bluuebunny/crossref_metadata_2025 huggingface dataset:
Data Augmentation-Based Photovoltaic Power Prediction
Authors: Wang Xifeng , Shen Yijun , Song Haiyu , Liu Shichao
Published: February 2025

In recent years, as the grid-connected installed capacity of photovoltaic (PV) power generation has increased by leaps and bounds, it has assumed considerable importance in predicting PV power output. However, the power prediction of newly built small-scale PV power plants often suffers from many unexpected problems, such as data redundancy, data noise, data sample imbalance, or even missing key data. Motivated by the above facts, this paper proposes a data augmentation-based prediction framework for PV power. Firstly, the daily power distance measurement is used to analyze feature correlation and filter erroneous data. Then, the autoencoder network trained based on the random discarding mechanism is used to restore the PV power generation data. At the same time, a specific data augmentation method for the PV power curve is designed to eliminate the influence of data sample imbalance. In the final experimental section, compared with the latest method, this method achieved the highest MAE accuracy of 8.26% and RMSE accuracy of 10.96%, which proves the effectiveness of this method.
Improving Synchronization and Stability in Integrated Electricity, Gas, and Heating Networks via LSTM-Based Optimization
Authors: Wu Xiaoyu , Cao Yuchen , Wu Hengtian , Qi Shaokang , Zhao Mengen , Feng Yuan , Yu Qinyi
Published: February 2025

This paper introduces an innovative optimization framework that integrates Long Short-Term Memory (LSTM) networks to enhance the synchronization and stability of urban integrated multi-energy systems (MESs), which include electricity, gas, and heating networks. The need for a holistic approach to manage these interconnected systems is driven by the increasing complexity of urban energy demands and the imperative to adhere to stringent environmental standards. The proposed methodology leverages LSTM networks for dynamic state estimation, enabling real-time and accurate predictions of energy demands and operational states across the different energy networks. This approach allows for the optimization of energy flows by adapting to fluctuations in demand and supply with high precision, which traditional static models are unable to do. By comprehensively modeling the unique operational characteristics and interdependencies of the electricity, gas, and heating networks, the framework ensures that the integrated system operates efficiently, remains stable under varying loads, and meets regulatory compliance for emissions. A synthesized case study simulating the operation of an integrated MES—including the IEEE 123-bus system for electricity, a modeled Belgian high-caloric gas network, and a Danish district heating system—illustrates the effectiveness of the proposed model. The study results indicate significant improvements in operational efficiency, reductions in emissions, and enhanced system stability. Key contributions of this paper include the development of a multi-layered optimization framework that addresses the dynamics of MESs, integration of environmental and regulatory compliance within the operational strategy, and a robust validation of the LSTM-based model against simulated anomalies and real-world scenarios.
Optimization of Biomass to Bio-Syntetic Natural Gas Production: Modeling and Assessment of the AIRE Project Plant Concept
Authors: Di Bisceglie Emanuele , Papa Alessandro Antonio , Vitale Armando , Pasqual Laverdura Umberto , Di Carlo Andrea , Bocci Enrico
Published: February 2025

This study focuses on the modeling, simulation, and optimization of an integrated biomass gasification and methanation process to produce bio-synthetic natural gas (Bio-SNG) as part of the AIRE project. The process was simulated using Aspen Plus® software (V14), incorporating experimental results from pilot-scale gasification setups. Key steps involved syngas production in a dual fluidized bed gasifier and its subsequent conversion to Bio-SNG in a methanation section. Heat integration strategies were implemented to enhance system results demonstrate that optimized heat recovery, achieved by utilizing exothermic methanation reactions to preheat gasification inputs, eliminates the need for auxiliary fuel in the gasification process. The optimized system achieved a thermal recovery rate of 80%, a cold gas efficiency of 79%, a Bio-SNG production rate of 0.4 Nm3/kgBiom, and a methane content of 85 vol.%. These optimizations reduced CO2 emissions by 10% while increasing overall energy efficiency. This work highlights the potential of integrating biomass gasification and methanation processes with heat recovery for sustainable methane production. The findings provide a basis for scaling up the process and further exploring syngas utilization pathways to produce renewable energy carriers.
Effects of Nitrogen Addition and Drought on Soil Microbial Diversity and Community Composition in a Young Tree Community
Authors: Bian Yanyan , Wu Xingli , Zhu Yulin , Xiong Xin , Xi Dan , Yang Qingpei , Liu Jun , Song Qingni , Ni Guorong , Fang Xiong
Published: February 2025

