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9339 KiB

Open AccessArticle

Research on the Multi-Sensory Experience Design of Interior Spaces from the Perspective of Spatial Perception: A Case Study of Suzhou Coffee Roasting Factory

by Haochen Xu, Jinxiang Zhao, Changjiang Jin, Ning Zhu and Ye Chai

Buildings 2025, 15(8), 1393; https://doi.org/10.3390/buildings15081393 (registering DOI) - 21 Apr 2025

Abstract

With globalization and the transformation of socio-cultural structures, the focus of spatial design has shifted from functionality to perceptual experience and atmospheric creation. This study draws on the spatial perception theory and the phenomenology of perception to examine how sensory subjects perceive and [...] Read more.

With globalization and the transformation of socio-cultural structures, the focus of spatial design has shifted from functionality to perceptual experience and atmospheric creation. This study draws on the spatial perception theory and the phenomenology of perception to examine how sensory subjects perceive and respond to the physical attributes of space. It explores key elements that shape spatial experiences, including lighting, color, spatial form, sound, material, and scent, all of which contribute to the construction of emotional ambiance and the perceptual character of interior environments. Based on this foundation, this study proposes multi-sensory design strategies for interior spaces, including the following: (1) visual perception: modifying color and lighting to establish emotional ambiance and enhance spatial depth; (2) auditory perception: crafting soundscapes that deepen immersion; (3) tactile perception: designing both direct and indirect tactile experiences; and (4) olfactory and gustatory perception: incorporating scent design to evoke memory and forge emotional connections. To demonstrate the practical potential of these strategies, this study presents a conceptual design case of a coffee roasting factory in Suzhou. The design integrates visual, auditory, tactile, olfactory, and gustatory elements to enhance users’ overall spatial perception through multi-sensory coordination. This study ultimately seeks to provide theoretical insights into practical design strategies, highlighting the importance of perceptual experience in improving spatial quality and guiding future interior design practice. Full article

(This article belongs to the Special Issue Art and Design for Healing and Wellness in the Built Environment)

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Open AccessArticle

Improving Thermal Environment of Power Generation Cabin via Vapor Chamber in Cold Regions

by Hao Zhai, Xianyi Jiang and Chengbin Zhang

Processes 2025, 13(4), 1260; https://doi.org/10.3390/pr13041260 (registering DOI) - 21 Apr 2025

Abstract

This study introduces the innovative application of a vapor chamber to mitigate fuel freezing and temperature disparity in power generation cabins operating under extreme cold conditions. A vapor chamber was designed and implemented within a low-temperature power generation platform in Daqing, China, where [...] Read more.

This study introduces the innovative application of a vapor chamber to mitigate fuel freezing and temperature disparity in power generation cabins operating under extreme cold conditions. A vapor chamber was designed and implemented within a low-temperature power generation platform in Daqing, China, where outdoor temperatures were below −20 °C. The research focused on evaluating the thermal performance of the cabin under natural and forced convection conditions, with and without the vapor chamber. The experimental investigations assessed the effects of the vapor chamber on the thermal dynamics of the power generation cabin, particularly the temperature of the bottom fuel oil and the air temperature distribution. The results indicated that without the vapor chamber significant temperature disparities and potential risks to electrical equipment were present. The vapor chamber effectively utilizes the heat generated by the diesel engine, thus accelerating the heating rate of the fuel at the bottom. It reduces the duration of the decrease in the oil temperature of the upper and lower layers during the initial start-up from 0.44 h and 0.5 h to 0.31 h and 0.35 h, respectively, effectively preventing the risk of fuel freezing in the initial start-up stage. In addition, the installation of the vaporization chamber significantly improves the temperature uniformity of the air inside the cabin. The maximum temperature difference between the upper and lower air in the cabin decreases by 33 °C, effectively improving the overall thermal environment. Full article

(This article belongs to the Section Energy Systems)

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3019 KiB

Open AccessArticle

A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition

by Lianglian Gu, Guangzhi Di, Danju Lv, Yan Zhang, Yueyun Yu, Wei Li and Ziqian Wang

Appl. Sci. 2025, 15(8), 4595; https://doi.org/10.3390/app15084595 (registering DOI) - 21 Apr 2025

Abstract

Birdsong is a valuable indicator of rich biodiversity and ecological significance. Although feature extraction has demonstrated satisfactory performance in classification, single-scale feature extraction methods may not fully capture the complexity of birdsong, potentially leading to suboptimal classification outcomes. The integration of multi-scale feature [...] Read more.

Birdsong is a valuable indicator of rich biodiversity and ecological significance. Although feature extraction has demonstrated satisfactory performance in classification, single-scale feature extraction methods may not fully capture the complexity of birdsong, potentially leading to suboptimal classification outcomes. The integration of multi-scale feature extraction and fusion enables the model to better handle scale variations, thereby enhancing its adaptability across different scales. To address this issue, we propose a multi-scale hybrid convolutional attention mechanism model (MUSCA). This method combines depthwise separable convolution and traditional convolution for feature extraction and incorporates self-attention and spatial attention mechanisms to refine spatial and channel features, thereby improving the effectiveness of multi-scale feature extraction. To further enhance multi-scale feature fusion, a layer-by-layer alignment feature fusion method is developed to establish a deeper correlation, thereby improving classification accuracy and robustness. Using the above method, we identified 20 bird species on three spectrograms, wavelet spectrogram, log-Mel spectrogram and log-spectrogram, with recognition rates of 93.79%, 96.97% and 95.44%, respectively. Compared with the resnet18 model, it increased by 3.26%, 1.88% and 3.09%, respectively. The results indicate that the MUSCA method proposed in this paper is competitive compared to recent and state-of-the-art methods. Full article

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Open AccessArticle

Morphology of External Genitalia in the Genus Acanthoponera Mayr, with Redescription of A. mucronata (Roger) Male (Hymenoptera: Formicidae: Ectatomminae)

by Stefano Cantone and Andrea Di Giulio

Insects 2025, 16(4), 436; https://doi.org/10.3390/insects16040436 (registering DOI) - 21 Apr 2025

Abstract

In this study, using scanning electron microscope (SEM) and optical microscopy, we give a detailed description of the Acanthoponera mucronata male, supplementing the former male-based genus diagnoses. In particular, we described for the first time the following characters: the morphology of the external [...] Read more.

