bluuebunny/crossref_metadata_2025 huggingface dataset:
Weedy Rice Infestation in Malaysia: What Do We Know and Where Do We Go?
Authors: Mahmod Intan Filzah , Syed Bakar Sharifah Nurnabilah , Mispan Muhamad Shakirin , Supandi Farahaniza , Mohamed Zulqarnain , Saiman Mohd Zuwairi
Published: August 2024

Weedy rice is a notorious weed in the rice agroecosystem. Its infestation has caused significant economic loss in many rice-production countries, including Malaysia. In Malaysia, weedy rice consists of a genetic admixture of cultivated and wild traits, resulting in a highly competitive ability against cultivated rice for resources. The morphology and genetic plasticity of weedy rice provide several adaptive advantages, enabling it to thrive in rice agroecosystems and become a serious weed infesting Malaysian rice granaries. Previous strategies in combating weedy rice have often been less effective since weedy rice is easily adapted to a wide range of environments. In this review, we address the weedy rice issue in Malaysia and the progress of weedy rice studies. A future direction on weedy rice research via omics study is also discussed to propose solutions for improvement in weed control, good agronomic practices, and food security.
Digitalization Drives the Green Transformation of Agriculture-Related Enterprises: A Case Study of A-Share Agriculture-Related Listed Companies
Authors: Yuan Yue , Guo Xiaoyang , Shen Yang
Published: August 2024

The advent of new digital technologies has catalyzed a disruptive technological revolution, fostering significant industrial changes and advancing the green transformation of the economy and society. This paper investigates the influence of digitization on the green transformation of agribusiness firms, focusing on agriculture-related companies listed on the Shanghai and Shenzhen A-share markets from 2013 to 2021. Employing fixed-effect and mediated-effect models, the study examines the mechanisms through which digitization impacts these enterprises. The findings indicate that the relationship between digitization and green transformation in agribusiness is non-linear; a certain threshold of digitization must be achieved before it positively affects green transformation. The effect of digitization on green transformation varies according to the nature of business ownership, company size, supply chain flexibility, and regional environmental regulations. The study reveals that digitization influences green transformation through several mechanisms that promote economies of scale, technological innovation, and structural adjustments. While economies of scale derived from digitization do not directly support green transformation, they facilitate technological innovation and structural adjustments that enhance green initiatives in agribusiness.
Effects of Biogas Digestate on Winter Wheat Yield, Nitrogen Balance, and Nitrous Oxide Emissions under Organic Farming Conditions
Authors: Winkhart Felizitas , Schmid Harald , Hülsbergen Kurt-Jürgen
Published: August 2024

Biogas digestate is increasingly used in organic farming to improve soil nutrient supply and sustainably increase yields. However, biogas digestate can also lead to environmentally relevant N2O emissions. The benefits, opportunities, and risks associated with the use of digestate as a fertilizer in organic farming are a subject of ongoing debate, in part due to a lack of conclusive experimental results. A field trial conducted in southern Germany examined the short-term and long-term impacts of digestate fertilization on winter wheat yield, nitrogen use efficiency, and N2O-N emissions. The four-year results from the years 2019 to 2022 are presented. Digestate was applied with a nitrogen input of up to 265 kg ha−1, with 129 kg ha−1 NH4+-N. The application of digestate resulted in a significant increase in wheat yield, with an average increase of 53% (2019) to 83% (2022) compared to the unfertilized control. It is notable that the treatment applied for the first time did not reach the yield of the long-term fertilized treatment, with a yield gap of 0.5 to 1.2 Mg ha−1 (6% to 15%). The highest N2O-N emissions (up to 3.30 kg ha−1) in the vegetation period from spring to autumn were measured in the long-term fertilized treatment. However, very high N2O-N emissions (up to 3.72 kg ha−1) also occurred in two years in winter in the unfertilized treatment. An increase in soil inorganic N stocks and N2O-N emissions was observed following the wheat harvest and subsequent tillage in all treatments. No significant differences were identified between the fertilizer treatments with regard to product-related emissions. The experimental results demonstrate that N2O-N emissions are not solely a consequence of N fertilization, but can also be attributed to tillage, post-harvest practices, and previous crops, with considerable variability depending on weather conditions. The experimental data provide comprehensive insight into the influence of cultivation, soil characteristics, and meteorological conditions on N2O-N emissions at an agricultural site in southern Germany.
Rapid Detection of Getah Virus Antibodies in Horses Using a Recombinant E2 Protein-Based Immunochromatographic Strip
Authors: Zhong Dengke , Zheng Jiayang , Ma Zhiyong , Wang Yan , Wei Jianchao
Published: August 2024

