bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing heavy metals and developing a fingerprint for Mahikeng paints using ICP-MS analysis: implications for environmental health
Authors: Olukotun S. F. , John S. O. O. , Kupi T. G. , Oladejo O. F. , Mathuthu J. , Shittu H. O. , Mathuthu M. 
Published: October 2024

Abstract

Despite global efforts to mitigate lead in paints, studies reveal persisting lead levels above safety thresholds in household paints in many nations. Alongside lead, other heavy metals (HMs) in paints pose health risks. The study aims to assess lead content and heavy metals levels, and develop a fingerprint for paints in Mahikeng, the capital of North West Province, South Africa, using ICP-MS analysis. We purchased and analyzed 30 paint samples from Mahikeng. The most prominent and nontoxic elements detected are Nitrogen (N), Calcium (Ca), Iron (Fe), Carbon (C), Aluminum (Al), and Phosphorus (P). Lead concentrations ranged from 0 ppm to 4.17 ppm, below South Africa's 600 ppm MPLL. Other HMs detected included Beryllium (Be), Chromium (Cr), Nickel (Ni), Arsenic (As), Cadmium (Cd), Antimony (Sb), Mercury (Hg), as well as radionuclides Barium (Ba), Strontium (Sr), Thorium (Th), and Uranium (U). Their concentrations range from 0 ppm to 810.57 ppm, with most elements found at relatively low levels. The obtained Pb isotopic ratios and rare earth elements (REE) patterns were used to develop a fingerprint. These findings offer insights into the environmental health implications of lead and heavy metals contamination by the paints, as well as the identification of their sources. This research contributes to sustainable cities and communities by promoting responsible consumption and production practices, enhancing quality education on environmental health, and supporting good health and well-being through the reduction of hazardous exposures.
A New Approach for Evaluating Maize Transgressive Segregants and Their Three-Way Cross Potential in the S4 Convergent Breeding Population
Authors: Widiayani Nuniek , Anshori Muhammad Fuad , Nasaruddin Nasaruddin , Farid Muh , Ridwan Ifayanti , Bahrun Abd. Haris , Azrai Muhammad , Nur Amin , Khaerani Purnama Isti , Suwarno Willy Bayuardi , Syahruddin Karlina , Khan Naeem , Alotaibi Majed A. , Seleiman Mahmoud F. 
Published: October 2024

Abstract

The development of transgressive segregant (TS) selection on convergent breeding populations of S4 maize is a concept that is rarely applied. Gene construction that focuses on the action of dominant genes and inbreeding depression are obstacles to this development. However, the development of TS is necessary to accelerate maize pipelines. Therefore, the objectives of this study were (1) to develop the concept of transgressive segregant selection and (2) to select S4 TS maize to be developed as hybrid cross parents. This study was also designed with an augmented design consisting of 6 blocks. The factors focused on maize genotypes were divided into two groups: unrepeated maize genotypes, 32 TS lines, and maize hybrid genotypes repeated in each block, namely, JH 37, NASA 29, BISI 18, and SINHAS 1. The combination of ratio analysis, path analysis, best linear unbiased prediction, relative fitness, and selection indices is a fair approach for assessing the genetic potential of the S4 TS. The selection index formed was 0.53 ear weight + 0.24 seed yield percentage + yield, which works on the fitness of BLUPs. The index selection resulted in 11 S4 transgressive segregant lines being further evaluated for their hybrid potential, with the TS line CB2.23.1 being the best. In addition, the three-way cross-hybrid evaluation results also recommended SG 3.35.12 × JH37 and CB 2.23.1 × JH37 as potential hybrid lines. However, these segregants are expected to focus on identifying and combining power and combinations of diallel crosses in the future.
Automatic Feature Selection Based Machine Learning Models for Hardware Trojan Detection
Authors: Dubey Shivam , Rathor Vijaypal Singh 
Published: October 2024

