bluuebunny/crossref_metadata_2025 huggingface dataset:
A Modified MobileNetV3 Model Using an Attention Mechanism for Eight-Class Classification of Breast Cancer Pathological Images
Authors: Guo Chang , Zhou Qingjian , Jiao Jia , Li Qingyang , Zhu Lin
Published: August 2024

Addressing the challenge of achieving precise subtype classification of breast cancer histopathology images with limited resources, a lightweight model incorporating multi-stage information fusion and an attention mechanism is proposed for this task. Using MobileNetV3 as the backbone, a multi-stage fusion strategy captures the rich image information in breast cancer histopathology images. Additionally, the selective kernel (SK) attention mechanism is introduced in the initial stages of feature extraction, while an improved squeeze-and-excitation coordinate attention (SCA) mechanism is integrated in the later stages to enhance the extraction of both underlying and semantic features. The final feature representations for subtype classification are determined based on the attention map weights computed at each stage. The experimental results demonstrate the model’s outstanding recognition performance on the BreakHis dataset, achieving subtype classification accuracies of 96.259%, 94.763%, 95.511%, and 94.015% at four different magnifications.
Additive Manufacturing of PH 13-8 Mo Family: A Review
Authors: Aydin Gökçe , Andersson Joel , Valiente Bermejo Maria Asuncion
Published: August 2024

The PH 13-8 Mo family of steels belong to the martensitic precipitation hardening stainless steels (MPHSSs) category, which exhibits a good combination of mechanical properties and corrosion resistance. Additive manufacturing (AM) offers advantages, including reduced material waste and the capability to produce complex, near-net-shape parts. Consequently, the application of AM techniques to the PH 13-8 Mo family is being increasingly explored across various industries. This review paper presents the existing literature on the topic and provides an overview. The review starts by presenting information about the PH 13-8 Mo family, including microstructure, chemical compositions, heat treatments, and mechanical properties. Afterwards, the work focuses on presenting the microstructure and resulting properties of PH 13-8 Mo family processed by three different additive manufacturing processes: Powder Bed Fusion using a Laser Beam (PBF-LB), Directed Energy Deposition using an Electric Arc (DED-Arc), and Directed Energy Deposition using a Laser Beam (DED-LB), both in their as-built condition and following post-processing heat treatments. The review concludes with a summary and outlook that highlights existing knowledge gaps and underscores the need for further research to tailor the microstructural evolution and enhance the properties. The findings indicate that AM of the PH 13-8 Mo family has the potential for industrial applications, yet further studies are necessary to optimize its performance.
Design of Adits for People Passing Spacing in High Altitude Highway Tunnels in Cold Regions
Authors: Cui Yuang , Liu Zhiqiang
Published: August 2024

Existing research into this topic primarily focuses on low-altitude areas, neglecting the impact of extreme environmental conditions such as low temperature, low oxygen level, and low pressure in high-altitude regions. Based on the smoke diffusion theory, a series of CFD numerical simulations were conducted in order to investigate the characteristics of smoke diffusion in the highway tunnel at high altitude. The results indicated that the increase in altitude would enhance the longitudinal propagation velocity of smoke, leading to a more pronounced impact on temperature, CO concentration, and visibility at characteristic heights. Meanwhile, the altitude intensifies the inhibitory impact of longitudinal ventilation on smoke diffusion upwind of the fire source and augments the acceleration effect on smoke diffusion downwind, thereby impeding personnel evacuation on the downwind side. By taking the hazardous range at a characteristic height under the impact of wind velocity and the deceleration of evacuation velocity due to altitude into consideration, a new recommended reduction factor was deduced to design adits for people passing spacing in highway tunnels at high altitude. The findings can serve as a valuable reference for the personal evacuation in high-altitude highway tunnel fires and the design of spacing between adits for people passing within such tunnels.
Urban Food Deserts and Cardiovascular Health: Evaluating the Impact of Nutritional Inequities on Elderly Populations in Santiago
Authors: Landaeta-Díaz Leslie , Vergara-Perucich Francisco , Aguirre-Nuñez Carlos , Cancino-Contreras Francisca , Correa-Parra Juan , Ulloa-León Felipe
Published: August 2024

This study examines the link between food deserts and cardiovascular health in older adults in Santiago’s Metropolitan Region, Chile. As the population ages and chronic diseases rise, understanding the impact of food accessibility on health is essential. Using multiscale geographically weighted regression, we analyzed data from the Cardiovascular Health Program, socioeconomic indicators, and food desert maps, sourced from the Chilean Ministry of Health and other databases. Spatial analysis, including Voronoi diagrams, assessed the influence of food deserts on health outcomes. Findings show a significant correlation between limited access to healthy foods and higher cardiovascular disease rates, especially in economically disadvantaged areas. The regression model is significant to contribute to the explanation of disease prevalence, emphasizing the impact of food availability on health. This study highlights the importance of considering spatial factors in urban planning and public health policies. By showcasing the role of food environments in health disparities, it advocates for integrated health interventions. Targeted urban planning to address food deserts can enhance access to healthy foods, improving cardiovascular health and well-being among Santiago’s elderly. The findings provide insights for policymakers to create healthier urban environments and stress the need for sustainable food systems to support public health.
A Route to Selective Arsenate Adsorption in Phosphate Solutions via Ternary Metal Biopolymer Composites
Authors: Bui Nam T. , Steiger Bernd G. K. , Wilson Lee D.
Published: August 2024

