bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of Non-Standard Unicode Characters on Security and Comprehension in Large Language Models
Authors: Daniel Johan S , Pal Anand 
Published: December 2024

Abstract

In this report, we present a comparative analysis of the performance of 15 large language models (LLMs). Each model underwent a standardized test comprising 38 queries obfuscated using alphanumeric symbols of non-standard Unicode characters. The responses of each model were analyzed using three key metrics: jailbreaks (prompt injections that cause a large language model to follow instructions contrary to its intended use), hallucinations (generating incorrect or misleading information), and comprehension errors. Our work reveals the inherent vulnerabilities of LLMs when they encounter non-standard character sets. Our findings show that non-standard Unicode characters reduced the efficacy of guardrails implemented through Reinforcement Learning with Human Feedback (RLHF) and challenged the notion of "human-level language comprehension" in these models. On average, 42.44% of character sets trigger jailbreaks, 32.22% trigger hallucinations, and 24.34% of character sets are not understood by the models. Consequently, these models exhibit heightened vulnerability to content policy breaches and prompt leakage using non-standard Unicode characters. Our study highlights the need to incorporate non-standard Unicode text in LLM training data and algorithms to enhance the safety and capabilities of these models.
Global genomic population structure of wild and cultivated oat reveals signatures of chromosome rearrangements
Authors: Bekele Wubishet A. , Avni Raz , L.	Birkett Clayton , Itaya Asuka , Charlene	P.	Wight  , Bellavance Justin , Brodführer Sophie , Francisco	J.	Canales  , Carlson Craig H. , Fiebig Anne , Li Yongle , Michel Steve , Nandety Raja Sekhar , Waring David J. , Juan	D.	Arbelaez  , D.	Beattie Aaron , Caffe Melanie , Blanco Isabel A.	del , Jason	D.	Fiedler  , Gupta Rajeev , Gutierrez Lucia , John	C.	Harris  , A.	Harrison Stephen , Matthias	H.	Herrmann  , Yung-Fen		Huang  , Sanchez Julio Isidro y , Michael	S.	McMullen  , Fetch Jennifer	W.	Mitchell , Kirby	T.	Nilsen  , P.	Parkin Isobel	A. , YuanYing		Peng  , Kevin	P.	Smith  , Sutton Tim , Weikai		Yan  , Zwer Pamela , Diederichsen Axel , Klos Kathy Esvelt , Yong-Bi		Fu  , Howarth Catherine	 J. , Jannink Jean-Luc , Jellen Eric N. , Langdon Tim , Maughan Peter J. , Paczos-Grzeda Edyta , Prats Elena , Taner	Z.	Sen  , Martin		Mascher  , Nicholas	A.	Tinker  
Published: December 2024

Abstract

The genus Avena consists of approximately 30 wild and cultivated oat species. Cultivated oat is an important food crop, yet the broader genetic diversity within the Avena gene pool remains underexplored and underexploited. We characterized over 9,000 wild and cultivated hexaploid oat accessions of global origin using genotyping-by-sequencing. We explored population structure using multidimensional scaling and population-based clustering methods. We also conducted analyses to reveal chromosome regions associated with local adaptation, sometimes resulting from large scale chromosome rearrangements. Here, we report four distinct genetic populations within the wild species A. sterilis, a distinct population of cultivated A. byzantina, and multiple populations within cultivated A. sativa. Some chromosome regions associated with local adaptation are also associated with confirmed structural rearrangements on chromosomes 1A, 1C, 3C, 4C, and 7D. This work provides evidence suggesting multiple polyploid origins, multiple domestications, and/or reproductive barriers amongst Avena populations caused by differential chromosome structure.
Characterization of HIV Peer Support Based on The Dennis Peer Support Model - Accounts from Transwomen in Ghana, BSHG-018
Authors: Shamrock Osman Wumpini , Abu-Ba'are Gamji Rabiu , Hosseini Fatemeh Sadat , Dakpui Henry Delali , Zigah Edem Yaw , Boakye Francis , Guure Chris , Nelson LaRon E. , Wilton Leo 
Published: December 2024