Soil microorganisms are well known to play a crucial role in carbon and nutrient cycling within terrestrial ecosystems. Numerous research efforts have demonstrated that nitrogen deposition can change forest soil microbial diversity and community composition; however, it is still unclear how nitrogen deposition will affect the soil microbial diversity and community composition in subtropical forests under the background of increasing drought. Consequently, over a period of 2.5 years, we carried out an experiment using two N addition regimes and three soil water treatment levels to reveal the effects of nitrogen, drought, and the influence of their interaction on the diversity and community composition of soil microorganisms. Overall, we found that both N addition and drought decreased the bacterial Shannon and Simpson indices yet had no significant effect on fungal diversity. In the well-watered treatments, nitrogen addition did not significantly reduce bacterial diversity, while in the moderate drought and severe drought treatments, N addition significantly decreased bacterial diversity, reducing the Shannon and Simpson indices by 27.05% and 0.13%, respectively, in the severe drought treatment. Drought significantly altered the community composition of bacteria regardless of N addition. N addition significantly changed the community composition of bacteria under moderate drought treatments, while both N addition and drought had less significant effects on the fungal community composition. The soil water content, fine root biomass, and soil pH were significantly correlated with bacterial community composition, which explained 53.3%, 11.1%, and 8.7% of the changes in soil bacterial community composition, respectively. These results suggest that drought may intensify the inhibitory effect of nitrogen on bacterial diversity and change the magnitude and direction of the impact of nitrogen on the composition of the bacterial community.
Machine Learning and Deep Learning-Based Multi-Attribute Physical-Layer Authentication for Spoofing Detection in LoRaWAN
Authors: Pourghasem Azita , Kirner Raimund , Tsokanos Athanasios , Mporas Iosif , Mylonas Alexios
Published: February 2025

The use of wireless sensor networks (WSNs) in critical applications such as environmental monitoring, smart agriculture, and industrial automation has created significant security concerns, particularly due to the broadcasting nature of wireless communication. The absence of physical-layer authentication mechanisms exposes these networks to threats like spoofing, compromising data authenticity. This paper introduces a multi-attribute physical layer authentication (PLA) scheme to enhance WSN security by using physical attributes such as received signal strength indicator (RSSI), battery level (BL), and altitude. The LoRaWAN join procedure, a key risk due to plain text transmission without encryption during initial communication, is addressed in this study. To evaluate the proposed approach, a partially synthesized dataset was developed. Real-world RSSI values were sourced from the LoRa at the Edge Dataset, while BL and altitude columns were added to simulate realistic sensor behavior in a forest fire detection scenario. Machine learning (ML) models, including Logistic Regression (LR), Random Forest (RF), and K-Nearest Neighbors (KNN), were compared with deep learning (DL) models, such as Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN). The results showed that RF achieved the highest accuracy among machine learning models, while MLP and CNN delivered competitive performance with higher resource demands.
Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning
Authors: Efunogbon Abimbola , Liu Enjie , Qiu Renxie , Efunogbon Taiwo
Published: February 2025

This paper introduces Optimal 5G Network Sub-Slicing Orchestration (ONSSO), a novel machine learning framework for dynamic and autonomous 5G network slice orchestration. The framework leverages the LazyPredict module to automatically select optimal supervised learning algorithms based on real-time network conditions and historical data. We propose Enhanced Sub-Slice (eSS), a machine learning pipeline that enables granular resource allocation through network sub-slicing, reducing service denial risks and enhancing user experience. This leads to the introduction of Company Network as a Service (CNaaS), a new enterprise service model for mobile network operators (MNOs). The framework was evaluated using Google Colab for machine learning implementation and MATLAB/Simulink for dynamic testing. The results demonstrate that ONSSO improves MNO collaboration through real-time resource information sharing, reducing orchestration delays and advancing adaptive 5G network management solutions.
The Variation in a Fish Egg Community and Its Relationship with Environmental Factors in Daya Bay, South China Sea
Authors: Wang Sifan , Wu Fengxia , Rao Yiyong , Liu Huaxue , Zhang Shufei , Dai Ming , Gu Yangguang , Hou Gang , Huang Honghui
Published: February 2025

To assess the impact of the marine environment on the community of fish eggs in Daya Bay, we collected fish egg samples across four seasons during 2022. A total of 51 species of fish eggs were gathered. The fish egg community exhibited significant seasonal and spatial variations. The number of fish egg species was highest in spring and lowest in winter, while overall abundance peaked during summer. The abundance of eggs from large economic fish (Sparidae) showed a declining trend; however small pelagic fish (Leiognathidae, Engraulidae) have become dominant over time. Spatially, higher abundance is observed in the inner bay compared to the outer bay, particularly around coastal bank waters, near islands and reefs. Additionally, environmental factors and geographical location significantly influenced the fish egg community. For instance, Stolephorus continentalis and Photopectoralis bindus demonstrated a significant positive correlation with both temperature and water depth; conversely, Acanthopagrus schlegelii exhibited a negative correlation with water temperature.
Effects of Drip Irrigations with Different Irrigation Intervals and Levels on Nutritional Traits of Paddy Cultivars
Authors: Ciftci Beyza , Kardes Yusuf Murat , Varol Ihsan Serkan , Tas Ismail , Akcura Sevim , Coskun Yalcin , Karaman Kevser , Gokalp Zeki , Akcura Mevlut , Kaplan Mahmut
Published: February 2025