With the increased need for improved adsorbents for efficient water treatment, sodium alginate (NaAlg) and chitosan (Chi) represent promising platform biopolymers for the preparation of biocomposite adsorbents for the effective removal of waterborne oxyanion (arsenate (Asi) and orthophosphate (Pi)) contaminants. The TMCs were characterized by spectroscopy (infrared (IR), SEM with an energy dispersive X-ray (SEM-EDX)), point-of-zero-charge (PZC) measurements, and dye adsorption by employing p-nitrophenol at variable pH. Based on dye adsorption results, the adsorbent surface area (SA) was 271 m2/g for Al-TMC, 286 m2/g for Fe-TMC, and 311 m2/g for Cu-TMC. This indicates the role of adsorbent pore structure and swelling in water. Further, the role of either aluminum (Al), copper (Cu), or iron (Fe) for the preparation of TMCs for the selective Asi removal in the presence of Pi as a competitor anion was evaluated. While Al, Fe, and Cu coordinate to the biopolymer framework at C=O sites, only Fe coordinates to –NH2 sites. While Al coordinated via Al-O and interfacial hydroxy groups, Cu showed the formation of Cu2(OH)3NO3 in contrast to Fe, which observed FeOOH formation. Adsorption of Asi was highest for Al-TMC (80 mg/g), followed by Fe-TMC (77 mg/g) and Cu-TMC (31 mg/g). Adsorption of Pi was highest for Al-TMC (93 mg/g), followed by Fe-TMC (66 mg/g) and Cu-TMC (17 mg/g). While Al-TMC showed the highest adsorption capacity overall, only Fe-TMC (followed by Cu-TMC) showed strong arsenate selectivity over orthophosphate. The selectivity toward Asi in presence of Pi was determined and the binary separation factor (αt/c) and the selectivity coefficient (βt) were calculated, where Cu-TMC (αt/c = 6.1; βt = 4.4) and Fe-TMC (αt/c = 8.3; βt = 5.0) exceeded Al-TMC (αt/c = 1.5; βt = 1.2). This work contributes to the field of oxyanion-selective adsorbents via judicious selection of the metal salt precursor during the synthetic design of the ternary biocomposite systems, as demonstrated herein.
The Effect of Waxes on Rapeseed Oil Quality and Acrylamide Development in Potato Fries during Deep-Frying
Authors: Onacik-Gür Sylwia , Ptasznik Stanisław , Siekierko Urszula , Szczepańska Magdalena
Published: August 2024

This paper analyzed the effects of adding rice bran wax (RW) and beeswax (BW) to rapeseed oil as a deep-frying medium and the quality parameters of potato fries, including acrylamide contamination. It was found that the addition of RW and BW caused an increase in the oil’s acidity. However, oil deterioration based on peroxide values and fatty acid profiles did not differ very much from the oil without waxes. The study showed that the addition of rice bran wax at the level of 2.5 g/L of rapeseed oil was able to significantly decrease oil uptake in potato fries. The addition of waxes did not influence the color parameters in the first frying cycle; however, changes were observed later. The browning index indicated that 10 g of BW per liter may darken fries in comparison to rapeseed oil without additives. It was found that with an increase in wax content in the frying medium, potato fries contained more acrylamide.
Cracks in Arch Dams: An Overview of Documented Instances
Authors: Conde André , Toledo Miguel Á. , Salete Eduardo
Published: August 2024

It is essential to understand how failure mechanisms work in arch dams and, in particular, their most common manifestation: cracking. In this paper, the different types of cracking are explained in terms of their causes and consequences. Then, an exhaustive literature review is carried out that results in a detailed compilation of the characteristics of 38 cracked arch dams from all over the world, including crack characteristics (zone, position, dimensions and probable cause). This review is restricted to only those dams for which information on the position of the cracks or dam displacements is publicly available. As part of the review, a brief summary of key data for each dam is included, as well as a compilation of published crack diagrams. The positions of the cracks of all the dams are classified using diagrams in relation to the type of dam and the origin of the crack. Finally, the distribution of some dam parameters and crack features is analyzed by studying the relationships between them.
Site-Specific Spectra for the City of Mexicali, Mexico, Obtained from April 2010 Earthquake Records
Authors: Rodríguez Lozoya Héctor E. , Domínguez Reyes Tonatiuh , Aguilar-Meléndez Armando , Trejo Soto Manuel Edwiges , García-Elías Alejandro , Huerta-Chua Jesús
Published: August 2024

