bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantitation of EPA and DHA in Fish Oil Dietary Supplements Sold in the United States
Authors: Hilton Joseph E. , Setzer William N. , Satyal Prabodh , Poudel Ambika , Ashcroft Sawyer , Satyal Rakesh
Published: September 2024

The dietary supplement industry is a growing market with a variety of suppliers, producers, and distributors in terms of size and quality. Because of the little amount of regulation placed on the dietary supplement industry compared to the pharmaceutical industry in the United States, there is less pressure on supplement companies ensure that their labels accurately reflect the actual product’s content. There are few studies on the quality of fish oil sold in the U.S., particularly about quantitation of ω-3 fatty acids, specifically eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). The aim of this study was to compare the amount of ω-3 fatty acids found in fish oil capsules and liquids bought from local stores to their label claim. Of the 16 fish oil products tested, 6 were ± 20% outside of the label claim for EPA and 10 of 15 were ± 20% outside of the label claim for DHA. One product did not have a label claim for DHA. The large variance in the amount of omega-3s from the product label could mislead consumers to ingesting significantly more or less than expected.
Prolonged Visual Evoked Potential Latencies in Dogs Naturally Infected with Canine Distemper Virus
Authors: Gutiérrez Mary , Delucchi Luis , Bielli Alejandro , Verdes José Manuel
Published: September 2024

Canine distemper (CD) is a deadly, multi-system infection caused by a Morbillivirus. The Canine Distemper Virus (CDV) frequently affects the nervous system with demyelinating leukoenceph-alitis, the most common neurological manifestation. The disease has been linked to multiple sclerosis (MS) in humans due to the similar clinical presentation and pathophysiological mecha-nisms. In MS, visual evoked potentials (VEP) have been identified as a reliable marker for disease progression, enabling early detection of clinically suspected lesions. The aim of this study was to determine if there are any abnormalities in VEP responses in dogs with neurological CD. Visual evoked potentials and Electroretinogram (ERG) were recorded at both the cranial and spinal levels in dogs naturally infected with CDV and in healthy dogs. The results revealed a bilateral increase in the latency of N1, P1, N2, P2 and N3 waves of the VEP, without any alterations in their amplitudes. No significant differences were observed in the ERG, between both groups. These results suggest that altered VEP responses could serve as an early diagnostic indicator of neu-rological damage caused by distemper. Therefore, conducting these studies could potentially aid in the detection of central nervous conduction disorders during the subclinical phases of the disease.
Designing Personalized Learning Paths for Foreign Language Acquisition Using Big Data: Theoretical and Empirical Analysis
Authors: Xia Yina , Shin Seong-Yoon , Shin Kwang-Seong
Published: September 2024

This study introduces the Data-Driven Personalized Learning Model (DDPLM), a sophisticated framework designed to enhance foreign language acquisition through the integration of big data analytics. Implemented within the educational platforms Edmodo and Duolingo, DDPLM utilizes real-time data processing to tailor learning paths and content dynamically to individual learner needs. Our findings indicate significant improvements in language learning efficiency, engagement, and retention. The model's adaptability across different digital environments showcases its potential scalability and effectiveness in various educational contexts. Additionally, the research addresses the critical role of personalized feedback and adaptive challenges in maintaining learner motivation and promoting deeper linguistic comprehension. The outcomes suggest that DDPLM significantly transforms traditional language education, making it more personalized, efficient, and aligned with individual learning preferences.
FGFR2 in the Development and Progression of Cutaneous Squamous Cell Cancer
Authors: Kallenberger Ethan M. , Khandelwal Alok , Nath Priyatosh , Nguyen Shaun A. , DiGiovanni John , Nathan Cherie-Ann O.
Published: September 2024

Cutaneous Squamous Cell Carcinoma (cSCC) is an increasingly common malignancy of the skin and the leading cause of death from skin cancer in adults over the age of 85. Fibroblast Growth Factor Receptor 2 (FGFR2) has been identified as an important effector of signaling pathways that lead to the growth and development of cSCC. In recent years, there have been numerous studies evaluating the role FGFR2 plays in multiple cancers, its contribution to resistance to anti-cancer therapy, and new drugs that may be used to inhibit FGFR2. This review will provide an overview of our current understanding of FGFR2 and potential mechanisms in which we can target FGFR2 in cSCC. The goals of this review are the following: 1) to highlight our current knowledge on the role of FGFR2 in healthy skin and contrast this with its role in the development of cancer; 2) to further explain the specific molecular mechanisms that FGFR2 uses to promote tumorigenesis; 3) to describe how FGFR2 contributes to more invasive disease, 4) to describe its immunosuppressive effects in skin, and 5) to evaluate its effect on current anticancer therapy and discuss therapies on the horizon to target FGFR2 related malignancy.
Extended-Spectrum Beta Lactamase-Producing E. Coli in Freshwater Lakes Along an Anthropogenic Impact Gradient
Authors: Bivins Aaron , Srivastava Abhishek , Katara Anant , Weise Katherine , Singh Kachhawaha Akanksha
Published: September 2024

