bluuebunny/crossref_metadata_2025 huggingface dataset:
Improved Fruit Fly Algorithm to Solve No-Idle Permutation Flow Shop Scheduling Problem
Authors: Zeng Fangchi , Cui Junjia
Published: February 2025

The no-idle permutation flow shop scheduling problem (NIPFSP), as a current hot topic, is widely present in practical production scenarios in industries such as aviation and electronics. However, existing methods may face challenges such as excessive computational time or insufficient solution quality when solving large-scale NIFSSP instances. In this paper, a discrete fruit fly optimization algorithm (DFFO) is proposed for solving the NIPFSP. DFFO consists of three phases, i.e., the smell search phase based on the variable neighborhood, the visual search phase based on the probabilistic model, and the local search phase. In the smell search phase, multiple perturbation operators are constructed to further expand the search range of the solution; in the visual search phase, a probabilistic model is constructed to generate a series of positional sequences using some elite groups, and the concept of shared sequences is adopted to generate new individuals based on the positional sequences and shared sequences. In the local search stage, the optimal individuals are refined with the help of an iterative greedy algorithm, so that the fruit flies are directed to more promising regions. Finally, the test results show that DFFO’s performance is at least 28.1% better than other algorithms, which verifies that DFFO is an efficient method to solve NIPFSP.
Research on Output Prediction Method of Large-Scale Photovoltaic Power Station Based on Gradient-Boosting Decision Trees
Authors: Xie Rongyi , Pan Guobing , Liang Chun , Lin Beimin , Yu Ouyang
Published: February 2025

As a large number of large-scale photovoltaic (PV) stations are integrated into the power grid, the penetration rate of PV power is growing higher and higher. The intermittency and volatility of PV power generation bring great pressure to the safe and stable operation of the distribution network. In order to realize scientific energy dispatching and optimization, the predicted output of large PV stations is the data basis and prerequisite. The output prediction method of large PV stations is studied in this paper, and a prediction method based on gradient-boosting decision trees is proposed. In the method, the original data are first collected, and the sample set is established through the steps of data interpolation, supplement, and integration, and then the sample set is pre-processed by data cleaning and normalization. The model training and PV output prediction during the test period are carried out based on the pre-processed data. Finally, the prediction results are imported into the error analysis module. The feasibility and accuracy of the proposed method are analyzed by comparing it with the traditional method. The results show that the normalized mean absolute error (nMAE) and normalized root mean square error (nRMSE) of the proposed method are 7.31% and 11.78%, respectively, while the nMAE and nRMSE of the traditional method are 11.67% and 20.39%, respectively. Thus, the prediction performance of the proposed method is superior to that of the traditional method.
Mechanism of Casing Deformation of Shale Gas Platform Wells in Luzhou Block Before Fracturing and Countermeasures for Prevention and Control
Authors: Zhang Xiaojun , Li Jun , Zhao Yuxuan , Cao Wei , Zhang Wenbo , Lu Zongyu , Liu Gonghui
Published: February 2025

The deep shale gas resources in the Luzhou area of the southern Sichuan Basin are abundant and have been identified as a key replacement field for natural gas development following the medium-to-shallow shale gas fields in Changning and Weiyuan. However, the frequent occurrence of “pre-deformation without fracturing” in horizontal wells has significantly restricted large-scale production. In this study, the Lu203 and Yang101 well areas were analyzed to investigate the characteristics of casing deformation and the correlation with faults and natural fractures (fracture systems). A numerical model of multi-stage fracturing for platform wells was established based on microseismic event data, and the effects of fracturing on the stress and casing stress of adjacent wells were simulated and analyzed. The results indicate that the development of fracture systems is the primary cause of the “pre-deformation without fracturing” phenomenon. The propagation of fracturing fluid through fractures significantly increases the stress and loading around adjacent wells, causing casing stress to exceed its yield strength. To mitigate this issue, a method involving the injection of approximately 10 MPa of internal casing pressure into unfractured wells was proposed, effectively reducing the risk of casing deformation and failure. This provides technical support for the efficient development of deep shale gas.
Utilizing Life Cycle Assessment to Optimize Processes and Identify Emission Reduction Potential in Rice Husk-Derived Nanosilica Production
Authors: Gu Shan , Yang Li , Liang Xiaoye , Zhou Jingsong
Published: February 2025