In this study, using scanning electron microscope (SEM) and optical microscopy, we give a detailed description of the Acanthoponera mucronata male, supplementing the former male-based genus diagnoses. In particular, we described for the first time the following characters: the morphology of the external genitalia, the peculiar antennal cleaning and the absence of the metapleural gland orifice. In addition, we show the pretarsal claws and the ventral excavation in the gaster that represent diagnostic male features of all Acanthoponera species, never imaged before. The use of modern taxonomic standards is particularly important in order to make these data available to future comparative analyses. Full article

(This article belongs to the Section Social Insects and Apiculture)

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Open AccessArticle

The Development of a Methodology for Assessing Data Value Through the Identification of Key Determinants

by Daye Lee and Byungun Yoon

Systems 2025, 13(4), 305; https://doi.org/10.3390/systems13040305 (registering DOI) - 21 Apr 2025

Abstract

This study introduces a methodology for assessing data value by identifying the key determinants that influence it. As data represents critical assets in modern business, companies must evaluate and use them strategically to maintain competitiveness. However, the intangible and complex nature of data [...] Read more.

This study introduces a methodology for assessing data value by identifying the key determinants that influence it. As data represents critical assets in modern business, companies must evaluate and use them strategically to maintain competitiveness. However, the intangible and complex nature of data makes objective valuation difficult. The proposed methodology categorizes data value determinants into two groups: essential value factors (completeness, accuracy, uniqueness, and consistency) and value-of-use factors (risk, timeliness, restrictive use, accessibility, and utility). This study analyzes the impact of each factor on the data value using quantitative methods. A regression analysis reveals the influence, interactions, and relative importance of these determinants. A real-world case study on the “Papers with Code” platform—widely used in machine learning research—demonstrates the methodology in practice. The results indicate that essential value factors, such as Percentage Correct and Task, have the strongest positive effect on data value, which underscores the importance of accuracy and relevance to specific applications. In contrast, factors such as Similar Datasets and Benchmarks reduce the data value, which highlights the need for uniqueness and differentiation in determining the value of a company’s data assets. This study provides practical guidelines for companies on the key factors to focus on when evaluating and managing data value. This study offers practical guidance on prioritizing value-related factors and enables more effective investment and utilization strategies. By addressing current limitations in data valuation and presenting a new approach, this study enhances data-driven decision-making and strengthens its associated competitive advantage. Full article

(This article belongs to the Special Issue Data-Driven Methods in Business Process Management)

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Open AccessArticle

Investigation of the Process Optimization for L-PBF Hastelloy X Alloy on Microstructure and Mechanical Properties

by Phuangphaga Daram, Masahiro Kusano and Makoto Watanabe

Materials 2025, 18(8), 1890; https://doi.org/10.3390/ma18081890 (registering DOI) - 21 Apr 2025

Abstract

The purpose of this study is to investigate the effects of process parameters on the microstructure and mechanical properties of the Hastelloy X (HX) alloy using a laser powder bed fusion (L-PBF) process. A combined experimental and numerical approach was used to evaluate [...] Read more.

The purpose of this study is to investigate the effects of process parameters on the microstructure and mechanical properties of the Hastelloy X (HX) alloy using a laser powder bed fusion (L-PBF) process. A combined experimental and numerical approach was used to evaluate the influence of the energy density distribution and temperature evolution on the microstructure, defects, and mechanical properties. After the specimens were built on SUS304 substrate by the L-PBF, the microstructure and defects in the specimens were analyzed by SEM and EBSD analysis methods, and then the hardness and the tensile tests were performed. The cooling rate under different laser conditions was obtained by the finite element method (FEM). The results show that a low volume energy density (VED) was applied to the unmelted powder particles, and a high energy density resulted in spherical defects. In addition, the microstructures were found to coarsen with increasing the energy density along with a tendency to strengthen the (001) texture orientation in both x–y and x–z planes. Compared to the parts with the thermal history from numerical results, the low cooling rate with high energy density had larger crystal grains elongated along the building direction, coarser sub-grains, resulting in a reduction in microhardness and yield strength together with an increase in elongation for the L-PBF HX alloy. The presented results provide new insights into the effects of parameters and the cooling rates. It can play an important role in optimizing the L-PBF processing parameters, identifying the cause of defects, and controlling the cooling rates for the crystallographic texture in such a way as to guide the development of better metrics for designing processing parameters with the desired mechanical properties. Full article

(This article belongs to the Special Issue Processing–Microstructure–Properties Loop in Materials and Metallurgical Engineering)

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Open AccessArticle

Detection and Pattern Recognition of Chemical Warfare Agents by MOS-Based MEMS Gas Sensor Array

by Mengxue Xu, Xiaochun Hu, Hongpeng Zhang, Ting Miao, Lan Ma, Jing Liang, Yuefeng Zhu, Haiyan Zhu, Zhenxing Cheng and Xuhui Sun

Sensors 2025, 25(8), 2633; https://doi.org/10.3390/s25082633 (registering DOI) - 21 Apr 2025

Abstract

Chemical warfare agents (CWAs), including hydrogen cyanide (AC), 2-[fluoro(methyl)phosphoryl]oxypropane (GB), 3-[fluoro(methyl)phosphoryl]oxy-2,2-dimethylbutane (GD), ethyl S-(2-diisopropylaminoethyl) methylphosphonothioate (VX), and di-2-chloroethyl sulfide (HD), pose a great threat to public safety; therefore, it is important to develop sensing technology for CWAs. Herein, a sensor array consisting of [...] Read more.