The World Health Organization recently proposed the Tricycle Protocol for harmonized surveillance of extended-spectrum beta lactamase-producing E. coli (ESBL-Ec) as a proxy for AMR among humans, animals, and the environment. We implemented the Tricycle Protocol with ceftriaxone (CRO) to screen for ESBL-Ec using membrane filtration (TBX-CRO) and Colilert Quanti-Tray (CQT-CRO) workflows in parallel applied to water samples collected from four lakes. ESBL-Ec and E. coli abundance and prevalence, and ESBL-Ec to E. coli ratios were significantly higher in the two highly impacted lakes (receiving sewage treatment plant effluent, open defecation site adjacent to informal settlements) compared to less impacted lakes (wildlife sanctuary, recreational site). The cumulative burden of antibiotic compounds was also higher in the highly impacted lakes. Even in the “pristine” lake in a bird sanctuary, ESBL-Ec and an antibiotic compound were present. Overall, the CQT-CRO format yielded reasonable qualitative (K = 0.603) and quantitative agreement (intra-class correlation coefficient = 0.849) and linearity (r2 = 0.785) with the TBX-CRO format adapted from the Tricycle Protocol. However, CQT-CRO doubled the prevalence of ESBL-Ec in settings with decreased E. coli abundance. Consistent with observations for other third-generation cephalosporins, CRO performed poorly for screening the ESBL phenotype. Further, confirmed ESBL-Ec isolates were not associated with an increased number of resistance phenotypes compared with non-ESBL isolates. Instead, the number of resistance phenotypes was associated with the lake from which the sample was collected. In high-burden settings, AMR surveillance efficiency could be increased by directly testing generic E. coli isolates for ESBL phenotype and antibiotic susceptibility. Our experience also suggests that sequencing ESBL-Ec will likely be required to determine epidemiologically appropriate risk mitigation strategies where ESBL-Ec are prevalent. Resource limitations could jeopardize the relevance of the Tricycle Protocol for changing the trajectory of AMR in high-burden low-resource settings globally.
’Seeing is Believing’: How Neutron Crystallography Informs Enzyme Mechanism by Visualizing Unique Water Species
Authors: Wan Qun , Bennett Brad C.
Published: September 2024

Hydrogen is the lightest atom and composes roughly half of the atomic content in macromolecules, yet their location can only be inferred or predicted in most macromolecular structures. This is because hydrogen can rarely be directly observed by the most common structure determination techniques (such as X-ray crystallography and electron cryo-microscopy). However, knowledge of hydrogen atom positions, especially for enzymes, can reveal protonation states of titratable active site residues, hydrogen bonding patterns, and the orientation of water molecules. Though we know they are present, this vital layer of information, which can inform a myriad of biological processes, is frustratingly invisible to us. The good news is that, even at modest resolution, neutron crystallography (NC) can reveal this layer and has emerged this century as a powerful tool for elucidating enzyme catalytic mechanisms. Due to its strong and coherent scattering of neutrons, incorporation of deuterium into the protein crystal amplifies the power of NC. This is especially true when solvation and the specific participation of key water molecules are crucial for catalysis. Neutron data allow the modeling of all three atoms in water molecules and have even revealed previously unobserved and unique species such as hydronium (D3O+) and deuteroxide (OD-) ions as well as lone deuterons (D+). Herein, we briefly review why neutrons are ideal probes for identifying catalytically important water molecules and these unique water-like species, limitations in interpretation, and four vignettes of enzyme success stories from disparate research groups. One of these groups was that of Dr. Chris G. Dealwis, who died unexpectedly in 2022. As a memorial appreciation of his scientific career, we will also highlight his interest and contributions to the neutron crystallography field. As both the authors were mentored by Chris, we feel we have a unique perspective on his love of molecular structure and admiration for neutrons as a tool to query those structures.
Impaired Development of Collagen Antibody-Induced Arthritis in Rab44-Deficient Mice
Authors: Yamaguchi Yu , Kadowaki Tomoko , Sakai Eiko , Noguromi Mayuko , Oyakawa Shun , Tsukuba Takayuki
Published: September 2024

Rheumatoid arthritis (RA) is an autoimmune disease characterized by immune cell-mediated joint inflammation and subsequent osteoclast-dependent bone destruction. Collagen antibody-induced arthritis (CAIA) is a useful mouse model for examining the inflammatory mechanisms in human RA. Previously, we identified the novel gene Rab44, which is a member of the large Rab GTPase family and is highly expressed in immune-related cells and osteoclasts. In this study, we induced CAIA in Rab44-knockout (KO) mice to investigate the effects of Rab44 on inflammation, cell filtration, and bone destruction. Compared with wild-type (WT) mice, Rab44 KO mice showed reduced inflammation in arthritis under CAIA-inducing conditions. Rab44 KO CAIA mice exhibited reduced cell filtration in the radiocarpal joints. Consistent with these findings, Rab44-KO CAIA mice showed decreased mRNA levels of arthritis-related marker genes including inflammation, cartilage turnover, bone formation, and bone absorption markers. Rab44-KO CAIA mice exhibited predominant infiltration of M2-type macrophages at inflammatory sites and reduced bone loss compared to WT CAIA mice. These results indicate that Rab44 deficiency reduces the progression of inflammation in CAIA in mice.
Dissolved Hydrogen Concentration in the Psychrophilic Anaerobic Digestion of Food Leftovers and Its Correlation to VFA/ TIC Ratio
Authors: Platosova Daniela , Rusin Jiri , Svoboda Radek , Smutna Katerina
Published: September 2024