The prevalence and impact of Getah virus (GETV) are significant concerns in China. GETV can infect a wide range of animals, including horses, pigs, sheep, cattle, birds, and humans, resulting in substantial losses in the livestock and agricultural industries. GETV infection can cause the development of ulcers and inflammation in the mouth and gums of horses, which result in pain and discomfort and lead to symptoms such as reduced appetite, drooling, and difficulty chewing. As a result, there is a pressing need for efficient and rapid disease diagnosis methods. However, the currently available diagnostic methods have limitations in terms of operational time, equipment, and the experience of the individuals using them. In this study, a rapid, specific, and sensitive detection method was developed using a colloidal gold-based immunochromatographic strip (ICS) for the detection of antibodies against GETV in horses. To prepare the ICS, the antigen domain of the E2 glycoprotein of GETV was expressed using the Escherichia coli expression system after analysis with DNAstar v7.1 software. The nitrocellulose membrane was coated with rE2 protein or SPA to form the test line and control line, respectively. After optimizing the reaction conditions, the sensitivity, specificity, and repeatability of the strip were verified. The results showed that the test strip had a detection limit of up to 1:320 dilutions for GETV-positive serum, with no cross-reactivity observed with other equine-susceptible pathogens such as equine arteritis virus (EAV), equine herpesvirus-1 (EHV-I), equine infectious anemia virus (EIAV), equine influenza virus (EIV), African horse sickness virus (AHSV), and Japanese encephalitis virus (JEV). Furthermore, the ICS exhibited a concordance rate of 94.0% when testing 182 clinical serum samples compared to the virus neutralization test. Overall, this ICS diagnosis method will be an effective tool for the rapid detection of GETV in the field.
Study on the Dynamic Evolution of Overburden Bed Separation Based on a Bed Separation Water Disaster
Authors: Yang Tao , Sun Jiarui , Zhang Jie , Lin Haifei , Bao Ruoyu , Wu Jianjun , Zhang Yiming , Deng Jiayue
Published: August 2024

Water inrush from separation layers is a special type of water inrush disaster that occurs during coal mining. It is characterized by the absence of precursors before the inrush, a sudden and massive influx of water, strong destructive power, and significant difficulty in prevention and control. This disaster causes substantial economic losses and casualties in mines. To study the dynamic evolution characteristics of the overlying rock strata in Guojiahe coal mine, field investigations, theoretical analysis, physical simulations, and numerical simulations were conducted. The development state of the overlying rock strata and the height of the water-conducting fractures in the 1304 working face during continuous mining were analyzed, and the spatial position of the strata where water hazards could form was determined. Through theoretical calculations, the height of the water-conducting fracture zone was found to be approximately 205 m, forming a water-storable separation space about 248 m above the coal seam roof. A physical similarity model was constructed to study the development state of overlying rock strata separation in the 1304 working face. Experimental results indicate that, as the working face advances, the rock strata gradually develop lateral separation spaces from bottom to top, and vertical fractures increase progressively in a similar manner. The spaces and fractures formed in the early stages of the working face advance gradually close and form a compaction zone. However, new fractures develop near the working face side. As mining continues, the asynchronous movement of the overlying rock promotes the development of vertical fractures, providing water-conducting channels for water inrush from the separation. Based on numerical simulation analysis of the development state of overlying rock strata separation and the height of the water-conducting fracture zone, it can be concluded that the water-storable separation space develops between the 8th layer of the formation of sandy mudstone and the 9th layer of the Yijun Formation conglomerate, which is 248 m from the coal seam roof. When excavated to 280 m, the separation space reaches its maximum, with a width of about 19 m and a height of about 1.2 m, and the water storage capacity increases to its maximum. The height of the water-conducting fracture zone eventually increases to 202 m.
Coupled Non-Ordinary State-Based Peridynamics Model for Ductile and Brittle Solids Subjected to Thermal Shocks
Authors: Li Hui , Zhang Hanbo , Zhang Yixiong , Bai Xiaoming , Shao Xuejiao , Wu Bingyang
Published: August 2024

A coupled thermomechanical non-ordinary state-based peridynamics (NOSB-PD) model is developed to simulate the dynamic response arising from temperature and to predict the crack propagation with thermal shocks in brittle and ductile solids. A unified multiaxial constitutive model with damage growth is proposed to simultaneously describe the ductile and brittle fracture mechanisms. The main idea is the use of Lemaitre’s model to describe ductile damage behavior and the use of tensile strength instead of yield stress in Lemaitre’s model to describe brittle damage behavior. A damage-related fracture criterion is presented in the PD framework to predict crack propagation, which avoids numerical oscillations when using the traditional bond stretch criterion. To capture the dynamic plastic response induced by thermal shocks, the time and stress integration are achieved by an alternating solving strategy and implicit return-mapping algorithm. Several numerical examples are presented to show the performance of the proposed model. Firstly, a thermomechanical problem simulation based on both the proposed model and the FEM illustrate the accuracy of the proposed model in studying the thermal deformation. Moreover, a benchmark brittle fracture example of the Kalthoff–Winkler impact test is simulated, and the crack path and angle are similar to the experimental observations. In addition, the simulation of ductile fracture under different loads illustrates the effect of temperature on crack propagation. Finally, the simulation of the 2D quenching test shows the ability of the proposed model in predicting crack propagation under thermal shocks.
Correction: Gao et al. A Thermal Error Prediction Method of High-Speed Motorized Spindle Based on Pelican Optimization Algorithm and CNN-LSTM. Appl. Sci. 2024, 14, 381
Authors: Gao Ying , Xia Xiaojun , Guo Yinrui
Published: August 2024