A consistent life cycle assessment (LCA) methodology was employed to show how the type of alkali (NaOH or Na2CO3) used for extracting water glass from rice husks, as well as the type of acid (HCl, H2SO4, or HNO3) used for precipitating water glass to nanosilica, affects the environmental emissions of rice husk-derived nanosilica (RH nanosilica). Six nanosilica production scenarios were explicitly compared to determine the most environmentally friendly route. The LCA results show that under the same circumstances, the majority of the environmental emissions of sodium hydroxide (NaOH) are significantly better than those of sodium carbonate (Na2CO3), except for the MAETP and ODP indicators. Similarly, except for the MAETP indicator, the environmental emissions of hydrochloric acid (HCl) are generally superior to those of sulfuric acid (H2SO4) and nitric acid (HNO3). NaOH and HCl were selected as preferable for the extraction of silica from rice husks and the precipitation of water glass, respectively. In addition, the preferred route underwent further in-depth optimization with the aim of achieving optimal environmental emissions for RH nanosilica. The effects of electricity, diesel, fertilizers, and pesticides on the life cycle emission factors of RH nanosilica were examined. The results demonstrate that replacing traditional coal power with cleaner alternatives, such as wind, hydropower, solar power (both photovoltaic and thermal), and biogas electricity, can result in a substantial decrease in the life cycle emission factors of nanosilica, with reductions varying between 20% and 60%. An effective method to reduce emissions associated with diesel, fertilizers, and pesticides is to adopt effective measures to decrease their consumption. These findings provide valuable theoretical foundations and insights for the industrial application of RH nanosilica. These results have great significance for guiding and promoting the industrialization process of nanosilica derived from rice husks and accelerating its commercialization.
Extraction of Carbon Nanodots from Benzoin Resin Soot for Multifaceted Antibacterial Applications
Authors: Thangavel Pranesh , Hasan Nazim , Raja Gnanadeepam , Jawhari Ahmed Hussain , Gopal Judy
Published: February 2025

Antimicrobial compounds have been gaining paramount importance in recent years since there has been a lot of concern over the increasing threats from microbial infections. Medicinal smoke has always been received with warmth and positivity, and being an age-old traditional process, it has been practiced down the generations. Nanomaterials have become highly promising when it comes to antimicrobial applications, and carbon nanomaterials have also gained preeminence. In the following study, carbon nanodots were extracted and characterized from benzoin resin smoke and tested for their antibacterial, anti-oral bacterial, and anti-biofilm properties. 2–15 nm-sized carbon nanodots were isolated, and their antibacterial activity was validated. Carbon coatings were made on glass using the benzoin resin-extracted carbon nanodots using a simple dip–dry technique. These coatings were confirmed to possess antibiofilm activity against pathogenic Staphylococcus aureus biofilms. With rising concerns over chemically synthesized nanomaterials, green extraction of carbon nanodots from benzoin smoke will come in handy for multiple biomedical applications.
A Comparative Review of IG-541 System Use in Total Flooding Application for Energized Electrical Fire
Authors: Loo Kheng Hooi , Lee Tin Sin , Bee Soo Tueen
Published: February 2025

Clean agent fire suppression systems are commonly used to protect areas containing valuable or critical equipment, especially in data centers and electrical substations, where traditional fire suppression methods are less effective or pose additional risks. This review evaluates the IG-541 fire suppression system as an alternative to halocarbon-based agents like HFC-227ea and FK-5-1-12, which are being phased out under environmental regulations, focusing on their application in energized electrical fires. IG-541 offers environmental advantages, including zero ozone depletion potential, no global warming potential, and negligible atmospheric lifetime, making it compliant with stringent environmental regulations. This review compares IG-541 with halocarbon agents across parameters such as extinguishing efficacy, safety considerations, environmental impacts, cost impacts, and system design considerations. Key findings underscore IG-541’s effectiveness in reducing fire damage without producing harmful by-products or exacerbating climate change. Furthermore, the study highlights the regulatory frameworks influencing the phase-out of halocarbon agents and the transition toward environmentally sustainable alternatives. While IG-541 emerges as a promising replacement for halocarbon agents, further exploration into its application in varied fire scenarios and energy-intensive environments is recommended to optimize its deployment.
Biofuels from Microalgae: A Review on Microalgae Cultivation, Biodiesel Production Techniques and Storage Stability
Authors: Sharma Amit Kumar , Jaryal Shivangi , Sharma Shubham , Dhyani Archana , Tewari Bhagya Sindhu , Mahato Neelima
Published: February 2025

Rising global energy demands, depleting fossil fuel reserves, and growing environmental concerns have led to an increasing demand for clean and renewable energy sources. Recently, microalgae biofuels have emerged as a promising and sustainable energy source due to their high biomass productivity, lipid content, and wastewater treatment capabilities. However, the viability of microalgae biofuels as a commercial-scale renewable fuel remains uncertain due to high production costs and storage stability issues. This review focuses on advanced technologies aimed at enhancing both the production of microalgae biodiesel and its storage stability. It explores the potential and challenges of recent developments in microalgae cultivation systems, particularly those factors that have contributed to increased lipid content in microalgae biomass. The study also examines the role of industrial wastewater in promoting microalgae growth and provides an overview of recent advances in biodiesel production. Additionally, it discusses various strategies to improve the storage stability of biodiesel, a critical consideration for the commercialization of microalgae biodiesel.
In the Lap of the Buddha: Intimacy in Tibetan Ritual
Authors: Warner Cameron David
Published: February 2025