Chemical warfare agents (CWAs), including hydrogen cyanide (AC), 2-[fluoro(methyl)phosphoryl]oxypropane (GB), 3-[fluoro(methyl)phosphoryl]oxy-2,2-dimethylbutane (GD), ethyl S-(2-diisopropylaminoethyl) methylphosphonothioate (VX), and di-2-chloroethyl sulfide (HD), pose a great threat to public safety; therefore, it is important to develop sensing technology for CWAs. Herein, a sensor array consisting of 24 metal oxide semiconductor (MOS)-based MEMS sensors with good gas sensing performance, a simple device structure (0.9 mm × 0.9 mm), and low power consumption (<10 mW on average) was developed. The experimental results show that there are always several sensors among the 24 sensors that show good sensing performance in relation to each CWA, such as a relatively significant response, a broad detection range (AC: 5.8–89 ppm; GB: 0.04–0.47 ppm; GD: 0.06–4.7 ppm; VX: 9.978 × 10⁻⁴–1.101 × 10⁻³; HD: 0.61–4.9 ppm), and a low detection limit that is lower than the immediately dangerous for life and health (IDLH) level of the five CWAs. This indicates that these sensors can meet the needs for qualitative detection and can provide an early warning regarding low concentrations of CWAs. In addition, features were extracted from the initial kinetic characteristics and dynamic change characteristics of the sensing response. Finally, principal component analysis (PCA) and machine learning algorithms were applied for CWA classification. The obtained PCA plots showed significant differences between groups, and the narrow neural network among the machine learning algorithms achieves a prediction accuracy of nearly 100.0%. In summary, the proposed MOS-based MEMS sensor array driven by pattern recognition algorithms can be integrated into portable devices, showing great potential and practical applications in the rapid, in situ, and on-site detection and identification of CWAs. Full article

(This article belongs to the Section Chemical Sensors)

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Open AccessEditorial

Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing: Towards Data-Driven Decision-Making and Precise Medicine

by Haipeng Liu and Rajesh Kumar Tripathy

Diagnostics 2025, 15(8), 1051; https://doi.org/10.3390/diagnostics15081051 (registering DOI) - 21 Apr 2025

Abstract

Artificial intelligence (AI) is reshaping the landscape of healthcare data [...] Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning for Healthcare Data Processing and Analyzing)

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Open AccessArticle

Genetic Profiles of Ten African Swine Fever Virus Strains from Outbreaks in Select Provinces of Luzon, Visayas, and Mindanao, Philippines, Between 2021 and 2023

by Andrew D. Montecillo, Zyne K. Baybay, Jimwel Bryan Christopher Ferrer, Wreahlen Cariaso, Airish Pantua, John Paulo Jose, Rachel Madera, Jishu Shi, Karla Cristine Doysabas, Alan Dargantes, Kassey Alsylle T. Dargantes, Anna Rochelle A. Boongaling, Alfredo P. Manglicmot, Lucille C. Villegas and Homer D. Pantua

Viruses 2025, 17(4), 588; https://doi.org/10.3390/v17040588 (registering DOI) - 21 Apr 2025

Abstract

An African Swine Fever (ASF) outbreak was first recorded in the Philippines in July 2019. Since then, the disease has spread across provinces in Luzon, Visayas, and Mindanao, causing severe economic consequences for the country’s swine industry. Here, we report the genome sequencing [...] Read more.

An African Swine Fever (ASF) outbreak was first recorded in the Philippines in July 2019. Since then, the disease has spread across provinces in Luzon, Visayas, and Mindanao, causing severe economic consequences for the country’s swine industry. Here, we report the genome sequencing of ASF virus strains from outbreaks in several provinces of the Philippines between 2021 and 2023, using a long-read tiled amplicon sequencing approach. The coding-complete genomes generated ranged from 187,609 to 189,540 bp in length, with GC contents of 38.4% to 38.5%. Notably, a strain from the Bataan province had a 1.9 kb deletion at the 5′-end, affecting several coding regions. The strains were characterized using 13 genes and regions; namely the B646L gene, the CD2v serogroup, the central variable region (CVR) of the B602L gene, the intergenic region (IGR) between the I73R and I329L genes, the IGR between A179L and A137R, O174L, K145R, Bt/Sj, J268L, and ECO2, the multigene family (MGF) 505-5R, and the MGF 505-9R and 10R regions. The ASFV strains were mostly related to Asian and European p72 genotype II strains. Genetic profiling provides valuable information on the diversity of local strains of ASFV in the Philippines, which are useful for epidemiology, diagnostics, and vaccine development. Full article

(This article belongs to the Collection African Swine Fever Virus (ASFV))

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Open AccessReview

New Insight into Microbial Exploitation to Produce Bioactive Molecules from Agrifood and By-Products’ Fermentation

by Paola Foti, Cinzia Caggia and Flora Valeria Romeo

Foods 2025, 14(8), 1439; https://doi.org/10.3390/foods14081439 (registering DOI) - 21 Apr 2025

Abstract

Consumers are increasingly interested in a healthy lifestyle, and choosing foods and ingredients with proven human health benefits has become a current trend. Recently, scientific evidence has proven that the use of microorganisms in different food matrices appears to play a key role [...] Read more.

Consumers are increasingly interested in a healthy lifestyle, and choosing foods and ingredients with proven human health benefits has become a current trend. Recently, scientific evidence has proven that the use of microorganisms in different food matrices appears to play a key role in the production of bioactive molecules with biological effects on human health. In particular, selected microorganisms with specific traits can be exploited for the production of specific molecules with high nutraceutical value that can be used in the food industry. This review aims to explore the most recent studies that correlate the use of microorganisms to produce high-value molecules through fermentation and synthetic biology, confirming their strategic role in obtaining nutraceuticals for human consumption with health-promoting effects. Full article

(This article belongs to the Special Issue Food Waste as Ingredients in Functional Food Development: Sources, Extractions and Functionality)

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Open AccessArticle

Pre-Solve Methodologies for Short-Run Identification of Critical Sectors in the ACSR Overhead Lines While Using Dynamic Line Rating Models for Resource Sustainability

by Hugo Algarvio

Sustainability 2025, 17(8), 3758; https://doi.org/10.3390/su17083758 (registering DOI) - 21 Apr 2025

Abstract

Most transmission system operators (TSOs) use seasonally static models considering extreme weather conditions, serving as a reference for computing the transmission capacity of power lines. The use of dynamic line rating (DLR) models can avoid the construction of new lines, market splitting, false [...] Read more.