In the original publication [...]
Assessing the Impact of Lichens on Saint Simeon Church, Paşabağ Valley (Cappadocia, Turkey): Potential Damaging Effects versus Protection from Rainfall and Winds
Authors: Casanova Municchia Annalaura , Giordani Paolo , Taniguchi Yoko , Caneva Giulia
Published: August 2024

The impact of lichens on the conservation of monuments, such as the World Heritage Site (WHS) of Cappadocian churches, presents a multifaceted challenge for conservators. Previous studies have shown that lichens can both induce deterioration processes of stone through their penetration into the substrate and chemical interactions as well as provide bioprotection, forming encrustations including calcium oxalate layers, which help mitigating the effects of weathering, reducing water penetration and eolian erosion. Evaluating the impact of lichens requires a comprehensive understanding of various factors, which include the type of rock substrate, the colonizing lichen species, the monument architecture, and the prevailing physic-chemical weathering processes. This study aims to provide a comprehensive analysis of the impact of lichen colonization on Saint Simeon Church in the Paşabağı Valley (Turkey) with a multidisciplinary approach to investigate the interplay between lichens, microclimatic conditions, and the degradation of stone. Specifically, this study examines the influence of wind-driven rain (WDR) occurrences on lichen distribution and stone weathering to develop comprehensive conservation strategies. The results confirmed the previous observations and showed a prevalence of the protective role of lichens over their deterioration. The northwest side of the church, despite being heavily impacted by environmental factors such as WDR and freezing–thawing cycles, showed reduced deterioration due to extensive lichen coverage. In contrast, the northeast side, with lower lichen colonization, demonstrated more severe deterioration. These findings suggest that integrating the protective aspects of lichen colonization into conservation strategies can enhance their preservation.
Comparative Analysis of Deep Convolutional Neural Network—Bidirectional Long Short-Term Memory and Machine Learning Methods in Intrusion Detection Systems
Authors: Udurume Miracle , Shakhov Vladimir , Koo Insoo
Published: August 2024

Particularly in Internet of Things (IoT) scenarios, the rapid growth and diversity of network traffic pose a growing challenge to network intrusion detection systems (NIDs). In this work, we perform a comparative analysis of lightweight machine learning models, such as logistic regression (LR) and k-nearest neighbors (KNNs), alongside other machine learning models, such as decision trees (DTs), support vector machines (SVMs), multilayer perceptron (MLP), and random forests (RFs) with deep learning architectures, specifically a convolutional neural network (CNN) coupled with bidirectional long short-term memory (BiLSTM), for intrusion detection. We assess these models’ scalability, performance, and robustness using the NSL-KDD and UNSW-NB15 benchmark datasets. We evaluate important metrics, such as accuracy, precision, recall, F1-score, and false alarm rate, to offer insights into the effectiveness of each model in securing network systems within IoT deployments. Notably, the study emphasizes the utilization of lightweight machine learning models, highlighting their efficiency in achieving high detection accuracy while maintaining lower computational costs. Furthermore, standard deviation metrics have been incorporated into the accuracy evaluations, enhancing the reliability and comprehensiveness of our results. Using the CNN-BiLSTM model, we achieved noteworthy accuracies of 99.89% and 98.95% on the NSL-KDD and UNSW-NB15 datasets, respectively. However, the CNN-BiLSTM model outperforms lightweight traditional machine learning methods by a margin ranging from 1.5% to 3.5%. This study contributes to the ongoing efforts to enhance network security in IoT scenarios by exploring a trade-off between traditional machine learning and deep learning techniques.
A Multi-Strategy Improvement Secretary Bird Optimization Algorithm for Engineering Optimization Problems
Authors: Qin Song , Liu Junling , Bai Xiaobo , Hu Gang
Published: August 2024

Based on a meta-heuristic secretary bird optimization algorithm (SBOA), this paper develops a multi-strategy improvement secretary bird optimization algorithm (MISBOA) to further enhance the solving accuracy and convergence speed for engineering optimization problems. Firstly, a feedback regulation mechanism based on incremental PID control is used to update the whole population according to the output value. Then, in the hunting stage, a golden sinusoidal guidance strategy is employed to enhance the success rate of capture. Meanwhile, to keep the population diverse, a cooperative camouflage strategy and an update strategy based on cosine similarity are introduced into the escaping stage. Analyzing the results in solving the CEC2022 test suite, the MISBOA both get the best comprehensive performance when the dimensions are set as 10 and 20. Especially when the dimension is increased, the advantage of MISBOA is further expanded, which ranks first on 10 test functions, accounting for 83.33% of the total. It illustrates the introduction of improvement strategies that effectively enhance the searching accuracy and stability of MISBOA for various problems. For five real-world optimization problems, the MISBOA also has the best performance on the fitness values, indicating a stronger searching ability with higher accuracy and stability. Finally, when it is used to solve the shape optimization problem of the combined quartic generalized Ball interpolation (CQGBI) curve, the shape can be designed to be smoother according to the obtained parameters based on MISBOA to improve power generation efficiency.
Surgicogenomics: The Role of Genetics in Deep Brain Stimulation in Parkinson’s Disease Patients
Authors: Kalinderi Kallirhoe , Papaliagkas Vasileios , Fidani Liana
Published: August 2024