Rice serves as the primary food source for the majority of the world’s population. In terms of irrigation water, the highest volume of irrigation water is utilized in paddy irrigation. Excessive water use causes both waste of limited water resources and various environmental problems. The drip irrigation method with high water use efficiency will reduce both the need for irrigation water and the environmental footprint of paddy production. This study was conducted to investigate the effects of two different irrigation intervals (2 and 4 days) and four irrigation levels (150%, 125%, 100%, and 75% of evaporation from a Class-A pan) on the nutritional traits of three different paddy cultivars (Ronaldo, Baldo, and Osmancık). Increasing irrigation intervals and decreasing irrigation levels reduced the nutritional properties (protein, oil, starch) of the rice grains. In addition, increasing irrigation levels also increased the phytic acid and dietary fiber contents. The highest protein (7.14%) and total starch (87.10%) contents were obtained from the 150% irrigation treatments. The highest amylose content (20.74%) was obtained from the 75% irrigation treatment. In general, it was found that irrigation levels should be applied at 125% and 150% to increase the mineral content of rice grains. Although water deficits decreased the nutritional properties of the paddy cultivars, drip irrigation at an appropriate level did not have any negative effects on nutritional traits.
Analyzing Winter Crash Dynamics Using Spatial Analysis and Crash Frequency Prediction Models with SHAP Interpretability
Authors: Shuai Zehua , Kwon Tae J.
Published: February 2025

This study investigates the application of machine learning (ML) to understand and mitigate winter road risks while addressing model interpretability. Using 26,970 winter crash records collected over four years in Edmonton, Canada, we developed and compared three ML-based winter crash frequency models: XGBoost, Random Forest, and LightGBM. To enhance interpretability, we applied SHapley Additive exPlanations (SHAP), providing insights into feature contributions. Our analysis incorporated micro-level variables such as collision records, weather conditions, and road characteristics, as well as macro-level variables such as land use patterns, spatial characteristics (via Hot Spot Analysis), and traffic exposure (estimated using Ordinary Kriging). Among the models tested, XGBoost outperformed others, achieving a testing R2 of 92.67%, MAE of 3.64, and RMSE of 5.77. SHAP analyses on XGBoost provided both global and local explanations. At a global level, road type, speed limit, and traffic enforcement cameras were identified as key factors influencing crash frequency while locally, distinct features of high- and low-crash locations were highlighted, supporting targeted risk mitigation strategies. By bridging the gap between model accuracy and interpretability, this study demonstrates the value of interpretable ML models in improving winter road safety, offering actionable insights for informed decision-making and resource allocation in winter road maintenance.
Physician Assistant/Associate Urology Workforce: A National Analysis
Authors: Hooker Roderick S. , Bruza-Augatis Mirela , Puckett Kasey , Kozikowski Andrzej , Doran Todd J.
Published: February 2025

Introduction/Background: The urology workforce is shifting in terms of the number of physicians per capita, age, gender, and availability. To meet this growing need, physician assistants/associates (PAs) and nurse practitioners (NPs) are part of this workforce backfilling effort. However, limited studies have been conducted on the demographic and employment attributes of PAs practicing in urology. Thus, using a national dataset, this study aims to compare the attributes of PAs in the urology workforce compared with PAs in all other surgical and medical disciplines. Methods: We analyzed the practice of PAs in urology using data from the 2022 National Commission on Certification of PAs (NCCPA). This study drew on responses from 117,748 board-certified PAs who reported their medical and surgical specialty. Our analysis involved descriptive and inferential statistics, comparing the demographic and practice attributes of PAs in urology (n = 1199) with PAs in all other medical disciplines (n = 116,549). Results: In 2022, 1199 (1.0%) PAs were reported to be clinically active in urology. Among PAs in urology, 68.1% self-identified as female, with a median age of 39 [IQR: 32–48]. Compared to PAs in other medical disciplines, PAs in urology resided in urban locations (94.5% vs. 92.5%, p = 0.002). They were also more likely to practice in office-based settings (53.6% vs. 37.0%), work over 40 h weekly (37.9% vs. 29.3%), and partake in telemedicine (52.0% vs. 40.1%; all p < 0.001). No statistical differences were found among PAs in urology versus PAs in all other medical fields related to job satisfaction (p = 0.763), symptoms of burnout (p = 0.124), and retirement plans in the next 5 years (p = 0.442). Conclusions: Given the predicted shortfalls of urologists and their changing demographic composition, this study has important implications for practice in the urology workforce. Our findings can inform workforce planning, recruitment strategies, and organizational policies to support the expansion of PAs in urology and help address shortages in this discipline.
Consciousness Research Through Pain
Authors: Shin Dong Ah , Chang Min Cheol
Published: February 2025

