a testimonial sensibility, a quality of listening necessary for the establishment of epistemic justice. The discussion focuses on how this way of working can be applied to other public service settings and how epistemic justice concepts can be included in more traditional trauma informed care models to encourage an ethic of listening that has political and social, in addition to therapeutic, outcomes.
Knowledge, attitude, and practice of healthcare workers regarding dengue fever in Mazandaran Province, northern Iran
Authors: Nikookar Seyed Hassan , Moosazadeh Mahmood , Fazeli-Dinan Mahmoud , Zaim Morteza , Sedaghat Mohammad Mehdi , Enayati Ahmadali
Published: July 2023

Background

Dengue fever is a rapidly emerging infection worldwide with a high public health burden. Adequate training of healthcare workers is essential to warrant the timely provision of health services to improve the outcome of dengue management.

Methods

This is an analytical cross-sectional study, conducted to assess the knowledge, attitudes and practice (KAP) of healthcare workers regarding dengue from April 2021 to March 2022 in Mazandaran Province, northern Iran. Data was collected using a researcher-made structured questionnaire, prepared as Google Forms, and sent to target groups through social media and email. Data analysis was performed by SPSS 22 software using descriptive and inferential statistics (Chi-square) at a significant level of 5%.

Results

Most of the respondents had heard about dengue (83.8%); media (32.7%) and academic education (25%) were the main sources of information. Respondents had less knowledge associated with dengue symptoms (52%) than prevention and control (69%), transmission (72.2%) and clinical management (81%). Based on the 70% cut-off point, the majority of the participants had a good attitude (81%) and practice (73%). However, only 49.6% of the respondents showed good practice regarding dengue local transmission. A significant difference was observed between participants knowledge on clinical management with occupation; attitude with gender and occupation; and practice with gender (p < 0.05).

Conclusion

The results of this study revealed gaps in some dimensions of KAP in healthcare workers, therefore, a greater focus should be placed on future training programs to raise knowledge and attitude leading to sound practice and behavior for adequate management of dengue.
Persuasive Technology and computational manipulation: hypernudging out of mental self-determination
Authors: Faraoni Stefano
Published: July 2023

Artificial Intelligence, unperceived, can acquire the user's data, find connections not visible by a human being, profile the users, and aim at persuading them, resulting in Persuasive Technology (PT). During the persuasive process, PT can use manipulation, finding and using routes to affect System 1, the primordial brain of individuals, in the absence of their awareness, undermining their decision-making processes. Multiple international and European bodies recognized that AI systems could use manipulation at an unprecedented degree via second-generation dark patterns such as the hypernudge and that computational manipulation constitutes a risk for autonomy and different, overlapping, fundamental rights such as privacy, informational self-determination and freedom of thought. However, there is a lack of shared ideas regarding which fundamental rights are violated by computational manipulation and which fundamental rights can protect individuals against it. The right to be let alone and the right to hold and express a thought differ from the right to create a thought, being in control of the decision-making process and free from cognitive interferences operated by computational manipulation. Therefore, this paper argues in favor of recognizing a newly emerged fundamental right, the right to mental self-determination, tailored to the unprecedented abilities of AI-driven manipulative technologies.
Impact of COVID-19 pandemic on insomnia and sleep efficiency in parents and caregivers of young children
Authors: Jiao Nana , Pituch Keenan A. , Petrov Megan E.
Published: July 2023

Introduction

During the COVID-19 pandemic, sleep problems were highly prevalent. However, few studies assess sleep quality among parents and caregivers with young children. This study aimed to describe the impacts of the COVID-19 pandemic on sleep patterns among parents and caregivers with young children (<6 years) and identify the factors associated with insomnia and sleep efficiency.

Methods

An internet sample of 136 caregivers (age: 35 ± 9.7 y, 70.6% female) were recruited internationally from May 21, 2020 to July 1, 2020. Participants completed the Center for Epidemiological Studies–Depression Scale-10 (CES-D-10), Sleep Hygiene and Practices Scale (SHPS), Coronavirus Impact Scale (CIS), Insomnia Severity Index (ISI), and sleep patterns prior to and during the pandemic. Hierarchical regressions were conducted to examine the factors associated with insomnia and sleep efficiency.

Results

Nearly 40% of the caregivers reported household incomes <$10,000. More than half reported clinical levels of depressive symptoms (59.2%) and low sleep efficiency (65.8%). Approximately 90% reported that their sleep-wake routine was altered with delayed bedtime and midpoint, and more naps and nightmares. Almost half (51.5%) were experiencing clinically meaningful insomnia symptoms. Greater insomnia symptom severity was independently associated with lower income, greater depressive symptoms, poor sleep hygiene behaviors, altered sleep-wake routine, and greater COVID-related disruptions in daily life. The predictors associated with poor sleep efficiency during the pandemic were lower income and poor sleep efficiency before the pandemic.