This paper evaluates the usefulness of measuring the concentration of dissolved hydrogen in an anaerobic fermenter for maintaining the process stability. A laboratory test of two-stage psychrophilic anaerobic digestion of food leftovers from a university canteen was carried out in a vertical reactor with a total working volume of 0.255 m3 without stirring. In the course of the experiment lasting 1050 days, with an average organic loading of 15.45 kgVS m-3 d-1 for the 1st stage and 0.657 kgVS m-3 d-1 for the 2nd stage, a specific biogas production of 0.123 Nm3 per kilogram of substrate and 0.448 Nm3 per kilogram of total solids and 0.480 Nm3 per kilogram of volatile solids, respectively, was achieved. The average methane content in biogas was 55.9 % vol. Slightly higher gas productions were measured in the batch BMP test. The concentration of hydrogen in the mixed biogas from both reactor stages occasionally exceeded 1000 ppm and averaged 134 ppm, the concentration of dissolved hydrogen measured by AMT MS 08 sensor in the overloaded second stage was often 0.10-0.23 mg dm-3 and correlated with the total concentration of lower fatty acids and with the VFA/ TIC parameter. The dissolved hydrogen concentration from AMT instrument was not found to be a reliable timely indicator of overload or process stability.
Multi-Sensor Soil Probe and Machine Learning Modeling
Authors: Grunwald Sabine , Murad MOF , Farrington Stephen , Wallace Woody , Rooney Daniel
Published: September 2024

We present a data-driven, in situ proximal multi-sensor digital soil mapping approach to develop digital twins for multiple agricultural fields. A novel Digital Soil CoreTM (DSC) Probe was engineered that contains seven sensors, each of a distinct modality, including sleeve friction, tip force, dielectric permittivity, electrical resistivity, soil imagery, acoustics, and visible and near-infrared spectroscopy. The DSC System integrates components the DSC Probe, DSC software, and deployment equipment to sense soil characteristics at a high vertical spatial resolution (mm scale) along in situ soil profiles up to a depth of 120 cm in about 60 sec. The DSC Probe in situ proximal data are harmonized into a data cube providing vertical high-density knowledge associated with physical-chemical-biological soil conditions. In contrast, conventional ex situ soil samples derived from soil cores, soil pits, or surface samples analyzed using laboratory and other methods are bound by substantially coarser spatial resolution and multiple compounding errors. Our objective was to investigate the effects of mismatched scale between high-resolution in situ proximal sensor data and coarser resolution ex situ soil laboratory measurements to develop soil prediction models. Our study was conducted in central California soil in almond orchards. We collected DSC sensor data and spatially co-located soil cores that were sliced into narrow layers for laboratory-based soil measurements. Partial Least Squares Regression (PLSR) cross-validation was used to compare results testing four data integration methods. Method A reduced the high-resolution sensor data to discrete values paired with layer-based soil laboratory measurements. Method B used stochastic distributions of sensor data paired with layer-based soil laboratory measurements. Method C allocated the same soil analytical data to each one of the high-resolution multi-sensor data within a soil layer. Method D linked the high-density multi-sensor soil data directly to crop responses (crop performance and behavior metrics) bypassing costly laboratory soil analysis. Overall, the soil models derived from Method C outperformed Methods A and B. Soil predictions derived using Method D were most cost-effective for directly assessing soil-crop relationships, making this method well-suited for industrial-scale precision agriculture applications.
Accident Prediction in Coal Mine Drilling Using Fuzzy Inference Based on Multi-Scale Feature Extraction
Authors: Zhang Youzhen , Yao Ke , Li Wangnian
Published: September 2024

To ensure the safety of coal mine drilling operation and reduce losses caused by accidents, this study proposes a fuzzy-reasoning-based early prediction method for in-hole accidents during underground coal mine drilling processes. First, the mechanism of in-hole accidents during underground drilling in coal mines was analyzed, and the changes in different accident-related parameters were summarized. Second, based on the suddenness of different accidents, they were distinguished into sudden-change and slow-change accidents, and the corresponding features were extracted from short- and long-timescale information, respectively. Subsequently, a rule base was constructed based on the analysis of field data and manual experience, and different accident occurrence probabilities were determined using fuzzy reasoning to realize the early prediction of in-hole accidents in the coal mine drilling process. Finally, WinCC was used to design the upper computer interface for in-hole accident prediction in underground drilling processes in coal mines. It displays the results of three types of in-hole accident prediction: drill bit failure, stuck pipe, and bit drop.
REAL-TIME PARAMETRIC DIAGNOSTICS OF MARINE DIESEL ENGINES
Authors: Varbanets R. , Minchev D. , Kucherenko Y. , Zalozh V.
Published: September 2024