Most transmission system operators (TSOs) use seasonally static models considering extreme weather conditions, serving as a reference for computing the transmission capacity of power lines. The use of dynamic line rating (DLR) models can avoid the construction of new lines, market splitting, false congestions and the degradation of lines in a cost-effective way. The operation of power systems is planned based on market results, which consider transactions hours ahead of real-time operation using forecasts with errors. The same is true for the DLR. So, during real-time operation TSOs should rapidly compute the DLR of overhead lines to avoid considering an ampacity above their lines’ design, reflecting the real-time weather conditions. Considering that the DLR of the lines can affect the power flow of an entire region, the use of the complete indirect DLR methodology has a high computation burden for all sectors and lines in a region. So, this article presents and tests three pre-solve methodologies able to rapidly identify the critical sector of each line. These methodologies solve the problem of the high computation burden of the CIGRÉ thermodynamic model of overhead lines. They have been tested by using real data of the transmission grid and the weather conditions for two different regions in Portugal, leading to errors in the computation of the DLR lower than 1% in relation to the complete CIGRÉ model, identifying the critical sector in significantly less time. Full article

(This article belongs to the Special Issue Sustainable Energy Transition: Advanced Control and Optimization Techniques for Power Grid Resilience and Efficiency)

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Open AccessArticle

Patient Experience with Intranasal Esketamine in Treatment-Resistant Depression: Insights from a Multicentric Italian Study (REAL-ESKperience)

by Marco Di Nicola, Maria Pepe, Giacomo d’Andrea, Ilaria Marcelli, Mauro Pettorruso, Ileana Andriola, Stefano Barlati, Matteo Carminati, Carlo Ignazio Cattaneo, Massimo Clerici, Domenico De Berardis, Sergio De Filippis, Bernardo Dell’Osso, Giorgio Di Lorenzo, Giuseppe Maina, Mirko Manchia, Matteo Marcatili, Vassilis Martiadis, Cinzia Niolu, Antonino Petralia, Gianluca Rosso, Gianluca Serafini, Maria Salvina Signorelli, Tommaso Vannucchi, Matteo Vismara, Raffaella Zanardi, Antonio Vita, Gabriele Sani, Giovanni Martinotti and REAL-ESKperience Study Group Show full author list Hide full author list

J. Pers. Med. 2025, 15(4), 161; https://doi.org/10.3390/jpm15040161 (registering DOI) - 21 Apr 2025

Abstract

Background. Treatment-resistant depression (TRD) is a prevalent, high-burden disorder. Esketamine nasal spray (ESK-NS) has been approved for, T.R.D.; and efficacy has been observed in both clinical trials and real-world studies. However, observations integrating patients’ perspective on this treatment are limited. This multicentric [...] Read more.

Background. Treatment-resistant depression (TRD) is a prevalent, high-burden disorder. Esketamine nasal spray (ESK-NS) has been approved for, T.R.D.; and efficacy has been observed in both clinical trials and real-world studies. However, observations integrating patients’ perspective on this treatment are limited. This multicentric Italian study explored experiences with ESK-NS in TRD patients, focusing on perceived therapeutic effects and overall satisfaction. Methods. A self-report survey was administered to 236 outpatients with TRD (55.1% females, 54.1 ± 14.1 years) treated with ESK-NS for at least three consecutive months within standard clinical care. Based on satisfaction levels, participants were classified as “unsatisfied” (10.2%), “partially satisfied” (19.1%), “satisfied” (44.4%), or “very satisfied” (26.3%), and compared for sociodemographic, clinical characteristics, and feedback on perceived benefits. Artificial intelligence (OpenAI) served to categorize responses to an open-ended question. Results. Enhanced quality of life was reported by 88.4% of participants. Significant differences emerged in earliest self-perceived benefits, most relevant effects, and impact on global functioning across groups. Specifically, “very satisfied” patients described the following: early improvements in depressed mood, suicidal thoughts, and restlessness; decreased suicidal thoughts among the most significant effects; and functional gains across all domains. OpenAI identified experiences of personal growth and rediscovery and a desire for tailored settings and approaches as recurring topics. Conclusions. Most patients reported a positive perception of ESK-NS treatment. The most satisfied participants highlighted significant benefits to depressed mood, suicidal thoughts, and overall functioning. Patient-reported experiences offer insights into different psychopathological dimensions, including functional outcomes and quality of life. Integrating these perspectives into clinical practice might assist treatment personalization, improving patients’ adherence and satisfaction. Full article

(This article belongs to the Section Personalized Therapy and Drug Delivery)

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Open AccessArticle

Single-Cell RNA Sequencing Outperforms Single-Nucleus RNA Sequencing in Analyzing Pancreatic Cell Diversity and Gene Expression in Goats

by Jie Cheng, Tianxi Zhang, Yan Cheng, Kefyalew Gebeyew, Zhiliang Tan and Zhixiong He

Int. J. Mol. Sci. 2025, 26(8), 3916; https://doi.org/10.3390/ijms26083916 (registering DOI) - 21 Apr 2025

Abstract

The objective of this study was to determine whether single-cell RNA sequencing (scRNA-seq) or single-nucleus RNA sequencing (snRNA-seq) was more effective for studying the goat pancreas. Pancreas tissues from three healthy 10-day-old female Xiangdong black goats were processed into single-cell and single-nucleus suspensions. [...] Read more.

The objective of this study was to determine whether single-cell RNA sequencing (scRNA-seq) or single-nucleus RNA sequencing (snRNA-seq) was more effective for studying the goat pancreas. Pancreas tissues from three healthy 10-day-old female Xiangdong black goats were processed into single-cell and single-nucleus suspensions. These suspensions were then used to compare cellular composition and gene expression levels following library construction and sequencing. Both scRNA-seq and snRNA-seq were eligible for primary analysis but produced different cell identification profiles in pancreatic tissue. Both methods successfully annotated pancreatic acinar cells, ductal cells, alpha cells, beta cells, and endothelial cells. However, pancreatic stellate cells, immune cells, and delta cells were uniquely annotated by scRNA-seq, while pancreatic stem cells were uniquely identified by snRNA-seq. Furthermore, the genes related to digestive enzymes showed a higher expression in scRNA-seq than in snRNA-seq. In the present study, scRNA-seq detected a great diversity of pancreatic cell types and was more effective in profiling key genes than snRNA-seq, demonstrating that scRNA-seq was better suited for studying the goat pancreas. However, the choice between scRNA-seq and snRNA-seq should consider the sample compatibility, technical differences, and experimental objectives. Full article

