bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stylistic Study of the Late Mesolithic Industries in Western France: Combined Principal Coordinate Analysis and Use-Wear Analysis
Authors: Hauguel-Bleuven Lola , Calvo-Gómez Jorge , Marchand Gregor 
Published: July 2023

Abstract
The notion of style has been the subject of much research and theoretical development in prehistoric archaeology. This vast concept touches several fields, including the morphology of artefacts, technical gestures and their function. The lithic typology of arrowhead armatures was widely developed in the twentieth century for the study of the French Mesolithic and includes some of these aspects. The functional, technical, or aesthetic nature of the morphological characteristics of these artefacts has received little attention, while the mapping of types has been overvalued This article presents a new approach combining statistical analysis with the use-wear analysis of arrowhead from several Breton sites, dated to the Late Mesolithic.
ACCLIMATIZATING TECHNIQUES OF TOMATO ANEMONEFISH (AMPHIPRION FRENATUS BREVOORT, 1856) BROOD STOCKS
Authors: Loc Ha Le Thi , Van Phan Thi Hong 
Published: July 2023

Tomato Anemonefish (Amphiprion frenatus) brood stocks from the wild were able to adapt and breed in capture condition. The results from experiments showed that temperature, salinity and presence of Magnificent Sea Anemone (Heteractis magnifica) closely related to ability of spawning, re-spawning of the brood stocks as well as egg adhesion to collectors. In stable condition of 26 0 C with the presence of the Magnificent Sea Anemone, at salinity of 33 - 35‰, the brood stocks started spawning after 51 days of acclimatization. The brood stocks spawned twice monthly in average and re-spawned after 3 to 5 days. Embryonic development on the collectors lasted 5 to 9 days. At salinity of 27 to 29‰, the re-spawning time lasted 5 to 9 days, the embryos were completely de-adhesive from the collectors after 9 days of spawning. At salinity of 40 to 42‰, the brood stocks stopped spawning.
Virtual Reality Simulation of Earthquake Response of Buildings Using AI
Authors: Negi Ankit 
Published: July 2023

Virtual Reality Simulation of Earthquake Response of Buildings using AI is an innovative approach to designing and evaluating the structural performance of buildings during earthquakes. The simulation is based on AI technology, which provides a realistic and immersive experience of earthquake scenarios in a virtual environment. The goal is to understand the behaviour of buildings during an earthquake and identify potential weaknesses in the structure to improve the building's resilience. The VR simulation is designed to identify the physical behaviour of buildings during earthquakes, including their response to ground shaking, vibration, and deformation. The AI algorithms enable the simulation to react in real-time to the user's input and the dynamic response of the building. The simulation generates a visual and audio output that accurately represents the scenario, providing an immersive experience to the user.
The simulation can be used to assess the performance of existing buildings and provide feedback on their resilience to earthquakes. It can also be used in the design and construction of new buildings to identify and mitigate potential risks. The AI algorithms can analyse the data generated by the simulation to provide insights into the building's structural behaviour and suggest improvements. Virtual Reality Simulation of Earthquake Response of Buildings Using AI is a cutting-edge technology that has the potential to revolutionise the way we design and evaluate buildings for earthquake resilience. It provides a safe and cost-effective way to study the behaviour of buildings during earthquakes, which can save lives and prevent property damage in earthquake-prone areas.
Analysis the Structural Propagations under Stochastic Variables with Arbitrary Probability Distributions Using Machine Learning
Authors: Pandey Rohit 
Published: July 2023