Abstract

Transwomen women in Ghana face significant challenges in accessing HIV prevention and care services due to the immense marginalization and discrimination they encounter from the healthcare system and professionals. Given these challenges, it is imperative to have change agents and advocates in the form of peer educators to support transwomen and ease their difficulties, particularly for those in under-resourced communities, in accessing and utilizing HIV care services. To understand the role of peer educators in facilitating HIV prevention and care services in slum communities in Ghana, we employed purposive and snowball sampling techniques to recruit 20 transwomen for in-depth qualitative interviews in the Greater Accra Metropolitan Area. Based on our utilization of the Dennis Peer Support Model, our results revealed that peer educators play significant roles in the lives of transwomen in the defining attribute of providing support. The emotional support for trans women indicated the mere presence of peers and their willingness to listen, show empathy by accompanying trans women to the HIV testing facilities were motivation enough to get tested. The information support attribute showed peers motivated transwomen to improve HIV health outcomes by providing information on antiretroviral therapies and dispelling misconceptions about HIV. Within the appraisal support attribute, peers provided transwomen with information on HIV which heightened their sense of responsibility in HIV prevention and care. This study highlights the crucial role of peer educators in supporting transwomen accessing HIV prevention and care services in urban slums to enhance positive health outcomes, emphasizing that transwomen can live fulfilling lives while living with HIV.
Establishment of machine translation model based on asymmetric Encoder-Decoder structure and its application in English grammar error detection
Authors: yu zhi 
Published: December 2024

Abstract

Asymmetric codecs have been successful in many deep learning tasks recently. It is a forward-looking research to apply this deep learning neural network to English grammar error detection tasks. The traditional machine translation model has poor semantic analysis ability, which leads to the low accuracy of the results when using the model to detect English grammatical errors. To solve this problem, this paper designs a deep learning neural network model based on asymmetric Encoder-Decoder. Firstly, English sentences need to be pre-processed and converted into a tuple probability table recognizable by the model, which is subsequently fed into the model of the encoder decoder. In the encoder: Pre-embedding is first implemented with the help of temporal convolutional network infrastructure to initially obtain the word mapping relationships in the sentences and then connected to BiLSTM for accurate word embedding to capture the word-to-word relationships in the sentences. In addition, for the context intermediate vector output by the encoder, in order to more accurately and adaptively extract relevant features with global information, the attention mechanism is used to process the features output from bilstm according to their corresponding weights. In the decoder, bigru is used to decode the context intermediate vector output from the encoder. And outputs the translation result after the decoding is completed. After training the machine translation model, word analysis is performed through the process of decoding the model. Finally, the experiment detected English grammatical errors, such as articles, prepositions, nouns, verbs and subject predicate agreement. The experimental results show that the designed model outperforms the comparison method in terms of evaluation metrics such as precision, recall and F1 values for English grammar detection. The necessity of each component of the model is verified, and it is shown that the m odel can effectively improve the accuracy of English grammar error detection. The research in this paper provides an important theoretical guidance for applying deep learning neural networks with asymmetric codecs to English grammar error detection.
A neuromorphic multi-scale approach for heart rate and state detection
Authors: De Luca Chiara , Tincani Mirco , Indiveri Giacomo , Donati Elisa 
Published: December 2024

Abstract

With the advent of novel sensor and machine learning technologies, it is becoming possible to develop wearable systems that perform continuous recording and processing of biosignals for health or body state assessment. For example, modern smartwatches can already track physiological functions, including heart rate and its anomalies, with high precision. However, stringent constraints on size and energy consumption pose significant challenges for always-on operation to detect trends across multiple time scales for extended periods of time. To address these challenges, we propose an alternative solution that exploits the ultra-low power consumption features of mixed-signal neuromorphic technologies. We present a biosignal processing architecture that integrates multimodal sensory inputs and processes them using the principles of neural computation to reliably detect trends in heart rate and physiological states. We validated this architecture on a mixed-signal neuromorphic processor and demonstrated its robust operation despite the inherent variability of the analog circuits present in the system. We show how the system can estimate both instantaneous heart rate and its long-term states discretized into distinct zones, effectively detecting monotonic changes over long time periods for recognizing pathological states such as agitation. This approach paves the way for a new generation of energy-efficient stand-alone wearable devices that are particularly suited for scenarios that require continuous health monitoring with minimal device maintenance.
Working Women: Their perceived stress levels and nonpharmacological yoga intervention for management
Authors: Khindri Nidhi , Yadav Jyotsna 
Published: December 2024

