bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing penmanship of Chinese handwriting: a deep learning-based approach
Authors: Xu Zebo , Mittal Prerit S. , Ahmed Mohd. Mohsin , Adak Chandranath , Cai Zhenguang G. 
Published: April 2024

AbstractThe rise of the digital era has led to a decline in handwriting as the primary mode of communication, resulting in negative effects on handwriting literacy, particularly in complex writing systems such as Chinese. The marginalization of handwriting has contributed to the deterioration of penmanship, defined as the ability to write aesthetically and legibly. Despite penmanship being widely acknowledged as a crucial factor in predicting language literacy, research on its evaluation remains limited, with existing assessments primarily dependent on expert subjective ratings. Recent initiatives have started to explore the application of convolutional neural networks (CNN) for automated penmanship assessment. In this study, we adopted a similar approach, developing a CNN-based automatic assessment system for penmanship in traditional Chinese handwriting. Utilizing an existing database of 39,207 accurately handwritten characters (penscripts) from 40 handwriters, we had three human raters evaluate each penscript’s penmanship on a 10-point scale and calculated an average penmanship score. We trained a CNN on 90% of the penscripts and their corresponding penmanship scores. Upon testing the CNN model on the remaining 10% of penscripts, it achieved a remarkable performance (overall 9.82% normalized Mean Absolute Percentage Error) in predicting human penmanship scores, illustrating its potential for assessing handwriters’ penmanship. To enhance accessibility, we developed a mobile application based on the CNN model, allowing users to conveniently evaluate their penmanship.
Edge and cloud computing approaches in the early diagnosis of skin cancer with attention-based vision transformer through hyperspectral imaging
Authors: La Salvia Marco , Torti Emanuele , Marenzi Elisa , Danese Giovanni , Leporati Francesco 
Published: April 2024

AbstractHyperspectral imaging is applied in the medical field for automated diagnosis of diseases, especially cancer. Among the various classification algorithms, the most suitable ones are machine and deep learning techniques. In particular, Vision Transformers represent an innovative deep architecture to classify skin cancers through hyperspectral images. However, such methodologies are computationally intensive, requiring parallel solutions to ensure fast classification. In this paper, a parallel Vision Transformer is evaluated exploiting technologies in the context of Edge and Cloud Computing, envisioning portable instruments’ development through the analysis of significant parameters, like processing times, power consumption and communication latency, where applicable. A low-power GPU, different models of desktop GPUs and a GPU for scientific computing were used. Cloud solutions show lower processing times, while Edge boards based on GPU feature the lowest energy consumption, thus resulting as the optimal choice regarding portable instrumentation with no compelling time constraints.
The impoverishment problem
Authors: Kind Amy 
Published: April 2024

AbstractWork in philosophy of mind often engages in descriptive phenomenology, i.e., in attempts to characterize the phenomenal character of our experience. Nagel’s famous discussion of what it’s like to be a bat demonstrates the difficulty of this enterprise (1974). But while Nagel located the difficulty in our absence of an objective vocabulary for describing experience, I argue that the problem runs deeper than that: we also lack an adequate subjective vocabulary for describing phenomenology. We struggle to describe our own phenomenal states in terms we ourselves find adequately expressive. This paper aims to flesh out why our phenomenological vocabulary is so impoverished – what I call the impoverishment problem. As I suggest, this problem has both practical and philosophical import. After fleshing out the problem in more detail, I draw some suggestive morals from the discussion in an effort to point the way forward towards a solution.
Time, temperature and media: the three keys to improve the recovery of Campylobacter fetus subsp. venerealis from preputial bull samples
Authors: Polo Coral , García-Seco Teresa , García Nerea , Fernández Víctor , Briones Víctor , Díez-Guerrier Alberto , Álvarez Julio , Domínguez Lucas , Pérez-Sancho Marta 
Published: April 2024

AbstractThe isolation of Campylobacter fetus subsp. venerealis (Cfv) from clinical samples is the gold standard for confirming cases of bovine genital campylobacteriosis, an important cause of infertility in cattle and a potential public health concern. Furthermore, isolation is also necessary for the development of autologous vaccines, characterization of strains for antimicrobial susceptibility patterns, etc. Nevertheless, the sensitivity of culture methods is usually low, and there is no standardized protocol to maximize the recovery of Cfv from clinical samples. The aim of the current study is to design a protocol for the culture of Cfv from preputial samples by evaluating the combination of different transport, enrichment and culture media considering the impact of certain factors (time between collection and enrichment, temperature, and use of filters). The use of modified Lander’s transport medium and storing the sample for 24 h at 21 ± 2 °C led to the highest recovery of Cfv CFUs. In contrast, the storage of the samples during 24–48 h in PBS and Thomann rarely allowed the recovery of Cfv regardless of the temperature. The enrichment medium yielding the best results was Preston (significantly higher recovery than Brucella medium), while Cfv could not be isolated with Bolton. Regarding our diagnostic assay (using Lander as transport medium and Preston as enrichment medium), the best protocol in terms of maximizing Cfv recovery as well as limiting contaminations is to culture the samples in i) solid media Preston or Skirrow, and ii) using 0.65 μm filters and incubating plates at 37 °C in microaerophilic conditions.
The Potential of Stormwater Management Strategies and Artificial Intelligence Modeling Tools to Improve Water Quality: A Review
Authors: Ramovha Ndivhuwo , Chadyiwa Martha , Ntuli Freeman , Sithole Thandiwe 
Published: April 2024