Analysing the structural propagations under stochastic variables with arbitrary probability distributions is a complex task due to the inherent uncertainties associated with such variables. Traditional analytical methods often rely on simplifying assumptions, limiting their applicability to scenarios with specific probability distributions. In this study, we propose a novel approach that leverages machine learning techniques to analyze and predict the structural propagations under stochastic variables with arbitrary probability distributions. The main objective of this research is to develop a machine learning-based framework capable of capturing the complex relationships between the stochastic variables and the resulting structural responses. To achieve this, a comprehensive dataset comprising input stochastic variables and corresponding structural responses is collected and pre-processed. Various machine learning algorithms, such as neural networks, random forests, and support vector machines, are trained on the dataset to learn the underlying patterns and correlations.
The trained models are then used to predict the structural propagations for new sets of stochastic variables, allowing for efficient and accurate analysis without the need for exhaustive analytical calculations. The use of machine learning enables the consideration of a wide range of probability distributions, ensuring a more realistic and comprehensive understanding of the structural behaviour under uncertainties. The proposed methodology offers several advantages over traditional analytical approaches. Firstly, it eliminates the need for restrictive assumptions about the probability distributions, enabling a more flexible analysis that can accommodate real-world scenarios. Additionally, the machine learning-based framework allows for efficient analysis by leveraging the computational power of modern algorithms, reducing the time and effort required for complex calculations.
The practical implications of this research are significant in fields such as civil engineering, aerospace, and materials science, where understanding and predicting structural behaviours under uncertainties are crucial. The ability to analyze structural propagations under stochastic variables with arbitrary probability distributions using machine learning opens up new possibilities for design optimization, risk assessment, and decision-making processes.
This study presents a novel approach that utilizes machine learning to analyze and predict the structural propagations under stochastic variables with arbitrary probability distributions. The developed framework offers a flexible and efficient solution for understanding the complex relationships between stochastic variables and structural responses. The findings of this research have significant implications for various industries and pave the way for further advancements in analyzing and managing uncertainties in structural engineering.
Study on the effects of Cognitive Behavioral Therapy on depression-related insomnia and its influence on suicide risk
Authors: Fagundes Dennys Lapenda , Sougey Everton Botelho , Silva Tatiana Santana , Andrade Leia Teixeira 
Published: July 2023

Abstract
Basis of the study
There is a growing interest in understanding the relationship between sleep and suicide. Although insomnia is commonly cited as a critical risk factor for suicidal thoughts and behavior, evidence on the treatment and management of insomnia and its effects on reducing risk and/or suicidal thinking remains unclear. In this sense, this is the first trial to test the effectiveness of CBTI in reducing suicidal behavior over a long period (8 weeks), associated with improvement in depressive symptoms.
Methods
Double-blind, randomized, controlled clinical trial, conducted over 8 weeks, to be constructed in accordance with the SPIRIT, and registered on the national clinical trials platform, with a duly calculated sample. Individuals aged 18 to 60 years will be included, who are diagnosed with depression according to DSM-V criteria and with symptoms of insomnia, and who report suicidal ideation with intent to die in the last week and/or suicide attempt in the last month. Randomized into 2 intervention groups, namely: Group A: therapeutic protocol including only Amitriptyline medication at a dosage of 25 mg for insomnia; Group B: therapeutic protocol including Amitriptyline medication at a dosage of 25 mg for insomnia and intervention with CBTI, with a protocol lasting 8 sessions. Primary outcomes will include assessment of insomnia severity (Insomnia Severity Index - ISI) and suicide risk (Columbia Suicide Risk Assessment Scale - C-SSRS); the secondary outcome will include severity of depressive symptoms (Montgomery-Asberg Depression Scale). Primary and secondary outcome measures will be collected at weeks 0, 2, 4, 6 and 8 of the intervention. And the follow up will be carried out for up to three months after the intervention with biweekly periodicity.
Discussion
To the best of our knowledge, this will be the first clinical trial to test the effectiveness of CBTI in reducing the risk of suicide in patients with depression and insomnia. The findings will contribute to the understanding of the therapeutic effects in the management of depression and insomnia, as well as risk behaviors, and may help guide the development of new care protocols aimed at clinical and pharmacological support in reducing the risk of suicide.
Trial registration:
Check-copy: http://www.ensaiosclinicos.gov.br/org Number: RBR-10b889rz/ Number UTN: U1111-1287-9616 (February 13, 2022).
Father-to-daughter transmission in late-onset OTC deficiency: an underestimated mechanism of inheritance of an X-linked disease
Authors: Siri Barbara , Olivieri Giorgia , Lepri Francesca Romana , Pom Martin , Goffredo Bianca Maria , Commone Anna , Novelli Antonio , Haberle Johannes , Dionisi-Vici Carlo 
Published: July 2023