Following the re-opening of the Rasa Trulnang Tsuklhakhang, the central temple in Lhasa, all of the new images of Buddhas, bodhisattvas, and famous lamas were placed behind glass except for those in the sancta sanctorum, the “Jokhang”. When a pilgrim approaches the central figure, the Jowo Śākyamuni, she climbs a ladder on his right side, lays a ceremonial scarf across his lap, and then lays her head there, like a child seeking solace from her mother. A wealthy pilgrim might return in the late afternoon, when the temple is closed to visitors, to sponsor a regilding ceremony, in which the sponsor can spend up to an hour nearly alone with the Jowo watching his whole body be repainted in gold. Based on participant observation, pilgrimage guides, and verses of praise offered to the Jowo, this paper considers how the cult of the Jowo uses moments of private intimacy to bridge the distance, both physically and historically, between a devotee and the Buddha.
Logos and Garden: Joseph Ratzinger—Benedict XVI on Eco-Theology
Authors: Nichols Aidan
Published: February 2025

The Logos has made a Garden. That short sentence might sum up the ‘eco-theology’ (or, as some would have it, theo-ecology) of Joseph Ratzinger—Benedict XVI. It is ripe for unpacking, by considering not only his general approach to the God–world relation (always presupposed as this is in his comments on environmental issues) but also how his theological insights into the created realm, understood as humanity’s common home (oikos), might now be taken further in the great pope’s footsteps, drawing on the doctrinal and hagiographical traditions of the Church.
External and Internal Threats to the Freedom of Religion or Belief of Indigenous Peoples in Latin America
Authors: Petri Dennis P. , Klocek Jason
Published: February 2025

Indigenous Peoples in Latin America face a dual challenge of social vulnerability and violations of freedom of religion or belief (FoRB). Representing 8% of the region’s population yet disproportionately affected by poverty, displacement, and health disparities, these communities also endure persistent religious discrimination—a challenge historically overlooked in policy and scholarship. This article advances the understanding of FoRB violations against Indigenous Peoples by distinguishing between external threats, targeting collective religious practices, and internal threats, undermining individual religious rights. Using a cross-case analysis of Paraguay, Mexico, and Colombia, this study illustrates the varied manifestations of these threats, relying on novel data from interviews, open-source platforms, and the Violent Incidents Database of the Observatory of Religious Freedom in Latin America and the International Institute for Religious Freedom. Focusing on Latin America as a “least likely case” for FoRB violations, the article highlights significant disparities in FoRB protections for Indigenous Peoples, challenging the assumption of Latin America as a region of relative religious freedom. By shedding light on these violations, the study underscores their broader implications for political stability and human rights. The findings call for greater attention to the intersections of religious freedom and Indigenous rights, offering a framework applicable to global contexts. This work also highlights the need for sustained data collection and targeted interventions to address these challenges effectively.
Estimating QoE from Encrypted Video Conferencing Traffic
Authors: Sidorov Michael , Birman Raz , Hadar Ofer , Dvir Amit
Published: February 2025

Traffic encryption is vital for internet security but complicates analytical applications like video delivery optimization or quality of experience (QoE) estimation, which often rely on clear text data. While many models address the problem of QoE prediction in video streaming, the video conferencing (VC) domain remains underexplored despite rising demand for these applications. Existing models often provide low-resolution predictions, categorizing QoE into broad classes such as “high” or “low”, rather than providing precise, continuous predictions. Moreover, most models focus on clear-text rather than encrypted traffic. This paper addresses these challenges by analyzing a large dataset of Zoom sessions and training five classical machine learning (ML) models and two custom deep neural networks (DNNs) to predict three QoE indicators: frames per second (FPS), resolution (R), and the naturalness image quality evaluator (NIQE). The models achieve mean error rates of 8.27%, 7.56%, and 2.08% for FPS, R, and NIQE, respectively, using a 10-fold cross-validation technique. This approach advances QoE assessment for encrypted traffic in VC applications.
Autonomous Sea Floor Coverage with Constrained Input Autonomous Underwater Vehicles: Integrated Path Planning and Control
Authors: Gkesoulis Athanasios K. , Georgakis Panagiotis , Karras George C. , Bechlioulis Charalampos P.
Published: February 2025

Autonomous underwater vehicles (AUVs) tasked with seafloor coverage require a robust integration of path planning and control strategies to operate in adverse real-world environments including obstacles, disturbances, and physical constraints. In this work, we present a fully integrated framework that combines an optimal coverage path planning approach with a robust constrained control algorithm. The path planner leverages a priori information of the target area to achieve maximal coverage, minimize path turns, and ensure obstacle avoidance. On the control side, we employ a reference modification technique that guarantees prescribed waypoint tracking performance under input constraints. The resulting integrated solution is validated in a high-fidelity simulation environment employing ROS, Gazebo, and ArduSub Software-in-the-Loop (SITL) on a BlueROV2 platform. The simulation results demonstrate the synergy between path planning and control, illustrating the framework’s effectiveness and readiness for practical seafloor operations such as underwater debris detection.
A Systematic Study of Popular Software Packages and AI/ML Models for Calibrating In Situ Air Quality Data: An Example with Purple Air Sensors
Authors: Smith Seren , Trefonides Theodore , Srirenganathan Malarvizhi Anusha , LaGarde Shyra , Liu Jiakang , Jia Xiaoguo , Wang Zifu , Cain Jacob , Huang Thomas , Pourhomayoun Mohammad , Llewellyn Grace , Phyo Wai , Hasheminassab Sina , Roberts Joe , Marlis Kevin , Duffy Daniel Q. , Yang Chaowei
Published: February 2025