Parkinson’s disease (PD) is the second-most common neurodegenerative disease, affecting 1% of people aged over 60. Currently, there is only symptomatic relief for PD patients, with levodopa being the gold standard of PD treatment. Deep brain stimulation (DBS) is a surgical option to treat PD patients. DBS improves motor functions and may also allow a significant reduction in dopaminergic medication. Important parameters for DBS outcomes are the disease duration, the age of disease onset, responsiveness to levodopa and cognitive or psychiatric comorbidities. Emerging data also highlight the need to carefully consider the genetic background in the preoperative assessment of PD patients who are candidates for DBS, as genetic factors may affect the effectiveness of DBS in these patients. This review article discusses the role of genetics in DBS for PD patients, in an attempt to better understand inter-individual variability in DBS response, control of motor PD symptoms and appearance of non-motor symptoms, especially cognitive decline.
Retrofitting a Fifth Generation District Heating and Cooling Network for Heating and Cooling in a UK Hospital Campus
Authors: Lalor Jonathan , Gillich Aaron
Published: August 2024

There is an increasingly rich literature on the decarbonisation of heat and the evolution of heat networks. This paper investigates whether a novel fifth Generation District Heating and Cooling Network (5GDHC) could be retrofitted to an existing National Health Service (NHS) hospital campus for the purpose of heating and cooling. The building load was simulated and input into a custom-written script to carry out a series of parametric studies and optimise design options. The model was calibrated against site data available from hospital facilities management. The research found that it is feasible to use a 5GDHC consisting of a large single mass of water to utilise inter-seasonal thermal storage. A natural water resource such as an aquifer was not required. The model tested sizing options and found that larger thermal storage, heat pumps and chillers reduce operating costs and improve flexibility. The paper closes with a discussion of the practical factors in retrofitting 5GDHC networks to a densely occupied and highly constrained campus environment. The findings are novel in further describing the circumstances for which 5GDHC networks are suitable.
From Building Information Modeling to Building Energy Modeling: Optimization Study for Efficient Transformation
Authors: An Na , Li Xin , Yang Huaqiu , Pang Xiufeng , Gao Guoheng , Ding Ding
Published: August 2024

The conversion from building information modeling (BIM) to building energy modeling (BEM) based on the industry foundation classes (IFC) data standard is a crucial step for efficient building energy design and energy performance analysis. The scope encompasses analyzing limitations in existing BIM-to-BEM workflows and proposing an optimized strategy that addresses data loss and modeling inconsistencies. The research question revolves around enhancing conversion efficiency and precision, with the hypothesis validated through literature review, development of a conversion tool, and case study verification. The data collection and evaluation methods involve streamlining the conversion process by incorporating BIM model optimization, automatic repair of damaged geometric information, and automatic thermal zone division. The main findings reveal that the optimized strategy and tool significantly reduce information duplication, improve the precision of energy simulations, and validate the hypothesis, thereby contributing to more efficient and accurate building energy design and analysis.
Molecular Factors Predicting Ovarian Chemotoxicity in Fertile Women: A Systematic Review
Authors: Raimondo Diego , Raffone Antonio , Neola Daniele , Genovese Federica , Travaglino Antonio , Aguzzi Alberto , De Gobbi Valeria , Virgilio Agnese , Di Santo Sara , Vicenti Rossella , Magnani Valentina , Guida Maurizio , Pippucci Tommaso , Seracchioli Renato
Published: August 2024

Background: Recent advances in cancer diagnosis and treatment have significantly improved survival rates among women of reproductive age facing cancer. However, the potential iatrogenic loss of fertility caused by chemotherapeutic agents underscores the need to understand and predict chemotherapy-induced ovarian damage. This study addresses this gap by systematically reviewing the literature to investigate genetic markers associated with chemotherapy-induced ovarian failure (CIOF). Objective: The primary objective is to identify genetic markers linked to CIOF, contributing to a comprehensive understanding of the factors influencing fertility preservation in female cancer survivors. Methods: A systematic review was conducted using PubMed, EMBASE, Web of Science, Scopus, and OVID electronic databases from inception through December 2023. Studies were included if they featured genomic assessments of genes or polymorphisms related to CIOF in women with histologically confirmed tumors. Exclusion criteria comprised in vitro and animal studies, reviews, and pilot studies. The resulting four human-based studies were scrutinized for insights into genetic influences on CIOF. Results: Of the 5179 articles initially identified, four studies met the inclusion criteria, focusing on alkylating agents, particularly cyclophosphamide, and anthracyclines. Su et al. explored CYP3A41B variants, revealing modified associations with CIOF based on age. Charo et al. investigated GSTA1 and CYP2C19 polymorphisms, emphasizing the need to consider age and tamoxifen therapy in assessing associations. Oktay et al. delved into the impact of BRCA mutations on anti-Müllerian hormone (AMH) levels post-chemotherapy, supported by in vitro assays. Van der Perk et al. focused on childhood cancer survivors and revealed significant associations of CYP3A43 and CYP2B6*2 SNPs with AMH levels. Conclusions: This systematic review analyzes evidence regarding genetic markers influencing CIOF, emphasizing the complex interplay of age, specific genetic variants, and chemotherapy regimens. The findings underscore the need for a personalized approach in assessing CIOF risk, integrating genetic markers with traditional ovarian reserve testing. The implications of this study extend to potential advancements in fertility preservation strategies, offering clinicians a comprehensive baseline assessment for tailored interventions based on each patient’s unique genetic profile. Further research is essential to validate these findings and establish a robust framework for integrating genetic markers into clinical practice.
Tissue- and Temporal-Dependent Dynamics of Myeloablation in Response to Gemcitabine Chemotherapy
Authors: Kitelinger Lydia E. , Thim Eric A. , Zipkowitz Sarah Y. , Price Richard J. , Bullock Timothy N. J.
Published: August 2024