Using modern high-performance microcontrollers with wireless interfaces, built-in ADC and low overall consumption, it is possible to develop a portable real-time parametric diagnostic system for marine engines. The system is based on the use of modern Android/iOS gadgets that receive information from sensors via Bluetooth and then make the necessary calculations and display charts and data in real time. The system being developed uses a combination of a gas pressure sensor in the working cylinder and a vibroacoustic sensor, which expands the diagnostic capabilities of marine diesel engines under operating conditions. This solution makes it possible to diagnose the fuel injection system, the valves control mechanism, as well as some other engine systems. In order to develop a portable diagnostic system for marine diesel engines, it is first necessary to solve the problem of analytically determining TDC, since such a system does not involve the use of special sensors for this. Parameters of irregular engine operation are considered, which can be calculated in real time using time diagrams of pressure and vibration. Methods for expressly assessing the stability of the functioning of the main engine systems by monitoring and analyzing a number of successive operating cycles are considered. To assess the unevenness of engine operation, a dispersion estimate of deviations of the main parameters is used. For a comprehensive assessment of engine stability in real time, the CII (cycle’s irregularity index) criterion is proposed. The assessment of fuel injection irregularity in the engine cylinder can be carried out by analyzing the phase fronts of the vibration signal of the injector. Coefficients for assessing the irregularity of fuel injection and gas distribution valves timing are also proposed, using dispersion analysis of vibrograms fronts. The data processing methods proposed in the article will make it possible to analyze the stability of operating cycles, as well as to perform optimal tuning of engine systems and monitor the results during operation.
Intervening Role of Brand Image in Purchasing Decisions: Path Analysis
Authors: Utami Neni , Sujianto Agus Eko , Asiyah Binti Nur , Jamaludin Husna Binti
Published: September 2024

This study aims to analyze the influence of digital marketing and e-wom on purchasing decisions for halal cosmetic products on gen z in Blitar city, as well as the intervening influence of brand image between digital marketing and e-wom on purchasing decisions for halal cosmetic products on gen z. The novelty of this research lies in the research location, namely in Blitar city. Data was collected by distributing questionnaires measured on a 5-point Likert scale to 250 Gen Z in Blitar City who purchased halal cosmetic products at least twice in the last 6 months. The collected research data were subjected to research instrument tests, classical assumption tests, path tests, and hypothesis tests. The results showed that digital marketing and brand image have an effect on purchasing decisions, e-wom has no impact on buying decisions, and brand image can intervene in the influence between digital marketing and e-wom on purchasing decisions. The results of this study contribute as evaluation material for companies to pay more attention to e-wom by improving sales services, collaborating with content creators in reviewing products, building online communities, and holding events that can provoke consumers to share their best experiences in using products to increase the number and quality of reviews related to halal cosmetics both on social media and shopping platforms so that potential consumers feel they are taking the right steps by making a purchase.

 
ANIMATING VALUES: EXPLORING METHODS OF INSTILLING RELIGIOUS AND MORAL PRINCIPLES IN EARLY CHILDHOOD THROUGH ANIMATED SERIES
Authors: Partono Partono , Ahmadi Ahmadi
Published: September 2024

Religious and moral values are considered one of the most important aspects of a child's development. Gaining information about the methods or practices used to instill these values can be beneficial for early childhood education. This research aims to describe the methods of instilling religious and moral values in early childhood through animated film series. Using a qualitative research approach with a postpositivist paradigm, this study employs a case study methodology as its primary approach. Data collection was conducted through three methods: interviews, observations, and documentation. The data analysis approach includes data reduction, data presentation, and conclusion drawing. The results of the study indicate that there are four methods for instilling religious and moral values in early childhood through animated film series. These methods include habituation, exemplification, reinforcement, and the combination of theory and practice.
THE EFFECT OF THE PROJECT-BASED LEARNING MODEL ON THE ABILITY OF RATIONAL COUNTING IN GROUP B AT TK ISLAM AZ-ZAHRAH PALEMBANG
Authors: Khana Ameliya Nada , Andika Windi Dwi
Published: September 2024

The basic ability of counting in children is important for the development of mathematics at a later stage so the selection of learning models can affect this ability. This study aims to see the effect of rational counting ability in group B of Az-Zahrah Islamic Kindergarten through a project-based learning model. The type of research used is pre-experiment with a shot case study design. The sampling technique used purposive sampling, namely class B4 with a total of 16 children using posttest and observation which will then be analyzed using a one-sample T-test. The results obtained are that the project-based learning model positively influences the ability of rational counting in group B of Az- Zahrah Palembang Islamic Kindergarten. This can be seen in the child’s score in each aspect, such as rote counting, one-to-one correspondence, counting, and cardinal, which has developed as expected. The results of this research can be an alternative for schools or educators in developing counting skills for group B through project learning.
CHALLENGES IN IMPLEMENTING INCLUSIVE EDUCATION IN AN INDONESIAN KINDERGARTEN; A CASE STUDY
Authors: Pasiningsih Pasiningsih
Published: September 2024