Background/Objectives: Consciousness is a complex and elusive phenomenon encompassing self-awareness, sensory perception, emotions, and cognition. Despite significant advances in neuroscience, understanding the neural mechanisms underlying consciousness remains challenging. Pain, as a subjective and multifaceted experience, offers a unique lens for exploring consciousness by integrating sensory inputs with emotional and cognitive dimensions. This study examines the relationship between consciousness and pain, highlighting the potential of pain as a model for understanding the interplay between subjective experience and neural activity. Methods: Literature review. Results: Key theories of consciousness, such as the Global Workspace Theory and the Integrated Information Theory, provide diverse frameworks for interpreting the emergence of consciousness. Similarly, pain research emphasizes the role of subjective interpretation and emotional context in shaping sensory experiences, reflecting broader challenges in consciousness studies. The limitations of current methodologies, particularly the difficulty of objectively measuring subjective phenomena, like pain and consciousness, are also addressed. This highlights the importance of neural correlates, with a particular focus on brain regions, such as the anterior cingulate cortex and the insula, which bridge sensory and emotional experiences. By analyzing the shared attributes of pain and consciousness, this study underscores the potential for pain to serve as a measurable proxy in consciousness research. Conclusions: Ultimately, it contributes to unraveling the neural and philosophical underpinnings of consciousness, offering implications for mental health treatment and advancements in artificial intelligence. This study fills a critical gap by leveraging pain as a measurable and reproducible model for exploring the neural and subjective mechanisms of consciousness. By combining theoretical frameworks with empirical evidence, it offers novel insights into how consciousness emerges from neural processes.
Severity Matters: How COVID-19 Severity Impacts Long-Term Effects on Symptoms, Physical Activity and Functionality—An Observational Study
Authors: Pérez-Gisbert Laura , Morales-García Concepción , Sánchez-Martínez José Antonio , González-Gutiérrez María Victoria , Valenza Marie Carmen , Torres-Sánchez Irene
Published: February 2025

Background/Objectives: The existing literature has described the common symptoms and long-term effects of coronavirus disease (COVID-19). However, there is a lack of detailed information on how different degrees of disease severity affect survivors differently. This study aims to fill that gap by evaluating the symptoms, physical activity, and functionality of COVID-19 survivors across a spectrum of severity levels, comparing them with those of healthy individuals. Methods: An observational study was carried out following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) criteria and checklist. Participants were divided into 5 groups based on COVID-19 severity according to the World Health Organization classification: healthy (COVID-19-negative), mild (symptomatic without pneumonia or dyspnoea), moderate (pneumonia and dyspnoea without hospitalisation), severe (severe pneumonia requiring hospitalisation), and critical (severe pneumonia with admission to the intensive care unit). Descriptive variables, symptoms (Fatigue Borg Scale, Fatigue Impact Scale, Fatigue Severity Scale, Dyspnoea Borg Scale, Visual Analogue Scale, Hospital Anxiety and Depression Scale, and European Quality of Life-5 Dimensions), physical activity (the International Physical Activity Questionnaire) and functionality (Patient-Specific Functional Scale, Short Physical Performance Battery, Arm Curl test, and 2 min step test) were measured. Results: A total of 304 participants were included: healthy (n = 42), mild (n = 143), moderate (n = 49), severe (n = 52), and critical (n = 18) COVID-19 patients. The impact of COVID-19 on surviving patients varies significantly with the severity of the disease. The results show that the hospitalisation time, age, and comorbidities of the patients are greater in those with a greater severity of the disease. Patients with more severe COVID-19 also experience greater frailty, dysphagia, fatigue, dyspnoea, and pain. Additionally, those with severe cases have poorer overall health, reduced physical activity, and diminished functionality. No evidence of post-COVID-19 anxiety or depression is found in the sample, even considering the timeframe between the negative test and the assessment. Conclusions: Patients with higher COVID-19 severity (severe or critical) experience more symptoms than those with lower COVID-19 severity (mild or moderate). Additionally, those with severe cases have poorer overall health, reduced physical activity and diminished functionality. Register: Clinicaltrials.gov: NCT05731817.
Examining Burnout Among Intern Physicians During the COVID-19 Pandemic: Insights and Solutions from Qualitative Research
Authors: Surawattanasakul Vithawat , Kiratipaisarl Wuttipat , Phetsayanavin Vitchayut , Pholvivat Chantarateera , Auernaruemonsuk Natcha , Lamlert Chanon , Soonthornvinit Warisa , Hengboriboon Lakkana , Siviroj Penprapa
Published: February 2025

