bluuebunny/crossref_metadata_2025 huggingface dataset:
Therapeutic Potential of Dimethyl Fumarate for the Treatment of High-Fat/High-Sucrose Diet-Induced Obesity
Authors: Valenca Helber da Maia , Mota Evelyn Caribé , Silva Andressa Caetano da Fonseca Andrade , Figueiredo-Junior Alexsandro Tavares , Verdini Fernanda , Romana-Souza Bruna , Renovato-Martins Mariana , Lanzetti Manuella , Valenca Samuel dos Santos , Moraes João Alfredo
Published: December 2024

Obesity is characterized by an imbalance between energy intake and expenditure that triggers abnormal growth of adipose tissues. Dimethyl fumarate (DMF) and its primary active metabolite, monomethyl fumarate (MMF), are Nrf2 activators and have been recognized as strategic antioxidants. This study aimed to evaluate the potential of MMF and DMF to interfere with adipogenesis and obesity, and identify the molecular mechanisms involved. The 3T3-L1 preadipocytes were incubated with differentiation medium (MIX) and simultaneously treated with different concentrations of MMF. In addition, male C57BL/6 mice were fed a standard diet or high-fat/high-sucrose diet (HFHSD) for 16 weeks, during the last 4 of which, they received oral DMF treatment. Exposure to MMF prevented the development of MIX-induced adipogenesis by reducing the expression of transcription factors that drive adipocyte differentiation and by decreasing triglyceride levels. In addition, various antioxidant and anti-inflammatory effects were observed after treatment with MMF as evidenced by the modulation of transcription factor activities and reduction in reactive oxygen species, adipokine, proinflammatory cytokine and resistin levels. In vivo treatment with DMF reduced calorie intake, body weight, and visceral and subcutaneous fat mass in HFHSD mice. Furthermore, DMF administration led to a better glycemic response as well as lower leptin and adiponectin plasma levels in these animals. Our data demonstrate that DMF and its metabolite MMF interfere with adipogenesis and prevent the key features of diet-induced obesity. Considering DMF is already a commercial drug used to treat psoriasis and multiple sclerosis, its pharmacological application for the treatment of obesity and related metabolic disorders holds promise.
Metabolomic and Transcriptomic Analysis Reveals Flavonoid-Mediated Regulation of Seed Antioxidant Properties in Peanut Seed Vigor
Authors: Gong Fangping , Cao Di , Li Zhuo , Fan Yi , Zhang Yaru , Zhang Lin , Zhao Kai , Qiu Ding , Li Zhongfeng , Ren Rui , Ma Xingli , Zhang Xingguo , Zhao Kunkun , Yin Dongmei
Published: December 2024

Peanut (Arachis hypogaea L.) is an oilseed crop grown worldwide. Flavonoids have profound benefits for plant growth and development because of their powerful antioxidant properties. Seed vigor is an important indicator of seed quality. However, how flavonoids impact seed vigor formation in large-seed peanuts is still poorly understood. Here, we profiled flavonoids, phytohormones, and transcriptomes of developing seeds of large-seed peanut varieties with low (ZP06) and high (H8107) seed vigor. A total of 165 flavonoids were identified, 51 of which were differentially accumulated in ZP06 and H8107. Lower levels of dihydromyricetin (0.28 times) and hesperetin-7-O-glucoside (0.26 times) were observed in ZP06 seeds than in H8107. All flavonoid biosynthesis structural genes were down-regulated in ZP06. The different hormone levels found in ZP06 and H8107 seeds could be associated with the expression of flavonoid biosynthesis genes via MYB and bHLH transcription factors. Dihydromyricetin could relate to ZP06′s poor seed vigor by impacting its seed antioxidant properties. Thus, the presence of flavonoids in large-seed peanuts could contribute to their physiological quality and germination potential through controlling the accumulation of reactive oxygen species to improve seed antioxidant properties.
The Novel Elemene Derivative, OMe-Ph-Elemene, Attenuates Oxidative Phosphorylation and Facilitates Apoptosis by Inducing Intracellular Reactive Oxygen Species
Authors: Guo Jianhua , Wang Jiayi , Fan Shuhao , Gao Mucong , Liu Guodu , Xia Yong
Published: December 2024