Accurate air pollution monitoring is critical to understand and mitigate the impacts of air pollution on human health and ecosystems. Due to the limited number and geographical coverage of advanced, highly accurate sensors monitoring air pollutants, many low-cost and low-accuracy sensors have been deployed. Calibrating low-cost sensors is essential to fill the geographical gap in sensor coverage. We systematically examined how different machine learning (ML) models and open-source packages could help improve the accuracy of particulate matter (PM) 2.5 data collected by Purple Air sensors. Eleven ML models and five packages were examined. This systematic study found that both models and packages impacted accuracy, while the random training/testing split ratio (e.g., 80/20 vs. 70/30) had minimal impact (0.745% difference for R2). Long Short-Term Memory (LSTM) models trained in RStudio and TensorFlow excelled, with high R2 scores of 0.856 and 0.857 and low Root Mean Squared Errors (RMSEs) of 4.25 µg/m3 and 4.26 µg/m3, respectively. However, LSTM models may be too slow (1.5 h) or computation-intensive for applications with fast response requirements. Tree-boosted models including XGBoost (0.7612, 5.377 µg/m3) in RStudio and Random Forest (RF) (0.7632, 5.366 µg/m3) in TensorFlow offered good performance with shorter training times (<1 min) and may be suitable for such applications. These findings suggest that AI/ML models, particularly LSTM models, can effectively calibrate low-cost sensors to produce precise, localized air quality data. This research is among the most comprehensive studies on AI/ML for air pollutant calibration. We also discussed limitations, applicability to other sensors, and the explanations for good model performances. This research can be adapted to enhance air quality monitoring for public health risk assessments, support broader environmental health initiatives, and inform policy decisions.
CLUMM: Contrastive Learning for Unobtrusive Motion Monitoring
Authors: Gyamenah Pius , Iyer Hari , Jeong Heejin , Guo Shenghan
Published: February 2025

Traditional approaches for human monitoring and motion recognition often rely on wearable sensors, which, while effective, are obtrusive and cause significant discomfort to workers. More recent approaches have employed unobtrusive, real-time sensing using cameras mounted in the manufacturing environment. While these methods generate large volumes of rich data, they require extensive labeling and analysis for machine learning applications. Additionally, these cameras frequently capture irrelevant environmental information, which can hinder the performance of deep learning algorithms. To address these limitations, this paper introduces a novel framework that leverages a contrastive learning approach to learn rich representations from raw images without the need for manual labeling. This framework mitigates the effect of environmental complexity by focusing on critical joint coordinates relevant to manufacturing tasks. This approach ensures that the model learns directly from human-specific data, effectively reducing the impact of the surrounding environment. A custom dataset of human subjects simulating various tasks in a workplace setting is used for training and evaluation. By fine-tuning the learned model for a downstream motion classification task, we achieve up to 90% accuracy, demonstrating the effectiveness of our proposed solution in real-time human motion monitoring.
Integration of Artificial Neural Network Regression and Principal Component Analysis for Indoor Visible Light Positioning
Authors: Fite Negasa Berhanu , Wegari Getachew Mamo , Steendam Heidi
Published: February 2025

The advancement of artificial intelligence has brought visible-light positioning (VLP) to the forefront of indoor positioning research, enabling precise localization without additional infrastructure. However, the complex interplay between light propagation phenomena and environmental factors in indoor spaces presents significant challenges for VLP systems. Additionally, the pose of the light-emitting diodes is prior unknown, adding another layer of complexity to the positioning process. Dynamic indoor environments further complicate matters due to user mobility and obstacles, which can affect system accuracy. In this study, user movement is simulated using a constructed dataset with systematically varied receiver positions, reflecting realistic motion patterns rather than real-time movement. While the experimental setup considers a fixed obstacle scenario, the training and testing datasets incorporate position variations to emulate user displacement. Given these dataset characteristics, it is crucial to employ robust positioning techniques that can handle environmental variations. Conventional methods, such as received signal strength (RSS)-based techniques, face practical implementation hurdles due to fluctuations in transmitted optical power and modeling imperfections. Leveraging machine learning techniques, particularly regression-based artificial neural networks (ANNs), offer a promising alternative. ANNs excel at modeling the intricate relationships within data, making them well-suited for handling the complex dynamics of indoor lighting environments. To address the computational complexities arising from high-dimensional data, this research incorporates principal component analysis (PCA) as a method for reducing dimensionality. PCA eases the computational burden, accelerates training speeds by normalizing the data, and reduces loss rates, thereby enhancing the overall efficacy and feasibility of the proposed VLP framework. Rigorous experimentation and validation demonstrate the potential of employing principal components. Experimental results show significant improvements across multiple evaluation metrics for a constellation comprising eight LEDs mounted in a rectangular structure measuring a room dimension of 12 m × 18 m × 6.8 m, with a photodiode (PD) receiver. Specifically, the mean squared error (MSE) values for the training and testing samples are 0.0062 and 0.0456 cm, respectively. Furthermore, the R-squared values of 99.31% and 94.74% for training and testing, respectively, signify a robust predictive performance of the model with low model loss. These findings underscore the efficacy of the proposed PCA-ANN regression model in optimizing VLP systems and providing reliable indoor positioning services.
Towards Nature-Positive Smart Cities: Bridging the Gap Between Technology and Ecology
Authors: Russo Alessio
Published: February 2025