Abstract

Background:
Work-related stress significantly impacts women’s mental health, leading to associated health issues requiring pharmacological interventions. With 37% of working women globally affected by stress, effective and accessible interventions are urgently needed.
Objective:
To evaluate the efficacy of Bhramari Pranayama (BP) in managing stress among working women using the Perceived Stress Scale-10 (PSS-10) and a validated self-help tool: Bhramari Pranayama Standard Operating Procedure (BPSOP).
Methods:
A single-arm clinical trial was conducted on 67 women aged 22–40 years with moderate to severe stress (PSS scores: 14–40). Exclusion criteria included low-stress scores (<13), severe psychological or systemic disorders, cardiovascular diseases, and ear infections. Participants underwent a 3-day in-person BP training followed by 20 days of intervention via the online Zoom platform. BP was practiced for 30 minutes daily (15 rounds) in the evenings. Pre- and post-intervention PSS-10 scores were compared.
Results:
The mean pre-intervention PSS score was 22.51, while the post-intervention mean was 14.28, showing a significant reduction. The calculated p-value (.000) was statistically significant, and the effect size (1.7) indicated a large magnitude of improvement, with post-intervention scores at the 95.5 percentile of pre-intervention values.
Conclusion:
Bhramari Pranayama is effective in reducing stress among working women. This effort is in line with Sustainable Development Goal (SDG) number 3 and highlights the need for action towards the fulfillment of SDG 5,8,10. Limitations include the single-arm design, small sample size, and online delivery. Future studies can explore its impact on hormonal stress markers and expand the findings through randomized controlled trials. The validated BPSOP tool offers a promising, accessible self-help intervention for stress management. The clinical trial was registered with the Clinical Trial Registry of India (CTRI) on 17th April 2023 with the registration number CTRI/2023/04/051650.
In vitro characterization of some of the anti-cancer effects of Borage officinalis flower extracts on human gastric adenocarcinoma cell line, (AGS), cell line
Authors: Salih Mohamad Trefa 
Published: December 2024

Worldwide, gastric cancer is considered to be a major health issue, which is common in eastern Asia, with high death rates recorded, that calls for immediate attention and investigation. This article is exploring the effects of methanol and acetone extracts of borage, Borage officinals, flowers with a range of concentrations (10, 50, 100, 250, and 500 μg/ml) on the human gastric cancer, AGS cell line. Fifteen gm of flower powder was suspended separately in 300ml of 50% acetone and placed on shaker in dark for 24 hours at room temperature. The results showed that in a dose dependent manner, methanol extract exhibit anti-proliferative effects whereas acetone extract revealed an inhibitory effect only at a higher dose 500 μg/ml. The clonogenicity of AGS cells was revealed to be significantly (p˂0.001) diminished where production of holoclone was reduced where cells treated with both extracts as compared to control group. AGS cells apoptosis levels were induced by both extracts when compared to control group. RT-PCR results depicted significant (p˂0.001) upregulation of both p21 and EGR1 when cells treated with both methanol and acetone extracts respectively, while both of NDRG1 and PTEN genes were upregulated when acetone extract was used, it’s worth mentioning, that these genes, p21, PTEN, and NDRG1 are considered to be tumor suppressors. In conclusion, borage, Borage officinals, extracts had anti-cancer and cytotoxic effects on AGS cell line and apoptosis was induced. Thus, this plant might be suggested as a future candidate for cancer treatment therapy.
The Reduction of Post-harvest Losses is Crucial for a Successful Cassava Value Chain and Food Security in Africa
Authors: Vutula Noncedo 
Published: December 2024

Introduction
This research paper examines the impact of post-harvest losses (PHLs) on the cassava value chain in Africa and provides potential methods to reduce post-harvest losses.