For triple-negative breast cancer (TNBC), the most aggressive subset of breast cancer, immune cell infiltrates have prognostic implications. The presence of myeloid-derived suppressor cells supports tumor progression, while tumor-infiltrating lymphocytes (TILs) correlate with improved survival and responsiveness to immunotherapy. Manipulating the abundance of these populations may enhance tumor immunity. Gemcitabine (GEM), a clinically employed chemotherapeutic, is reported to be systemically myeloablative, and thus it is a potentially useful adjunct therapy for promoting anti-tumor immunity. However, knowledge about the immunological effects of GEM intratumorally is limited. Thus, we directly compared the impact of systemic GEM on immune cell presence and functionality in the tumor microenvironment (TME) to its effects in the periphery. We found that GEM is not myeloablative in the TME; rather, we observed sustained, significant reductions in TILs and dendritic cells—crucial components in initiating an adaptive immune response. We also performed bulk-RNA sequencing to identify immunological alterations transcriptionally induced by GEM. While we found evidence of upregulation in the interferon-gamma (IFN-γ) response pathway, we determined that GEM-mediated growth control is not dependent on IFN-γ. Overall, our findings yield new insights into the tissue- and temporal-dependent immune ablative effects of GEM, contrasting the paradigm that this therapy is specifically myeloablative.
Ultra-Structural Surface Characteristics of Dental Silane Monolayers
Authors: Liu Xiaotian , Shum Winnie Wing-Yee , Tsoi James Kit-Hon
Published: August 2024

This study aims to study the formation quality of the film of dental silanes. Two dental silanes, 3-methacryloxyproyltrimethoxysilane (MPS) and 3-acryloyloxypropyltrimethoxysilane (ACPS), were deposited on the silica glass-equivalent model surface (i.e., n-type silicon(100) wafer) by varying the deposition time (5 h and 22 h). The film quality was then evaluated by ellipsometry, surface contact angle (CA) and surface free energy (SFE), atomic force microscopy (AFM) and X-ray photoelectron spectroscopy (XPS) in survey and high-resolution modes on Si2p, O1s and C1s. Ellipsometry confirmed that both silanes at the two different deposition times would produce 0.85–1.22 nm thick self-assembled monolayer on the silicon wafer surface. While the water CA of silanized surfaces (60.7–71.5°) was larger than the surface without silane (29.6°), the SFE values of all silanes (40.0–44.5 mN/m) were slightly less than that of the wafer surface (46.3 mN/m). AFM revealed that the MPS with 22 h silanization yielded a significantly higher roughness (0.597 μm) than other groups (0.254–0.297 μm). High-resolution XPS on C1s identified a prominent peak at 288.5 eV, which corresponds to methacrylate O-C*=O, i.e., the silane monolayer is extended fully in the vertical direction, while others are in defect states. This study proves that different dental silanes under various dipping times yield different chemical qualities of the film even if they look thin physically.
Rice Yield Forecasting Using Hybrid Quantum Deep Learning Model
Authors: Setiadi De Rosal Ignatius Moses , Susanto Ajib , Nugroho Kristiawan , Muslikh Ahmad Rofiqul , Ojugo Arnold Adimabua , Gan Hong-Seng
Published: August 2024