Background/Objectives: The extensive exposure of physicians to the COVID-19 pandemic has contributed to occupational stress and burnout in their daily lives. This study aimed to explore the lived experiences of intern physicians who experienced burnout during the COVID-19 pandemic and to identify potential solutions to enhance clinical practices in future pandemics. Methods: This study employed a qualitative, phenomenological study utilizing in-depth interviews. The participants were 19 first-year intern physicians from public hospitals in Thailand, selected through a purposeful sampling approach who had experienced burnout. Semi-structured interviews were conducted face-to-face and via online platforms. A thematic narrative analysis approach was used. Results: Phenomenological explorations included two parts: the first explored physicians’ workplace conditions while providing patient care, and the second focused on their proposed solutions for policy changes in clinical practices and hospital management. Four main themes in the first part were derived: (1) emotional suffering and burnout; (2) engaging with a high-intensity workplace; (3) hostile work environments; and (4) deterioration of relationships with staff and colleagues. The second part identified three main themes: (1) changes in policy of clinical practices; (2) effective hospital management; and (3) building interpersonal skills. Conclusions: The COVID-19 pandemic has exacerbated challenges faced by intern physicians, such as high-pressure working conditions, deteriorated relationships with colleagues, and ineffective management, all of which contribute to burnout. These challenges require targeted policy changes in clinical practices, effective hospital management, and building interpersonal skills. Recommendations include improved clinical practices, increased academic support, comprehensive orientation programs, effective communication, teamwork assistance, stress management, and transforming organizational culture to value physicians during internships.
Barriers to Providing Optimal Care in Idaho from the Perspective of Healthcare Providers: A Descriptive Analysis
Authors: Ericsson Alexis A. , McCurry Allie D. , Tesnohlidek Lucas A. , Kearsley B. Kelton , Hansen-Oja Morgan L. , Glivar Gillian C. , Ward Allie M. , Craig Kathryn J. , Chung Eva B. , Smith Skyler J. , Alomar Tabarak O. , La Mue Luke A. , Lopez Karina S. , Goodwin Jake R. , Kieu Thinh T. , Dingel Audrey J. , Rockwell Hill Catherine M. , Casanova Madeline P. , Moore Jonathan D. , Wiet Ryan , Baker Russell T.
Published: February 2025

Background/Objectives: Few studies have assessed barriers to providing care from the perspective of interprofessional healthcare providers. Despite Idaho’s predominantly rural geography, limited research exists assessing barriers to providing care within the state. This study sought to assess barriers to providing optimal healthcare using a sample of 400 healthcare providers at 22 clinic sites across the state. Methods: A barriers to providing optimal care 9-factor, 41-item survey was modified from an existing survey. Healthcare providers rated barrier items using an 11-point Likert scale. The survey was distributed to a convenience sample of healthcare providers in 22 different clinic sites in rural Idaho. Results: Four hundred interprofessional healthcare providers in Idaho across 13 professional disciplines completed surveys. Items in the Service Access (mean = 7.14), Patient Complexity (mean = 6.59), and Resource Limitations (mean = 6.18) factors were reported as the most commonly perceived barriers to providing optimal care. Conclusions: Few studies have assessed rural interprofessional providers’ perceived barriers to providing optimal, high-quality, care, specifically in the rural state of Idaho, where healthcare services are often not equitable compared to urban regions. The results suggest that commonly perceived barriers exist throughout the state, particularly Service Access, Patient Complexity, and Resource Limitations. Further research is needed to develop data-driven decisions to address these concerns.
Awareness and Attitude of the General Population Towards Inherited Hemoglobinopathies in the Premarital Screening Program in the Northern Region of Saudi Arabia
Authors: Hafiz Mariah N. , Suhail Nida , Mohammed Zakariya M. S. , Elzein Husham O. , Almasmoum Hibah A. , Abass Awad E. , Jawad Mohammed M. , Trabelsi Saoussen
Published: February 2025

Background: Premarital screening (PMS) is a nationwide program that helps high-risk individuals make decisions to avoid genetic and sexually transmitted diseases from spreading to their spouse or future offspring. This study examined the knowledge and attitudes towards inherited hemoglobinopathies in PMS among the people of Northern Border Region in Saudi Arabia and their relationship to various sociodemographic factors. Methods: A cross-sectional study was undertaken in the Northern region of Saudi Arabia from January to March 2024. Data were gathered via questionnaire from 478 Saudi participants aged 18 years and older. The chi-square test was employed to determine the association between categorical variables. Results: All participants in the study were familiar with the PMS program. A significant portion of participants, 79.3%, acknowledged that consanguinity can increase the risk of hereditary blood disorders, while 69.9% believed that if both parents are carriers of the same genetic blood disease, their child may inherit it. Higher education, female gender, and age group (30–40) were found to be the main predictors of knowledge regarding PMS. Most of the participants (98.5%) had a positive attitude regarding the necessity of PMS as a prerequisite for marriage completion. About 82.8% indicated they would not continue with the marriage if the PMS results were incompatible. Conclusions: The study indicates a growing awareness and positive attitude towards premarital screening among the general population, with an increasing number of individuals opting for it. The findings suggest that PMS programs contribute to informed decision making, as evidenced by the rise in participants choosing to forgo marriage due to partner incompatibility. The study recommends the enhancement of health education campaigns by considering demographic factors such as age, education, and marital status. Additionally, it advocates for expanding the scope of PMS to include a wider range of health and genetic disorders to improve its overall efficacy.
Inclusive Museum Engagement: Multisensory Storytelling of Cagli Warriors’ Journey and the Via Flamina Landscape Through Interactive Tactile Experiences and Digital Replicas
Authors: Clini Paolo , Nespeca Romina , Ferretti Umberto , Galazzi Federica , Bernacchia Monica
Published: February 2025