AbstractStormwater management modeling tools have been utilized to enhance stormwater operating systems, assess modeling system efficiency, and evaluate the impacts of urban growth on stormwater runoff and water quality. This review explores the potential of stormwater management strategies and Artificial Intelligence modeling tools in enhancing water quality. The study focuses on evaluating stormwater modeling tools for planning and improving stormwater systems, assessing modeling efficiency, and understanding the impacts of new development on stormwater runoff and water quality. Various stormwater modeling tools are compared to aid in water management in urban and rural settings, which is crucial due to increasing storm intensity from climate change. The review debates the advantages and limitations of different modeling tools, particularly in modeling stormwater quantity and quality under different scenarios. It also examines tools used for predicting and analysing stormwater runoff during storm events in diverse locations. The assessment of modeling tools is centred on their support for Green Infrastructure (GI) practices, considering factors like modeling accuracy, data availability, and requirements. The study highlights the importance of these tools in managing water in urban areas and safeguarding water sources during stormwater events. Notably, the accuracy and efficacy of stormwater modeling tools are influenced by input data quality, calibration methods, and standardization metrics, with the widely used Stormwater Management Model (SWMM) being a common modeling tool.
Assessment of Climate Change-Induced Water Scarcity Risk by Using a Coupled System Dynamics and Bayesian Network Modeling Approaches
Authors: Dehghani Sara , Massah Bavani Alireza , Roozbahani Abbas , Sahin Oz 
Published: April 2024

AbstractThe water scarcity risk induced by climate change is contributing to a sequence of hydrological and socioeconomic impacts. Certain numbers of related impacts are locked in already and are expected to be much greater in the future. So, there is still a lack of understanding of its dynamics, origin, propagation, and the mutual interaction of its drivers. In recent years, several model-based approaches have been introduced to tackle the complexity, dynamics, and uncertainty of water scarcity specifically. However, the coupled modeling while addressing different aspects of the risk of water scarcity under the climate change scenarios has been rarely done. For bridging this gap, in this research, the combination of complementary System Dynamics modeling and Bayesian Network was applied to Qazvin Plain in Iran with five AOGCM models under two Shared Socioeconomic Pathways (SSP) scenarios (126 and 585). Key findings of this research show: 1) Baseline risk assessment indicates a low probability of water scarcity; however, in the future 30-year time horizon with continuous change in hazard, vulnerability, and exposure for SSP126, the risk fell in the extreme category with an average probability of 41%. Under SSP585, the risk varies between extreme and high categories with an average probability of 47%. 2) Economic development, particularly regional gross domestic product (RGDP) in 2045–2054 in SSP585 can diminish the negative projected consequences of climate change and therefore investments in adaptation policies could offset negative consequences, highlighting the role of economic growth in climate resilience. 3) It is projected that crop yield and income will receive the largest negative effects due to cutting back the agriculture area. 4) Considering the interplay of climate change, economic development, and water extraction policies is essential for the design, operation, and management of water-related activities. The proposed integrated methodology provides a comprehensive framework for understanding climate change-induced water scarcity risks, their drivers, and potential consequences. This approach facilitates adaptive decision-making to address the evolving challenges posed by climate change.
Analysis of Data Generation and Preparation for Porosity Prediction in Cold Spray using Machine Learning
Authors: Eberle Martin , Pinches Samuel , Osborne Max , Qin Kai , Ang Andrew 
Published: April 2024

AbstractCold spray is an additive manufacturing and coating process in which powder particles are accelerated to supersonic speeds without melting them and then deposit on a surface to form a layer of a coating. Process parameters and materials affect the characteristics of manufactured parts and therefore must be chosen with care. Machine learning (ML) techniques have been specifically applied in additive manufacturing for tasks such as predicting and characterizing porosity. Machine learning algorithms can learn how a variation in the input spray parameters affects annotated output data, such as experimentally measured part properties. In this work, a dataset was developed from experiments reported in published academic papers, to train ML algorithms for the porosity prediction of cold spray manufactured parts. Data cleaning steps, such as null value replacement and categorical feature handling, were applied to prepare the dataset for the training of different ML models. The dataset was split into training and testing portions, and floating feature selection and hyperparameter optimization were performed using parts of the training set. A final evaluation of all trained models, using the test portion of the dataset, showed that a prediction accuracy with an average deviation of 0-2% porosity of the predicted values compared to the true values can be achieved.
Graphical Abstract
Learning-assisted optimization for transmission switching
Authors: Pineda Salvador , Morales Juan Miguel , Jiménez-Cordero Asunción 
Published: April 2024