In recent advancements in agricultural technology, quantum mechanics and deep learning integration have shown promising potential to revolutionize rice yield forecasting methods. This research introduces a novel Hybrid Quantum Deep Learning model that leverages the intricate processing capabilities of quantum computing combined with the robust pattern recognition prowess of deep learning algorithms such as Extreme Gradient Boosting (XGBoost) and Bidirectional Long Short-Term Memory (Bi-LSTM). Bi-LSTM networks are used for temporal feature extraction and quantum circuits for quantum feature processing. Quantum circuits leverage quantum superposition and entanglement to enhance data representation by capturing intricate feature interactions. These enriched quantum features are combined with the temporal features extracted by Bi-LSTM and fed into an XGBoost regressor. By synthesizing quantum feature processing and classical machine learning techniques, our model aims to improve prediction accuracy significantly. Based on measurements of mean square error (MSE), the coefficient of determination (R2), and mean average error (MAE), the results are 1.191621 × 10−5, 0.999929482, and 0.001392724, respectively. This value is so close to perfect that it helps make essential decisions in global agricultural planning and management.
A New Species of Smithophis Giri et al. 2019 from the Indo-Burma Region
Authors: Mirza Zeeshan A. , Bhardwaj Virender K. , Lalmuanawma Jote Chawntual , Choure Girish , Lalremsanga Hmar Tlawmte , Vabeiryureilai Mathipi , Captain Ashok , Zagade Akshay , Patel Harshil
Published: August 2024

The natricine snake genus Smithophis Giri, Gower, Das, Lalremsanga, Lalronunga, Captain, and Deepak, 2019, is represented by four species, three of which are distributed in northeast India and Bangladesh, and a single species in Yunnan and Myanmar. In the past, S. bicolor (Blyth, 1855) was said to be widespread across northeast India and Myanmar; however, recent studies have shown it to be a species complex. Here, we describe a new species of the complex from the Indian state of Mizoram that resembles S. bicolor. The new species differs in bearing a patterned dorsum, a darker venter, and moderately keeled sacral scales. Re-examination of types of S. arunachalensis Das, Deepak, Captain, Wade, and Gower, 2020, shows the presence of strongly keeled sacral keels in males, which is an important diagnostic character. A revised key to members of the genus is presented with notes on S. arunachalensis.
Cardiovascular Risk in Patients with Inflammatory Bowel Diseases—The Role of Endothelial Dysfunction
Authors: Livzan Maria A. , Bikbavova Galiya R. , Lisyutenko Natalya S. , Romanyuk Alisa E. , Drapkina Oxana M.
Published: August 2024

Inflammatory bowel disease (IBD) is associated with an increased risk of cardiovascular disease (CVD). Cardiovascular pathology in people with IBD has not been well studied to date, and a direct link between cardiovascular events and IBD has not been established. The mechanisms underlying this association include the parallel and dynamic interaction of inflammation, modulation of the composition of the gut microbiota, endothelial dysfunction, thrombogenicity, and increased endothelial and epithelial permeability. Endothelial dysfunction is a common aspect of the pathogenesis of IBD and atherosclerotic CVD and can be considered one of the most important factors leading to the development and progression of cardiovascular pathology in patients with IBD. The purpose of this literature review is to describe the mechanisms underlying the development of endothelial dysfunction and disorders of the structure and function of the gut–vascular barrier in the pathogenesis of the cardiovascular manifestation of IBD.
The Usefulness of Line-Field Confocal Optical Coherence Tomography in Monitoring Epidermal Changes in Atopic Dermatitis in Response to Treatment: A Pilot Study
Authors: Dryżałowska Zuzanna , Blicharz Leszek , Michalczyk Agnieszka , Koscian Jan , Maj Małgorzata , Czuwara Joanna , Rudnicka Lidia
Published: August 2024

Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease. Due to its high prevalence, considerable morbidity, and chronicity, there is a need for the accurate in vivo evaluation of treatment efficacy. Line-field confocal optical coherence tomography (LC-OCT) is a new emerging imaging technique able to perform a non-invasive, real-time examination of the epidermis and the upper dermis. LC-OCT may represent a promising tool in the diagnosis and treatment follow-up of chronic eczematous skin diseases with barrier defects. Objectives: We aimed to investigate the role of LC-OCT in the non-invasive monitoring of the treatment effect on five patients with severe atopic dermatitis during dupilumab treatment. Materials and Methods: LC-OCT imaging was performed on five patients (three women and two men) aged between 14 and 85 years old at the baseline and at 2, 4, and 6 weeks of treatment with dupilumab. The LC-OCT scans were performed at two sites, the lesional skin in the antecubital fossa and the extensor part of the arm, considered a control site on each patient for comparison. The captured images were later evaluated. Descriptive statistics and a t-test were used to compare the analyzed parameters over time and between involved atopic skin and clinically healthy skin. Results: The LC-OCT imaging was able to detect the difference in stratum corneum (SC) thickness and quality and epidermal thickness (ET) and the changes before and after treatment with high accuracy. The main findings include a significant reduction in the epidermal and stratum corneum thickness and decreased epidermal spongiosis and inflammation, with better quality of the stratum corneum indicating restoration of its tightness at both lesional and control sites. Conclusions: This study demonstrates that clinical improvement of affected and unaffected atopic skin under dupilumab treatment correlates with the LC-OCT findings. LC-OCT represents a novel, non-invasive tool examining the in vivo skin barrier and inflammation and can help to monitor the treatment efficacy among patients with atopic dermatitis in daily practice.
Evaluation of Thermal Stress on Heterogeneous IoT-Based Federated Learning
Authors: Gu Yi , Zhao Liang , Liu Tianze , Wu Shaoen
Published: August 2024