The incidence and mortality rates of colorectal cancer have been steadily increasing, making it one of the most prevalent cancers globally. Although current chemotherapy drugs have shown some efficacy in treating this disease, their associated side effects necessitate the development of more effective treatments and medications. The clinical application of elemene is widely utilized in tumor treatment; however, its efficacy is hindered by the requirement for high dosage and suboptimal anticancer effects. Thus, we have made modifications and enhancements to elemene, resulting in the development of a novel compound named (E)-8-(3,4,5-OMe-Ph)-β-Elemene (abbreviated as OMe-Ph-Elemene) that demonstrates significantly enhanced efficacy in suppressing colorectal cancer. We conducted an in vivo study and demonstrated the potential of OMe-Ph-Elemene in suppressing the growth of colorectal cancer xenograft tumors in zebrafish. The in vitro experiments revealed that OMe-Ph-Elemene effectively inhibited the proliferation and migration of colorectal cancer SW480 and HT-29 cells by inducing reactive oxygen species (ROS)-caused apoptosis and inhibiting mitochondrial oxidative phosphorylation. The mechanism was elucidated through high-throughput proteomic analysis and molecular biological analysis, revealing that OMe-Ph-Elemene induced cellular oxidative stress by downregulating CISD3 and promoted cell apoptosis by downregulating TRIAP1 and upregulating HMOX1. Furthermore, OMe-Ph-Elemene suppressed colorectal cancer cells’ mitochondrial oxidative phosphorylation by downregulating NDUFA7. In summary, the utilization of the elemene parent nucleus structure has led to the derivation of a novel tumor suppressor compound characterized by high efficacy and low toxicity, thereby providing a significant reference for the development of innovative drugs for colorectal cancer treatment.
Quantitative Analysis of Caffeine in Roasted Coffee: A Comparison of Brewing Methods
Authors: Mystkowska Iwona , Dmitrowicz Aleksandra , Sijko-Szpańska Monika
Published: December 2024

Coffee is one of the most widely consumed beverages in the world due to its sensory and health benefits. The caffeine content, a bioactive compound of coffee, depends on many factors, including the brewing method, which is the subject of ongoing scientific research. In addition, various methods are used in studies to determine the caffeine content. However, it is worth noting that there is considerable variation in the individual analytical parameters within these methods. The aim of this study was to update the data on the effects of different brewing methods on the caffeine content of the brew and to present the current state of knowledge on techniques for the determination of this compound. A literature review was conducted, taking into account the latest studies in this field. The results showed that the caffeine content (mg/100 mL) of the brew prepared with the Cold Brew method was 48.50–179.95, Espresso—50.40–965.60, French Press—52.00–123.90, AeroPress—56.35–120.92, and Moka—128.00–539.90. These methods were characterized by different brewing parameters (time, water temperature and pressure, ratio of coffee to water), which differentiated the caffeine content. In addition, some methods were characterized by a wide range of caffeine content, suggesting that even minor variations in brewing method parameters may affect the content of this ingredient. High-performance liquid chromatography (HPLC) was the predominant method used. The detector wavelengths, along with other parameters of the HPLC method, such as gradient profiles and column temperatures, can affect the precision and accuracy of the analysis, and these differences can modify analyte retention and detection, leading to discrepancies in results. These results point to the need for studies that consider various brewing methods and a wide range of coffee types, including roast and origin, to accurately determine the effects of these factors on caffeine content determined by one precise method.
Intermolecular Interactions in Mixed Choline Acetate and Maleic Acid Systems
Authors: Di Muzio Simone , Paolone Annalisa , Trequattrini Francesco , Palumbo Oriele
Published: December 2024