Discussion

The study highlighted the factors associated with insomnia and poor sleep efficiency during the COVID-19 pandemic. Interventions are needed to support caregivers' sleep during global crises.
Diversity and utilization of indigenous wild edible plants and their contribution to food security in Turkana County, Kenya
Authors: Oduor Francis Odhiambo , Kaindi Dasel Wambua Mulwa , Abong George Ooko , Thuita Faith , Termote Céline
Published: July 2023

Introduction

Indigenous Wild edible plants (IWEPs) are consumed daily in some form by at least one in seven people worldwide. Many of them are rich in essential nutrients with the potential for dietary and nutrition improvement particularly for poor households. They are, however, often overlooked. This study investigated diversity, consumption frequency, and perceptions of IWEPs and the contribution they make to the food security of communities in Turkana County, northern Kenya. Our findings are aimed at stimulating targeted discussions among stakeholders involved in food security programs on best way to overcome the poverty stigma associated with IWEPs consumption and to promote their utilization for food security, nutritional and dietary improvement, and enhanced community resilience.

Methods

Applying a mixed-methods approach, we collected data using 12 gender-disaggregated focus group discussions and a questionnaire applied to a random sample of 360 households.

Results and Discussion

Participants identified 73 IWEPs, of which 24 were consumed in the preceding six months by 48.5% of households. Almost all surveyed households (96%) were classified as severely food insecure, and food insecurity did not differ significantly between households that consumed IWEPs and those that did not. Our results indicate that more IWEPs consumers than non-consumers reported eating foods they had not wanted to consume to cope with food scarcity, as well as having to eat fewer meals than normal. Just over half of the respondents (57.1%) held positive attitudes towards IWEPs, which was positively associated with a higher likelihood of IWEPs consumption. Long distances to harvest sites, lack of knowledge about the plants, their seasonality, and how to cook them appetizingly, coupled with overall unfavorable perceptions, are probable reasons for non-consumption of IWEPs among the survey respondents. In line with other studies cited on wild foods, we conclude that IWEPs have the potential to bridge food and nutritional deficits in food insecure households in the study area, although currently their consumption remains limited. Given this potential, further analysis of IWEPs’ nutritional composition and restoration of wild edible foods to local areas should be given priority, as well as interventions that help to overcome the challenges to their consumption and promote their wider use.
Fabric-Based, Pneumatic Exosuit for Lower-Back Support in Manual-Handling Tasks
Authors: Cuttilan Amit Nirmal , Natividad Rainier F. , Yeow Raye Chen-Hua
Published: July 2023

Lower-back pain (LBP) is a major cause of occupational disability and morbidity. This study investigates the effectiveness of a fabric-based pneumatic exosuit in reducing discomfort and lumbar muscle activation in healthy individuals who are performing manual-handling tasks. The suit combines the comfort of soft exosuits and the support of rigid exoskeletons. Ten healthy subjects performed a circuit of lifting tasks, simulating manual-handling work, with and without AireLevate support. We assessed the comfort levels and ease of task completion via a questionnaire after each manual-handling task. There was no difference in spinal range of motion, local discomfort, or general discomfort of activities with or without the AireLevate. There was a statistically significant reduction in muscle activation of the erector spinae at the L-5 level with AireLevate support (p < 0.02). This study demonstrates the exosuit’s ability in reducing lower-back muscle activation during manual-handling tasks, while maintaining comfort and mobility. Practitioner summary: We developed a soft exosuit which was shown to significantly reduce the muscle action of the erector muscles of the lumbar spine. In addition, participants perceived that the suit was easy to use and did not limit manual-handling tasks.
Genetics and Production of Safe, High-Quality Milk by Dairy Cattle
Authors: Yang Sitian , Cao Xiang , Wang Yu , Li Cong , Chen Zhi
Published: July 2023

A crucial part of the livestock industry is dairy cattle which contribute significantly to the livestock economy [...]
Visualization of Lidar-Based 3D Droplet Distribution Detection for Air-Assisted Spraying
Authors: Wang Zhichong , Zhang Yang , Li Tian , Müller Joachim , He Xiongkui
Published: July 2023