In the biodiversity and climate emergency, a holistic approach is needed for the development of smart cities. This perspective paper proposed a novel conceptual framework for nature-positive smart cities in a socio-technical-ecological system (STES), which bridged the gap between technological advancement and ecological principles within the existing smart city approach, enabling cities to transition towards a biodiversity-led paradigm. Based on recent literature on smart cities and nature-positive cities, this framework combines the principles of nature-positive cities and smart cities with the technological capabilities of Nature 4.0, using tools such as AI, sensors, IoT, big data analytics, and machine learning. The literature shows that smart green spaces have already been developed worldwide; therefore, education is needed for personnel working in local government to effectively use this new technology. The paper presents examples of how smart technologies can be utilised within urban green spaces to maximise ecosystem services and biodiversity. Finally, it provides recommendations and areas for future research, concluding with a call for specific policy interventions to facilitate the transition towards nature-positive smart cities.
Carbon Emissions Trading and Employment: Evidence from China
Authors: Qiang Mengmeng , Lai Hangyi , Lyu Zhenxi
Published: February 2025

Using carbon emissions trading (CET) to lower carbon emissions and stabilize employment are critical components of both environmental and social sustainability. However, it remains unclear whether there is a conflict between these two objectives. Based on panel data from 267 Chinese cities spanning from 2005 to 2019, this study employs a difference-in-differences (DID) model to evaluate the impact of CET on employment. The results show that CET significantly increases employment by 16.3%. The underlying reason for this growth is that CET boosts output in regulated industries, which not only promotes employment growth in these industries, but also through industrial linkages, and drives significant growth in unregulated sectors, particularly in production and consumption services. Moreover, the study finds that the employment effect is closely related to the characteristics of CET. Higher market liquidity and trading volume amplify CET’s employment impact, while carbon prices exhibit an inverted U-shaped relationship with employment, peaking at around 30 yuan per ton. The findings indicate that CET is a promising policy tool that, if well-designed, can deliver the dual benefits of carbon reduction and job creation.
Analysis of Annual Drought Episodes Using Complex Networks
Authors: Spiliotis Konstantinos , Voudouris Konstantinos , Vangelis Harris , Spiliotis Mike
Published: February 2025

In this work, a new method to analyze the drought episodes based on the annual precipitation time series and utilizing complex networks theory is proposed. The precipitation time series is transformed into a complex network using the visibility algorithm.Then, several network measures are computed to characterize the underlying connectivity. The proposed analysis identifies important nodes which correspond to the low annual precipitation volume, providing a way to assess drought intensity without the use of the mean value and standard deviation, which are sensitive to climate change. Additionally, using community detection algorithms and network centrality measures, the method identifies ∼10-year and ∼4-year cycles within a period of 57 years. Using macroscopic measures like network distributions, we can identify rare high-intensity drought events. Finally, network analysis shows that the closeness centrality measure is in very good agreement with the well-known Standardised Precipitation Index (SPI) and thus can be used to characterize drought intensity.
The Impact of Dual Environmental Regulations on Carbon Intensity: A Global Perspective
Authors: Wang Jianshi , Li Han , Wang Chengxin , Ren Wanxia
Published: February 2025

Greenhouse gas emissions are a leading cause of global warming, posing significant threats to both the natural environment and the sustainable development of global economies and societies. Environmental regulations have been crucial in reducing these emissions and improving ecological conditions. This study presents the first theoretical analysis of the mechanisms through which formal and informal environmental regulations influence carbon intensity. A panel data model was constructed to empirically test and analyze the impact of these regulations on carbon intensity across 118 countries. The findings reveal that both formal and informal environmental regulations exerted a reduced influence on global carbon emission intensity, affirming their significance in promoting energy conservation and emission reduction. Moreover, a synergistic effect was observed between the two types of regulations, indicating that their coordinated enforcement could further mitigate carbon emission intensity. Regional analysis revealed that formal environmental regulation exhibited a dampening effect on carbon emission intensity in both high- and low-carbon countries. However, the moderating impact of informal environmental regulation was found to be markedly stronger in low-carbon countries compared to their high-carbon counterparts. The synergy between these two forms of regulation had a more pronounced influence on the carbon emission intensity of high-carbon countries, underscoring their potent emission reduction effect when implemented in tandem. The insights gained from this study can aid policymakers in developing effective environmental policies aimed at realizing energy conservation and emission reduction goals, thereby contributing to the mitigation of global warming and the promotion of sustainable development.
Open Source Simulation for Compression Analysis of Corrugated Boards
Authors: Metar Mohmad-Akram , Fitas Ricardo
Published: February 2025