Abstract
Background
Ornithine Transcarbamylase Deficiency (OTCD) is an X-linked urea cycle disorder characterized by acute hyperammonemic episodes. Hemizygous males are usually affected by a severe/fatal neonatal-onset form or, less frequently, by a late-onset form with milder disease course, depending on the residual enzymatic activity. Hyperammonemia can occur any time during life and patients could remain non- or mis-diagnosed due to unspecific symptoms. In heterozygous females, clinical presentation varies based on the extent of X chromosome inactivation. Maternal transmission in X-linked disease is the rule, but in late-onset OTCD, due to the milder phenotype of affected males, paternal transmission to the females is possible. So far, father-to-daughter transmission of OTCD has been reported only in 4 Japanese families.
Results
We identified in 2 Caucasian families, paternal transmission of late-onset OTCD with severe/fatal outcome in affected males and 1 heterozygous female. Furthermore, a careful pedigree analysis of other published reports allowed to identify 7 additional families with father-to-daughter inheritance of OTCD.
Conclusions
Our study highlights how the diagnosis and pedigree analysis of late-onset OTCD may represent a real challenge for clinicians. Therefore, the occurrence of paternal transmission in OTCD should not be underestimated, due to the relevant implications for disease inheritance and risk of recurrence.
Nucleophilic substitution of hydrogen and synthesis of nickel-, copper-, and cobalt-porphyrinates – double functionalized at the β-positions
Authors: Mikus Agnieszka , Ostrowski Stanisław 
Published: July 2023

Abstract
2-Nitro-5,10,15,20-tetraphenylporphyrin–nickel(II), –copper(II), and –cobalt(II) complexes reacted with carbanions of halomethyl aryl sulphones and para-chlorophenoxyacetonitrile according to the vicarious nucleophilic substitution scheme (VNS). This leads to the substitution of hydrogen products, in the position ortho- to the NO2 group, thus giving β,β-disubstituted derivatives on the same pyrrole ring. The yields were satisfactory, up to 95%. These types of products, being practically unavailable by alternative methods, are attractive starting materials for synthesis of more complex porphyrinoid moieties.
Systematic Approach to Determining True Long Memory on PM10 Data
Authors: Isma'il Lawan Adamu , Awang Norhashidah , Kane Ibrahim Lawal 
Published: July 2023

Abstract
Particulate matter pollutants are common atmospheric air pollutants in Malaysia which have numerous harmful effects on the environment, the flora and fauna, as well as human health. Long memory process may spuriously be detected due to presence of break in the time series data since a short memory process with occasional structural break can show a slower rate of decay in the autocorrelation function and other properties of fractionally integrated \(I\left(d\right)\) process. Numerous research around the globe confirmed evidence of long memory on particulate matter pollutant, but few or none in Malaysia have tested for it and investigated whether the persistence (if evident) is truly detected or merely spurious due to presence of neglected structural breaks. In this paper, we examined the statistical properties of daily PM10 emission in fourteen Malaysian air quality stations over the period 1 January 2011 through 31 December 2020 by applying a fractional integration framework on both the original and partitioned series that encountered structural break as confirmed by the OLS-based CUSUM test. Both the original and sub-series (before and after the break regime) are found to be characterized by long memory with orders of integration within the range \(\left(0, 1\right)\) implying the evidence of mean reversion form of long memory. Thus, this evidence confirms the presence of true long memory not due to structural break. We also obtained higher values for Kurtosis statistic implying that the emission fluctuates significantly.
Spatio–temporal trends of air quality, Kampala City, Uganda, 2020–2022
Authors: Ninsiima Mackline , Ndyabakira Alex , Zalwango Sarah , Migisha Richard , Kadobera Daniel , Biribawa Claire , Bulage Lilian , Ario Alex Riolexus , Harris Julie R. , Ayen Daniel Okello 
Published: July 2023