This study is a case study exploring the challenges of implementing inclusive education at Star Kindergarten, a private kindergarten in Yogyakarta, Indonesia. Data collection was conducted through observations and interviews with two teachers who have students with disabilities in their classes. The data were then analyzed using the three-step analysis method recommended by Miles and Huberman. The findings indicate that both students and teachers have positive attitudes towards inclusive education. However, three challenges may hinder the implementation of inclusive education at Star Kindergarten, despite the school serving children from upper-middle-class socioeconomic backgrounds. These challenges are the teachers’ lack of knowledge due to not coming from early childhood education programs, insufficient training provided by the school, and minimal parental support. Professional training for teachers and parent-school collaboration is necessary to support the implementation of inclusive education in kindergarten.
STRATEGIES FOR IMPLEMENTHING THE HIDDEN CURRICULUM IN THE DEVELOPMENT OF RELIGIOUS AND MORAL VALUES IN EARLY CHILDREN
Authors: Latifah Ibdaul
Published: September 2024

The aim of this research is to identify the existence of a hidden curriculum in early childhood learning in developing aspects of religious and moral values as well as teacher strategies in implementing the hidden curriculum at PAUD Siti Rahma Pabelan. Data collection procedures use interviews, observation and documentation. Interviews were conducted with the school principal and teacher council at PAUD Siti Rahma Pabelan using structured interview techniques. Observation sheets are used to discover hidden curriculum in learning. Documentation is used to identify between the core curriculum and the hidden curriculum. Data analysis was carried out by collecting data from interviews, observation sheets and documentation, then reducing it, presenting it and drawing conclusions. The results of this research explain that there are several hidden curriculum in the learning process which are divided into programmed activities, non-programmed activities and incidental activities. The hidden curriculum is implemented in various ways, including storytelling, playing, habituation, and reward and punishment. The novelty of this research is describes activities regarding the hidden curriculum in early childhood institutions and strategies for implementing it so that teachers better understand and comprehend the importance of the hidden curriculum for the development of children’s religious and moral values.
DEVELOPMENT OF REGULATIONS ON FACTUAL ACTIONS POST-LAW ON GOVERNMENT ADMINISTRATION: 2024 ELECTION PROCESS DISPUTE STUDY IN CASU PRIMA POLITICAL PARTY
Authors: Said Ahmad , Herawati Ratna , - Sulistiani
Published: September 2024

Factual actions in state administrative law have experienced developments after the issue of government administration law. This factual action is active in nature and is always preceded by a written decision from state administrative officials (TUN). However, in practice, factual actions involving written decisions are still subject to district courts accepting claims for unlawful acts (PMH) such as official decisions of state administrative officials (TUN) which are naturally the public law. This can be seen in the Jakarta District Court Decision Number: 757/Pdt.G/2022/PN which has punished the General Election Commission of the Republic of Indonesia (KPU RI) to postpone the General Election (Pemilu) and pay compensation. The aim of the research is to analyze the problems of the authority of written factual actions and to reformulate fair laws in the PTUN judicial system as well as the amount of awarded compensation procedures. Meanwhile, this research uses normative legal methods with statutory, conceptual and case approaches. The data collection method is by means of secondary data. The results of this research show that in the development of factual actions in state administrative law there has been an expansion of meaning to include written decisions, so that if there is a dispute over the election process between participants and the organizers of the General Election (Pemilu) then dispute resolution becomes the realm of absolute competence of the State Administrative Court (PTUN) after taking legal adjudication efforts at the Election Supervisory Body (Bawaslu). For this reason, in the future, legal reformulation efforts are needed regarding the expansion of the meaning of written decisions to also include factual actions contained in Law Number 51 of 2009 concerning PTUN, and changes to Government Regulation Number 43 of 1991 in the nominal amount of compensation awarded in PTUN according to real conditions regarding factual actions that give rise to PMH by the government.
Postoperative Dysesthesia Secondary to Thermal Injury Following Biportal Endoscopy for Lumbar Canal Stenosis: A Report of 3 Cases and Technical Details of Radiofrequency
Authors: Lokanath Yadhu K. , Jabri Hussam Essam , Chen Kuo-Tai , Kim Jin-Sung
Published: September 2024

Postoperative dysesthesia following lumbar spine surgery is a highly bothersome symptom in the follow-up period. Although many factors contribute to this condition, the most important one is the use of radiofrequency in endoscopic spine surgery. Nonetheless, there is widespread acceptance regarding the use of radiofrequency in endoscopic spine surgery, and the literature contains few reports related to thermal damage to nerves. We describe 3 cases of nondermatomal dysesthesia syndrome in patients who had undergone lumbar unilateral biportal endoscopic surgery for lumbar spinal stenosis. All diagnostic modalities were equivocal, and no pathological lesion was identified. Therefore, we interpreted the dysesthesia as secondary to thermal damage to neural structures, resulting from the radiofrequency.
Doleschallia bisaltide (Lepidoptera: Nymphalidae) profile and oviposition preference on Graptophyllum pictum (L.) Griff.
Authors: Lestari Peni , Mardiningsih Tri Lestari , Sartiami Dewi , Rohaeni Wage Ratna , Rohimatun Rohimatun , Obura Bruce Ochieng , Manzila Ifa
Published: September 2024