(This article belongs to the Special Issue Molecular Basis of Pancreatic Secretion and Metabolism)

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Open AccessArticle

Assessing Urban Activity and Accessibility in the 20 min City Concept

by Tsetsentsengel Munkhbayar, Zolzaya Dashdorj, Hun-Hee Cho, Jun-Woo Lee, Tae-Koo Kang and Erdenebaatar Altangerel

Electronics 2025, 14(8), 1693; https://doi.org/10.3390/electronics14081693 (registering DOI) - 21 Apr 2025

Abstract

The 20 min city concept ensures that essential services—such as work, education, healthcare, and recreation—are accessible within a 20 min walk or transit ride. This study evaluates urban accessibility in Ulaanbaatar by analyzing Points of Interest (POIs) and public bus transit networks using [...] Read more.

The 20 min city concept ensures that essential services—such as work, education, healthcare, and recreation—are accessible within a 20 min walk or transit ride. This study evaluates urban accessibility in Ulaanbaatar by analyzing Points of Interest (POIs) and public bus transit networks using spatial analytics and deep learning techniques. Our finding highlights that geographical area characterization is a good proxy for predicting ridership in transit networks. For instance, healthcare and medical areas show a strong correlation with similar ridership behaviors. However, some areas lack nearby bus stations, leading to poorly placed transit stops with low walking scores. To address this, we propose the use of a Quad-Bus approach to identify optimal bus station locations in urban and suburban areas, considering amenity density and deep learning ridership models to diagnose and remedy accessibility gaps. This approach is evaluated using walking and transit scores for distances ranging from 5 to 20 min in the case of Ulaanbaatar city. Results show a moderate overall link between amenity density and ridership (r = 0.44), rising to 0.53 around healthcare clusters. However, >500 high-activity partitions contain no bus stop, and 40% of the city scores below 50 on a 0–100 walking index. Half of urban areas lack a stop within 300 m, leaving 60% of residents beyond a 10 min walk. Quad-Bus reallocations close many of these gaps, boosting walk and transit scores simultaneously. This research offers valuable insights for enhancing mobility, reducing car dependency, and optimizing urban planning to create equitable and sustainable 20 min city models. Full article

(This article belongs to the Special Issue Machine/Deep Learning Applications and Intelligent Systems)

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Open AccessArticle

Comparative Analysis of Adverse Effects: Protein Kinase Inhibitors Versus Traditional Anticancer Therapies

by Ioana Lavinia Radulian, Georgiana Nitulescu, Anca Zanfirescu and George Mihai Nitulescu

Sci. Pharm. 2025, 93(2), 20; https://doi.org/10.3390/scipharm93020020 (registering DOI) - 21 Apr 2025

Abstract

The adverse effects of protein kinase inhibitors (PKIs) and other anticancer therapies were compared using FDA Adverse Events Reporting System (FAERS) data. The dataset included 159 FDA-approved anticancer drugs (71 PKIs, 88 nonPKIs) and analyzed 8216 unique adverse event (AE) terms. PKIs showed [...] Read more.

The adverse effects of protein kinase inhibitors (PKIs) and other anticancer therapies were compared using FDA Adverse Events Reporting System (FAERS) data. The dataset included 159 FDA-approved anticancer drugs (71 PKIs, 88 nonPKIs) and analyzed 8216 unique adverse event (AE) terms. PKIs showed fewer systemic toxicities, with an average of 230.1 distinct AEs per drug, compared to 537.7 in nonPKIs. Hematologic AEs were significantly lower in PKIs (e.g., febrile neutropenia: 1.93% vs. 5.25%; thrombocytopenia: 2.18% vs. 3.87%), coupled with a lower incidence of infections (6.87% vs. 14.2%) and immunosuppressive effects. However, gastrointestinal and skin-related AEs were more common in PKIs (e.g., diarrhea: 13.95% vs. 8.36%). A higher proportion AEs in the PKI group (14.57%) were classified under “Investigations”, compared to the nonPKI group (9.87%). The frequency of “Skin and subcutaneous tissue disorders” AEs was twice as high in the PKI group. Clustering analysis grouped drugs by AE profiles, showing that PKIs formed more homogeneous clusters, while nonPKIs had broader variability. Multi-kinase inhibitors with VEGFR activity were linked to dermatologic AEs, likely due to EGFR inhibition in basal keratinocytes. Despite PKIs’ targeted mechanisms, resistance remains a challenge, requiring biomarker-driven strategies. This study highlights PKIs’ improved tolerability but emphasizes using personalized treatment approaches to optimize efficacy and safety. Full article

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Open AccessArticle

Multiomics Analysis of a Micronutrient-Rich Dietary Pattern and the Aging Genotype 9p21 on the Plasma Proteome of Young Adults

by Sara Mahdavi, Katie Rosychuk, David J. A. Jenkins, Andrew J. Percy, Christoph H. Borchers and Ahmed El-Sohemy

Nutrients 2025, 17(8), 1398; https://doi.org/10.3390/nu17081398 (registering DOI) - 21 Apr 2025

Abstract

Background: Diet is one of the most significant modifiable lifestyle factors influencing human health, contributing to both morbidity and mortality. Genetic variations in the pleiotropic 9p21 risk locus further shape premature aging, disease susceptibility, and have been strongly linked to cardiovascular disease [...] Read more.