Federated learning is a novel paradigm allowing the training of a global machine-learning model on distributed devices. It shares model parameters instead of private raw data during the entire model training process. While federated learning enables machine learning processes to take place collaboratively on Internet of Things (IoT) devices, compared to data centers, IoT devices with limited resource budgets typically have less security protection and are more vulnerable to potential thermal stress. Current research on the evaluation of federated learning is mainly based on the simulation of multi-clients/processes on a single machine/device. However, there is a gap in understanding the performance of federated learning under thermal stress in real-world distributed low-power heterogeneous IoT devices. Our previous work was among the first to evaluate the performance of federated learning under thermal stress on real-world IoT-based distributed systems. In this paper, we extended our work to a larger scale of heterogeneous real-world IoT-based distributed systems to further evaluate the performance of federated learning under thermal stress. To the best of our knowledge, the presented work is among the first to evaluate the performance of federated learning under thermal stress on real-world heterogeneous IoT-based systems. We conducted comprehensive experiments using the MNIST dataset and various performance metrics, including training time, CPU and GPU utilization rate, temperature, and power consumption. We varied the proportion of clients under thermal stress in each group of experiments and systematically quantified the effectiveness and real-world impact of thermal stress on the low-end heterogeneous IoT-based federated learning system. We added 67% more training epochs and 50% more clients compared with our previous work. The experimental results demonstrate that thermal stress is still effective on IoT-based federated learning systems as the entire global model and device performance degrade when even a small ratio of IoT devices are being impacted. Experimental results have also shown that the more influenced client under thermal stress within the federated learning system (FLS) tends to have a more major impact on the performance of FLS under thermal stress.
Differential–Linear Approximations of CHAM
Authors: Roh Dongyoung
Published: August 2024

CHAM is a family of lightweight block ciphers designed for resource-constrained environments like IoT devices and embedded systems, which require low power consumption and high performance. Despite numerous cryptanalytic evaluations, the security of CHAM remains robust. Differential–linear cryptanalysis, a method that combines two of the strongest attack methods on block ciphers—differential cryptanalysis and linear cryptanalysis—has been successfully applied to many block ciphers. This study introduces the first concrete differential–linear approximations of CHAM, marking a significant advancement in the cryptanalysis of this cipher family. Utilizing a Boolean satisfiability problem framework, we present a 46-round differential–linear approximation of CHAM-64/128 with a correlation of 2−31.08 and a 58-round approximation for CHAM-128/128 and CHAM-128/256 with correlations of 2−58.86 and 2−59.08, respectively. These findings significantly exceed the designers’ expectations for differential–linear approximations using CHAM. Furthermore, the 46-round differential–linear approximation of CHAM-64/128 is the best distinguisher of CHAM-64/128 to date in a single-key attack model. Notably, our findings do not threaten the security of CHAM but provide deeper insights into its cryptanalytic resistance.
A Grid-Forming Converter with MVDC Supply and Integrated Step-Down Transformer: Modeling, Control Perspectives, and Control Hardware-in-the-Loop (C-HIL) Verification
Authors: Golestan Saeed , Barrios Manuel , Golmohamadi Hessam , Iov Florin , Bak-Jensen Birgitte , Monfared Mohammad
Published: August 2024

A grid-forming voltage source converter with an integrated step-down transformer could be a promising solution for supplying low-voltage alternating current loads from a medium-voltage direct current supply. However, it may require a control system that gathers feedback signals from both the primary and secondary sides of the transformer, which in turn complicates the derivation of a standard form linear model. The absence of such a model complicates control tuning, as well as the assessment of dynamics and stability of the converter system. The objective of this paper is to address this gap in knowledge. For the case study, a conventional H-bridge converter with a step-down transformer and an αβ-frame dual-loop grid-forming controller is considered. Initially, comprehensive guidelines on deriving a standard form linear model for this converter system are presented. Then, the impact of controlling the VSC in a dq frame and the changes in the transformer vector group on the small-signal model of the VSC are analyzed. The aspects of control tuning are also discussed in detail, and the model’s accuracy and efficacy are validated both theoretically and through control hardware-in-the-loop (C-HIL) tests using a Typhoon HIL setup.
Inter- and Intra-Specific Variation in Leaf Functional Traits at Different Maturity Levels in a Tropical Monsoon Forest
Authors: Wu Miaolan , Liu Yue , He Zhihang , Gu Xiaojuan , Yu Yaohong , Tao Yuzhu , Zhou Qing , Mo Qifeng
Published: August 2024