Doleschallia bisaltide Cramer (Lepidoptera: Nymphalidae) is a main pest on Graptophyllum pictum (L.) Griff. in the Lepidopteran order, the first-instar larval performance is determined by adult oviposition. An adult oviposition preference can be used to determine the resistance level of some G. pictum accessions in early-stage breeding programs. The study aimed to identify the profile of D. bisaltide and its oviposition preferences on G. pictum accessions. The herbivore-host interaction was utilized as a guide to select cultivars resistant to D. bisaltide and identify repellent characteristics for the insect. The research was conducted at the Indonesian Spice and Medicinal Crops Research Institute (ISMCRI), using nested Randomized Complete Block Design (RCBD). G. pictum accessions nested on flight periods. The study revealed that insect mating occurred from dusk to night, while oviposition occurred the following morning. The adult mating was dominantly in the humid area, suggesting a potential population outbreak during the rainy season. The highest oviposition preference was observed for accessions with higher anthocyanin-to-chlorophyll content. In contrast, the lowest oviposition was recorded for accession 12 content of higher chlorophyll-to-anthocyanins. D. bisaltide oviposition preferences were influenced by leaf pigment, saponins, and the C/N ratio. Phytochemicals such as flavonoids and glycosides in G.pictum leaves acted as oviposition stimulants for specialists such as D. bisaltide.
Spatial and temporal distribution characteristics and evolution of traditional villages: the Qi River Basin, China
Authors: Zhang YunXing , Han NaNa , Zhang Baien , Lu ChenChen , Yang MeiYu , Zhai FeiFei , Li HaiDong
Published: September 2024

The Qi River Basin, renowned as one of the cradles of Chinese civilization and it's also a river that flows in the Psalms, boasts a rich cultural heritage. Traditional villages within this region play a pivotal role in preserving its cultural legacy and are of great importance for sustainable development. This research focuses on investigating the temporal and spatial transformations of traditional villages in the Qi River Basin. By conducting a thorough literature review, consulting local chronicles, and undertaking field surveys, historical information on the ancient Qi River and its significant tributaries was collected and validated using ArcGIS 10.8. Spatial analysis techniques, such as nearest neighbor index, kernel density estimation, standard deviation ellipse, geographic concentration index, and buffer analysis, were applied to analyze the spatiotemporal dynamics of traditional villages. Through a detailed exploration of various factors influencing village evolution, especially the role of rivers in driving changes, several key findings emerged: (1) Traditional villages in the Qi River Basin exhibit a clustered distribution pattern; (2) The shifting centroid of traditional villages over six historical periods demonstrates a directional trend from southwest to northeast, with contemporary villages facing the risk of disappearance; (3) Factors like proximity to water, elevation, slope, and socio-cultural elements are intricately linked to traditional villages, with water proximity emerging as a primary influencer. This study enhances our comprehension of the interplay between traditional village development and river basin, providing valuable theoretical insights for promoting the sustainable development of traditional villages in China.
HKNI: Fusing Neighbor Information for Hyper-Relational Knowledge Graph Completion
Authors: Liu Shuang , liu daqing , Chen Peng , Xu Liangyang , Kolmanič Simon
Published: September 2024

The hyper-relation knowledge graph constructed by hyper-relation facts can effectively express most of the information in the real world. Similar to traditional knowledge graphs, it also has more or less missing information, which makes link prediction on the hyper-relation knowledge graph equally important.However, due to the extension from binary relations to n-ary relations, the hyper-relation knowledge graph has both graph representations based on hypergraphs and sequential representations based on semantics.Most existing models mainly focus on handling only one type of representation within the internal structure of hyper-relations, which limits the comprehensive learning of hyper-relation knowledge graphs.Moreover, there is a large amount of neighbor information in hyper-relation knowledge graphs, but most existing models simply feed the sequential representations into Transformer layers, which greatly reduces the model’s ability to handle neighbors.Therefore, based on the two aforementioned issues, this paper presents a Hyper-Relation Knowledge Graph Completion model that integrates neighbor information(HKNI). HKNI not only effectively handles the two types of representations within the hyper-relation, but also efficiently processes neighbor information.In the end, we compared our model with existing baseline models and experimental results demonstrated that our model achieves the best performance in link prediction tasks.
An anomalous traffic detection method for distributed photovoltaic safety state baseline
Authors: Mu Chen , Nige Li , Yong Li , Bo Zhang , Yongcai Xiao , Tengyan Wang , Ziang Lu
Published: September 2024

With the rapid growth of resource integration in modern power systems, these resources are diverse, large-scale, and situated in complex and open physical environments, making them relatively vulnerable to cyber-attacks due to weaker security measures. To address this challenge, this paper proposes an identity authentication architecture system that integrates software and hardware. In the software fingerprint section, we extract packet characteristics and statistical features through network probing, and combine them with time difference sequence features obtained from side-channel monitoring to generate the software fingerprint of the power smart terminal by direct concatenation. This method incorporates various characteristic information, enhancing the recognition accuracy of the fingerprint features. In the hardware fingerprint section, we generate hardware fingerprints by extracting the preamble signal and performing statistical feature analysis. Finally, using an ensemble learning method, we integrate the software and hardware fingerprints to generate device fingerprint features. This approach effectively addresses the security authentication issue of power equipment based on High-Level Power Line Communication (HPLC), achieving a recognition rate of over 95% under most machine learning classification methods.
Hierarchy-based domain adversarial neural network for bearing fault diagnosis under variable working conditions
Authors: Zheng Yuanlin , Wang Ting , Liu Jie , Zhang Xueqian , Xie Jingsong
Published: September 2024