Mixed systems based on ionic liquids are promising innovative solvents due to their properties, which are strictly connected to the interactions that arise among the components. The present work investigates the intermolecular interactions of a mixed choline acetate and maleic acid system and their modifications with increasing acid content. MM/DFT calculations provided indications about the possible geometric configurations of the systems while Non-Covalent Interaction analysis was useful to describe and distinguish secondary interactions. The number of available configurations decreases at high acid concentration. Moreover, intramolecular hydrogen bonding was observed in all configurations except that in the most stable one of the lowest acid content mixture. Concomitantly, far-infrared spectroscopy was used to investigate intermolecular interactions and provided support to the computational results.
Predicting Online Shopping Behavior: Using Machine Learning and Google Analytics to Classify User Engagement
Authors: Gkikas Dimitris C. , Theodoridis Prokopis K.
Published: December 2024

User engagement metrics, including engaged sessions, average engagement time, bounce rate, and conversions, provide significant insights into online behavior. This study utilizes Google Analytics data insights and predictive statistics to analyze these metrics and apply classification models to enhance digital marketing strategies. Relationships among key metrics including event count, sessions, purchase revenue, transactions, and bounce rate, were examined using descriptive statistics, revealing factors affecting user engagement. Machine learning classifiers, such as decision trees (DTs), Naive Bayes (NB), and k-nearest neighbors (k-NN), were assessed for their effectiveness in classifying engagement levels. DTs achieved a classification accuracy of 97.98%, outperforming NB (65.00%) and k-NN (97.90%). Furthermore, techniques like pruning are applied for performance optimization. Primarily, this paper goas is to generate a series of recommendations to help the decision-makers and marketers optimizing the marketing strategies. This study highlights the significance of artificial intelligence (AI) integration in digital marketing as a best practice for optimizing decision-making processes.
A Two-Stage Optimization Model for Airport Stand Allocation and Ground Support Vehicle Scheduling
Authors: Yao Mengyun , Hu Minghua , Yin Jianan , Su Jiaming , Yin Mengxuan
Published: December 2024

To address the issues of inefficient resource allocation and severe ground congestion at hub airports during aircraft turnaround operations, a two-stage optimization model is constructed to coordinate the scheduling of stands and ground support vehicles. The model focuses on analyzing the scheduling rules, operational patterns, and collaborative mechanisms between stand allocation and ground support vehicles, taking into account the coupling relationship between airport operational and support resources. The pre-allocation of stands is conducted under the constraints of limited support resources, and the results are used as inputs for ground support vehicle scheduling. This combined optimization of stands and vehicle resources enhances the overall resource efficiency. The NSGA-II algorithm, combining local search strategies (LS-NSGA-II), is used to solve the model. Computational experiments conducted at Shenzhen Airport show some improvements: For the stand allocation model, the model incorporates ground support service constraints for tow tractors and driving distances for ferry buses, thereby avoiding potential service conflicts and resource wastage. Secondly, for the scheduling of vehicles, by analyzing the operational patterns and service characteristics of different vehicles, the model improved vehicle utilization efficiency by 37.5%, reduced travel distance by 20.4%, and decreased waiting times by 57.6%, compared to the first-come-first-served strategy currently employed at airports.
Attenuating Oxidative Damage with Macelignan in Glutamate-Induced HT22 Hippocampal Cells
Authors: He Mei Tong , Jung Kiwon , Park Chan-Woong , Chin Young-Won , Kang Ki Sung
Published: December 2024