This paper presents a case study from the Archaeological and Via Flaminia Museum in Cagli (Italy), developed within the ERASMUS+ Next-Museum project, which explores inclusive approaches through the digital transformation of small museums and their connection to the surrounding territory. A key goal was to “return” bronze statuettes to the museum, symbolically compensating the community for their absence. The initiative integrates accessibility and multisensory storytelling following “Design for All” principles. Three installations were implemented: tactile replicas of the statuettes produced through 3D printing, a sensorized table for interactive storytelling, and a story map displayed on a touchscreen for exploring local archaeological heritage. The design prioritized inclusivity, particularly for visitors with visual impairments, while addressing practical constraints such as the need for a mobile and flexible setup within a limited budget. Verification and validation tests were conducted with visually impaired participants during the pre-opening phase, and the installations were later evaluated using the User Experience Questionnaire, complemented by qualitative feedback. These evaluations highlight the potential of phygital experiences to foster engagement with cultural heritage while addressing technological and design challenges.
Home Environment as a Therapeutic Target for Prevention and Treatment of Chronic Diseases: Delivering Restorative Living Spaces, Patient Education and Self-Care by Bridging Biophilic Design, E-Commerce and Digital Health Technologies
Authors: Huntsman Dorothy Day , Bulaj Grzegorz
Published: February 2025

A high prevalence of chronic diseases exposes diverse healthcare pain points due to the limited effectiveness of pharmaceutical drugs and biologics, sedentary lifestyles, insufficient health literacy, chronic stress, unsatisfactory patient experience, environmental pollution and competition with commercial determinants of health. To improve patient care and long-term outcomes, the impact of the home environment is overlooked and underutilized by healthcare. This cross-disciplinary work describes perspectives on (1) the home environment as a therapeutic target for the prevention and treatment of chronic diseases and (2) transforming health-centric household goods e-commerce platforms into digital health interventions. We provide a rationale for creating therapeutic home environments grounded in biophilic design (multisensory, environmental enrichment) and supporting physical activities, quality sleep, nutrition, music, stress reduction, self-efficacy, social support and health education, hence providing clinical benefits through the modulation of the autonomic nervous system, neuroplasticity and behavior change. These pleiotropic “active non-pharmacological ingredients” can be personalized for people living with depression, anxiety, migraine, chronic pain, cancer, cardiovascular and other conditions. We discuss prospects for integrating e-commerce with digital health platforms to create “therapeutic home environment” interventions delivered through digital therapeutics and their combinations with prescription drugs. This multimodal approach can enhance patient engagement while bridging consumer spending with healthcare outcomes.
Therapeutic Potential of Adina rubella Hance Stem and Picroside III as a Differentiation Inducer in AML Cells via Mitochondrial ROS Accumulation
Authors: Kwon Chan-Seong , Jeon Byeol-Eun , Lee Ji-Eun , Kim Hyeon-Young , Kang Ryun-Young , Kim Keun-Hu , Lee Eun-Ju , Jang Ju-Yeon , Kim Tae-Jin , Shin Ho-Jin , Kim Sang-Woo
Published: February 2025

Acute myeloid leukemia (AML) is characterized by the accumulation of immature myeloid cells and a differentiation block, highlighting the urgent need for novel differentiation-inducing therapies. This study evaluated Adina rubella Hance (ARH) stem as a potent differentiation inducer by systematically screening 200 plant extracts. ARH stem promoted phenotypic differentiation in AML cells. In addition to its differentiation-inducing effects, ARH stem exhibited strong antileukemic activities, such as inhibiting cell proliferation, inducing cell death, and enhancing mitochondrial reactive oxygen species (mtROS) levels, the latter of which is critical for its differentiation-promoting activity. Comparative analysis with the extracts from other parts of the plant confirmed the superior efficacy of the stem extract because of its unique chemical composition. Ultra-high-performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry analysis identified Picroside III as a major active compound within the stem extract, capable of recapitulating ARH stem-induced differentiation and demonstrating significant antileukemic properties. These findings underscore the therapeutic potential of ARH stem and its active component, Picroside III, as promising agents for differentiation-based treatment strategies in AML.
The Role of Gut Microbiota-Derived Trimethylamine N-Oxide in the Pathogenesis and Treatment of Mild Cognitive Impairment
Authors: Xie Haihua , Jiang Jia , Cao Sihui , Xu Xuan , Zhou Jingyin , Zhang Ruhan , Huang Bo , Lu Penghui , Peng Liang , Liu Mi
Published: February 2025