The April 2010 earthquake (Mw = 7.2), which occurred about 40 km to the southeast of the city of Mexicali, Mexico, caused significant damage to buildings. To improve knowledge of the seismic response of the soil due to the occurrence of earthquakes, a response spectrum at 5% damping was calculated. A comparison between the spectral ordinates obtained in this study and the spectra proposed by the regulations of the Federal Electricity Commission (CFE for its acronym in Spanish) in its seismic design for civil works manual, which is currently used as the design standard throughout the country, was made. We calculated response spectra using records from the April 2010 earthquake and a stratigraphic profile of the city to calculate a transfer function. We first corrected the records for site effect due to stations being over sedimentary soil, and then used them as Green functions to perform a numerical simulation of propagation through the stratigraphic profile to obtain a simulated surface record from which response spectra were calculated. Additionally, ambient seismic noise was measured at the same site to get the dominant period (To). We observed that the transfer function was similar to the spectral quotient up to 5 Hz and that To calculated in both ways gave similar values. The comparison suggests that the design spectrum of the CFE regulation can be considered as a representative spectrum for Mexicali for periods greater than 1.3 s, but not for the zone of short periods.
High-Precision Skin Disease Diagnosis through Deep Learning on Dermoscopic Images
Authors: Malik Sadia Ghani , Jamil Syed Shahryar , Aziz Abdul , Ullah Sana , Ullah Inam , Abohashrh Mohammed
Published: August 2024

Dermatological conditions are primarily prevalent in humans and are primarily caused by environmental and climatic fluctuations, as well as various other reasons. Timely identification is the most effective remedy to avert minor ailments from escalating into severe conditions. Diagnosing skin illnesses is consistently challenging for health practitioners. Presently, they rely on conventional methods, such as examining the condition of the skin. State-of-the-art technologies can enhance the accuracy of skin disease diagnosis by utilizing data-driven approaches. This paper presents a Computer Assisted Diagnosis (CAD) framework that has been developed to detect skin illnesses at an early stage. We suggest a computationally efficient and lightweight deep learning model that utilizes a CNN architecture. We then do thorough experiments to compare the performance of shallow and deep learning models. The CNN model under consideration consists of seven convolutional layers and has obtained an accuracy of 87.64% when applied to three distinct disease categories. The studies were conducted using the International Skin Imaging Collaboration (ISIC) dataset, which exclusively consists of dermoscopic images. This study enhances the field of skin disease diagnostics by utilizing state-of-the-art technology, attaining exceptional levels of accuracy, and striving for efficiency improvements. The unique features and future considerations of this technology create opportunities for additional advancements in the automated diagnosis of skin diseases and tailored treatment.
Proof of Concept of a New Revision Procedure for Ceramic Inlays of Acetabular Cups Using a Shape-Memory Alloy Actuator System
Authors: Rotsch Christian , Kemter-Esser Karoline , Dohndorf Johanna , Funke Kerstin , Heyde Christoph-Eckhard , Drossel Welf-Guntram
Published: August 2024

The revision of ceramic inlays of acetabular cups is a challenging surgical procedure. The mechanical impact during the inlay extraction process can damage the ceramic or metal cup rim. To avoid these risks, a concept for a new revision procedure was developed. It is based on an actuator system, which allows a non-destructive release of the ceramic inlay. To integrate the actuator system, different design concepts of acetabular cup components were investigated, and an actuator based on shape-memory alloy (SMA) wires was developed. The process chain for the actuator, starting from nickel-titanium wires manufactured into the actuator geometry by laser welding and thermo-mechanical treatment for the shape setting process up to the functionality evaluation of the actuator system, was implemented on a laboratory scale. The new revision procedure is based on a phase transformation of the SMA wire actuator, which was obtained through two methods—applying an electrical current by an instrument and rinsing the wire with heated water. The phase transformation of the actuator resulted in a contraction between 3.2% and 4.3% compared to its length after pre-stretching and was able to release the ceramic inlay from the cup. Therefore, the developed actuator design and process chain is a proof of concept towards a new revision procedure for modular acetabular cups.
Habitat Loss in the IUCN Extent: Climate Change-Induced Threat on the Red Goral (Naemorhedus baileyi) in the Temperate Mountains of South Asia
Authors: Abedin Imon , Mukherjee Tanoy , Abedin Joynal , Kim Hyun-Woo , Kundu Shantanu
Published: August 2024