Abstract

Hardware Trojan (HT) is a significant threat to the integrity and security of the Integrated Circuit (IC). Hardware Trojans can be implanted in any stage of IC development. Detection of HT is difficult during testing due to its stealthy nature and IC’s complex design. Therefore, researchers have focused on developing HT detection methods to mitigate the threats of HT. In recent years, machine learning (ML) has emerged as a promising approach that uses various Trojan-related circuit features for detecting the hardware Trojans. The previously proposed ML-based methods either use all known features or use manual/random feature selection. Due to this, these methods are complex and provide low performance. This work proposes a new ML-based method that encompasses different automatic feature selection algorithms, such as Principal Component Analysis (PCA) and AutoEncoder, for effective Trojan detection in ICs. We also use algorithms like Lasso, Ridge, and Elastic-net to select the best features based on their coefficient values. Using feature selection enables us to identify the most influential features for accurately detecting hardware Trojans. Further, we develop different ML models by integrating these automatic feature selection algorithms to achieve high performance (in terms of accuracy and F1-score) during HT detection. The experimental evaluation shows that the proposed Autoencoder achieves 99.5% average accuracy and 99.28% F1 score, and PCA provides on average 98.6% accuracy and 98.35% F1-Score with different proposed machine learning models. The proposed XGBoost, Random forest-based ML model achieves 100% accuracy and F1-score with Autoencoder while detecting HT in different datasets prepared using Trust-hub benchmarks. The proposed model exhibits a notable improvement in the F1-score compared to the best-known existing ML-based model, ranging from 0.1% to 2.1%. The proposed method’s top-performing model (RF with Autoencoder) achieves significant enhancements in the F1-score, ranging from 2% to 53%, over the performance of existing ML-based hub trust detection methods.
Trained dogs can detect canine urothelial carcinoma of the bladder
Authors: Desmas-Bazelle Isabelle , Rooney Nicola Jane , Morant Steve , Harris Rob , Volckaert Veerle , Goodfellow Mark , Aziz Sophie , Guest Claire Marie 
Published: October 2024

Abstract

Dog have been trained to detect a variety of human cancers. However, studies examining their ability to find canine cancer are sparse and show variable results. Whilst dogs were shown to be successful at detecting malignant tumors from saliva samples, they were previously unsuccessful at finding canine urothelial carcinoma (UC) from urine. We used advanced training methods to explore if dogs can accurately detect urothelial carcinoma (UC) from canine urine. Urine samples from healthy dogs without urinary tract abnormalities (control), with non-malignant urinary tract disease (control), and with UC (positive) were collected prospectively. Urothelial carcinoma was diagnosed using cytology and/or histopathology. All dogs were naïve of chemotherapy, underwent bladder ultrasound and urinalysis. Residual urine remaining after conventional diagnostic procedures was frozen in glass jars until analysis and used for dog training and testing. Three dogs previously trained to detect human cancer underwent up to 29 days of additional training. They were each presented with 20 control samples (from either 13 or 14 unique individuals), 10 UC positive samples (from between 5 and 7 individuals), in a double-blind trial and their behavioral responses recorded. Overall, 80% of UC were correctly indicated, and 91.7% of controls correctly ignored (68% and 87% when just considering only unique donor dogs). Individual dogs showed sensitivities ranging from 70 to 90%, and specificities ranging from 85 to 95%. This study suggests that, when using appropriate methods, dogs can be trained to detect canine UC from urine, and could serve as an additional rapid, non-invasive diagnostic test for the disease.
The Role of Cross-Border E-commerce Platforms in Digital Economy: Empower Firms to Gain Global Market Insights to Increase Global Competitiveness
Authors: Chen Yubo , Wang Qi , Xie Jinhong , Zhang Jurui 
Published: October 2024

Abstract

With the fast development of digital economy, cross-border e-commerce platforms provide unprecedented opportunities for firms particularly small businesses to expand their business to global markets. While these platforms create tremendous competition for sellers, they also provide a unique platform to empower firms to gain market insights without physically entering different international markets. This article investigates such unique role of the cross-border e-commerce platforms from a data rich social network perspective. Specifically, by aggregating thousands of sellers and buyers, these digital platforms own massive data on the complex buyer–seller social networks, in which sellers can become connected with one another through common buyers with whom they have traded. Based on a proprietary dataset from a leading Chinese cross-border e-commerce platform, this study shows how the implicit social interactions in this digital platform affect sellers’ performance, how such impacts vary in developed versus emerging markets, and how they were affected by macro-economic shocks such as the global financial crisis. Our research provides important implications for firms going global in the digital economy age, and on the critical role of digital platforms in empowering small businesses.
Investigating solar cycles using mathematical morphology
Authors: Grangeiro Cicero. D. , Duarte Tharcísyo S. S. , Costa Jefferson S. da , Coelho Hugo R. , de Oliveira Gilson F. 
Published: October 2024