Plant functional traits are an important indicator for the comprehensive evaluation of community stability and resilience. Therefore, exploring the variations and relationships among leaf functional traits at different maturity levels during forest restoration can deepen the understanding of plant adaptation strategies and community assembly. In this study, we measured the leaf area (LA), specific leaf area (SLA), photosynthetic pigments, non-structural carbohydrates (NSCs), leaf nitrogen content (LNC), and leaf phosphorus content (LPC) of dominant tree species in three communities with different maturity levels (defined by species composition, biodiversity and spatial structure) in a monsoon forest located in the northern margin of the tropics in China, and explored the variation and relationships among different leaf traits at individual, species, and community scales. The results showed that maturity levels significantly affected leaf functional traits. With the increase in maturity levels, SLA increased, and leaf SS and NSCs decreased, while other leaf functional traits did not show a consistent pattern. In different communities, NSCs, Chl (a:b), SS:St or Car had a trade-off or synergistic relationship with leaf economic spectrum. Additionally, the LPC, LNC, and starch were the key traits in response to selection pressure at maturity levels, inter-specific and intra-specific scales, respectively, and the trait–trait relationships were stronger or more extreme as the scale was narrowed. Therefore, when evaluating the development and succession of tropical monsoon forest communities, the selection of leaf functional characteristics and the determination of the research scale should be comprehensively and systematically considered.
Multi-Class Intrusion Detection Based on Transformer for IoT Networks Using CIC-IoT-2023 Dataset
Authors: Tseng Shu-Ming , Wang Yan-Qi , Wang Yung-Chung
Published: August 2024

This study uses deep learning methods to explore the Internet of Things (IoT) network intrusion detection method based on the CIC-IoT-2023 dataset. This dataset contains extensive data on real-life IoT environments. Based on this, this study proposes an effective intrusion detection method. Apply seven deep learning models, including Transformer, to analyze network traffic characteristics and identify abnormal behavior and potential intrusions through binary and multivariate classifications. Compared with other papers, we not only use a Transformer model, but we also consider the model’s performance in the multi-class classification. Although the accuracy of the Transformer model used in the binary classification is lower than that of DNN and CNN + LSTM hybrid models, it achieves better results in the multi-class classification. The accuracy of binary classification of our model is 0.74% higher than that of papers that also use Transformer on TON-IOT. In the multi-class classification, our best-performing model combination is Transformer, which reaches 99.40% accuracy. Its accuracy is 3.8%, 0.65%, and 0.29% higher than the 95.60%, 98.75%, and 99.11% figures recorded in papers using the same dataset, respectively.
A Survey on Emerging Blockchain Technology Platforms for Securing the Internet of Things
Authors: Kareem Yunus , Djenouri Djamel , Ghadafi Essam
Published: August 2024

The adoption of blockchain platforms to bolster the security of Internet of Things (IoT) systems has attracted significant attention in recent years. Currently, there is a lack of comprehensive and systematic survey papers in the literature addressing these platforms. This paper discusses six of the most popular emerging blockchain platforms adopted by IoT systems and analyses their usage in state-of-the-art works to solve security problems. The platform was compared in terms of security features and other requirements. Findings from the study reveal that most blockchain components contribute directly or indirectly to IoT security. Blockchain platform components such as cryptography, consensus mechanism, and hashing are common ways that security is achieved in all blockchain platform for IoT. Technologies like Interplanetary File System (IPFS) and Transport Layer Security (TLS) can further enhance data and communication security when used alongside blockchain. To enhance the applicability of blockchain in resource-constrained IoT environments, future research should focus on refining cryptographic algorithms and consensus mechanisms to optimise performance and security.
Forest Fires: Silvicultural Prevention and Mathematical Models for Predicting Fire Propagation in Southern Italy
Authors: Marziliano Pasquale A. , Lombardi Fabio , Cataldo Maria F. , Mercuri Michele , Papandrea Salvatore F. , Manti Leonardo M. , Bagnato Silvio , Alì Giuseppe , Fusaro Pierpaolo , Pantano Pietro S. , Scuro Carmelo
Published: August 2024

In the Mediterranean basin, coniferous reforestation mainly comprises forest stands highly susceptible to fires. When silvicultural treatments have not been performed for decades after plantation, these stands often exhibit high vertical and horizontal tree density, along with a significant occurrence of lying and standing deadwood, thereby increasing the fuel load. On average, these pine forests are characterized by high values of above-ground biomass, ranging from 175 to 254 Mg ha−1 for the younger and the older ones, respectively. The theoretical heat energy produced per surface unit, in the case of the total combustion of the above-ground biomass, is also high, varying from 300 to 450 MJ ha−1 depending on the stage of stand development. In this study, we demonstrated the importance of silvicultural interventions in reducing the pyrological potential in pine reforested stands located in southern Italy, also giving attention to the water savings needed during extinction phases. In detail, we applied a preliminary mathematical reaction-diffusion model aimed at predicting the development of forest fires. The model was applied using data obtained through the estimation of the pyrological potential in terms of heat energy produced per surface unit (1 hectare) and the variation in the critical surface intensity. We verified that, when silvicultural interventions are applied, they induce a reduction of heat energy ranging between 17 and 21%, while the extinguishing water saved ranges between 600 and 1000 Mg ha−1. Moreover, when the silvicultural interventions are implemented, the probability of the transition from surface fire to crown fire can be reduced by up to 31%. The most effective results on fire risk mitigation are mainly obtained when thinning aimed at reducing canopy and tree density is carried out in the younger phases of the reforested pine stands.