bluuebunny/crossref_metadata_2025_split huggingface dataset:
Feed Plants, Ethnoveterinary Medicine, and Biocultural Values: Insights on the Luchuan Pig from Hakka Communities in China
Authors: Liufu Yongqin , Zhou Jilong , Fu Qiongyao , Shao Min , Xie Yaozhang , Luo Binsheng 
Published: August 2023

Abstract

Background
The Luchuan pig is an indigenous breed from Luchuan County, China, with cultural and genetic significance. However, traditional knowledge and conservation status have not been systematically documented.
Methods
Using ethnobiological methods, we surveyed 72 Luchuan pig farmers in 7 townships during 2021–2023. Semi-structured interviews and participant observation were conducted to document traditional knowledge and management practices.
Results
The locals reported 51 plant species used as pig feed, with 30 wild species. Growth-stage-specific feeding and seasonal adjustment practices were documented. We recorded 62 ethnoveterinary plant uses, mainly for treating pigs' heat stress and skin conditions. Luchuan pigs play central roles in Hakka customs, rituals, and cuisine. While the ecological farming model minimizes environmental impacts, challenges remain for conserving and promoting local pigs.
Conclusions
The Luchuan Hakka people possess rich traditional knowledge and management experience in raising Luchuan pigs. Our study provides extensive documentation of traditional knowledge and recommends integrating cultural and genetic aspects for sustaining this biocultural heritage. Findings can inform initiatives supporting local breed conservation globally.
Genetic algorithms based optimized hybrid deep learning model for explainable Alzheimer's prediction based on temporal multimodal cognitive data
Authors: Saleh Hager , ElRashidy Nora , Elaziz Mohamed Abd , Aseeri Ahmad O. , El-Sappagh Shaker 
Published: August 2023

Abstract
Alzheimer's Disease (AD) is an irreversible neurodegenerative disease. Its early detection is crucial to stop disease progression at an early stage. Most deep learning (DL) literature focused on neuroimage analysis. However, there is no noticed effect of these studies in the real environment. Model's robustness, cost, and interpretability are considered the main reasons for these limitations. The medical intuition of physicians is to evaluate the clinical biomarkers of patients then test their neuroimages. Cognitive scores provide an medically acceptable and cost-effective alternative for the neuroimages to predict AD progression. Each score is calculated from a collection of sub-scores which provide a deeper insight about patient conditions. No study in the literature have explored the role of these multimodal time series sub-scores to predict AD progression.
We propose a hybrid CNN-LSTM DL model for predicting AD progression based on the fusion of four longitudinal cognitive sub-scores modalities. Bayesian optimizer has been used to select the best DL architecture. A genetic algorithms based feature selection optimization step has been added to the pipeline to select the best features from extracted deep representations of CNN-LSTM. The SoftMax classifier has been replaced by a robust and optimized random forest classifier. Extensive experiments using the ADNI dataset investigated the role of each optimization step, and the proposed model achieved the best results compared to other DL and classical machine learning models. The resulting model is robust, but it is a black box and it is difficult to understand the logic behind its decisions. Trustworthy AI models must be robust and explainable. We used SHAP and LIME to provide explainability features for the proposed model. The resulting trustworthy model has a great potential to be used to provide decision support in the real environments.
Design Efficacy Evaluation of a LIM-SD-based Approach for Ecological Engineered Landscaping Design: A Case Study of An Urban River Wetland
Authors: Huang Yan , Li Tianjie 
Published: August 2023

Abstract

Ecological engineered landscaping (EEL) is a type of design work which incorporates various ecological principles and aims to provide aesthetic and ecological characteristics to semi-natural environments. Digital landscape techniques, such as Landscape Information Modelling (LIM) and Stable Diffusion (SD) -based approach, has significant potential to support EEL projects for both environmental engineers and landscape architects. This study aims to quantitatively evaluate the design efficacy and feasibility of the LIM-SD-based approach applied for an urban river wetland EEL design task. An experiment was used to quantify the EEL design efficacy within the digital approach for students from different backgrounds, i.e., industrial design postgraduates (ID), landscape architecture undergraduates (UG), landscape architecture postgraduates (PG). Experts were invited to evaluate the EEL design works of the participants according to 4 criteria, i.e., "Site adequacy", "Aesthetic", "Spatial arrangement" and "Eco-engineering", while the vegetation coverage rate (VCR) and proportion of riparian areas (PRA) of each design is automatically calculate by a parametric program. Participants’ objective experiences are also evaluated by questionnaires. It indicates that LIM-SD-based approach can improve most designers' productivity in EEL design tasks. The ranking of scores for most of the quantitative indicators is PG > UG > ID. Explanation of some phenomena, i.e., the mismatch between LIM and SD generated renderings and the different objective experiences of the participants, are provided. Future research can further investigate the impact of digital EEL workflows on landscape architects’ and environmental engineers’ performance.
Enhancing load frequency control with plug-in electric vehicle integration in non-reheat thermal power systems
Authors: Garg Man Mohan , Shukla Rakesh Rajan , Panda Anup Kumar , Das Debapriya 
Published: August 2023