Abstract

In this study, we report on the processing and analysis of around 9700 images of the solar disk, obtained from the SOHO and SDO satellites, available in the Helioviewer database. Using our own code, PyMoST (Python Morphological Sunspot Tracker), which employs a mathematical morphology technique, we were able to automatically track and analyze approximately 50000 sunspots in these images, covering the period from January 1, 1998 to July 31, 2024. By using PyMoST we were able to obtain the distribution of the number of spots as a function of solar latitude and time, accurately reproducing solar cycles 23, 24 and the current phase of solar cycle 25, in agreement with the literature. In addition, our code was also able to determine the areas and number of sunspots, showing excellent correlation with the results reported by the Royal Greenwich Observatory (RGO), Kodaikanal Solar Observatory (KSO) and the International Sunspot Number - Solar Influences Data Analysis Center (SIDIC), thus consolidating the efficiency of our tool and its importance for the investigation of solar magnetic activity.
Synthesis of Reliable Logic Question Answering Data for Large Language Model with Aid of Operations Research Program
Authors: Li You , jiang baozheng , Mu Qing , qiu wenying , jiang weiwei , zhang hoaxiang , gu weixi 
Published: October 2024

Abstract

Logical reasoning is a highly valued capability in language models. However, there is a shortage of high-quality data for logical reasoning, and manually generating a large amount of such data is both expensive and time-consuming. We introduce a novel and reliable logic question answering data synthesis framework called Operations Research Aided Synthesis (ORAS). This framework employs operations research techniques to systematize the synthesis of high-quality training examples that enhance the reasoning ability of large language models (LLMs). ORAS integrates mathematical programming methods to generate question-answer pairs. ORAS employs a dual-phase approach: the first phase uses optimization algorithms to constructand evaluate the logic systems by solving the problem using a mathematical solver. The second phase translates the logic systemto real world problems, and generate answers according to the information from the mathematical solver. Using data generated from ORAS, fine-tuned models improved by 38% for Qwen-7B.
Global Submesoscale Ocean Dynamics Unveiled by Wide-Swath Satellite Altimetry
Authors: Archer Matthew , Wang Jinbo , Klein Patrice , Fu Lee-Lueng 
Published: October 2024

Abstract

Ocean submesoscale (1–100 km) processes and their significant impact on Earth’s climate system have been increasingly emphasized in recent decades by high-resolution numerical models and regional observations. However, the dynamics and energy associated with these processes, including submesoscale eddies and small-wavelength nonlinear tidal waves, have never been observed before from a global perspective. Where, when, and how much do these submesoscale processes contribute to the large-scale ocean circulation and climate system? Data from the recently launched Surface Water and Ocean Topography (SWOT) satellite not only confirm the characteristics of submesoscale eddies and waves, but also suggest that their potential impacts on ocean energetics, the marine ecosystem, atmospheric weather, and Earth’s climate system are much larger than anticipated. SWOT ushers in a new era of global ocean observing, placing submesoscale ocean dynamics as a critical element of the Earth’s climate system.
Efficient Conceptual Knowledge Removal in Large Language Models: Methods and Evaluations
Authors: Dimitriou Miyim , Rogowski Daniel , Anderson Michael , Vanderbilt Eric , Carmichael Lawrence 
Published: October 2024

Abstract

The increasing use of deep neural networks has led to models that accumulate vast amounts of knowledge from their training data, often retaining outdated or biased information that needs to be selectively removed. Novel techniques are required to efficiently erase specific conceptual knowledge from these models while maintaining overall performance and avoiding computationally expensive re-training processes. This paper introduces a scalable framework for conceptual knowledge removal through targeted weight modification and sparse fine-tuning, demonstrating how specific knowledge representations can be isolated and erased without significant degradation to the model's broader capabilities. The methodology achieves high precision in knowledge suppression by leveraging probing techniques and gradient-based optimization, ensuring minimal disruption to general task performance. Extensive experimental evaluations confirm the effectiveness of the proposed approach, highlighting its application to scenarios where adaptive model refinement is essential for maintaining both accuracy and ethical integrity. Contributions to the field include the development of a flexible and efficient mechanism for knowledge erasure, applicable across various architectures, that minimizes computational overhead while enhancing the model's responsiveness to dynamic knowledge requirements.
Integrated geophysical and GIS approaches for groundwater potential assessment: a case study of Aladja, Delta State, Nigeria
Authors: Ojo Odunayo Tope , Chiaka Ike Joy , Nwokeabia Charity Nkiru 
Published: October 2024