Background: Diet is one of the most significant modifiable lifestyle factors influencing human health, contributing to both morbidity and mortality. Genetic variations in the pleiotropic 9p21 risk locus further shape premature aging, disease susceptibility, and have been strongly linked to cardiovascular disease (CVD), metabolic disorders, certain cancers, and neurodegenerative conditions. However, given that this region was discovered based on Genome-Wide Association Studies, the mechanisms by which 9p21 exerts its effects remain poorly understood and its interactions with diet and biomarkers are insufficiently explored. Methods: This study investigated the association between the rs2383206 SNP in 9p21, dietary patterns, and plasma proteomic biomarkers in a multi-ethnic cohort of 1280 young adults from the Toronto Nutrigenomics and Health Study. Participants’ dietary intake was assessed using a validated food frequency questionnaire, and dietary patterns were categorized using principal component analysis. Plasma proteomics analyses quantified 54 abundant proteins involved in the cardiometabolic and inflammatory pathways. Genotyping identified individuals who were homozygous for the 9p21 risk allele (GG), known to confer the highest susceptibility risk to premature aging and multiple chronic diseases. Results: A significant interaction was observed between the 9p21 genotype and adherence to a micronutrient-rich Prudent dietary pattern for eight plasma proteins (α₁ Antichymotrypsin, Complement C4 β chain, Complement C4 γ chain, Complement C9, Fibrinogen α chain, Hemopexin, and Serum amyloid P-component). However, only Complement C4-γ showed a pattern consistent with the risks associated with the 9p21 genotype and adherence to a Prudent diet. Individuals with the high-risk GG genotype had significantly higher concentrations of Complement C4-γ, but only among those with a low adherence to a Prudent diet. Conclusions: These findings suggest that Prudent dietary patterns rich in micronutrients may counteract genetic-mediated proinflammatory susceptibility by modulating key proteomic biomarkers in young adults, highlighting the potential for tailored dietary interventions to mitigate disease risk. This study also introduces a novel framework for post hoc micronutrient resolution within dietary pattern analysis, offering a new lens to interpret nutrient synergies in gene–diet interaction research. Full article

(This article belongs to the Special Issue Recent Advances in Nutrigenomics and Nutrigenetics)

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Open AccessSystematic Review

Climate-Induced Migration in India and Bangladesh: A Systematic Review of Drivers, Impacts, and Adaptation Mechanisms

by Devangana Gupta, Pankaj Kumar, Naoyuki Okano and Manish Sharma

Climate 2025, 13(4), 81; https://doi.org/10.3390/cli13040081 (registering DOI) - 21 Apr 2025

Abstract

Climate-induced migration has emerged as a major concern in India and Bangladesh, due to their geographical vulnerability and socioeconomic conditions. Coastal areas, such as the Sundarbans and the Ganges–Brahmaputra Delta, face relentless threats due to rising sea levels, cyclones, and floods. These factors [...] Read more.

Climate-induced migration has emerged as a major concern in India and Bangladesh, due to their geographical vulnerability and socioeconomic conditions. Coastal areas, such as the Sundarbans and the Ganges–Brahmaputra Delta, face relentless threats due to rising sea levels, cyclones, and floods. These factors force millions to relocate, resulting in rural–urban transitions and cross-border movements that worsen urban challenges and socioeconomic vulnerabilities. For this, a systematic literature review of the Scopus database was undertaken using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A detailed review analysis of 65 papers was carried out. The study highlighted key climatic and non-climatic drivers of migration, including natural disasters, resource depletion, poverty, and poor governance. Despite existing adaptation strategies, such as early warning systems, micro-insurance, and climate-resilient practices, gaps remain in addressing long-term resilience and legal recognition for climate migrants. The research emphasizes the need for a holistic, multi-stakeholder approach, integrating adaptive infrastructure, sustainable livelihoods, and international cooperation. Recommendations include bridging research gaps, increasing community participation, and implementing global frameworks, like the Fund for Responding to Loss and Damage. Addressing climate migration through fair, inclusive measures is essential for building resilience and ensuring long-term development in the region. Full article

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Open AccessArticle

Impact of East Pacific La Niña on Caribbean Climate

by Mark R. Jury

Atmosphere 2025, 16(4), 485; https://doi.org/10.3390/atmos16040485 (registering DOI) - 21 Apr 2025

Abstract

Statistical cluster analysis applied to monthly 1–100 m ocean temperatures reveals El Niño–Southern Oscillation (ENSO) dipole patterns with a leading mode having opposing centers of action across the dateline and tropical east Pacific. We focus on the La Niña cold phase and study [...] Read more.

Statistical cluster analysis applied to monthly 1–100 m ocean temperatures reveals El Niño–Southern Oscillation (ENSO) dipole patterns with a leading mode having opposing centers of action across the dateline and tropical east Pacific. We focus on the La Niña cold phase and study its impact on the Caribbean climate over the period of 1980–2024. East dipole time scores are used to identify composite years, and anomaly patterns are calculated for Jan-Jun and Jul-Dec. Convective responses over the Caribbean exhibit seasonal contrasts: dry winter–spring and wet summer–autumn. Trade winds and currents across the southern Caribbean weaken and lead to anomalous warming of upper ocean temperatures. Sustained coastal upwelling off Peru and Ecuador during east La Niña is teleconnected with easterly wind shear and tropical cyclogenesis over the Caribbean during summer, leading to costly impacts. This ocean–atmosphere coupling is quite different from the more common central Pacific ENSO dipole. Full article

(This article belongs to the Special Issue Recent Advances in Air-Sea Interactions, Climate Variability, and Predictability (2nd Edition))

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Open AccessArticle

Efficient Training of Deep Spiking Neural Networks Using a Modified Learning Rate Scheduler

by Sung-Hyun Cha and Dong-Sun Kim

Mathematics 2025, 13(8), 1361; https://doi.org/10.3390/math13081361 (registering DOI) - 21 Apr 2025

Abstract

Deep neural networks (DNNs) have achieved high accuracy in various applications, but with the rapid growth of AI and the increasing scale and complexity of datasets, their computational cost and power consumption have become even more significant challenges. Spiking neural networks (SNNs), inspired [...] Read more.