Abstract
As the percentage of renewable energy source within the energy production mix has expanded, it's become gradually difficult to determine the appropriate values for controller gains and model parameters. Controlling system frequency of interconnected power system during sudden load disturbance requires careful consideration of the controller gain values and small signal stability model parameters. This study proposes an innovative methodology for ascertaining controller gain values and model parameters for plug-in electric vehicles (PEVs) in load frequency control (LFC) applications. Proposed method uses the root locus (RL) approach to find the suitable controller gain values and model parameters for PEVs. This paper offers a thorough mathematical description of the proposed RL approach. Routh approximation method is used for reduced-order modelling (ROM), which comprises thermal and PEV systems, to reduce complexity of higher-order system while designing controllers. Fractional order proportional-integral-derivative controller (FO-PID) is proposed, and its parameters are adjusted using particle swarm optimization (PSO) optimization tool. To validate the efficacy of suggested method, a comprehensive comparison of time response parameters and performance indices (PI) is carefully carried out. Also, the various PEVs state of charge (SOC) levels is investigated, and effects of these levels are studied in LFC. The proposed method for determining gain value is highly reliable and efficient, outperforming existing methodologies in the literature.
Performance analysis of a hybrid optical amplifier based 480-Gbps DWDM-FSO system under the effect of different atmospheric conditions
Authors: Obaid Hafiz Muhammad , Shahid Hifsa , Akram Adeel , Zafrullah Muhammad 
Published: August 2023

Abstract
Free space optical (FSO) systems offer a desirable and affordable way of providing communication services in remote locations. They provide secure transmission without the requirement for licensing and with lower implementation costs. However, due to the impact of atmospheric turbulences on the effectiveness of FSO systems, their implementation faces significant challenges. Dense wavelength division multiplexing (DWDM) has shown promise in addressing the growing bandwidth needs in optical networks. This paper introduces a hybrid optical amplifier based system, combining DWDM and FSO technology. The proposed DWDM-FSO system operates at a rate of 480 Gbps and consists of 12 channels, each capable of transmitting data at 40 Gbps. The system’s performance is evaluated and compared by determining the Q-factor and bit error rate (BER) for both the cases when hybrid amplifier is employed and with no hybrid amplifier within the C-band, specifically focusing on wavelengths around 1550 nm. Moreover, the length of the FSO link is increased to assess the corresponding Q-factor and BER. Resultantly, the maximum distance for the FSO link is determined, ensuring that it remains within acceptable Q-factor and BER thresholds. Furthermore, the effectiveness of the FSO system is also evaluated and compared under different atmospheric conditions, considering both scenarios: when employing a hybrid amplifier and when not utilizing one.
Segmentation based early Melanoma Detection Using Random Forest Algorithm
Authors: Abate Mesfin , Hussein Jemal , Varadarajan V. K. , Zemene Solomon 
Published: August 2023

Abstract
This article proposes a method to detect melanoma at the early stage before it becomes something severe. The lesion of melanoma has five stages, stage zero to stage four, of which stage one and stage two are early stages. Melanoma is mainly caused by UV radiation, unhealthy lifestyle, hereditary etc.; In addition, age and gender (sex) are also part of the risk factors causing melanoma. So people ought to be aware of what skin disease they have and what precautions and measures they must be taken at its early stage so as to treat it. Because fatal and dangerous cancers must be managed either through prevention or immediate reaction as it occurs. The purpose of this work is to detect melanoma by applying segmentation method at its early stage using random forest classification algorithms. Therefore, segmentation process is implemented as a means of its detection by taking 600 image datasets which are randomly selected from SIIM- ISIC-2020 training dataset. And attained an accuracy of 95%, a precision of 97.3%, a specificity of 95.4%, a selectivity of 95.3% and an f1 score of 96.3. Hopefully, this result will help physicians treat diseases at an early stage and thus prevent further damage.
Intrusion Detection based on Concept Drift Detection &amp; Online Incremental Learning
Authors: Jemili Farah , Jouini Khaled , Korbaa Ouajdi 
Published: August 2023