In the packaging industry, corrugated boards are widely used due to many factors like biodegradability, a high strength-to-weight ratio, and also ease of manufacturing. In this study, the finite element analysis of corrugated cardboards under the flat compression test was performed using the open source FEA software Salome-meca. A corrugated board consists of a flute sandwiched between a top and bottom liner. This study was performed with the help of Python scripting in order to iteratively perform many studies by varying the geometric shape of the flute. The pressure distribution along the top and the bottom liner was analyzed. The load–deflection curve for the corrugated cardboard was also analyzed as a part of this study. The boundary condition and the loading condition were chosen in such a way as to correctly represent the situation in real life using the flat crush test in the lab. The contact zone was identified a priori and defined during the preparation of the study. Finally, Code-Aster (the solver utilized by Salome-Meca) was used to solve the finite element solution to the problem.
Quantum-Resistant Lattice-Based Proxy Signature
Authors: Luo Jiaoyan , Zuo Liming , Liu Hao
Published: February 2025

With the advancement of quantum computing, the utilization of quantum algorithms such as Shor’s algorithm enables the efficient resolution of problems that are intractable in classical computing paradigms, posing a significant threat to traditional signature schemes. Lattice-based cryptography is considered one of the most promising post-quantum cryptographic algorithms due to its computational advantages and potential resistance to quantum attacks. Proxy signature is an authorization mechanism that allows the original signer to delegate the signing power to a proxy. The security of existing proxy signature schemes is mostly based on classical hard problems, which cannot guarantee security under quantum attacks. Therefore, this paper combines lattice-based cryptography with proxy signatures to propose a new lattice-based proxy signature scheme (NLBPS). NLBPS constructs signatures using lattice-based trapdoor sampling algorithms and preimage sampling algorithms. Comparative analysis shows that the proposed scheme has relatively smaller key and signature sizes compared to some existing lattice-based proxy signature schemes, and it also offers a certain improvement in computational efficiency.
A Note on the Continuous and Nowhere Differentiable Function
Authors: Kęska Sergiusz
Published: February 2025

We continue the studies of derivatives of the non-harmonic series ∑cnexp(iλnx). We consider replacing sinλnx with some step function fn:[0,1)→{0,1,2}. The purpose of this paper is to show that f(x)=∑fn(x)2n is continuous and nowhere differentiable in (0,1). All functions fn, used to construct f, are created from symmetrical blocks of type M: [0,1,0] and blocks of type L: [0,1,2] and R: [2,1,0], located symmetrically with respect to M. The function f1 takes the value zero in the intervals [0,1/3), [2/3,1) and the value one in the interval [1/3,2/3), i.e., it consists of the block of type M: [0,1,0]. Function f2 takes the value two in the intervals [2/9,3/9) and [6/9,7/9); the value one in the intervals [1/9,2/9), [4/9,5/9), and [7/9,8/9); and the value zero in the remaining intervals. This means a composition of blocks of [0,1,2][0,1,0][2,1,0], i.e., LMR blocks. The function f3 is a symmetric composition of blocks, LLMLMRMRR. These blocks are discontinuous analogs of the functions that produces Schoenberg curves.
Multiparametric Magnetic Resonance Imaging Findings of the Pancreas: A Comparison in Patients with Type 1 and 2 Diabetes
Authors: Higashi Mayumi , Tanabe Masahiro , Tanabe Katsuya , Okuya Shigeru , Takeda Koumei , Nagao Yuko , Ito Katsuyoshi
Published: February 2025

Background/Objectives: Diabetes-related pancreatic changes on MRI remain unclear. Thus, we evaluated the pancreatic changes on MRI in patients with both type 1 diabetes (T1D) and type 2 diabetes (T2D) using multiparametric MRI. Methods: This prospective study involved patients with T1D or T2D who underwent upper abdominal 3-T MRI. Additionally, patients without impaired glucose metabolism were retrospectively included as a control. The imaging data included pancreatic anteroposterior (AP) diameter, pancreas-to-muscle signal intensity ratio (SIR) on fat-suppressed T1-weighted image (FS-T1WI), apparent diffusion coefficient (ADC) value, T1 value on T1 map, proton density fat fraction (PDFF), and mean secretion grade of pancreatic juice flow on cine-dynamic magnetic resonance cholangiopancreatography (MRCP). The MR measurements were compared using one-way analysis of variance and the Kruskal–Wallis test. Results: Sixty-one patients with T1D (n = 7) or T2D (n = 54) and 21 control patients were evaluated. The pancreatic AP diameters were significantly smaller in patients with T1D than in patients with T2D (p < 0.05). The average SIR on FS-T1WI was significantly lower in patients with T1D than in controls (p < 0.001). The average ADC and T1 values of the pancreas were significantly higher in patients with T1D than in patients with T2D (p < 0.01) and controls (p < 0.05). The mean secretion grade of pancreatic juice flow was significantly lower in patients with T1D than in controls (p = 0.019). The average PDFF of the pancreas was significantly higher in patients with T2D than in controls (p = 0.029). Conclusions: Patients with T1D had reduced pancreas size, increased pancreatic T1 and ADC values, and decreased pancreatic juice flow on cine-dynamic MRCP, whereas patients with T2D had increased pancreatic fat content.
An Evaluation of Green Hotels in Singapore, Sentosa Island: A Big Data Study Through Online Review
Authors: Aliyah Ummi , Williady Angellie , Kim Hak-Seon
Published: February 2025