ABSTRACT
This study explores the groundwater potential in Aladja, Udu, Local Government Area of Delta State, Nigeria, using an integrated approach combining electrical resistivity techniques with Remote Sensing (RS) and Geographic Information System (GIS) methods. The primary aim is to delineate aquifers and comprehensively assess groundwater resources. Methods include Vertical Electrical Sounding (VES) to gather resistivity data, RS and GIS for analyzing geological and hydrological parameters such as lineament density, drainage density, slope, soil type, and land use. The resistivity values ranged from 147 to 460.50 Ωm, with aquifer thicknesses varying between 6.50 and 36.30 m. The RS and GIS analysis indicated high groundwater potential in regions characterized by significant lineament density and moderate slopes. The study highlights the complementarity of VES and RS/GIS in groundwater exploration, revealing substantial groundwater resources in the northwestern regions of the study area. This approach underscores the importance of integrating these methods for a more accurate assessment of groundwater potential. The novelty of this study lies in validating RS and GIS findings with VES data, ensuring reliable groundwater potential mapping and effective resource management. This integrated methodology offers a robust framework for sustainable groundwater resource assessment and management in similar geological settings.
Design, Synthesis, Docking Studies, and Biological Activity of Novel Analogs of Cyclophosphamide as Potential Anticancer Agents
Authors: Gholivand Khodayar , Rostami Soobieh Alemi , Sabaghian Marzie , Sadeghi-Mohammadi Sanam , Babaei Azam , Malekshah Rahime Eshaghi , Naderi-Manesh Hossein 
Published: October 2024

Introduction:
This study aimed to present the synthesis and characterization
of four novel analogs of cyclophosphamide (2, 3, 4, 7) and their related precursors (1, 5,
6) and assess their anticancer activity against breast cancerous (MCF-7) and normal
(HUVEC) cells.

Method:
Notably, 2-(bis(2-chloroethyl)amino)-1,3,2-diazaphospholidine 2-oxide ((2))
and 2-(bis(2-hydroxyethyl)amino)-1,3,2-diazaphospholidine 2-oxide ((7)) exhibited concentration-
dependent cytotoxicity against the MCF-7 cell line, with IC50 values of 8.98
and 28.74 μM, respectively.

Result:
Annexin V/PI staining and ROS assays demonstrated reduced cell viability and
mitochondrial dysfunction. in silico studies involving DFT-D optimization and Molegro
virtual docking against B-DNA dodecamer and STAT3 receptors revealed enhanced interactions
for certain compounds compared to cyclophosphamide.

Conclusion:
Importantly, the in silico and in vitro results corroborated each other, supporting
the potential anticancer efficacy of these novel analogs.
CDT1 is a Potential Therapeutic Target for the Progression of NAFLD to HCC and the Exacerbation of Cancer
Authors: He Xingyu , Ma Jun , Yan Xue , Yang Xiangyu , Wang Ping , Zhang Lijie , Li Na , Shi Zheng 
Published: October 2024

Aims:
This study aimed to identify potential therapeutic targets in the progression from
non-alcoholic fatty liver disease (NAFLD) to hepatocellular carcinoma (HCC), with a focus on
genes that could influence disease development and progression.

Background:
Hepatocellular carcinoma, significantly driven by non-alcoholic fatty liver disease,
represents a major global health challenge due to late-stage diagnosis and limited treatment options.
This study utilizes bioinformatics to analyze data from GEO and TCGA, aiming to uncover
molecular biomarkers that bridge NAFLD to HCC. Through identifying critical genes and pathways,
our research seeks to advance early detection and develop targeted therapies, potentially improving
prognosis and personalizing treatment for NAFLD-HCC patients.

Objective:
Identify key genes that differ between NAFLD and HCC; Analyze these genes to understand
their roles in disease progression; Validate the functions of these genes in NAFLD to HCC
transition.