Air-assisted spraying is a commonly used spraying method for orchard plant protection operations. However, its spraying parameters have complex effects on droplet distribution. The lack of large-scale 3D droplet density distribution measurement methods of equipment has limited the optimization of spraying parameters. Therefore, there is a need to develop a method that can quickly obtain 3D droplet distribution. In this study, a 2D LiDAR was used to quickly scan moving droplets in the air, and a test method that can obtain the visualization of 3D droplet distribution was constructed by using the traveling mode of the machine perpendicular to the scanning plane. The 3D droplet distribution at different positions of the nozzle installed in the air-assisted system was tested at different fan rotation speeds, and the methods for signal processing, point cloud noise reduction, and point cloud division for 2D LiDAR were developed. The results showed that the LiDAR-based method for detecting 3D droplet distribution is feasible, fast, and environmentally friendly.
Spatiotemporal Dynamics of Land Use and Land Cover through Physical–Hydraulic Indices: Insights in the São Francisco River Transboundary Region, Brazilian Semiarid Area
Authors: Sousa Lizandra de Barros de , Montenegro Abelardo Antônio de Assunção , Silva Marcos Vinícius da , Lopes Pabrício Marcos Oliveira , Silva José Raliuson Inácio , Silva Thieres George Freire da , Lins Frederico Abraão Costa , Silva Patrícia Costa
Published: July 2023

This article presents a study on the spatiotemporal dynamics of land cover and use, vegetation indices, and water content in the semiarid region of Pernambuco, Brazil. This study is based on an analysis of satellite images from the years 2016, 2018, and 2019 using the MapBiomas platform. The results show changes in the predominant land cover classes over time, with an increase in the caatinga area and a decrease in the pasture area. An analysis of the vegetation indices (NDVI and LAI) indicated low vegetation cover and biomass in the study area, with a slight increase in the NDVI in 2018. An analysis of the Modified Normalized Difference Water Index (MNDWI) showed that the water content in the study area was generally low, with no significant variations over time. An increase in the water bodies, mainly due to the construction of a reservoir, was noted. The results of this study have provided important information for natural resource management in the region, including the development of strategies for the sustainable use and management of natural resources, particularly water resources, vegetation cover, and soil conservation.
Soil Attributes Mapping with Online Near-Infrared Spectroscopy Requires Spatio-Temporal Local Calibrations
Authors: Canal Filho Ricardo , Molin José Paulo , Wei Marcelo Chan Fu , Silva Eudocio Rafael Otavio da
Published: July 2023

Building machine learning (ML) calibrations using near-infrared (NIR) soil spectroscopy direct in agricultural areas (online NIR), soil attributes can be fine-scale mapped in a faster and more cost-effective manner, guiding management decisions to ensure the maintenance of soil functions. However, a financially and environmentally unattractive density of 3–5 laboratory soil samples per ha is required to build these calibrations. Since no reports have evaluated if they are reusable or if a new calibration is required for each acquisition, this study’s objective was to acquire online NIR spectra in an agricultural field where ML models were previously built and validated, assessing their performance over time. Two spectral acquisitions were held over a fallow tropical field, separated by 21 days. Soil properties (clay, organic matter, cation exchange capacity, pH, phosphorus, potassium, calcium, and magnesium) were predicted using principal components regression models calibrated with day 1 spectra. Day 1 and day 21 predicted values and maps interpolated by ordinary kriging were compared. Spectra characteristics (morphology, features, and intensity) were evaluated. Predicted values from the two days were not correlated, as no causal relationship was found for the only Pearson’s correlation coefficient (r) significative at 99% (p < 0.01) (calcium, with r = 0.22 in the comparison pairing the nearest neighbors from the two days). For clay, organic matter, and cation exchange capacity, despite their robust prediction on day 1, no significative r values were found, ranging from −0.14 to 0.32, when comparing day 1 with day 21. The maps of the two days presented no similar spatial distribution, hindering their use for management decisions. Soil moisture is a suggested source of variation, but the analysis indicated that it was not the only one, requiring further investigation of the effect of soil surface conditions and environmental variables. Although further investigations should be performed, the results presented suggest that online NIR spectra ML models require spatio-temporal local calibrations to perform properly.
Image Structure-Induced Semantic Pyramid Network for Inpainting
Authors: Huang Rong , Zheng Yuhui
Published: July 2023