The purpose of this study is to evaluate green hotels in Sentosa Island, Singapore, with big data analysis utilizing online reviews regarding environmental sustainability practices. Tourism, while providing significant economic benefits, also contributes to environmental degradation, particularly through the hotel industry, which accounts for a substantial share of global greenhouse gas emissions. Sustainable tourism practices are becoming increasingly popular as the public becomes more aware of the environment. As a result, green hotels emerged as a solution and hotels are taking steps to become eco-friendly. Based on the 3579 online reviews, the findings indicate that green practices, including water and energy conservation, play a crucial role in enhancing customer satisfaction, alongside traditional hospitality elements such as service quality and amenities. The integration underscores the importance of incorporating sustainability into core operations without compromising the high standards of service that guests expect. This research contributes to the understanding of sustainable hospitality practices, offering actionable recommendations for policymakers and hotel managers to foster environmentally friendly practices while maintaining customer satisfaction.
Prevalence of Aphid-Transmitted Potyviruses in Pumpkin and Winter Squash in Georgia, USA
Authors: Acharya Nirmala , Kumar Manish , Bag Sudeep , Riley David G. , Diaz-Perez Juan C. , Simmons Alvin M. , Coolong Timothy , McAvoy Theodore
Published: February 2025

Viruses are a major pathogen challenging the sustainable production of cucurbits worldwide. Pumpkin and winter squash showed severe virus-like symptoms during the fall of 2022 and 2023 in Georgia, USA. Symptomatic leaves were collected from the field and processed for small RNA sequencing for virus identification using high-throughput sequencing (HTS). HTS analysis revealed the presence of two aphid-transmitted viruses (ATVs), zucchini yellow mosaic virus (ZYMV) and papaya ringspot virus (PRSV), along with three whitefly-transmitted viruses, cucurbit chlorotic yellows virus, cucurbit yellow stunting disorder virus, and cucurbit leaf crumple virus. The results of our study suggest a significant shift in ATV’s abundance in these two crops between 2022 and 2023. According to the qPCR data in the fall of 2022, pumpkins experience an incidence of 56.25% and 31.25% of PRSV and ZYMV, respectively. Similarly, winter squash shows an incidence of 50% and 32.14% of PRSV and ZYMV, respectively. Mixed infection of both viruses was also observed in these two crops. In 2023, we observed a predominance of ZYMV in pumpkin and winter squash (61.25% and 42.50%, respectively). However, PRSV was not detected in pumpkins, and it was detected at a negligible level (0.62%) in winter squash using qPCR. Phylogenetic analysis of ZYMV-encoded coat protein (CP) and helper component-protease (HC-Pro) from Georgia suggests a close relationship with the European isolates. Conversely, PRSV-encoded CP and NIa-VPg show a more diverse evolutionary history. Overall, this research will provide valuable insights into the dynamics of ZYMV and PRSV in pumpkin and winter squash crops within the southeastern United States.
Innate Immune Sensors and Cell Death—Frontiers Coordinating Homeostasis, Immunity, and Inflammation in Skin
Authors: Soe Ye Mon , Sim Seen Ling , Kumari Snehlata
Published: February 2025

The skin provides a life-sustaining interface between the body and the external environment. A dynamic communication among immune and non-immune cells in the skin is essential to ensure body homeostasis. Dysregulated cellular communication can lead to the manifestation of inflammatory skin conditions. In this review, we will focus on the following two key frontiers in the skin: innate immune sensors and cell death, as well as their cellular crosstalk in the context of skin homeostasis and inflammation. This review will highlight the recent advancements and mechanisms of how these pathways integrate signals and orchestrate skin immunity, focusing on inflammatory skin diseases and skin infections in mice and humans.
Therapeutic Assessment of Diverse Doxycycline-Based Formulations in Promoting Deep Corneal Wound Healing: Evidence from a Rat Model
Authors: Chan Sze-Min , Tseng Ching-Li , Huang Wei-Hsiang , Lin Chung-Tien
Published: February 2025