The bearing fault is one of the primary factors affecting the safe and stable running of mechanical systems. To guarantee the normal and reliable running of the entire equipment, it is crucial to promptly and accurately monitor the operating conditions of bearings. Conventional fault diagnosis methods usually depend upon the assumption that the training and test data are consistently distributed and independent. However, this premise poses challenges to the resolution of fault diagnosis issues for changeable running conditions. To tackle the aforementioned problem, a novel hierarchy-based domain adversarial neural network (H-DANN) is introduced in this paper. For the proposed H-DANN model, it is mainly constructed based on the DANN. The domain discriminator enables the feature extractor to abstract domain-independent features and allows classifier transfer across different operating environments. Furthermore, to extract rich discriminative features, a hierarchy-based feature extractor is proposed based on a novel feature pyramid network (FPN) modified by the CNN-BiLSTM network. Finally, the results of two bearing datasets indicate that the H-DANN model is adept at precisely recognizing bearing fault categories under different running environments, outperforming some state-of-the-art models.
CO2 capture using biochar derived from municipal residual sludge conditioned with chitosan
Authors: Yu Yue , Li Tongqing , Gui Jiacheng , Chen Ming , Zheng Qiushi , Liao Yang , Yang Yueyue , Wu Yan , Liu Chang
Published: September 2024

To achieve the dual objectives of pollution reduction and carbon mitigation, this study examined the effects of biochar derived from urban residual sludge conditioned with chitosan (SBCC) on the CO2 capture capacity. Using raw sludge biochar (RSB) as the control group, and focusing on the preparation temperature of the biochar and the adsorption temperature of CO2 as main parameters, the study explored the CO2 adsorption performance of modified sludge biochar. The mechanism of CO2 adsorption by SBCC was elucidated through the analysis of the surface morphology, elemental composition, functional groups, and surface area of the sludge biochar. Results indicate that the optimal preparation and adsorption temperatures for RSB are 800°C and 30°C, respectively, with a maximum CO2 adsorption capacity of 28.36 mg/g. For SBCC, the optimal temperatures are 600°C and 30°C, respectively, achieving a maximum CO2 adsorption of 89.88 mg/g. Compared to RSB, SBCC primarily exhibits a chemisorption process, with its adsorption mechanism involving strong dipole-quadrupole interactions between nitrogen atoms and CO2. In the sludge, chitosan undergoes partial hydrolysis through alkalinization, forming carboxyl groups. These carboxylic functional groups facilitate the formation of hydrogen bonds between CO2 and the carbon surface, as well as condensation reactions between alkaline functional groups and CO2. Moreover, SBCC also demonstrates good reusability. After five cycles of adsorption and desorption, it still retains up to 75% of its initial CO2 adsorption capacity.
Serum levels of auto-antibodies (rheumatoid factor and anti-double stranded DNA) in Nigerian COVID-19 patients as predictions of developing future autoimmune disorders
Authors: Oladokun Haleemat Yejide , Rahamon Sheu Kadiri , Surajudeen Yaqub Adebayo , Onifade AbdulFattah Adekunle , Arinola Ganiyu Olatunbosun
Published: September 2024

Background
A deeper knowledge of the roles played by severe, acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the development of autoantibodies is vital to understand the relationship between COVID-19, autoimmune disorders and proper management of the two diseases. Therefore, serum levels of rheumatoid factor (RF) and anti-double stranded DNA antibody (dsDNA ab) which are important factors of autoimmune disorders were determined in COVID-19 patients and apparently healthy control.
Methods
Serum levels of rheumatoid factor were determined using turbidimetric immunoassay. Serum anti-dsDNA antibody levels were determined using enzyme linked immunosorbent assay (ELISA) in both COVID-19 patients and healthy controls.
Results
Serum levels of RF and anti-dsDNA ab were similar in COVID-19 patients compared with controls. A significant positive correlation was observed between RF and anti-dsDNA ab in healthy controls. The correlation between RF and anti-dsDNA ab in COVID-19 patients was not significant.
Conclusion
SARS-CoV-2 infection did not have a profound effect on serum RF and anti-dsDNA ab levels in these infected individuals. Therefore, autoimmune disorders are not implicated in these COVID-19 patients and that the use of therapeutic antagonists to auto-antibodies may not be useful in the management of COVID patients, since they may not develop COVID-19 based autoimmune disorder.
The paradox of climate policy diffusion
Authors: Savin Ivan , Mundt Philipp , Bellanca Margherita
Published: September 2024