Climate change has severely impacted many species, causing rapid declines or extinctions within their essential ecological niches. This deterioration is expected to worsen, particularly in remote high-altitude regions like the Himalayas, which are home to diverse flora and fauna, including many mountainous ungulates. Unfortunately, many of these species lack adaptive strategies to cope with novel climatic conditions. The Red Goral (Naemorhedus baileyi) is a cliff-dwelling species classified as “Vulnerable” by the IUCN due to its small population and restricted range extent. This species has the most restricted range of all goral species, residing in the temperate mountains of northeastern India, northern Myanmar, and China. Given its restricted range and small population, this species is highly threatened by climate change and habitat disruptions, making habitat mapping and modeling crucial for effective conservation. This study employs an ensemble approach (BRT, GLM, MARS, and MaxEnt) in species distribution modeling to assess the distribution, habitat suitability, and connectivity of this species, addressing critical gaps in its understanding. The findings reveal deeply concerning trends, as the model identified only 21,363 km2 (13.01%) of the total IUCN extent as suitable habitat under current conditions. This limited extent is alarming, as it leaves the species with very little refuge to thrive. Furthermore, this situation is compounded by the fact that only around 22.29% of this identified suitable habitat falls within protected areas (PAs), further constraining the species’ ability to survive in a protected landscape. The future projections paint even degraded scenarios, with a predicted decline of over 34% and excessive fragmentation in suitable habitat extent. In addition, the present study identifies precipitation seasonality and elevation as the primary contributing predictors to the distribution of this species. Furthermore, the study identifies nine designated transboundary PAs within the IUCN extent of the Red Goral and the connectivity among them to highlight the crucial role in supporting the species’ survival over time. Moreover, the Dibang Wildlife Sanctuary (DWLS) and Hkakaborazi National Park are revealed as the PAs with the largest extent of suitable habitat in the present scenario. Furthermore, the highest mean connectivity was found between DWLS and Mehao Wildlife Sanctuary (0.0583), while the lowest connectivity was observed between Kamlang Wildlife Sanctuary and Namdapha National Park (0.0172). The study also suggests strategic management planning that is a vital foundation for future research and conservation initiatives, aiming to ensure the long-term survival of the species in its natural habitat.
3D Models Currently Proposed to Investigate Human Skin Aging and Explore Preventive and Reparative Approaches: A Descriptive Review
Authors: Lombardi Francesca , Augello Francesca Rosaria , Ciafarone Alessia , Ciummo Valeria , Altamura Serena , Cinque Benedetta , Palumbo Paola
Published: August 2024

Skin aging is influenced by intrinsic and extrinsic factors that progressively impair skin functionality over time. Investigating the skin aging process requires thorough research using innovative technologies. This review explores the use of in vitro human 3D culture models, serving as valuable alternatives to animal ones, in skin aging research. The aim is to highlight the benefits and necessity of improving the methodology in analyzing the molecular mechanisms underlying human skin aging. Traditional 2D models, including monolayers of keratinocytes, fibroblasts, or melanocytes, even if providing cost-effective and straightforward methods to study critical processes such as extracellular matrix degradation, pigmentation, and the effects of secretome on skin cells, fail to replicate the complex tissue architecture with its intricated interactions. Advanced 3D models (organoid cultures, “skin-on-chip” technologies, reconstructed human skin, and 3D bioprinting) considerably enhance the physiological relevance, enabling a more accurate representation of skin aging and its peculiar features. By reporting the advantages and limitations of 3D models, this review highlights the importance of using advanced in vitro systems to develop practical anti-aging preventive and reparative approaches and improve human translational research in this field. Further exploration of these technologies will provide new opportunities for previously unexplored knowledge on skin aging.
Effects of Obesity and Exercise on Hepatic and Pancreatic Lipid Content and Glucose Metabolism: PET Studies in Twins Discordant for BMI
Authors: Lietzén Martin S. , Mari Andrea , Ojala Ronja , Hentilä Jaakko , Koskensalo Kalle , Lautamäki Riikka , Löyttyniemi Eliisa , Parkkola Riitta , Saunavaara Virva , Kirjavainen Anna K. , Rajander Johan , Malm Tarja , Lahti Leo , Rinne Juha O. , Pietiläinen Kirsi H. , Iozzo Patricia , Hannukainen Jarna C.
Published: August 2024

Obesity and sedentarism are associated with increased liver and pancreatic fat content (LFC and PFC, respectively) as well as impaired organ metabolism. Exercise training is known to decrease organ ectopic fat but its effects on organ metabolism are unclear. Genetic background affects susceptibility to obesity and the response to training. We studied the effects of regular exercise training on LFC, PFC, and metabolism in monozygotic twin pairs discordant for BMI. We recruited 12 BMI-discordant monozygotic twin pairs (age 40.4, SD 4.5 years; BMI 32.9, SD 7.6, 8 female pairs). Ten pairs completed six months of training intervention. We measured hepatic insulin-stimulated glucose uptake using [18F]FDG-PET and fat content using magnetic resonance spectroscopy before and after the intervention. At baseline LFC, PFC, gamma-glutamyl transferase (GT), and hepatic glucose uptake were significantly higher in the heavier twins compared to the leaner co-twins (p = 0.018, p = 0.02 and p = 0.01, respectively). Response to training in liver glucose uptake and GT differed between the twins (Time*group p = 0.04 and p = 0.004, respectively). Liver glucose uptake tended to decrease, and GT decreased only in the heavier twins (p = 0.032). In BMI-discordant twins, heavier twins showed higher LFC and PFC, which may underlie the observed increase in liver glucose uptake and GT. These alterations were mitigated by exercise. The small number of participants makes the results preliminary, and future research with a larger pool of participants is warranted.
Integrating Computational Design and Experimental Approaches for Next-Generation Biologics
Authors: Son Ahrum , Park Jongham , Kim Woojin , Lee Wonseok , Yoon Yoonki , Ji Jaeho , Kim Hyunsoo
Published: August 2024