Methods
Cassava has the potential to address food insecurity and poverty in Africa. It can serve as a sustainable food source, animal feed, and an industrial product for manufacturing starch, alcohol, and fermented beverages. Cassava is highly perishable once harvested due to post-harvest physiological deterioration (PDD). PDD causes high post-harvest losses within 1-3 days of harvesting, making cassava unsuitable for consumption or trade.

Results
Previous studies showed that cassava has the potential to mitigate climate change and establish sustainable food systems in Africa. However, post-harvest losses in the cassava value chain remain a major challenge to food security in Africa.

Conclusion
Investing in post-harvest loss mechanisms can increase the shelf life of cassava yields and contribute towards food security.
Prevalence and Factors Associated with Chronic Energy Deficiency among Adolescent Girls and Young Mothers in Sub-Saharan Africa
Authors: Sawadogo Pengdewendé Maurice , Ugwu Nebechukwu Henry , Phiri Million , Arnaldo Carlos 
Published: December 2024

Background
Adolescent girls and young mothers (AGYM) have high nutritional needs, which puts them at risk of malnutrition. In particular, little is known about the prevalence and determinants of chronic energy deficiency (CED) of AGYM, which received less attention among researchers in sub-Saharan Africa.

Objective
The aim of the study was to examine the prevalence and associated factors of CED among AGYM in sub-Saharan Africa.

Methods
Data from the latest demographic and health surveys in four countries were used for this study: Gabon, Kenya, Malawi and Nigeria. Chronic energy deficiency, the outcome variable referred to as Body Mass Index (BMI), is classified as underweight for AGYM, whose BMI is less than 18.5kg/m2 according to the global standards established by the United Nations Children’s Fund (UNICEF) . Data analysis was done in three stages: descriptive, bivariate, and multivariable.

Results
The prevalence of chronic energy deficiency among AGYM was 7.2% in Malawi, 13.4% in Gabon, 14.9% in Kenya and 15.4% in Nigeria. The extent of chronic energy deficiency was particularly high among adolescent girls aged 15-19 in Gabon, AGYMs with no education in Nigeria and Kenya, and AGYMs from poor households in Nigeria, suggesting the necessity to provide systematic food rations to AGYMs of these specific groups. Finally, multivariable analysis shows that there are reduced odds of suffering from chronic energy deficiency among AGYM aged 20-24 years in Gabon and Kenya, compared with 15-19-year-olds. It is also revealed that education was negatively associated with chronic energy deficiency of AGYM in Nigeria and Kenya.

Conclusion
Mothers, particularly girls and young mothers, need more economic programmes to safeguard their reproductive rights through the promotion of nutritional information. This information is key for their nutritional protection, particularly for teenage girls (aged 15-19), the uneducated, and those living in poor households.
Analysis of Air Samples from the Cities of Mysuru and Madikeri
Authors: Marilingappa Jamuna , Servesh Anushka 
Published: December 2024

Introduction:
Air pollution is a major health hazard. This study aims to investigate
the concentration of the pollutants nitrogen oxides, sulfur dioxide, and respirable
Particulate Matter (PM10) generated from various sources like automobiles and industries
near the center of the cities in Mysuru and Madikeri.

Methods:
The major pollutants, as suggested by the Karnataka State Pollution Control
Board (KSPCB) in industrial areas include oxides of nitrogen (NOX), sulfur dioxide, and
respirable particulate matter (PM10). The concentration of these gases in the ambient air
was studied for about three months, from February to April 2017.

Results:
The results showed that the concentration of emissions of the above-cited gaseous
and suspended solid pollutants is higher than that of permissible concentrations as per
the standards provided by KSPCB for an industrial area.