Abstract
Intrusions are constantly evolving and changing, and to keep up with these changes, it is necessary to have models that detect these changes, also known as concept drifts, and offer the ability to update the model without starting the learning process from scratch. In our contribution, we have opted for a new approach to intrusion detection based on concept drift detection and online incremental learning, named DDM-ORF. Our approach is based on the Detection Drift Method (DDM) and Online Random Forest algorithm (ORF). The model has shown very good accuracy compared to traditional approaches and an ability to handle massive data, providing multi-class classification that allows for determining insights. The proposed system achieves very good classification results, along with good processing speed that meets real-world scenarios. Apache Spark Structured Streaming provides important functionalities for dealing with streaming data and enables the deployment of the proposed system DDM-ORF in real-world applications.
Ceratobasidioid mycobionts in Russian populations of Goodyera repens and mycorrhizal specificity in Goodyerinae subtribe (Orchidaceae)
Authors: Bibikov Nikita , Voronina Elena , Kamzolkina Olga , Yarmeeva Maria , Kurakov Alexander 
Published: August 2023

Abstract
Members of Ceratobasidiaceae family (more commonly known by its anamorphic name “rhizoctonias”) possess a variety of nutritional modes: plant pathogens, saprotrophs, endophytes and symbionts of orchid mycorrhiza. Links between nutritional modes and taxonomy of these fungi as well as their specificity towards plant host is still ambiguous. The scope of the present study was to explore biodiversity of ceratobasidioid mycobionts of sciophytic terrestrial orchid Goodyera repens, search for evolutionary stable clades within mycobionts of Goodyerinae subtribe uniform by plant host or geographic region and to establish possible connection between ceratobasidioid nutritional modes and morphological characteristics. We consider G. repens a generalist associated with a wide range of distantly related mycobionts. Two unidentified Ceratobasidium species and Thanatephorus ochraceus are reported from G. repens roots for the first time. Ceratobasidiaceae tend to form stable clades specific to either temperate or tropical region. Morphological characteristics of pathogenic and mycorrhizal rhizoctonia isolates tend to form a variety of transitional forms to correlate with nutritional mode.
How do substrate types affect the seasonal composition and functional feeding groups variation of benthic insects in an arid region (northeastern Algeria)?
Authors: MERADI Kenza , Bounar Rabah , Benzina Imène , Meradi Salah , Bachir Abdelkrim Si , Céréghino Régis 
Published: August 2023

Abstract
The diversity of bottom substrates is a primary driver of taxonomic richness and species abundance patterns of freshwater benthic insects in space and time. Here, we examine the influence of substrate composition on the seasonal patterns of benthic insect communities in streams of arid regions. Benthic insects were sampled monthly over a year at three sampling sites distributed along the Bouilef stream within the Belezma biosphere reserve (Algeria). Different substrate types (sand, gravel, pebbles, boulders, and emergent macrophytes) were sampled in the rainy and dry seasons. During the 2 studied seasons, a total of 8599 insects belonging to 6 orders, 26 families, and 39 genera/species were sampled and identified. Student's t-test analysis showed that season influences significantly the mean abundance of benthic insects, which increases in the rainy season. However, the season does not affect taxa richness. Substrate types influence taxa composition and variation of benthic insect communities. The results showed that pebbles and boulders are the most populated by gathering and filtering collectors in terms of taxa abundance. Emergent macrophytes are more favorable for herbivores shredders. Principal Coordinates Analysis ‘’PCoA’’ also showed that pebbles and boulders differed significantly from other substrates, whereas sand and gravel substrates exhibited similar taxa mean abundance. Such assessments can help propose conservation measures needed to successfully safeguard these fragile ecosystems.
Evaluating the expression level of miR-192 and miR-377 in serum of type 2 diabetic patients as potential biomarkers for prognosis of diabetic nephropathy
Authors: Khedrimirghaed Maryam , Ghaneialvar Hori , Mozafari Aliashraf , Heidari Mohammad Amin , Bastaminejad Saiyad 
Published: August 2023

Abstract

Background
Diabetic nephropathy is a common complication of diabetes, which can be prevented by early diagnosis and development. The aim of the present study was to evaluate the expression level of miR-192 and miR-377 in serum samples of diabetic subjects without nephropathy and diabetic nephropathy subjects to investigate these miRNAs as a biomarker for the diagnosis and prognosis of diabetic nephropathy.
Methods and Results
We reviewed 370 patients diagnosed with diabetes mellitus type 2 (T2D) (190 patients with diabetic nephropathy and 180 patients with diabetes without nephropathy), and 170 healthy control individuals without any history of T2D. Expression analysis of miR-192 and miR-377 was performed by Real-Time PCR. The expression level of miR-192 was significantly increased in the diabetic nephropathy and diabetic without nephropathy groups (p < 0.05 and p < 0.01). Also, the expression level of miR-377 was significantly increased in the diabetic group without nephropathy compared to the control group (P < 0.05), although miR-377 expression in the diabetic nephropathy samples was a significant decrease compared with the diabetic without nephropathy samples (P > 0.05). QRT-PCR results showed that the expressions of miR-192 and miR-377 were significantly lower and higher in the diabetic without nephropathy group than in micro-albuminuria and macro-albuminuria groups, respectively.
Conclusions
Overexpression of miR-192 and down-regulation of miR-377 were observed in diabetic nephropathy. Up-regulation of miR-192 can be used as a prognostic factor for diagnosing diabetic nephropathy.
Intelligent Characterization of Spark-Assisted Chemical Engraving (SACE) Process Using Time Series Classification
Authors: Sahebari Seyed Mahmoud Seyedi , Bassyouni Zahraa , Barari Ahmad , Ziki Jana D Abou 
Published: August 2023