Mild cognitive impairment (MCI) represents a transitional stage between normal aging and dementia, often considered critical for dementia prevention. Despite its significance, no effective clinical treatment for MCI has yet been established. Emerging evidence has demonstrated a strong association between trimethylamine-N-oxide (TMAO), a prominent metabolite derived from the gut microbiota, and MCI, highlighting its potential as a biomarker and therapeutic target. TMAO has been implicated in increasing MCI risk through its influence on factors such as hypertension, cardiovascular disease, depression, diabetes, and stroke. Moreover, it contributes to MCI by promoting oxidative stress, disrupting the blood–brain barrier, impairing synaptic plasticity, inducing inflammation, causing mitochondrial metabolic disturbances, and facilitating abnormal protein aggregation. This review further explores therapeutic strategies targeting TMAO to mitigate MCI progression.
Automated Fungal Identification with Deep Learning on Time-Lapse Images
Authors: Mansourvar Marjan , Charylo Karol Rafal , Frandsen Rasmus John Normand , Brewer Steen Smidth , Hoof Jakob Blæsbjerg
Published: February 2025

The identification of species within filamentous fungi is crucial in various fields such as agriculture, environmental monitoring, and medical mycology. Traditional identification methods based on morphology have a low demand for advanced equipment usage and heavily depend on manual observation and expertise. However, this approach may struggle to differentiate between species in a genus due to their potential visual similarities, making the process time-consuming and subjective. In this study, we present an AI-based fungal species recognition model that utilizes deep learning techniques applied to time-lapse images. The training dataset, derived from fungi strains in the IBT Culture Collection, comprised 26,451 high-resolution images representing 110 species from 35 genera. The dataset was divided into a training set and validation subsets. We implemented three advanced deep learning architectures—ResNet50, DenseNet-121, and Vision Transformer (ViT)—to assess their effectiveness in accurately classifying fungal species. By utilizing images from early growth stages (days 2–3.5) for training and testing and later stages (days 4–7) for validation, our approach shortens the fungal identification process by 2–3 days, significantly reducing the associated workload and costs. Among the models, the Vision Transformer achieved the highest accuracy of 92.6%, demonstrating the effectiveness of our method. This work contributes to the automation of fungal identification, providing a reliable and efficient solution for monitoring fungal growth and diversity over time, which would be useful for culture collections or other institutions that handle a large number of new isolates in their daily work.
Generative Artificial Intelligence and the Evolving Challenge of Deepfake Detection: A Systematic Analysis
Authors: Babaei Reza , Cheng Samuel , Duan Rui , Zhao Shangqing
Published: February 2025

Deepfake technology, which employs advanced generative artificial intelligence to create hyper-realistic synthetic media, poses significant challenges across various sectors, including security, entertainment, and education. This literature review explores the evolution of deepfake generation methods, ranging from traditional techniques to state-of-the-art models such as generative adversarial networks and diffusion models. We navigate through the effectiveness and limitations of various detection approaches, including machine learning, forensic analysis, and hybrid techniques, while highlighting the critical importance of interpretability and real-time performance in detection systems. Furthermore, we discuss the ethical implications and regulatory considerations surrounding deepfake technology, emphasizing the need for comprehensive frameworks to mitigate risks associated with misinformation and manipulation. Through a systematic review of the existing literature, our aim is to identify research gaps and future directions for the development of robust, adaptable detection systems that can keep pace with rapid advancements in deepfake generation.
A Land Spatial Optimization Approach for the Reutilization of Abandoned Mine Land: A Case Study of Ningbo, China
Authors: Cao Chenglong , Yang Liu , Zhang Wanqiu , Zhang Wenjun , Lin Gang , Liu Kun
Published: February 2025

As a mining country, China faces enormous challenges in the context of the global commitment to achieve carbon neutrality. In order to achieve this goal, the Chinese government is actively promoting the green and low-carbon transformation of the energy system. Consequently, an increasing number of mines with poor production capacity and depleted resources are being closed down or eliminated, leading to a large quantity of stranded land resources that are now idle. However, in the process of rapid economic development, China is facing serious problems, such as land shortage and land use conflicts. Abandoned mining land (AML), as a kind of reserve land resource, has an important regulating role in solving the dilemma of land resource tension faced by national land spatial planning. In order to realize the rational planning and utilization of AML, this study proposes a high-precision AML planning model and simulates the planning of AML in multiple policy scenarios, using Ningbo City as an example. The results show that AML has great economic and ecological potential; the economic development scenario (EDS) enhanced the economic benefits of the mine region by 396%, and the ecological protection scenario (EPS) enhanced the ecological benefits of the mine region by 74.61%, when compared with the baseline scenario (BAU). The overall level of optimization is as follows: EDS > EPS > BAU. In addition, the optimal utilization of AML in all three scenarios significantly enhanced the ecological quality of the mining region, and the enhancement effect was EPS > BAU > EDS. Therefore, AML, as a kind of free land resource, has an important supporting effect for the spatial planning of the national territory. Furthermore, it is of great significance to scientifically and reasonably guide the optimal utilization of AML, according to the policy planning for future development, in order to achieve efficient economic development and improve the quality of the ecological environment.
Exploring Self-Organization in Community-Led Urban Regeneration: A Comparative Analysis of Chinese Approaches
Authors: Lang Wei , Fu Danhong , Chen Tingting
Published: February 2025