AbstractThe design of new strategies that exploit methods from machine learning to facilitate the resolution of challenging and large-scale mathematical optimization problems has recently become an avenue of prolific and promising research. In this paper, we propose a novel learning procedure to assist in the solution of a well-known computationally difficult optimization problem in power systems: The Direct Current Optimal Transmission Switching (DC-OTS) problem. The DC-OTS problem consists in finding the configuration of the power network that results in the cheapest dispatch of the power generating units. With the increasing variability in the operating conditions of power grids, the DC-OTS problem has lately sparked renewed interest, because operational strategies that include topological network changes have proved to be effective and efficient in helping maintain the balance between generation and demand. The DC-OTS problem includes a set of binaries that determine the on/off status of the switchable transmission lines. Therefore, it takes the form of a mixed-integer program, which is NP-hard in general. In this paper, we propose an approach to tackle the DC-OTS problem that leverages known solutions to past instances of the problem to speed up the mixed-integer optimization of a new unseen model. Although our approach does not offer optimality guarantees, a series of numerical experiments run on a real-life power system dataset show that it features a very high success rate in identifying the optimal grid topology (especially when compared to alternative competing heuristics), while rendering remarkable speed-up factors.
A field investigation on the soil management practices in a productive vineyard considering C sequestration and water resistance of soil structure
Authors: Šimanský Vladimír , Jonczak Jerzy , Bordoloi Sanandam , Horák Ján 
Published: April 2024

AbstractOptimal soil structure is linked to soil organic matter (SOM). Different soil management practice alternates SOM but the storing C to soil structure formation in vineyard soils is not sufficiently studied. In addition, understanding the impact of soil management practices in vineyard on changes in SOM and soil structure is crucial for sustainable viticulture. In this study conducted in a productive vineyard near Nitra city, Slovakia, we investigated the dynamics of individual size-fractions of water-stable aggregates (WSA) as results of changes in soil organic matter (SOM) under different soil management practices. Five distinct management practices – Intensive tillage system (T); Intensive tillage system with poultry manure (T + M); Strip grass (G); Fertilization in lower rates of NPK (NPK 1) to grass strips; and Fertilization in higher level of NPK (NPK 2) to grass strips were adopted to to be tested. The results have shown that the highest content of soil organic carbon (SOC) 20.3 ± 3.68 g kg−1, labile carbon (CL) 2.71 ± 0.78 g kg−1, and the most favor soil structure were identified in NPK 1, however, the most effectively improved soil structure and C sequestration (SOC: 19.9 ± 3.88 g kg−1; CL: 2.53 ± 0.83 g kg−1) was under G treatment. SOC had a more significant influence on the content of water-stable aggregates than CL. Intense changes in SOM took place in larger size-fractions of water-stable macroaggregates (WSAma) above 2 mm, while smaller WSAma below 2 mm and water-stable microaggregates were more stable.
Acute, Chronic, and Everyday Physical Pain in Borderline Personality Disorder
Authors: Nance Melissa , Stetsiv Khrystyna , McNamara Ian A. , Carpenter Ryan W. , Hepp Johanna 
Published: April 2024

Abstract
Purpose of Review
Physical pain is an underrecognized area of dysregulation among those with borderline personality disorder (BPD). Disturbances are observed within the experience of acute, chronic, and everyday physical pain experiences for people with BPD. We aimed to synthesize research findings on multiple areas of dysregulation in BPD in order to highlight potential mechanisms underlying the association between BPD and physical pain dysregulation.

Recent Findings
Potential biological mechanisms include altered neural responses to painful stimuli within cognitive-affective regions of the brain, as well as potentially low basal levels of endogenous opioids. Emotion dysregulation broadly mediates dysregulation of physical pain. Certain psychological experiences may attenuate acute physical pain, such as dissociation, whereas others, such as negative affect, may exacerbate it. Social challenges between patients with BPD and healthcare providers may hinder appropriate treatment of chronic pain.