Abstract
Spark-Assisted Chemical Engraving (SACE) requires precise control over key factors to overcome gas film instability and achieve reproducible optimal resolution and machining speed. This paper presents a substantial advancement in the SACE micromanufacturing technique by introducing a composite algorithm. This algorithm leverages deep learning and time series classification, employing a Temporal Convolutional Network (TCN) and a Long Short-Term Memory (LSTM) architecture for sequence-to-sequence intelligent classification. These classifiers are trained and optimized using Bayesian optimization, achieving impressive accuracies of 97.12% for TCN and 96.64% for LSTM. The algorithm utilizes TCN's superior performance to calculate derived parameters like gas film formation time, lifetime, mean discharge current and energy, and discharging frequency. Its versatility is demonstrated across various experimental conditions, showcasing its potential for rapid and accurate systematic studies. By highlighting the algorithm's applicability in real-time process control for SACE, this study establishes a foundation for future advancements in the field of glass micro manufacturing.
Selective under-representation of Pacific peoples in population estimates for health indicator measurements in Aotearoa New Zealand misinforms policy making
Authors: Sonder Gerard JB , Grey Corina , Ryan Debbie , Cumming Jacqueline , Sporle Andrew , Hill Philip C 
Published: August 2023

Abstract
Background
The Aotearoa New Zealand ‘Census of Populations and Dwellings’ is a five yearly count of people and dwellings in. Best available populations (BAP) are subnational projections based on census data and demographic assumptions developed for healthcare planning and funding allocation but are also used as the denominator for health indicator monitoring. Pacific people are systematically undercounted, but the impact on health statistics is not well studied. For COVID-19 vaccination coverage, health service user (HSU) data were considered a more reliable denominator than BAP but introduced new biases. We examined the impact of both denominators on two health indicators, by ethnic group and geographic district.
Methods
We described how declining census response rates affected population data quality. We compared BAP and HSU data at district level. For the indicators ‘access to primary care’ and ‘cervical cancer screening uptake’ we replaced currently used BAP denominators with HSU and examined the impact for different ethnic groups in different geographic districts.
Results
Overall census 2018 response declined by 10%, but for Māori and Pacific people by 21% and 23%, respectively. This inequitably impacting BAP accuracy. Census undercount was highest in the district with the largest Pacific populations, where HSU exceeded BAP most. Notably, ‘access to primary care’ for Pacific people in this district consistently exceeds 100%. Using BAP, both health indicators are currently estimated as highest for Pacific people compared to other ethnic groups, but when based on HSU, they dropped to lowest. Similar, but less pronounced trends occurred in other districts. Changes in trends over time for both indicators coincided mostly with adjustments in BAP, rather than changes in the numerators.
Conclusions
The current use of BAP denominators for health statistics does not enable reliable monitoring of key health indicators for Pacific people. HSU denominators are also unsuitable for monitoring health. A real-time population register is strongly recommended as a new, transparent, way of obtaining more reliable, timely population data to guide policymaking and underpin a more equitable health system under the health reforms. Meanwhile, reporting of ethnic specific outcomes need to include a clear assessment of the potential for bias due to inaccurate population estimates.
A multi-physical structure-preserving method and its analysis for the conservative Allen-Cahn equation with nonlocal constraint
Authors: Liu Xu , Hong Qi , Liao Hong-lin , Gong Yuezheng 
Published: August 2023

Abstract
The conservative Allen-Cahn equation satisfies three important physical properties, namely the mass conservation law, the energy dissipation law and the maximum bound principle. However, very few numerical methods can preserve them at the same time. In this paper, we present a multi-physical structure preserving method for the conservative Allen-Cahn equation with nonlocal constraint by combining the averaged vector field method in time and the central finite difference scheme in space, which can conserve all three properties simultaneously at the fully discrete level. We propose an efficient linear iteration algorithm to solve the presented nonlinear scheme, and prove that the iteration satisfies the maximum bound principle and a contraction mapping property in the discrete L∞ norm. Furthermore, concise error estimates in the maximum norm are established on nonuniform time meshes. The theoretical findings of the proposed scheme are verified by several benchmark examples, where an adaptive time-stepping strategy is employed.
MSC Classification: 65M06 , 65M12 , 65M70