Self-organization has emerged as a critical approach in community-led urban regeneration, particularly in addressing the challenges of rapid urbanization and socio-spatial inequalities. In the Chinese context, where urban governance has traditionally been state-centric, self-organized initiatives represent a significant shift toward grassroots participation and localized decision-making. This study conducts a comparative analysis of four cases: Guangminglou Building No. 17 in Beijing, Kangjian Road 341 Building No. 7–8 in Shanghai, Honggang Garden Residence in Shenzhen, and Jiqun Street Building No. 2 in Guangzhou to examine self-organization in community-led urban regeneration in China. It reveals the following: (1) Residents’ active participation and co-construction awareness are the foundation of successful renewal. The construction of resident self-governance and democratic decision-making mechanisms should be strengthened. (2) The government’s policy support and financial guidance are vital. Local governments can mobilize social capital to participate by providing financial subsidies, tax incentives, and other means. (3) Urban planning should combine local cultural characteristics and community needs, avoiding the simple “demolish and rebuild” model. (4) Technical means can effectively improve efficiency and reduce information asymmetry and conflicts of interest. The findings suggest that self-organization is not merely a grassroots phenomenon but often operates within a hybrid governance framework, where collaboration between communities and local governments is essential. The study has significant implications for urban planning and policy, contributes to the growing discourse on participatory urban regeneration, and offers practical insights for fostering sustainable community-led initiatives.
CD56-Positive NK Cells and CD138-Positive Plasma Cells in Basal Decidua of Term Placentas in Singleton Pregnancies After Assisted Reproductive Technology Treatment of Endometriosis-Related Infertility
Authors: Dumancic Stipe , Bakotin Jakovac Marinela , Mimica Marko Drazen , Zekic Tomas Sandra , Marusic Jelena
Published: February 2025

A eutopic endometrium in endometriosis shows altered immune responses, including abnormalities of NK cells and expression of plasma cells, related to reproductive issues. This study investigated the counts of CD56-positive NK cells and CD138-positive plasma cells in the basal decidua of term placentas in singleton pregnancies after endometriosis-related infertility conceived by assisted reproductive technology (ART). This single-center, case-control study involved immunohistochemical analysis of CD56-positive NK cells and CD138-positive plasma cells in basal decidua using primary monoclonal mouse antibodies, followed by secondary antibodies using a standardized protocol. CD56 and CD138 immunohistochemically positive cells were reported as the total cell count for each studied antibody expressed per 1 mm2 of basal decidua (Olympus BX46 and Olympus Image Analyzer). Placental samples containing basal decidua from 36 participants with endometriosis-related infertility who conceived by ART, 31 participants with male factor infertility who conceived by ART and 40 healthy controls were included. Endometriosis decidua showed the lowest median count of CD56-positive NK cells (11.5 / mm2, p = 0.039) in BD compared to male factor group (25 / mm2) and healthy controls (24.5 / mm2). No differences were found for CD138-positive plasma cells counts between study groups. Basal decidua in pregnancies after endometriosis-related infertility showed reduced total count of CD56-positive NK cells, without differences in the CD138-positive plasma cell counts compared to control groups. Future studies should investigate how changes in NK cells throughout pregnancy affect the development of perinatal complications and placental pathologies in women with endometriosis, which could uncover potential diagnostic and therapeutic targets.
Properties of Electrode Induction Melting Gas Atomization- and Vacuum Induction Melting Atomization-Produced Powders and Their As-HIPed Blanks
Authors: Ren Xiaona , Wang Yao , Wang Zhenfan , Wang Peng , Wang Zihao , Yang Lebiao , Qi Weifeng , Li Xinggang , Ge Changchun
Published: February 2025

The main method for large-scaled preparing powder superalloys in the production process is inert gas atomization, particularly vacuum-induced gas atomization (VIGA). A novel technique called electrode-induced gas atomization (EIGA) with a crucible-free electrode was proposed to prepare non-inclusion superalloy powders. In this study, a Ni-based superalloy of FGH4096 powder was prepared using both the VIGA and EIGA methods, while blanks were prepared through direct hot isostatic pressing (as-HIPed) near-net-forming method. The particle size, morphology, microstructure, and mechanical properties of the powders and blanks were compared via a laser particle size analyzer, SEM, TEM, and room-temperature and 650 °C tensile tests. The results indicated that EIGA-prepared powders exhibited a finer particle size and better surface quality than the one prepared via VIGA, which showed reduced satellite powders. However, the as-HIPed blank of EIGA-prepared powders had a lower secondary γ’ ratio and slightly reduced strength compared to the as-HIPed blank of VIGA-prepared powders due to its slightly lower secondary γ’ phase ratio and less effective inhibition of dislocation movement. Furthermore, the overall performance of the two samples did not differ significantly due to the similar microstructural characteristics of the powders. However, the variation in particle size affects heat conduction during the HIP process, resulting in slight differences in blanks’ properties.