Abstract
Fine particulate matter (PM2.5) is among the health damaging air pollutants; levels >15 µg/m3 are associated with adverse health effects. We assessed spatio–temporal trends of air quality based on PM2.5 concentration in Kampala City during January 2020–June 2022. We abstracted PM2.5 concentrations generated by twenty–four Clarity© Node Solar–Powered monitors from January 1, 2020, to June 30, 2022, from the Clarity© dashboard. We computed 24–hour average PM2.5 concentrations by combining data from all monitors. Average PM2.5 concentrations per hour were compared by hour of the day. Seasonal Mann–Kendall statistical test was applied to assess significance of observed trends. Overall, the 24–hour average PM2.5 concentration from January 1, 2020, to June 30, 2022, was 59 µg/m3 (range: 18–182 µg/m3). A statistically significant decline in PM2.5 occurred throughout the assessment period from January 2020 to June 2022 (r = –0.27, p < 0.001). PM2.5 increased from April to June each year [2020 (55 µg/m3, r=0.56, p=0.006), 2021 (45 µg/m3, r=0.26, p=0.030), and 2022 (37 µg/m3, r=0.37, p=0.030)] and declined from July to September in 2021 (57 µg/m3, r=–0.43, p=0.008) and January to March in 2022 (60 µg/m3, r=–0.41, p=0.011). PM2.5 concentration peaked from 10am–midday (74–73 µg/m3) and 8pm–9pm (73–77 µg/m3). PM2.5 concentrations exceeded targeted safe levels on all days in Kampala City during 2020–2022.
Indices for measuring system efficiency of American cotton and legume intercropping in additive series of 1:1 ratio at rainfed vertisols
Authors: MANIKANDAN ANGAMUTHU , Blaise Desouza , Nalayini Periyakaruppan , Nagrare Vishlesh Shankar 
Published: July 2023

Abstract
Background and Aims
Cotton (Gossypium hirsutum L.) is grown at wide-row spacing and offers an opportunity to intercrop. We hypothesized that including a legume may improve the soil fertility and the system productivity. However, less is known about intercropping legume on rainfed vertisols.
Methods
During initial two years (2015–2017) field experiments were conducted for identifying efficient nitrogen (N) fixing legume [Blackgram (Bg), green gram (Gg), cowpea (Cp), soybean (Sb), cluster bean (Cb), pigeon pea (Pp), dolichos (Dc) and groundnut (Gn)] with high-density planting system (HDPS). Based on these results, a further study for three years (2018–2020) was conducted with the short, medium and long duration of shortlisted legumes to determine economic and operational feasibility.
Results
Legume intercrops reduced the seed cotton yield. However, cotton equivalent yield results showed that productivity of the cotton + cotton (Cc) and sole cotton (Cs), is lower (63, 30%) than legume intercropped cotton (CLi) excluding pigeon pea (Pp). The positive and higher values of land equivalent ratio (1.40–1.78) indicated the benefits of intercropping systems than of Pp (0.74 − 0.69) or Cs (1.00). Legumes also improved the area time equivalent ratio of 14–28% and 97% of land use efficiency than Cs. No competition and aggressivity was exhibited between legume intercrops and cotton.
Conclusion
These indices suggest that pigeon pea, dolichos and cotton are not suitable for intercropping in additive series with HDPS at the existing row spacing (1:1). Further, the indices with Bt-cultivars ensure the facilitative robustness of legume intercropping technology for sustainable production under rainfed areas.
Annual reproductive cycle and meat yield of the Wedge Clam Donax trunculus Linnaeus, 1758 from Black Sea
Authors: Yıldız Harun , Acarlı Sefa , Vural Pervin , Gündüz Fettah 
Published: July 2023