Macelignan, from Myristica fragrans (nutmeg), is a bioactive compound with various pharmacological properties, including anti-inflammatory and neuroprotective activities. The purpose of this work was to investigate the antioxidant and anti-apoptotic effects of macelignan in glutamate-treated HT22 mouse hippocampal neurons. Macelignan was extracted and identified in a methanol extract of M. fragrans seeds. The DPPH was used to assess the antioxidative activity of macelignan. Glutamate (5 mM) was used to induce neurotoxicity in the HT22 cells. Neuroprotective effects were measured using relevant biochemical and imaging assays, including cell viability, ROS production, nuclear staining, apoptotic cell death, and protein expression. Macelignan markedly and concentration-dependently enhanced DPPH radical scavenging activity. In the HT22 cell model, glutamate induced cell damage by decreasing cell viability, promoting ROS generation, and increasing apoptotic cell death according to cell morphological changes. However, macelignan treatment restored cell viability, inhibited ROS generation concentration-dependently, and reduced apoptosis. Moreover, glutamate significantly up-regulated the phosphorylation of MAPK-pathway-related proteins, which was reversed by macelignan treatment. In conclusion, macelignan shows notable neuroprotective effects on oxidative stress and apoptotic cell death in glutamate-induced cells, and this study provides useful information on its potential therapeutic implications in neurological disorders.
An Enhanced Crowned Porcupine Optimization Algorithm Based on Multiple Improvement Strategies
Authors: Lei Wenli , Gu Yifan , Huang Jianyu
Published: December 2024

The Crowned Porcupine Optimization (CPO) algorithm exhibits certain deficiencies in initialization efficiency, convergence speed, and adaptability. To address these issues, this paper proposes an enhanced Crowned Porcupine Optimization algorithm (ICPO) based on multiple improvement strategies. ICPO optimizes the initialization process by introducing Logistic chaotic mapping, thereby expanding the search space. It accelerates convergence through an elite retention strategy and enhances global search capability by integrating stochastic operations, mutation-like operations, and crossover-like operations to increase population diversity. Additionally, adaptive step tuning based on fitness values is employed to comprehensively improve the algorithm’s performance. To verify the effectiveness of ICPO, 23 standard functions were used for a comprehensive evaluation, and its practicality was further validated through optimization of actual engineering design problems. The experimental results demonstrate significant improvements in convergence speed, solution quality, and adaptability with ICPO.
Novel Artificial Neural Network Aided Structural Topology Optimization
Authors: Kong Xiangrui , Wu Yuching , Zhu Peng , Zhi Peng , Yang Qianfan
Published: December 2024

In this paper, novel artificial neural networks are adopted for the topology optimization of full structures at both coarse and fine scales. The novelty of the surrogate-based method is to use neural networks to optimize the relationship from boundary and mesh conditions to structure density distribution. The objective of this study is to explore the feasibility and effectiveness of deep learning techniques for structural topology optimization. The newly developed neural networks are used for optimizing various types of structures with different meshes, partition numbers, and parameters. The finite element computation takes more than 90% of the total operation time of the SIMP method, but it decreases to 40%. It is indicated that the computational cost for the whole structural design process is relatively low, while the accuracy is acceptable. The proposed artificial neural network method is used to perform topology optimization for some numerical examples such as the cantilever beam, the MBB beam, the L-shape beam, the column, and the rod-supported bridge. It is demonstrated that computational efficiency is considerably improved while the proposed neural network method is adopted.
Function-Oriented Applicability Evaluation of Technical Folding Based on Expert Knowledge
Authors: Wieja Franziska , Jacobs Georg , Stürmer Stefan , Boelsen Kathrin , Konrad Christian , Merz Judith , Moreno Gata Kevin , Seiter Alex
Published: December 2024

Technical folding systems are rigid, three-dimensional surface structures that can be folded at their joints. Due to their foldable structure, technical folding systems can produce lightweight structures that have a high load-bearing capacity and can also flexibly change their shape. In order to develop products with technical folding in a function-oriented product development process, a folding principle must be defined that realises the individual functions. Since there is hardly any expert knowledge on technical folding, in general, and on the use of the technical folding principle in particular, technical folding is not very widespread despite its promising properties. Therefore, there is a need for a software-supported evaluation of whether functions can be realised by the principle of technical folding. The software-supported applicability evaluation (EvalTech) developed here is based on case-based reasoning methods, as these can artificially simulate evaluations based on expert knowledge. The expert knowledge used for the evaluation includes functions, function structures and properties of known technical folding systems, which are compared with functions and function structures of the development task. EvalTech was successfully used in the development and realisation of a large-scale foldable cover for industrial robots.
Data Mining for the Characterization of a Paper Prototype Obtained with Bacterial Cellulose Derived from Banana and Pineapple By-Products
Authors: Valenzuela-Cobos Juan Diego , Pérez-Martínez Simón , Fiallos-Cárdenas Manuel , Guevara-Viejó Fabricio
Published: December 2024