Methods:
Initially, we identified a set of genes differentially expressed in both NAFLD and HCC
using second-generation sequencing data from the GEO and TCGA databases. We then employed
a Cox proportional hazards model and a Lasso regression model, applying machine learning techniques
to the large sample data from TCGA. This approach was used to screen for key disease-related
genes, and an external dataset was utilized for model validation. Additionally, pseudo-temporal
sequencing analysis of single-cell sequencing data was performed to further examine the variations
in these genes in NAFLD and HCC.

Results:
The machine learning analysis identified IGSF3, CENPW, CDT1, and CDC6 as key
genes. Furthermore, constructing a machine learning model for CDT1 revealed it to be the most
critical gene, with model validation yielding an ROC value greater than 0.80. The single-cell sequencing
data analysis confirmed significant variations in the four predicted key genes between
the NAFLD and HCC groups.

Conclusion:
Our study underscores the pivotal role of CDT1 in the progression from NAFLD to
HCC. This finding opens new avenues for early diagnosis and targeted therapy of HCC, highlighting
CDT1 as a potential therapeutic target.
Role of Immunotherapy in the Management of Advanced Hepatocellular Carcinoma: A Systematic Review
Authors: Addanki Raja Narendra Divakar , Mannava Anjali Srikanth , abbas ali Mustansir , Velmurugan Hemasri , Thangaraju Pugazhenthan , Sri Varshini Talagampala 
Published: October 2024

Abstract:
Introduction: The most prevalent histologic subtype of primary liver cancer, hepato-cellular carcinoma, is also the third most lethal malignancy worldwide and a significant cause of cancer death. When diagnosed, it is frequently too late for curative therapies. To add to the difficulty, it is resistant to conventional systemic therapies like chemotherapy. Method: Re-cently, more attention has been given to the use of immunotherapy for this condition. Immuno-therapy has had a major impact on the outcome of several malignancies; several studies have revealed the potential role of immunotherapy in advanced hepatocellular carcinoma. To provide an overview of the current literature, justification, and clinical evidence for the use of immuno-therapy in advanced hepatocellular carcinoma, we employed a systematic literature review (SLR) approach in this work. Additionally, we explored the benefits and rationales of various treatment combinations and described the molecular mechanisms behind resistance to a number of immunotherapy drugs. Results: A total of 188 articles from credible journals published be-tween 2013 and August 7, 2023, were screened. Eight of those articles were chosen for in-depth study. This systematic review concluded that the current immunotherapeutic approaches have the potential to improve outcomes for aHCC patients; however, several clinical and biological hurdles have to be overcome, and predictive markers are still eagerly needed. Conclusion: To fully realize the potential of immunotherapy agents and their combination therapies to enhance health outcomes for patients with aHCC, additional investigation and research are required.
Liquisolid Technique for Solubility Enhancement of Poorly Soluble Drug - A Brief Review
Authors: Prusty Amaresh , Jena Bikash Ranajn , Barik Vivek , Khamkat Piyali , Barik Bhakti Bhusan 
Published: October 2024

Abstract:
Most of the newly discovered drug candidates are lipophilic and poorly water-soluble,
making it a significant challenge for the pharmaceutical industry to formulate suitable drug delivery
systems. This review gives insight into an overview of the liquisolid technique (LST) and summarizes
the progress of its various applications in drug delivery. This novel technique involves converting
liquid drugs or drugs in a liquid state (such as solutions, suspensions, or emulsions) into dry, nonadherent,
free-flowing, and readily compressible powder mixtures by blending or spraying a liquid
dispersion onto specific powder carriers and coating materials. In Liquisolid systems, the liquid medication
is absorbed into the interior framework of carriers. Once the carrier's interior is saturated with
liquid medication, a liquid layer forms on the surface of the carrier particles, which is instantly adsorbed
by the fine coating material. As a result, a dry, free-flowing, and compressible powder mixture
is formed. Compared to other solubility enhancement techniques, s.a. micronization, inclusion complexation,
microencapsulation, nanosuspension, and self-nano emulsions, LST is relatively simple to
prepare and may offer a cost-effective solution to enhance the solubility of poorly water-soluble drugs
enhancing its bioavailability in drug formulation and delivery.