Abstract

The study aims to determine the reproductive cycle, the condition index, meat yield, shell component index, shell thickness index and gonad index of bivalve Donax trunculus inhabiting the Kefken-Babalı location on the Black Sea in relation to environmental parameters between November 2013 and October 2014. Its gonadal development began in November. D. trunculus was in the inactive stage during autumn. Spawning was observed from May (18°C) to August (28°C). Sex ratios were not equal and number of males was higher during the study period (1.13:1) with one hermaphrodite individual only. Being synchronous between sexes based on histological analysis, the reproductive cycle consists of six stages. The study showed that the most ideal period to harvest of D. trunculus was between December and May, especially when the meat yield and condition index were determined to be highest.
Native tree species prosper while exotics falter during gap-phase regeneration, but only where deer densities are near historical levels
Authors: Yacucci Anthony C. , Carson Walter P. , Martineau Jenna C. , Burns Curtis D. , Riley Brian P. , Royo Alejandro A. , Diggins Thomas P. , Renne Ian J. 
Published: July 2023

Abstract
Throughout eastern North America, chronic white-tailed deer overbrowsing and widespread exotic plant invasions degrade native plant species diversity and abundance. However, because deer densities are high across entire regions, it is challenging to find large areas where deer have been at low density for long periods. We tested the hypothesis that native plant diversity and abundance would be significantly higher during gap-phase regeneration where deer have been maintained at low densities (~ 7 deer/km²) compared to where deer have been chronically overabundant. Simultaneously, we tested the hypothesis that exotic plant diversity and abundance would be lower in gaps where deer have been maintained at low versus high densities. We also evaluated whether deer density interacts with gap age, gap size, and gaps in dry versus more mesic forest patches. To test these hypotheses, we studied 41 gaps throughout an 88 km2 fenced military post where deer were maintained near historical densities for 67 years compared to nearby forests where deer have been overabundant for decades. Deer were the primary filter. Within canopy gaps at low deer density, native species richness, diversity, and cover were 30–65% higher, and exotic species cover was 80% lower. Sapling height and density of multiple native canopy tree species (e.g., two Quercus spp.) were 5-20-fold higher within canopy gaps at low deer density. Moreover, 10 native woody species were indicators of low deer density, including three Quercus species, whereas no native woody species were indicators of high deer density. Our findings demonstrate that gaps develop a vigorous and diverse native sapling layer that is not plagued by exotics at low deer density. We propose that military posts may be conservation and regeneration hotspots whenever they regularly cull deer.
Evaluation and In vitro Study of Lung Cancer Cell therapeutic Target using Chitosan- Gold Nanoparticles Loaded Lapatinib
Authors: Rezaeiaghdam Hadi , Peymani Maryam , Akbarzadeh Abolfazl , Salehzadeh Ali , Rouhi Leila 
Published: July 2023

Abstract
Purpose
Long non-coding ribonucleic acids (lncRNAs) play a crucial role in cancer progression by affecting cell proliferation, apoptosis, and metastasis. Targeting lncRNAs with chemotherapy drugs and overcoming drug resistance pose significant challenges. This study aimed to identify candidate oncogenic lncRNAs in lung cancer through transcriptome analysis and propose a drug targeting these lncRNAs.
Methods
Using data from the Cancer Genome Atlas (TCGA) database, lncRNAs with significantly increased expression in lung adenocarcinoma and lung squamous cell carcinoma were identified (FDR < 0.01, log fold change >). The relationship between lncRNA expression and patient survival was assessed using Cox regression analysis. Furthermore, lncRNAs associated with other common cancers were selected. The GEO profile database was utilized to identify drugs that had a substantial impact on the expression of the identified lncRNAs. Chitosan gold nanoparticles were employed to demonstrate the efficacy of the selected drug, and in vitro experiments were conducted using A549 cells to evaluate cytotoxicity, apoptosis induction, and changes in lncRNA expression levels.
Results
The expression level of LINC01615 was found to be increased in lung cancer and several other cancers and was associated with poor prognosis in lung cancer patients. Lapatinib was identified as an effective drug to control the expression of LINC01615 in multiple cancers. In vitro studies using Lapatinib-chitosan-gold nanoparticles demonstrated reduced cell viability and induction of apoptosis in A549 cells, along with decreased expression of LINC01615.
Conclusion
LINC01615 holds potential as a predictive biomarker for survival in breast, kidney, liver, and lung cancer patients. Lapatinib-chitosan-gold nanoparticles offer an effective approach for controlling LINC01615 expression and cell viability in lung cancer.