The primary objective of this research is to evaluate the feasibility of two of the most prevalent agricultural residues in Ecuador, banana peels and pineapple peels, as a carbon source in the culture medium of Komagataeibacter hansenii for the production of bacterial cellulose (BC) and BC-based paper. This analysis includes an assessment of the productivity parameters of the obtained BC and the quality parameters of the BC-based paper, employing multivariate statistical methodologies. The experimental design consisted of fifteen treatments: T1 served as the control using the standard HS medium, while treatments T2–T8 used banana peel extracts (BPE), and treatments T9–T15 used pineapple peel extracts (PPE) at concentrations from 10% to 40% (v/v). Extracts were prepared with tailored pretreatments for each type of peel to optimize bioactive compound recovery. Standardized fermentation and purification conditions were applied, and once the cellulose was obtained, additives and coating agents were incorporated to produce paper samples from each treatment. The results indicated that higher BPE concentrations (T5, T6, T7, and T8) correlated significantly with increased Weight and Yield of BC, as well as improved grammage and water content in the BC-based paper. This highlights that efficient paper production is influenced by the quality of the bacterial cellulose used, with BPE-based media yielding optimal results due to their nutrient composition, which promotes bacterial growth and metabolic activity. This approach suggests a pathway for advancing sustainable and economical paper production.
Effect of a Virtual Biophilic Residential Environment on the Perception and Responses of Seniors
Authors: Lee Eun-Ji , Park Sung-Jun , Choi Joon-Ho
Published: December 2024

This study investigates the effects of a virtual biophilic residential environment on seniors’ physiological and subjective responses to evaluate its potential to promote healing and recovery. Thirty seniors were exposed to three different scales (units, buildings, complexes) of virtual biophilic residential environments that combined both physical and digital biophilic elements. Physiological responses, including heart rate, heart rate variability, and galvanic skin response, were measured alongside self-reported levels of satisfaction and immersion. The primary objective was to assess the effectiveness of physical and digital design interventions at each residential scale. The findings revealed that the virtual biophilic residential environment reduced physiological stress in seniors, with the most significant impact observed at the unit scale. Digital design interventions further enhance stress relief benefits, indicating that integrating physical and digital elements in biophilic residential environments can positively influence seniors’ stress levels. Additionally, significant correlations were identified between physiological responses and subjective perceptions of immersion and satisfaction. This study is valuable as an initial comparative analysis of the effectiveness of physical and digital approaches in biophilic design. This paper is a preliminary study and is significant in that it systematizes virtual environment research from an age-friendly perspective and expands approaches to biophilic design.
Coal Dust and Methane as a Hazard in Coal Preparation Plants
Authors: Lutyński Aleksander , Lutyński Marcin
Published: December 2024

This article presents the results of analysis of the hazards posed by coal mine dust and methane in the coal preparation plants of hard coal mines in Poland. It was shown how the number of workplaces in plants at risk of coal dust explosion and the highest permissible dust concentration changed in the period from 2003 to 2022 when compared with coal production. The methodology of assessing mine dust hazards was based on hazard ratios related to one million tons of hard coal enriched in preparation plants. As a result of the analysis, it was found that the explosion hazard index with zone 20 showed an increasing trend in the analyzed period, while the explosion hazard indices with zones 21 and 22 analyzed together and the maximum permissible dust concentration showed decreasing trends following a decrease in hard coal production. In the case of methane, no zone 0 explosion hazards were found, and there were only a few instances of zone 1 explosion hazards. However, it was determined that the explosion hazard index for zone 2 showed an increasing trend during the analyzed period, which is directly proportional to the coal produced and is a result of increasing depth of mining.
Mechanical Design and Testing of Mobile Monitoring Units for Security Systems
Authors: Semrád Karol , Fiľko Martin , Draganová Katarína , Novotňák Jozef , Kessler Jaroslav
Published: December 2024