Therapeutic protein engineering has revolutionized medicine by enabling the development of highly specific and potent treatments for a wide range of diseases. This review examines recent advances in computational and experimental approaches for engineering improved protein therapeutics. Key areas of focus include antibody engineering, enzyme replacement therapies, and cytokine-based drugs. Computational methods like structure-based design, machine learning integration, and protein language models have dramatically enhanced our ability to predict protein properties and guide engineering efforts. Experimental techniques such as directed evolution and rational design approaches continue to evolve, with high-throughput methods accelerating the discovery process. Applications of these methods have led to breakthroughs in affinity maturation, bispecific antibodies, enzyme stability enhancement, and the development of conditionally active cytokines. Emerging approaches like intracellular protein delivery, stimulus-responsive proteins, and de novo designed therapeutic proteins offer exciting new possibilities. However, challenges remain in predicting in vivo behavior, scalable manufacturing, immunogenicity mitigation, and targeted delivery. Addressing these challenges will require continued integration of computational and experimental methods, as well as a deeper understanding of protein behavior in complex physiological environments. As the field advances, we can anticipate increasingly sophisticated and effective protein therapeutics for treating human diseases.
A Methodical Framework Utilizing Transforms and Biomimetic Intelligence-Based Optimization with Machine Learning for Speech Emotion Recognition
Authors: Prabhakar Sunil Kumar , Won Dong-Ok
Published: August 2024

Speech emotion recognition (SER) tasks are conducted to extract emotional features from speech signals. The characteristic parameters are analyzed, and the speech emotional states are judged. At present, SER is an important aspect of artificial psychology and artificial intelligence, as it is widely implemented in many applications in the human–computer interface, medical, and entertainment fields. In this work, six transforms, namely, the synchrosqueezing transform, fractional Stockwell transform (FST), K-sine transform-dependent integrated system (KSTDIS), flexible analytic wavelet transform (FAWT), chirplet transform, and superlet transform, are initially applied to speech emotion signals. Once the transforms are applied and the features are extracted, the essential features are selected using three techniques: the Overlapping Information Feature Selection (OIFS) technique followed by two biomimetic intelligence-based optimization techniques, namely, Harris Hawks Optimization (HHO) and the Chameleon Swarm Algorithm (CSA). The selected features are then classified with the help of ten basic machine learning classifiers, with special emphasis given to the extreme learning machine (ELM) and twin extreme learning machine (TELM) classifiers. An experiment is conducted on four publicly available datasets, namely, EMOVO, RAVDESS, SAVEE, and Berlin Emo-DB. The best results are obtained as follows: the Chirplet + CSA + TELM combination obtains a classification accuracy of 80.63% on the EMOVO dataset, the FAWT + HHO + TELM combination obtains a classification accuracy of 85.76% on the RAVDESS dataset, the Chirplet + OIFS + TELM combination obtains a classification accuracy of 83.94% on the SAVEE dataset, and, finally, the KSTDIS + CSA + TELM combination obtains a classification accuracy of 89.77% on the Berlin Emo-DB dataset.
Recent Advances in Natural-Polymer-Based Hydrogels for Body Movement and Biomedical Monitoring
Authors: Liu Jing , Li Saisai , Li Shuoze , Tian Jinyue , Li Hang , Pan Zhifeng , Lu Lijun , Mao Yanchao
Published: August 2024

In recent years, the interest in medical monitoring for human health has been rapidly increasing due to widespread concern. Hydrogels are widely used in medical monitoring and other fields due to their excellent mechanical properties, electrical conductivity and adhesion. However, some of the non-degradable materials in hydrogels may cause some environmental damage and resource waste. Therefore, organic renewable natural polymers with excellent properties of biocompatibility, biodegradability, low cost and non-toxicity are expected to serve as an alternative to those non-degradable materials, and also provide a broad application prospect for the development of natural-polymer-based hydrogels as flexible electronic devices. This paper reviews the progress of research on many different types of natural-polymer-based hydrogels such as proteins and polysaccharides. The applications of natural-polymer-based hydrogels in body movement detection and biomedical monitoring are then discussed. Finally, the present challenges and future prospects of natural polymer-based hydrogels are summarized.
Overcoming Limited Access to Virus Infection Rapid Testing: Development of a Lateral Flow Test for SARS-CoV-2 with Locally Available Resources
Authors: Peri Ibáñez Estefanía S. , Mazzeo Agostina , Silva Carolina , Juncos Maria Juliana , Costa Navarro Guadalupe S. , Pallarés Horacio M. , Wolos Virginia J. , Fiszman Gabriel L. , Mundo Silvia L. , Caramelo Julio J. , Yanovsky Marcelo J. , Fingermann Matías , Castello Alejandro A. , Gamarnik Andrea V. , Peinetti Ana S. , Capdevila Daiana A.
Published: August 2024