Summary
Dysregulated physical pain is common in BPD and important in shaping health outcomes including elevated BPD symptoms, chronic pain conditions, and risk for problematic substance use.
Shading solutions for sustainable water management: impact of colors and intensities on evaporation and water quality
Authors: Zeadeh Doha , Albalasmeh Ammar , Mohawesh Osama , Unami Koichi 
Published: April 2024

AbstractThis study investigated the effectiveness of suspended shade covers in reducing evaporation rates and improving evaporation suppression efficiency. The experiment tested different shading colors (black, white, and green) and intensities (50, 70, and 90%) while considering ecological and economical aspects. Evaporation was determined using class-A evaporation pans, and various microclimatic variables were measured below the shade covers. Additionally, water quality parameters such as EC, phosphate, nitrate, and chloride concentrations were monitored. The results showed that black covers had the highest evaporation suppression efficiency with values of 56.8, 53.6, and 51.7% observed under 90% shading intensity for the black, green, and white covers, respectively. Despite variations in water quality parameters which all met Jordanian and FAO standards for irrigation water, the economic feasibility of installing these covers was found to be viable due to the resulting benefits in water conservation and crop production. However, selecting the best shading cover should consider the multipurpose use of agricultural reservoirs, including aquaculture, and further studies are recommended to investigate other overlapping aspects on a reservoir scale.
Groundwater quality assessment in the La Mojana region of northern Colombia: implications for consumption, irrigation, and human health risks
Authors: Marrugo-Negrete José , Paternina-Uribe Roberth , Enamorado-Montes German , Herrera-Arango Jairo , Rosso-Pinto Mauricio , Ospino-Contreras Juan , Pinedo-Hernández José 
Published: April 2024

AbstractWater quality is one of the most relevant issues related to water management, with water pollution and access to clean water for drinking and irrigation being common issues in developing countries. Groundwater is an available water source, especially where surface sources are scarce or unsuitable. In this sense, studies to improve knowledge of aquifers should be undertaken where information on groundwater resources is not available. In the current study, 50 groundwater samples were collected from rural and urban wells used by local communities for human consumption and to irrigate local crops. Several indices for drinking water and irrigation use have been used for the assessment of water quality. Conversely, the carcinogenic and noncarcinogenic human health risks due to exposure to heavy metals in the groundwater were estimated, as were the predominant hydrochemical characteristics. The groundwater water quality index (GWQI) shows values < 100 for all the samples; groundwater is recommended for drinking after disinfection. By contrast, the irrigation water quality index (IWQI) shows that 80% of groundwater samples pose a potential threat to irrigation, with restriction categories ranging from high to severe. The individual hazard quotients were < 1. However, the multielement and multipathway hazard index (HI) for children and adults were > 1, whereas As and Mn had a higher contribution to the HI. The Piper trilinear diagram shows that hydrochemical facies of Ca2+ and HCO3− are significantly dominant. By contrast, weathering of rocks and atmospheric precipitation is the dominant factors regulating the chemistry of groundwater in the aquifer system of La Mojana. Our findings indicate that there are significant potential noncarcinogenic health risks for local populations exposed to groundwater.
Modeling HIV-1 infection and NeuroHIV in hiPSCs-derived cerebral organoid cultures
Authors: Donadoni Martina , Cakir Senem , Bellizzi Anna , Swingler Michael , Sariyer Ilker K. 
Published: April 2024

AbstractThe human immunodeficiency virus (HIV) epidemic is an ongoing global health problem affecting 38 million people worldwide with nearly 1.6 million new infections every year. Despite the advent of combined antiretroviral therapy (cART), a large percentage of people with HIV (PWH) still develop neurological deficits, grouped into the term of HIV-associated neurocognitive disorders (HAND). Investigating the neuropathology of HIV is important for understanding mechanisms associated with cognitive impairment seen in PWH. The major obstacle for studying neuroHIV is the lack of suitable in vitro human culture models that could shed light into the HIV-CNS interactions. Recent advances in induced pluripotent stem cell (iPSC) culture and 3D brain organoid systems have allowed the generation of 2D and 3D culture methods that possess a potential to serve as a model of neurotropic viral diseases, including HIV. In this study, we first generated and characterized several hiPSC lines from healthy human donor skin fibroblast cells. hiPSCs were then used for the generation of microglia-containing human cerebral organoids (hCOs). Once fully characterized, hCOs were infected with HIV-1 in the presence and absence of cART regimens and viral infection was studied by cellular, molecular/biochemical, and virological assays. Our results revealed that hCOs were productively infected with HIV-1 as evident by viral p24-ELISA in culture media, RT-qPCR and RNAscope analysis of viral RNA, as well as ddPCR analysis of proviral HIV-1 in genomic DNA samples. More interestingly, replication and gene expression of HIV-1 were also greatly suppressed by cART in hCOs as early as 7 days post-infections. Our results suggest that hCOs derived from hiPSCs support HIV-1 replication and gene expression and may serve as a unique platform to better understand neuropathology of HIV infection in the brain.