Mobile monitoring systems are currently used in many applications related to environmental applications or the monitoring of health status. However, security monitoring systems are usually chosen for a specific object, area or perimeter. The main goal of our article is to present the mechanical design of mobile monitoring units. These units create the basis of a developed mobile monitoring security system, which can be applied to monitor any area of interest, even in demanding weather conditions, involving, for example, windiness or wide operational temperature ranges. Therefore, this article is focused on the mechanical design of mobile monitoring units, which are constructed not only so that they can withstand challenging environmental conditions, but also with regard to their simple transportation, manufacturing process and, if necessary, possible repairs. During the design, emphasis was also placed on the vibrations of the mobile monitoring units and their temperature dependence, because vibrations can significantly affect the correct functioning of the mobile monitoring security system and cause false alarm situations. To confirm the correctness of the simulation models, experiments were performed on the mobile monitoring unit prototypes.
A New Methodology for Optimization of Lashing Lines in the Securing Arrangement of Non-Standardized Cargo on Ships
Authors: Pérez-Canosa José M. , Orosa José A.
Published: December 2024

The stowage plan and the securing arrangement of non-standardized cargo are some of the most important aspects in terms of cargo safety and economic costs. For this reason, the optimization of this operation is crucial and a daily challenge for securing planners trying to fulfill both requirements. In the present paper, a new methodological optimization is proposed presenting novel mathematical models and new 3D maps useful for the people in charge of stowage and securing arrangement. For this goal, the materials followed were the Code of Safe Practice for Cargo Stowage and Securing (CSS) of the International Maritime Organization (IMO) because many ships’ Cargo Securing Manuals refer to this international standard. Using an initial case study and making use of response surface techniques of design of experiments (DOE), multiple numerical simulations were performed to obtain novel mathematical models, revealing a high precision. Moreover, new 3D maps were presented and are very interesting tools due to their ease of understanding. The obtained results were compared with other simulations carried out where different variables were employed. The presented models of this methodology can predict the best securing arrangement to fulfill the balance forces in any stowage position (vertical and longitudinal) using the minimum securing devices and keeping the standards of safety. These methodological tools offer valuable advice to the shipping industry with responsibilities involved in the securing design of non-standardized cargo items.
Selection of Grid Road Networks Based on Raster Data
Authors: Shen Yilang , Zhang Yiqing , Li Renzhu
Published: December 2024

In cartography, generalization is a key process used to simplify complex geographic information, making it suitable for display at different scales while maintaining its essential meaning. When representing high-density road networks on a fixed screen area, overcrowding and loss of clarity often occur. To solve these problems, a road selection operation can be applied. However, traditional methods have primarily focused on structured vector road networks, leaving unstructured raster road networks largely unaddressed. This study introduces a novel technique, Adaptive Road Width Selection (ARWS), designed to improve the multiscale visualization of compact road systems using unstructured raster datasets. The ARWS method begins by segmenting the original raster road network into multilevel superpixels of varying sizes, reflecting the road widths, through neighborhood analysis. Next, road superpixel matching and selection are performed based on the minimum angle and maximum distance rules, alongside shortest-path calculations. Finally, redundant intersection pixels are eliminated to generate the selection results. The proposed ARWS method was evaluated using road network data from Shenzhen, China, producing effective multiscale visualization outcomes. Unlike conventional techniques relying on structured vector data, ARWS excels in preserving the semantic attributes, overall structure, local connectivity, and integrity of road networks. It addresses the challenges of multiscale visualization in dense road networks, offering a robust solution for unstructured raster data.
A Modified Bearing Capacity Model for Inclined Shallow Anchor Cable with Experimental Verification
Authors: Zhang Zhenhua , Xu Guojuan , Dai Liangjun , Cheng Tao , Xi Banglu , Chen Mingliang , Yang Jiaqiang
Published: December 2024