The COVID-19 pandemic highlighted testing inequities in developing countries. Lack of lateral flow test (LFT) manufacturing capacity was a major COVID-19 response bottleneck in low- and middle-income regions. Here we report the development of an open-access LFT for SARS-CoV-2 detection comparable to commercial tests that requires only locally available supplies. The main critical resource is a locally developed horse polyclonal antibody (pAb) whose sensitivity and selectivity are greatly enhanced by affinity purification. We demonstrate that these Abs can perform similarly to commercial monoclonal antibodies (mAbs), as well as mAbs and other pAbs developed against the same antigen. We report a workflow for test optimization using nasopharyngeal swabs collected for RT-qPCR, spiked with the inactivated virus to determine analytical performance characteristics as the limit of detection, among others. Our final prototype showed a performance similar to available tests (sensitivity of 83.3% compared to RT-qPCR, and 90.9% compared to commercial antigen tests). Finally, we discuss the possibility and the challenges of utilizing affinity-purified pAbs as an alternative for the local development of antigen tests in an outbreak context and as a tool to address inequalities in access to rapid tests.
Recent Advances in Dielectrophoretic Manipulation and Separation of Microparticles and Biological Cells
Authors: Yao Junzhu , Zhao Kai , Lou Jia , Zhang Kaihuan
Published: August 2024

Dielectrophoresis (DEP) is an advanced microfluidic manipulation technique that is based on the interaction of polarized particles with the spatial gradient of a non-uniform electric field to achieve non-contact and highly selective manipulation of particles. In recent years, DEP has made remarkable progress in the field of microfluidics, and it has gradually transitioned from laboratory-scale research to high-throughput manipulation in practical applications. This paper reviews the recent advances in dielectric manipulation and separation of microparticles and biological cells and discusses in detail the design of chip structures for the two main methods, direct current dielectrophoresis (DC-DEP) and alternating current dielectrophoresis (AC-DEP). The working principles, technical implementation details, and other improved designs of electrode-based and insulator-based chips are summarized. Functional customization of DEP systems with specific capabilities, including separation, capture, purification, aggregation, and assembly of particles and cells, is then performed. The aim of this paper is to provide new ideas for the design of novel DEP micro/nano platforms with the desired high throughput for further development in practical applications.
Classification of Known and Unknown Study Items in a Memory Task Using Single-Trial Event-Related Potentials and Convolutional Neural Networks
Authors: Delgado-Munoz Jorge , Matsunaka Reiko , Hiraki Kazuo
Published: August 2024

This study examines the feasibility of using event-related potentials (ERPs) obtained from electroencephalographic (EEG) recordings as biomarkers for long-term memory item classification. Previous studies have identified old/new effects in memory paradigms associated with explicit long-term memory and familiarity. Recent advancements in convolutional neural networks (CNNs) have enabled the classification of ERP trials under different conditions and the identification of features related to neural processes at the single-trial level. We employed this approach to compare three CNN models with distinct architectures using experimental data. Participants (N = 25) performed an association memory task while recording ERPs that were used for training and validation of the CNN models. The EEGNET-based model achieved the most reliable performance in terms of precision, recall, and specificity compared with the shallow and deep convolutional approaches. The classification accuracy of this model reached 62% for known items and 66% for unknown items. Good overall accuracy requires a trade-off between recall and specificity and depends on the architecture of the model and the dataset size. These results suggest the possibility of integrating ERP and CNN into online learning tools and identifying the underlying processes related to long-term memorization.
A Learning Dendritic Neuron-Based Motion Direction Detective System and Its Application to Grayscale Images
Authors: Chen Tianqi , Todo Yuki , Takano Ryusei , Qiu Zhiyu , Hua Yuxiao , Tang Zheng
Published: August 2024

In recent research, dendritic neuron-based models have shown promise in effectively learning and recognizing object motion direction within binary images. Leveraging the dendritic neuron structure and On–Off Response mechanism within the primary cortex, this approach has notably reduced learning time and costs compared to traditional neural networks. This paper advances the existing model by integrating bio-inspired components into a learnable dendritic neuron-based artificial visual system (AVS), specifically incorporating mechanisms from horizontal and bipolar cells. This enhancement enables the model to proficiently identify object motion directions in grayscale images, aligning its threshold with human-like perception. The enhanced model demonstrates superior efficiency in motion direction recognition, requiring less data (90% less than other deep models) and less time for training. Experimental findings highlight the model’s remarkable robustness, indicating significant potential for real-world applications. The integration of bio-inspired features not only enhances performance but also opens avenues for further exploration in neural network research. Notably, the application of this model to realistic object recognition yields convincing accuracy at nearly 100%, underscoring its practical utility.
Factors Associated with Anxiety, Depression, and Quality of Life in Patients with Psoriasis: A Cross-Sectional Study
Authors: Cipolla Salvatore , Catapano Pierluigi , Bonamico Antonio Fiorino , De Santis Valeria , Murolo Roberta , Romano Francesca , Volpicelli Antonio , Perris Francesco , Lo Schiavo Ada , Fabrazzo Michele , Catapano Francesco
Published: August 2024