The existing deep-learning-based image inpainting algorithms often suffer from local structure disconnections and blurring when dealing with large irregular defective images. To solve these problems, an image structure-induced semantic pyramid network for inpainting is proposed. The model consists of two parts: the edge inpainting network and the content-filling network. U-Net-based edge inpainting network restores the edge of the image defect with residual blocks. The edge inpainting map is input into the pyramid content-filling network together with the image in the prior condition. In the content-filling network, the attention transfer module (ATM) is designed to reconfigure the encoding features of each scale step by step, and the recovered feature map is linked to the decoding layer and the corresponding potential feature fusion decoding to improve the global consistency of the image and finally obtain the restored image. The quantitative analysis shows that the average L1 loss is reduced by about 1.14%, the peak signal-to-noise ratio (PSNR) is improved by about 3.51, and the structural similarity (SSIM) is improved by about 0.163 on the CelebA-HQ and Places2 datasets compared with the current mainstream algorithms. The qualitative analysis shows that this model not only generates semantically sound content as a whole but also better matches the human visual perception in terms of local structural connectivity and texture synthesis.
Date Fruit Classification Based on Surface Quality Using Convolutional Neural Network Models
Authors: Almomen Mohammed , Al-Saeed Majed , Ahmad Hafiz Farooq
Published: July 2023

Classifying the quality of dates after harvesting the crop plays a significant role in reducing waste from date fruit production. About one million tons of date fruit is produced annually in Saudi Arabia. Part of this production goes to local factories to be produced and packaged to be ready for use. Classifying and sorting edible dates from inedible dates is one of the first and most important stages in the production process in the date fruit industry. As this process is still performed manually in date production factories in Saudi Arabia, this may cause an increase in the waste of date fruit and reduce the efficiency of production. Therefore, in our paper, we propose a system to automate the classification of dates fruit production. The proposed system focuses on classifying the quality of date fruit at the postharvesting stage. By automating the process of classifying date fruit at this stage, we can increase the production efficiency, raise the classification accuracy, control the product quality, and perform data analysis within the industry. As a result, this increases the market competitiveness, reduces production costs, and increases the productivity. The system was developed based on convolutional neural network models. For the purpose of training the models, we constructed a new image dataset that contains two main classes that have images of date fruit with excellent surface quality and another class for date fruit with poor surface quality. The results show that the used model can classify date fruit based on their surface quality with an accuracy of 97%.
Identification of Terahertz Link Modulation in Atmospheric Weather Conditions
Authors: Wu Zhendong , Qiao Yige , Ma Jianjun , Zhang Yuping , Li Dehua , Zhang Huiyun
Published: July 2023

With the rapid increase of wireless connectivity, current spectrum resources are not enough for significant requirements for large data capacity. Research interests are moving towards the high-frequency band in the terahertz range for wider bandwidth. However, multipath scattering and induced time delay, suffered by terahertz links propagating in outdoor weather, lead inevitably to increasing expenses in baseband signal processing. This trades away the advantage of low time latency and high stability, which are commonly considered as important merits of terahertz wireless communication techniques. To reduce the burden in signal processing and explore the feasibility of modulation identification in the terahertz band, a method of wireless link modulation identification is considered as a potential solution. In this work, it is investigated theoretically by employing two kinds of neural networks: the convolutional neural network (CNN) and the long short-term memory network (LSTM). Link deterioration caused by different atmospheric weather is introduced into the theoretical model, and the performance of this method is evaluated. Results show that the identification accuracy of the constructed neural networks can be up to 99%, which means such a method is efficient for identification of the modulation format of terahertz wireless links under different weather conditions. This work demonstrates the feasibility of modulation identification in outdoor terahertz communication scenarios and provides specific references.
Deep Belief Networks (DBN) with IoT-Based Alzheimer’s Disease Detection and Classification
Authors: Alqahtani Nayef , Alam Shadab , Aqeel Ibrahim , Shuaib Mohammed , Mohsen Khormi Ibrahim , Khan Surbhi Bhatia , Malibari Areej A.
Published: July 2023

Dementias that develop in older people test the limits of modern medicine. As far as dementia in older people goes, Alzheimer’s disease (AD) is by far the most prevalent form. For over fifty years, medical and exclusion criteria were used to diagnose AD, with an accuracy of only 85 per cent. This did not allow for a correct diagnosis, which could be validated only through postmortem examination. Diagnosis of AD can be sped up, and the course of the disease can be predicted by applying machine learning (ML) techniques to Magnetic Resonance Imaging (MRI) techniques. Dementia in specific seniors could be predicted using data from AD screenings and ML classifiers. Classifier performance for AD subjects can be enhanced by including demographic information from the MRI and the patient’s preexisting conditions. In this article, we have used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In addition, we proposed a framework for the AD/non-AD classification of dementia patients using longitudinal brain MRI features and Deep Belief Network (DBN) trained with the Mayfly Optimization Algorithm (MOA). An IoT-enabled portable MR imaging device is used to capture real-time patient MR images and identify anomalies in MRI scans to detect and classify AD. Our experiments validate that the predictive power of all models is greatly enhanced by including early information about comorbidities and medication characteristics. The random forest model outclasses other models in terms of precision. This research is the first to examine how AD forecasting can benefit from using multimodal time-series data. The ability to distinguish between healthy and diseased patients is demonstrated by the DBN-MOA accuracy of 97.456%, f-Score of 93.187 %, recall of 95.789 % and precision of 94.621% achieved by the proposed technique. The experimental results of this research demonstrate the efficacy, superiority, and applicability of the DBN-MOA algorithm developed for the purpose of AD diagnosis.
Peer-to-Peer Energy Trading Case Study Using an AI-Powered Community Energy Management System
Authors: Mahmoud Marwan , Slama Sami Ben
Published: July 2023