Conclusion:
The major precautions can be taken to reduce the concentration level of these
pollutants.
Nanoparticle Carriers for Drug Delivery: An Updated Review
Authors: Hajipour Somayeh , Ghiasvand Alireza 
Published: December 2024

Abstract:
The drug was initially administrated relying on pills, eye drops, ointments, and intravenous
solutions. In the last decades, several novel technologies have emerged to overcome significant
challenges including poor solubility, drug aggregation, low bioavailability, limited biodistribution,
poor absorption in the body, lack of selectivity, or to minimize the adverse effects of therapeutic
drugs. Drug delivery systems (DDS) can be designed to the technologies that carry drugs into or
throughout the body of humans or animals to enhance therapeutic efficacy. DDS can also be considered
for in vivo delivery, particularly for their use in peptide and protein therapeutics. Continued research
may show the trends and perspectives of how drugs are delivered. In addition, this article includes
comprehensive information regarding the trends and perspectives in DDS technologies.
A Comprehensive Review of Strategies of Topical Niosomes and Their Synergistic Effect for Enhanced Therapeutic Outcomes
Authors: Thilagam N. Bharathi Sai , Karthik V.P , Sowmya C. , Gnanasambandan R. 
Published: December 2024

Abstract:
The review aims to assess the potential of niosomes—nonionic surfactant-based vesicular
systems—as carriers for topical and transdermal drug delivery. Niosomes enable targeted and controlled
drug release while minimizing systemic toxicity. The investigation centers on their structure,
stability, and capacity to entrap both hydrophilic and lipophilic drugs, as well as their use in managing
various dermatological and systemic disorders. Recent studies have examined the formulation of
niosomes, particularly highlighting the roles of nonionic surfactants and cholesterol in enhancing the
stability and entrapment efficiency of these vesicles. Research on permeability enhancers has been
reviewed for their ability to work together to improve drug transport and bioavailability. It also provides
a detailed discussion on the use of niosomes in treating various dermatological conditions, as
well as their applications in systemic diseases, with a particular focus on co-delivery systems in cancer
therapies. Niosomes exhibit efficacy in drug delivery by providing an increase in penetration
through the stratum corneum, targeting hydrophilic and lipophilic drugs for dermatological and systemic
applications. The Development of niosomal therapy has expanded into immunization, antiinflammatory
treatments, and the control of pigmentation. Permeability enhancers further increase
their efficacy, bioavailability, and tissue localization. Anticancer treatment using niosomes for codelivery
of agents demonstrates synergistic effects with reduced side effects. Niosomes have tremendous
potential in advancing topical and transdermal drug delivery, offering controlled, targeted
release and improved patient outcomes. With optimized fabrication and comprehensive toxicity
evaluation, niosomes can potentially revolutionize topical therapies, making them safer, more effective,
and patient-friendly for a range of next-generation treatment options across dermatology and
beyond.
A Study on Learning Resources Recommendation Based on Multi-Domain Fusion Network
Authors: Zhang ShuQin , Wang HaoRan , Su XinYu 
Published: December 2024

Background:
Considering the singularity of collaborative filtering algorithms in
recommending learning resources and the problem that existing knowledge graph convolutional
networks cannot deeply mine the neighbourhood information of learning resource nodes in
application scenarios with less neighbourhood information, a multi-domain fusion convolutional
network learning resource recommendation model based on knowledge graphs is proposed
here.

Objective:
This study aimed to improve the accuracy and personalization of recommendations
of filtering algorithms.

Methods:
First, the model mapped learner nodes, learning resources, and their neighbour nodes
into low-dimensional dense vectors. Second, the multi-domain fusion layer and the multidomain
aggregator were used to obtain the fused multi-domain learning resource vector. Finally,
the learner vector and the multi-domain learning resource vector were fed into the prediction
layer to calculate the interaction probability.

Results:
To verify the effectiveness of the algorithm, we conducted a comparative experiment
using the publicly available datasets MOOPer and MOOCCubeX. The experimental results
showed that the proposed model outperformed baseline models, such as CKE, MKR, KGCN,
DEKGCN, and KGIN, in terms of evaluation metrics, such as AUC, ACC, and F1. At the same
time, when the neighborhood information was limited, the AUC, ACC, and F1 values of the
proposed model still maintained the optimal value.

Conclusion:
Compared to the optimal baseline model, the effectiveness of the proposed model
has been proven.