Background: Psoriasis is a chronic skin disorder affecting 2–3% of the global population, and is associated with several comorbidities, including psychiatric disorders. This study aimed to identify factors influencing anxiety, depression, and quality of life (QoL) in patients with psoriasis. Methods: This observational study included 112 patients diagnosed with psoriasis. Dermatological and psychiatric assessments were conducted using Psodisk, the Hamilton Anxiety Rating Scale (HAM-A), Hamilton Depression Rating Scale (HAM-D), Symptom Checklist-90-Revised (SCL-90-R), and 36-Item Short Form Health Survey (SF-36). Descriptive statistics, correlation analyses, and multivariate regression models were employed. Results: The sample was predominantly middle-aged males (mean age 48.91 years). Females (p < 0.001), patients with arthritis (p < 0.05), and those with a sedentary lifestyle (p < 0.05) showed higher anxiety and depression scores. Psodisk subscales significantly correlated with psychiatric symptoms and QoL measures (p < 0.001). Pain (B: 0.63, p < 0.05; B: −2.03, p < 0.01) and sleep disturbances (B: 0.68, p < 0.01; B: 0.60, p < 0.01; B: −1.46, p < 0.01; B: −1.57, p < 0.05; B: 3.91, p < 0.05) emerged as major predictors of poor mental health and reduced QoL. Conclusions. The study underscores the complex relationship between psoriasis, psychiatric comorbidities, and QoL. Key factors exacerbating anxiety and depression include female gender, arthritis, and sedentary lifestyle. Comprehensive management of psoriasis should address both dermatological and psychological aspects, with a focus on pain relief and improving sleep quality to enhance overall patient well-being.
Young Children’s Directed Question Asking in Preschool Classrooms
Authors: Wong Michelle , Choi Koeun , Barak Libby , Lapidow Elizabeth , Austin Jennifer , Shafto Patrick , Bonawitz Elizabeth
Published: August 2024

Question asking is a prevalent aspect of children’s speech, providing a means by which young learners can rapidly gain information about the world. Previous research has demonstrated that children exhibit sensitivity to the knowledge state of potential informants in laboratory settings. However, it remains unclear whether and how young children are inclined to direct questions that support learning deeper content to more knowledgeable informants in naturalistic classroom contexts. In this study, we examined children’s question-asking targets (adults, other preschoolers, self-talk) during an open-play period in a US preschool classroom and assessed how the cognitive and linguistic characteristics of questions varied as a function of the intended recipient. Further, we examined how these patterns changed with age. We recorded the spontaneous speech of individual children between the ages of 3 and 6 years (N = 30, totaling 2875 utterances) in 40-min open-period sessions in their preschool day, noting whether the speech was directed toward an adult, another child, or was stated to self. We publish this fully transcribed database with contextual and linguistic details coded as open access to all future researchers. We found that questions accounted for a greater proportion of preschoolers’ adult-directed speech than of their child-directed and self-directed speech, with a particular increase in questions that supported broader learning goals when directed to an adult. Younger children directed a higher proportion of learning questions to adults than themselves, whereas older children asked similar proportions of questions to both, suggesting a difference in younger and older children’s question-asking strategies. Although children used greater lexical diversity in questions than in other utterances, their question formulation in terms of length and diversity remained consistent across age and recipient types, reflecting their general linguistic abilities. Our findings reveal that children discriminately choose “what” and “whom” to ask in daily spontaneous conversations. Even in less-structured school contexts, preschoolers direct questions to the informant most likely to be able to provide an adequate answer.
Tool, Threat, Tutor, Talk, and Trend: College Students’ Attitudes toward ChatGPT
Authors: Yu Sen-Chi , Huang Yueh-Min , Wu Ting-Ting
Published: August 2024

The purposes of this study are to investigate college students’ attitudes toward ChatGPT and to understand whether gender makes any difference in their attitudes. We developed the ChatGPT attitude scale (CAS) and administrated it to a sample of 516 Taiwan college students. Through an exploratory factor analysis, the 5-T (Tool, Tutor, Talk, Trend, and Threat) model of CAS was extracted and validated via confirmatory factor analysis. The CAS exhibited good reliability and validity and can be used to explain ChatGPT attitudes. According to our findings, university students consider ChatGPT an important “Tool” in their daily life. Additionally, ChatGPT plays a significant “Tutor” role, assisting with language translation and knowledge learning. Besides its utilitarian functions, ChatGPT also serves as a “Talk” feature, offering interactive chat and emotional support. Currently, students also acknowledge ChatGPT as an important “Trend” of the times, but they are also deeply concerned about the potential “Threat” of content falsification and job displacement brought on by ChatGPT. In terms of gender differences, compared to females, males scored higher than females in the total scale and in the Tool, Tutor, and Trend subscales. However, there was no significant difference between males and females in the Talk and Threat subscales. This gender difference result differs from previous research on robots or social media.
The Effects of the Medium of Notetaking on the Delayed Learning Effect of College Students: A Mediated Moderation Model
Authors: Xu Lei , Cai Shuangshuang , Liu Yanxi , Chen Jiwen , Peng Shun , Andrasik Frank
Published: August 2024