The Internet of Energy (IoE) is a topic that industry and academics find intriguing and promising, since it can aid in developing technology for smart cities. This study suggests an innovative energy system with peer-to-peer trading and more sophisticated residential energy storage system management. It proposes a smart residential community strategy that includes household customers and nearby energy storage installations. Without constructing new energy-producing facilities, users can consume affordable renewable energy by exchanging energy with the community energy pool. The community energy pool can purchase any excess energy from consumers and renewable energy sources and sell it for a price higher than the feed-in tariff but lower than the going rate. The energy pricing of the power pool is based on a real-time link between supply and demand to stimulate local energy trade. Under this pricing structure, the cost of electricity may vary depending on the retail price, the number of consumers, and the amount of renewable energy. This maximizes the advantages for customers and the utilization of renewable energy. A Markov decision process (MDP) depicts the recommended power to maximize consumer advantages, increase renewable energy utilization, and provide the optimum option for the energy trading process. The reinforcement learning technique determined the best option in the renewable energy MDP and the energy exchange process. The fuzzy inference system, which takes into account infinite opportunities for the energy exchange process, enables Q-learning to be used in continuous state space problems (fuzzy Q-learning). The analysis of the suggested demand-side management system is successful. The efficacy of the advanced demand-side management system is assessed quantitatively by comparing the cost of power before and after the deployment of the proposed energy management system.
Research and Application of Semi-Supervised Category Dictionary Model Based on Transfer Learning
Authors: Dai Yuansheng , Liu Yingyi , Song Haoyu , He Bing , Yuan Haiwen , Zhang Boyang
Published: July 2023

Classification tasks are pivotal across diverse applications, yet the burgeoning amount of data, coupled with complicating factors such as noise, exacerbates the challenge of classifying complex data. For algorithms that require a large amount of data, the annotation work for datasets is also exceptionally complex and tedious. Drawing upon existing research, this paper first introduces a novel semi-supervised category dictionary model based on transfer learning (SSDT). This model is designed to construct a more representative category dictionary and to delineate the associations of information across different domains, utilizing the lens of conditional probability distribution. This approach is particularly apt for semi-supervised transfer learning scenarios. Subsequently, the proposed method is applied to the domain of bearing fault diagnosis. This model is suitable for transfer scenarios; moreover, its semi-supervised characteristic eliminates the need for labeling the entire input dataset, significantly reducing manual workload. Experimental results attest to the model’s practical utility. When benchmarked against other 6 models, the SSDT model demonstrates enhanced generalization performance.
Robust Multi-Criteria Traffic Network Equilibrium Problems with Path Capacity Constraints
Authors: Ma Xing-Xing , Xu Yang-Dong
Published: July 2023

With the progress of society and the diversification of transportation modes, people are faced with more and more complicated travel choices, and thus, multi-criteria route choosing optimization problems have drawn increased attention in recent years. A number of multi-criteria traffic network equilibrium problems have been proposed, but most of them do not involve data uncertainty nor computational methods. This paper focuses on the methods for solving robust multi-criteria traffic network equilibrium problems with path capacity constraints. The concepts of the robust vector equilibrium and the robust vector equilibrium with respect to the worst case are introduced, respectively. For the robust vector equilibrium, an equivalent min–max optimization problem is constructed. A direct search algorithm, in which the step size without derivatives and redundant parameters, is proposed for solving this min–max problem. In addition, we construct a smoothing optimization problem based on a variant version of ReLU activation function to compute the robust weak vector equilibrium flows with respect to the worst case and then find robust vector equilibrium flows with respect to the worst case by using the heaviside step function. Finally, extensive numerical examples are given to illustrate the excellence of our algorithms compared with existing algorithms. It is shown that the proposed min–max algorithm may take less time to find the robust vector equilibrium flows and the smoothing method can more effectively generate a subset of the robust vector equilibrium with respect to the worst case.