Doxycycline (Dxy), a broad-spectrum antibiotic with anti-inflammatory effects, is commonly used in ophthalmology but is unstable as a topical eyedrop, degrading quickly into inactive forms and requiring frequent application. To address this, gelatin nanoparticles (GNPs) loaded with Dxy (DNPs) were developed as a stable ophthalmic nanomedicine for enhancing corneal wound healing by inhibiting matrix metalloproteinases (MMPs). In this study, female Sprague–Dawley rats underwent lamellar keratectomy, and various Dxy formulations—oral, conventional eyedrops, and DNP-containing eyedrops—were evaluated for corneal wound repair. Clinical assessments included fluorescein staining, slit-lamp biomicroscopy, spectral-domain optical coherence tomography (SD-OCT) imaging, histopathology, and immunohistochemistry for MMP-2, MMP-9, and α-SMA. The DNP group (0.01% Dxy in DNPs, applied twice daily) demonstrated faster corneal thickness recovery and epithelial healing on days 7 and 14 compared to 0.1% Dxy eyedrop treatments applied twice or four times daily. DNP-treated eyes also showed reduced angiogenesis intensity and lower MMP-2 and MMP-9 immunoreactive scores, with enhanced stromal recovery and reduced neovascularization. These results highlight DNPs’ potential as a superior treatment for corneal wounds, providing effective healing with less frequent dosing and lower drug concentrations. This study supports DNPs’ potential for clinical application as a stable and efficient therapeutic agent in ophthalmology.
PRRSV RNA Detection and Predictive Values Between Different Sow and Neonatal Litter Sample Types
Authors: Li Peng , Machado Isadora , Petznick Thomas , Pratt Emily , Xiao Jinnan , Sievers Chris , Yeske Paul , Jayaraman Swami , Moraes Daniel C. A. , Cezar Guilherme , Mil-Homens Mafalda , Tong Hao , Will Kelly , Reicks Darwin , Kelly Jason , Osemeke Onyekachukwu H. , Silva Gustavo S. , Linhares Daniel C. L.
Published: February 2025

Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), a major challenge for the swine industry, can be transmitted both vertically and horizontally. Common sample types, including processing fluid, serum, and family oral fluid, can neither determine whether PRRSV infection originates vertically or horizontally nor directly reflects the sow’s PRRSV status. At around 90 days post live-virus inoculation (LVI), 555 sows were sampled by Tonsil-oral-scrubbing (TOSc) 2 weeks pre-farrowing and tested for PRRSV RNA. From these, 59 PRRSV-positive sows, and 88 PRRSV-negative sows matched by parity were conveniently selected. TOSc from sows, blood swabs from live piglet, and tongue fluid (TF) plus serum from dead piglet were collected individually from all study litters within 12 h post-farrowing. The pre-farrowing TOSc samples had significantly higher PRRSV positivity than TF, serum and blood swab pools, while dead piglet serum had significantly lower mean Ct values than all other sample types. TOSc samples had 25% positive predictive value (PPV) for “live litter” PRRSV status while the pre-farrow and post-farrow TOSc had 87.2% and 89.0% negative predictive value (NPV), respectively. In conclusion, we characterized PRRSV RNA detection among all sub-populations within a litter with easy-to-use TOSc samples and neonatal litter samples, suggesting the occurrence of vertical transmission 90 days post-LVI in sows. TOSc samples from sows had low PPV and high NPV for their respective litter’s PRRSV status.
The Impact of Ice on River Morphology and Hydraulic Structures: A Review
Authors: Cheng Tiejie , Wei Jinjin , Ni Jin , Wang Jun , Lu Haitian , Cheng Kai , Fu Hui
Published: February 2025

River ice formation during the winter period is a common phenomenon for most rivers in the northern hemisphere. The combined effect of hydraulic, thermodynamic, and geometric boundary conditions results in a highly complex system when compared to open channel conditions, particularly in regard to ice cover and ice jams. These differences have a considerable impact on the evolution of river morphology, sediment transport, and the stability of hydraulic structures. The presence of ice cover and ice jam results in an increase in river channel roughness, which in turn changes the velocity and shear stress distribution in the riverbed. The present review summarizes the current state-of-the-art research on river ice, including field observation, experimental study, and numerical simulation. Finally, the review concludes with an overview of the current state of research in the field, accompanied by an analysis of the challenges that remain and suggestions for future research directions.
Spatiotemporal Drought Analysis Using the Composite Drought Index (CDI) over Dobrogea, Romania
Authors: Serban Cristina , Maftei Carmen
Published: February 2025

This paper discusses a study that examined the severity of droughts and their changes in the Dobrogea region in southeastern Romania between 2001 and 2021 and develops a high-resolution (1 km) Composite Drought Index (CDI) dataset. To explore the effectiveness of the index, we carried out a correlation analysis between the CDI, the Standardized Precipitation Index (SPI), and the Standardized Precipitation Evapotranspiration Index (SPEI), which shows a strong positive relationship among these indices. Analysis of the CDI time series reveals an increase in drought frequency for the study period, due to high temperature and below-normal rainfall. Most parts of the region were affected by moderate, severe, or extreme droughts, except for the years 2002–2005 and 2013. The worst drought events were in 2011, 2012, and 2020, when the region was under severe land surface temperature stress, with values up to 39.13 °C. The central and northern areas of the region had the longest period of drought, at 22 months, which started in 2018 and culminated in 2020 when extreme drought covered over 70% of the region. Another major event was in 2015 when 95% of the region experienced severe drought. These results show the potential of the CDI as one of the significant indices in the assessment of drought and provide useful insights into drought monitoring in the future. More than that, we consider that the GPM IMERG satellite product can be used in the implementation of Drought Management Plans in Dobrogea in order to calculate drought indices derived from remote sensing data.