This study systematically probed the relationship between the medium of taking classroom notes (virtual variable, electronic notetaking = 0 vs. traditional notetaking = 1), the word count in each medium, as well as the review process, and the students’ delayed learning effect for each notetaking approach. Data were collected from 189 college students, with the influence of gender and prior knowledge being controlled. The conclusions were as follows. (1) The notetaking medium was positively correlated with delayed test scores, irrespective of whether reviews were allowed or not. (2) The mediating role of word count between notetaking medium and delayed test scores was moderated by review. That is, when reviews were allowed, a significant correlation was found between the medium of the notes and the delayed test scores; when reviews were not allowed, the mediating effect of word count was not significant.
Automated Defect Detection on Dry-Hanging Stone Curtain Walls through Colored Point Clouds
Authors: Yao Zhidong , Li Xuelai , Yan Guihai , Lin Zhongliang , Wang Gang , Liu Changyong , Yang Xincong
Published: August 2024

Stone curtain walls are widely used in contemporary architectures; however, their regular inspection is always labor-intensive, time-consuming, and hazardous due to the complex and enclosed spatial structure of these high-rise building enclosures. To address this issue, this study proposes an automated and novel inspection method, which is composed of the following three steps: First, we utilize 3D laser scanning technology to capture colored point cloud data of the stone curtain wall system; subsequently, by extracting and processing the integration of color and depth information, the stone panels and end sealants are precisely segmented; finally, various defects, such as cracks, unevenness, and irregularities, are automatically identified through artificial intelligence algorithms in a timely manner. To validate the proposed method, an on-site experiment was carried out to demonstrate the effectiveness in detecting multiple defects concurrently on stone curtain walls. The experimental results showed that our proposed method could provide a non-contact and automated inspection alternative for all the stone curtain walls with a high accuracy of anomaly detection, facilitating rational maintenance plans and strategies to ensure the safety and performance of these modern building enclosures.
Electrical Resistance Tomography (ERT) for Concrete Structure Applications: A Review
Authors: Jeon Dongho , Yoon Seyoon
Published: August 2024

Electrical resistance tomography (ERT) is gaining recognition as an effective, affordable, and nondestructive tool for monitoring and imaging concrete structures. This paper discusses ERT’s applications, including crack detection, moisture ingress monitoring, steel reinforcement assessment, and chloride level profiling within concrete. Recent advancements, such as time-lapse ERT and artificial intelligence (AI) integration, have enhanced image resolution and provided detailed data for infrastructure monitoring. However, challenges remain regarding the need for better spatial resolution, concrete-compatible electrodes, and integration with other nondestructive testing techniques. Addressing these issues will expand the applicability and reliability of the current ERT, making it an invaluable tool for infrastructure maintenance and monitoring.
Exploring the Economic Viability of Virtual Reality in Architectural, Engineering, and Construction Education
Authors: Farouk Abdelrahman M. , Naganathan Hariharan , Rahman Rahimi A. , Kim Jonghoon
Published: August 2024

The role of technology in education is becoming increasingly important, and the introduction of advanced technology and AI is transforming the way we learn. Virtual reality (VR) is an effective technology that enhances student engagement and improves learning outcomes. However, the cost of implementing VR is a significant concern for educational institutions, making integrating VR technology into education challenging. To address this challenge, this study aims to explore the costs associated with integrating VR into architectural, engineering, and construction (AEC) education. The study had three objectives: to identify relevant case studies that utilized VR in AEC education, to perform keyword analysis, and to conduct a cost analysis of the selected case studies. The thematic analysis identified VR applications in various categories, including VR platforms, construction safety training, design review simulators, civil engineering labs, building information modeling (BIM) integration, architectural design, and surveying engineering. The results revealed that the cost of VR varies based on the application, indicating that it is possible to implement VR in education even on a limited budget. This research provides valuable insights and recommendations for researchers and practitioners who want to adopt VR technology in AEC education effectively.
An NSCT-Based Multifrequency GPR Data-Fusion Method for Concealed Damage Detection
Authors: Wang Junfang , Li Xiangxiong , Zeng Huike , Lin Jianfu , Xue Shiming , Wang Jing , Zhou Yanfeng
Published: August 2024

Ground-penetrating radar (GPR) is widely employed as a non-destructive tool for subsurface detection of transport infrastructures. Typically, data collected by high-frequency antennas offer high resolution but limited penetration depth, whereas data from low-frequency antennas provide deeper penetration but lower resolution. To simultaneously achieve high resolution and deep penetration via a composite radargram, a Non-Subsampled Contourlet Transform (NSCT) algorithm-based multifrequency GPR data-fusion method is proposed by integrating NSCT with appropriate fusion rules, respectively, for high-frequency and low-frequency coefficients of decomposed radargrams and by incorporating quantitative assessment metrics. Despite the advantages of NSCT in image processing, its applications to GPR data fusion for concealed damage identification of transport infrastructures are rarely reported. Numerical simulation, tunnel model test, and on-site road test are conducted for performance validation. The comparison between the evaluation metrics before and after fusion demonstrates the effectiveness of the proposed fusion method. Both shallow and deep hollow targets hidden in the simulated concrete structure, real tunnel model, and road are identified through one radargram obtained by fusing different radargrams. The significance of this study is producing a high-quality composite radargram to enable multi-depth concealed damage detection and exempting human interference in the interpretation of multiple radargrams.