Most theoretical models of shallow anchor cables do not take the effect of anchor inclination into consideration, which is an important factor influencing load distribution, stress concentration, and failure mechanisms. In this paper, a modified bearing capacity was developed for a single anchor cable, taking the anchor inclination into consideration, based on the principle of limit equilibrium. Then, a series of indoor pull-out tests of single anchors with different inclinations were performed, where the effects of the anchor inclination on the bearing capacity and failure mechanisms were carefully analyzed. The experimental bearing capacities were compared to the predicted data of the proposed modified model, as well as other existing experimental results, aiming to verify the applicability and accuracy. The results show that the bearing capacity increases with decreasing anchor inclination because the vertical component of the force acting on the anchor cable increases. The failure models of the anchor cables, pulled out at different angles, exhibit an asymmetric “inverted trumpet” shape, which is caused by the varying stress distributions around the anchor cable during pull-out. In addition, the bearing capacities of the theory differ very little from the experimental and previous results, with a max error of nearly 10%. This study confirms that the proposed model reliably captures the effects of anchor inclination, providing valuable insights for designing inclined anchors in engineering practice.
Early Infection for Mass Production of Artificial Single-Stranded DNA with Escherichia coli
Authors: Hafner Nathalie , Nematzadeh Somehsaraei Nazanin , Honemann Maximilian N. , Dietz Hendrik , Weuster-Botz Dirk
Published: December 2024

Large quantities of artificial single-stranded DNA (ssDNA) with user-defined sequences are increasingly required to exploit the potential of DNA nanotechnology. Cross-contamination-free ssDNA production can be achieved using Escherichia coli with an optimized helper plasmid in high-cell-density cultivation via the secretion of phagemid particles containing ssDNA with user-defined sequences. In our study, we aimed to reduce the number of phagemid particles for the initiation of ssDNA production. We tested different infection densities, ranging from a multiplicity of infection (MOI) of 10−6–10−2 tfu cfu−1 at the start of the initial batch phase in a 2.5 L stirred tank bioreactor. A MOI of 10−3 tfu cfu−1 was the best compromise between process time and ssDNA concentration. Early initiation of ssDNA production with low MOI reduced the number of phagemid particles by a factor of 250,000. The early infection strategy was successfully scaled up to the 25 L scale, resulting in ssDNA concentrations of >100 mg L−1 within a process time of one day. Transferring the infection strategy to a 1000 L scale gained 65 mg L−1 ssDNA because of incomplete initial infection. The versatility of the early infection strategy was further proven with a second prolonged, user-defined ssDNA sequence.
Investigating an Enhanced Contour Blasting Technique Considering Rock Mass Structural Properties
Authors: Imashev Askar , Mussin Aibek , Adoko Amoussou Coffi
Published: December 2024

When excavating tunnels and underground openings with the drill-and-blast method, minimizing excavation overbreak is vital, as it reduces the costs associated with excavation stability, mucking, and rock support. Overbreak in excavations not only causes safety concerns but also increases the cost of construction and completion time. This paper proposes a contour blasting design in which the parameters are optimized based on the rock’s structural properties. Numerical modeling was used to identify the possible damage zones of rock with a change of the seismic load due to blasting. The results were used as input for the design of the proposed contour blasting with a low-brisance explosive. Experimental blasting tests were conducted at the Akbakai mine, located in Kazakhstan, and the performance of the design was examined. The proposed contour blasting was compared with the standard blasting method. The results indicated that the cross-section of excavation with the conventional blasting method varied between 10.5 and 12.1 m2, indicating an overbreak between 17 and 34%. However, with the suggested contour blasting, the overbreak was less than 13%. It was concluded that charging contour boreholes with low-brisance explosives is an effective method of reducing overbreak due to excessive explosive loads on the contour massif, which can improve the safety and profitability of mining operations.
A Study on the Impact of Different Delay Times on Rock Mass Throwing and Movement Characteristics Based on the FEM–SPH Method
Authors: Wang Guoqiang , Chen Hui , Zhao Jingkun
Published: December 2024