Deep neural networks (DNNs) have achieved high accuracy in various applications, but with the rapid growth of AI and the increasing scale and complexity of datasets, their computational cost and power consumption have become even more significant challenges. Spiking neural networks (SNNs), inspired by biological neurons, offer an energy-efficient alternative by using spike-based information processing. However, training SNNs is difficult due to the non-differentiability of their activation function and the challenges in constructing deep architectures. This study addresses these issues by integrating DNN-like backpropagation into SNNs using a supervised learning approach. A surrogate gradient descent based on the arctangent function is applied to approximate the non-differentiable activation function, enabling stable gradient-based learning. The study also explores the interplay between the spatial domain (layer-wise propagation) and the temporal domain (time step), ensuring proper gradient propagation using the chain rule. Additionally, mini-batch training, Adam optimization, and layer normalization are incorporated to improve training efficiency and mitigate gradient vanishing. A softmax-based probability representation and cross-entropy loss function are used to optimize classification performance. Along with these techniques, a deep SNN was designed to converge to the optimal point faster than other models in the early stages of training by utilizing a modified learning rate scheduler. The proposed learning method allows deep SNNs to achieve competitive accuracy while maintaining their inherent low-power characteristics. These findings contribute to making SNNs more practical for machine learning applications by combining the advantages of deep learning and biologically inspired computing. In summary, this study contributes to the field by analyzing and adapting deep learning techniques—such as dropout, layer normalization, mini-batch training, and Adam optimization—to the spiking domain, and by proposing a novel learning rate scheduler that enables faster convergence during early training phases with fewer epochs. Full article

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Open AccessArticle

Determining a Safe Distance Zone for Firefighters Using a High-Resolution Global Canopy Height Dataset—A Case in Türkiye

by Zennure Uçar

Forests 2025, 16(4), 709; https://doi.org/10.3390/f16040709 (registering DOI) - 21 Apr 2025

Abstract

Safety zones protect firefighters from bodily injury and death caused by exposure to dangerous heat levels. These zones are defined by maintaining a safe distance from combustible fuels, a safe separation distance (SSD) derived from flame height. This study aimed to determine safety [...] Read more.

Safety zones protect firefighters from bodily injury and death caused by exposure to dangerous heat levels. These zones are defined by maintaining a safe distance from combustible fuels, a safe separation distance (SSD) derived from flame height. This study aimed to determine safety zones, integrating an existing automated identification-of-safety-zone model with vegetation height derived from a freely available high-resolution global canopy height dataset for Manavgat Forest Management Directorate (FMD) in Türkiye. Flame height, terrain slope, size of a safety zone, and distance to the closest road were also used as input in this model. The results indicated that vegetation height from high-resolution global canopy height offered promising results for determining potential safety zones (SZs) associated with SSD. Integrating the global canopy height dataset into the existing model could assist in determining the safety zone in the absence of lidar. Thus, this spatial model would provide a framework for decision-makers to develop fire prevention and suppression strategies for higher fire risk areas, especially before and during a fire. Full article

(This article belongs to the Special Issue Advancements in Forest Engineering Technologies and Sustainable Practices)

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Open AccessArticle

Network Function Placement in Virtualized Radio Access Network with Reinforcement Learning Based on Graph Neural Network

by Mengting Yi, Mugang Lin and Wenhui Chen

Electronics 2025, 14(8), 1686; https://doi.org/10.3390/electronics14081686 (registering DOI) - 21 Apr 2025

Abstract

In 5G and beyond 5G networks, function placement is a crucial strategy for enhancing the flexibility and efficiency of the Radio Access Network (RAN). However, demonstrating optimal function splitting and placement to meet diverse user demands remains a significant challenge. The function placement [...] Read more.

In 5G and beyond 5G networks, function placement is a crucial strategy for enhancing the flexibility and efficiency of the Radio Access Network (RAN). However, demonstrating optimal function splitting and placement to meet diverse user demands remains a significant challenge. The function placement problem is known to be NP-hard, and previous studies have attempted to address it using Deep Reinforcement Learning (DRL) approaches. Nevertheless, many existing methods fail to capture the network state in RANs with specific topologies, leading to suboptimal decision-making and resource allocation. In this paper, we propose a method referred to as GDRL, which is a deep reinforcement learning approach that utilizes graph neural networks to address the functional placement problem. To ensure policy stability, we design a policy gradient algorithm called Graph Proximal Policy Optimization (GPPO), which integrates GNNs into both the actor and critic networks. By incorporating both node and edge features, the GDRL enhances feature extraction from the RAN’s nodes and links, providing richer observational data for decision-making and evaluation. This, in turn, enables more accurate and effective decision outcomes. In addition, we formulate the problem as a mixed-integer nonlinear programming model aimed at minimizing the number of active computational nodes while maximizing the centralization level of the virtualized RAN (vRAN). We evaluate the GDRL across different RAN scenarios with varying node configurations. The results demonstrate that our approach achieves superior network centralization and outperforms several existing methods in overall performance. Full article

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Open AccessArticle

First Report of Ethylenediaminetetraacetic Acid-Dependent Pseudo-Thrombocytopenia in Chile: Prevalence and Laboratory Insights

by Mario Balcázar-Villarroel, Florencia Carmine, Francisco Torrens, Katherine Birditt and Cristian Sandoval

Diagnostics 2025, 15(8), 1050; https://doi.org/10.3390/diagnostics15081050 (registering DOI) - 21 Apr 2025

Abstract

Background: Ethylenediaminetetraacetic acid-dependent pseudo thrombocytopenia (EDTA-PCTP) is defined as a false in vitro decrease in the platelet count performed in the EDTA tube due to the spontaneous formation of platelet aggregates that prevent a correct count in hematological auto analyzers. The frequency of [...] Read more.

Background: Ethylenediaminetetraacetic acid-dependent pseudo thrombocytopenia (EDTA-PCTP) is defined as a false in vitro decrease in the platelet count performed in the EDTA tube due to the spontaneous formation of platelet aggregates that prevent a correct count in hematological auto analyzers. The frequency of EDTA-PCTP varies depending on the population studied, ranging from 0.01% to 30.0%. In Chile, although the diagnosis of this condition is performed in clinical laboratories, only a few isolated reports have been described. Objectives: To determine the prevalence of EDTA-PCTP in a cohort of patients who attended an outpatient clinical laboratory in southern Chile over a period of almost 4 years. Methods: A retrospective analysis was conducted using the Laboratory Information System from January 2021 to November 2024 to identify patients with suspected and confirmed cases of EDTA-PCTP. Results: The prevalence rate observed was 0.044% (12 out of 27,480). Additionally, we established that platelet count measurement from the citrate tube at 2–5 h post-sampling was comparable to the platelet count from the EDTA/K2 tube at time 0 (p > 0.05) in these patients. Conclusions: We conclude that a relatively low prevalence of EDTA-PTCP was identified in a population of patients attending an outpatient laboratory in Chile, marking the first report of its kind in our country. Future studies may validate our findings to enhance understanding of EDTA-PTCP, thereby preventing incorrect diagnoses and treatments. Full article