Prior research produced contradicting evidence regarding the role of international influence in the diffusion of climate policies. To unravel this puzzle, we examine various policy instruments adopted by G20 countries, demonstrating that peer pressure stimulates convergence in the number of new policies adopted but divergence in their stringency. This suggests that policymakers emulate the appearance of their peers but not the rigor of regulation, creating opportunities for carbon leakage.
JEL Codes: C21, F18, F42, F64, Q56
Adaptive Real-Time Big Data Processing Framework: A Machine Learning and Reinforcement Learning Approach Using Random Forest and Q-Learning for Dynamic Resource Management
Authors: Hooda Akash , Hooda Arju , Yadav Disha
Published: September 2024

Real-time big data analytics is crucial for industries that handle high-velocity data streams, such as IoT, finance, e-commerce, and social media. Traditional big data processing frameworks often struggle to adapt to the dynamic nature of real-time data, leading to inefficiencies, increased latency, and higher operational costs. This research proposes an Adaptive Real-Time Big Data Processing Framework that integrates a Random Forest model for load prediction with Q-learning for dynamic resource allocation. The framework adjusts in real-time based on predictive insights and learned policies, optimizing system performance while minimizing costs.
Experimental results demonstrate that the framework reduces latency by 15%, improves throughput by 20%, compared to traditional methods. The Random Forest model achieved an efficiency of 93.62%, while the Q-learning approach further achieved 94.86%, reflecting superior resource utilization. These improvements indicate the framework's effectiveness in dynamically adapting to real-time data, ensuring high performance and cost-efficiency.
Machine Learning Approaches for Classifying and Characterizing Coral Diseases
Authors: Buren Emily W. Van , Beavers Kelsey M. , Cornelio Mariah N. , Stokes Alexia , Emery Madison , Demuth Jeffery P. , Wang Li , Mydlarz Laura D
Published: September 2024

Background: Anthropogenic climate change has had devastating effects on the Florida and Caribbean reef systems, primarily due to increased disease outbreaks. Climate change contributes to the rising frequency of marine diseases by expanding pathogen ranges and heightening host susceptibility due to environmental stress. Specifically, there has been a stark rise in marine disease events targeting multiple coral species, resulting in high mortality rates and declining reef biodiversity. Although many of these diseases present similar visual symptoms, they exhibit varying mortality rates and require distinct treatment protocols. Advances in coral transcriptomics research have enhanced our understanding of coral responses to different diseases, but more sophisticated methods are required to classify diseases that appear visually similar.
Results: This study provides the first machine learning algorithm that can classify two common coral diseases: stony coral tissue loss disease (SCTLD) and white plague (WP). This algorithm also identifies 463 biomarkers, with 275 unique to SCTLD and 167 unique to WP. These biomarkers highlight the differences in immune responses to the different disease outbreaks. These algorithms were tested and validated with samples collected in situ, supporting the biomarker efficacy of the identified biomarkers for disease classification. The final model was built with partial least squares discriminant analysis and the 463 biomarkers, resulting in a highly predictive model with an AUC of 0.9895 and low error rates.
Conclusion: This study provides a diagnostic tool that reliably distinguishes between phenotypically similar tissue loss diseases and identifies biomarkers that provide characterizations of SCTLD and WP.
Gender equality in academic medicine before, during and after COVID: what have we learned? A systematic review.
Authors: Burke Elaine , Darker Catherine , Godson-Treacy Isabelle Molly , Kavanagh Columb , Mockler David , Slattery Natasha , Hennessy Martina
Published: September 2024

BackgroundGender disparity at senior levels in academic medicine has been recognised for decades, but progress has been slow and confounded further by the COVID pandemic. While there are many papers describing this problem, there is little evidence for potential solutions. We aimed to describe the current evidence for interventions to enhance gender equality in academic medicine, and to compare interventions pre, during and post-COVID-19. We also wished to characterise the nature of the interventions, who delivered them, and whether they seek to “fix the women”, or target issues at organisational and systemic levels.MethodsWe searched five electronic databases in November 2022 and August 2023 and undertook hand-searching. We extracted data using a form developed for the study, and applied the TIDieR and Morahan frameworks to describe and characterise interventions. We used the QUADs tool to critically appraise included studies.ResultsThe search of electronic databases yielded 1,747 studies. A further 62 were identified through hand-searching. Following removal of duplicates, 764 articles were screened for eligibility, and 199 full-text articles were screened. Of these, 27 met the inclusion criteria. The most commonly reported interventions were career development or leadership skills programmes, followed by mentorship and multi-faceted interventions. Most papers reported positive findings, but many relied on subjective measures. Robustly designed studies often reported mixed findings. The majority of interventions aimed to “fix the women”, with few addressing inequality at organisational level. We found no studies describing interventions aimed specifically at mitigating the effects of the COVID pandemic, and none describing the effects of the pandemic on their interventions.ConclusionAcknowledging the possibility of publication delay, we found that despite strong evidence of the negative effects of the pandemic on women’s research productivity, there were no new interventions designed to mitigate this. Many existing interventions create “institutional housekeeping” by relying on women for their delivery, this can result in failure, especially during a crisis like COVID. Most studies were low to moderate quality. More robust research, and a more holistic approach is needed, moving away from “fixing the women” to address the organisational and systemic structures which underpin inequality.