(This article belongs to the Section Clinical Laboratory Medicine)

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Open AccessArticle

Evaluation of White Grape Marc Extract as an Additive to Extend the Shelf-Life of Fish Fillets

by María Isabel Sáez, Javier Sabio, Alba Galafat, Antonio Jesús Vizcaíno, Francisco Javier Alarcón-López and Tomás Francisco Martínez Moya

Foods 2025, 14(8), 1438; https://doi.org/10.3390/foods14081438 (registering DOI) - 21 Apr 2025

Abstract

In this study, an extract of white grape marc (GME), a by-product obtained during the winemaking process, was applied to the surface of gilthead seabream (Sparus aurata) fillets, which were then stored under refrigeration (4 °C) for a period of 14 [...] Read more.

In this study, an extract of white grape marc (GME), a by-product obtained during the winemaking process, was applied to the surface of gilthead seabream (Sparus aurata) fillets, which were then stored under refrigeration (4 °C) for a period of 14 days. The effects of GME were compared with those of ascorbic acid (one of the few additives authorized for fresh fish in the EU) and distilled water (as a control batch). Samples were taken at 1, 2, 4, 7, 9, 11, and 14 days postmortem (dpm) cold storage, and several objective quality parameters were measured (instrumental color, pH, water holding capacity, texture profile analysis—TPA, lipid oxidation, and microbial spoilage). The results showed that the grape extract significantly improved several of the parameters studied, not only compared to the control batch, but even compared to the ascorbic acid batch. Thus, GME slowed down the proliferation of psychrophilic bacteria, prevented the oxidation of muscle lipids, and even improved the firmness of the fillets compared to the other two experimental groups. On the other hand, minor effects on color, pH, or water retention capacity were observed. In the context of the scarcity of approved food additives for fresh fish in the EU and the strong consumer rejection of synthetic substances for this purpose, this grape extract could well represent a viable alternative. In addition to its natural origin, the use of GME as a food additive could also contribute to the valorization of winery by-products as part of a circular bioeconomy strategy. Full article

(This article belongs to the Special Issue Food with Extended Shelf Life Featuring Ingredients Derived from Fruits, Vegetables, and Wild Edible Plants: Nutritional, Functional, and Sensory Properties)

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Open AccessReview

Comprehensive Review of Edge Computing for Power Systems: State of the Art, Architecture, and Applications

by Fatma Yıldırım, Yunus Yalman, Kamil Çağatay Bayındır and Erman Terciyanlı

Appl. Sci. 2025, 15(8), 4592; https://doi.org/10.3390/app15084592 (registering DOI) - 21 Apr 2025

Abstract

The increasing complexity of conventional energy distribution systems, combined with the growing demand for efficient data processing, has necessitated the implementation of smart grid technologies and the integration of advanced computing paradigms such as edge computing. Traditional cloud-based solutions face significant challenges, including [...] Read more.

The increasing complexity of conventional energy distribution systems, combined with the growing demand for efficient data processing, has necessitated the implementation of smart grid technologies and the integration of advanced computing paradigms such as edge computing. Traditional cloud-based solutions face significant challenges, including high latency, limited bandwidth, and cybersecurity vulnerabilities, rendering them less effective for real-time smart grid applications. Edge computing enables localized data processing, which significantly reduces latency and optimizes bandwidth usage. These capabilities enhance the resilience and intelligence of modern energy systems. This paper presents a systematic review of edge computing in energy distribution systems, examining its architectures, methodologies, and real-world applications. Key application areas consist of real-time data transmission, smart metering, microgrid management, anomaly and fault detection, state estimation, and energy management. The analysis shows how edge computing improves secure communication, supports decentralized intelligence, and facilitates scalable energy optimization. Beyond these advantages, the review also identifies critical challenges such as interoperability issues, resource constraints, and security vulnerabilities. By categorizing edge computing applications, the findings provide a comprehensive reference for both researchers and industry professionals working on the development of next-generation energy management systems. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in the Novel Power System)

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Open AccessArticle

Antibacterial Activity of GO-Based Composites Enhanced by Phosphonate-Functionalized Ionic Liquids and Silver

by Xinyu Liu, Xing Zhao, Hongda Qiu, Weida Liang, Linlin Liu, Yunyu Sun, Lingling Zhao, Xiao Wang and Hongze Liang

Materials 2025, 18(8), 1889; https://doi.org/10.3390/ma18081889 (registering DOI) - 21 Apr 2025

Abstract

The development of antibiotic-independent antimicrobial materials is critical for addressing bacterial resistance to conventional antibiotics. Currently, there is a lack of comprehensive understanding of ionic liquid-modified composites in antimicrobial applications. Here, we innovatively prepared GO-based composites modified with phosphonate ionic liquids via a [...] Read more.

The development of antibiotic-independent antimicrobial materials is critical for addressing bacterial resistance to conventional antibiotics. Currently, there is a lack of comprehensive understanding of ionic liquid-modified composites in antimicrobial applications. Here, we innovatively prepared GO-based composites modified with phosphonate ionic liquids via a series of surface functionalizations. The resulting antibacterial composites exhibit significant broad-spectrum activity against both Gram-negative and Gram-positive bacteria, including drug-resistant strains, with stronger efficacy against Gram-negative species. Additionally, the material features excellent long-term reusability and the ability to inhibit/destroy biofilms, which is vital for combating persistent infections. Mechanistic studies reveal its antibacterial effects through multiple pathways: disrupting bacterial membranes, inducing ROS, and inactivating intracellular substances—mechanisms less likely to promote resistance. Overall, these phosphonate ionic liquid-modified polycationic materials demonstrate substantial potential in treating bacterial infections, offering a promising strategy to tackle antibiotic resistance challenges. Full article

(This article belongs to the Special Issue Ionic Liquids: New Trends in Advanced Applications)

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