Burst morphology is a crucial indicator for evaluating the effectiveness of blasting, as it directly reflects the actual state of the blasting results. The results of rock displacement following blasting partially reflect the effectiveness of throw blasting, while the rock ejection process serves as the macroscopic manifestation of the blasting method. To accurately assess the impact of different delay times on burst formation, this study addressed the issues of rock movement and ejection in underground blasting. Using three-dimensional modeling, we constructed a FEM–SPH model and utilized LS-DYNA numerical simulation software to investigate the movement patterns of rock in precise delayed blasting scenarios underground. This study explored the spatiotemporal evolution characteristics of rock movement post-blasting. Digital electronic detonators were used to set precise inter-row delay times of 25 ms, 50 ms, and 75 ms. The results revealed that the ejection distance of blasted rock in underground mining increased with longer inter-row delay times, while the slope angle of the blasted muck pile decreased as the delay time increased. Furthermore, at a micro level, the study found that a 75 ms delay created new free surfaces, providing effective compensation space for subsequent blasts, thereby improving blasting outcomes. Analysis of the 25 ms and 50 ms delay periods indicated a clamping effect on rock movement. Field comparisons of blasting results were conducted to validate the influence of precise delay times on the movement patterns and spatiotemporal evolution characteristics of blasted rock.
Impact Assessment of Coupling Mode of Hydrological Model and Machine Learning Model on Runoff Simulation: A Case of Washington
Authors: Zhang Junqi , Li Jing , Zhao Huiyizhe , Wang Wen , Lv Na , Zhang Bowen , Liu Yue , Yang Xinyu , Guo Mengjing , Dong Yuhao
Published: December 2024

The inherent uncertainties in traditional hydrological models present significant challenges for accurately simulating runoff. Combining machine learning models with traditional hydrological models is an essential approach to enhancing the runoff modeling capabilities of hydrological models. However, research on the impact of mixed models on runoff simulation capability is limited. Therefore, this study uses the traditional hydrological model Simplified Daily Hydrological Model (SIMHYD) and the machine learning model Long Short Term Memory (LSTM) to construct two coupled models: a direct coupling model and a dynamically improved predictive validity hybrid model. These models were evaluated using the US CAMELS dataset to assess the impact of the two model combination methods on runoff modeling capabilities. The results indicate that the runoff modeling capabilities of both combination methods were improved compared to individual models, with the combined forecasting model for dynamic prediction effectiveness (DPE) demonstrating the optimal modeling capability. Compared with LSTM, the mixed model showed a median increase of 12.8% in Nash Sutcliffe efficiency (NSE) of daily runoff during the validation period, and a 12.5% increase compared to SIMHYD. In addition, compared with the LSTM model, the median Nash Sutcliffe efficiency (NSE) of the hybrid model simulating high flow results increased by 23.6%, and compared with SIMHYD, it increased by 28.4%. At the same time, the stability of the hybrid model simulating low flow was significantly improved. In performance testing involving varying training period lengths, the DPE model trained for 12 years exhibited the best performance, showing a 3.5% and 1.5% increase in the median NSE compared to training periods of 6 years and 18 years, respectively.
An AI-Driven Particle Filter Technology for Battery System State Estimation and RUL Prediction
Authors: Ahwiadi Mohamed , Wang Wilson
Published: December 2024

The increasing demand for reliable and safe Lithium-ion (Li-ion) batteries requires more accurate estimation of state of health (SOH) and remaining useful life (RUL) prediction. However, the inherent complexity and non-linear dynamics of Li-ion batteries present specific challenges to traditional methods of SOH modeling. Although particle filter (PF) techniques can handle nonlinear dynamics, they still face challenges, including particle degeneracy and loss of diversity, that reduce their ability to effectively model the nonlinear degradation mechanisms of batteries. To tackle these limitations, this paper presents a novel artificial intelligence-driven PF (AI-PF) technology for battery health modeling and prognosis. The main contributions of the AI-PF technique are as follows: (1) A novel dynamic sample degeneracy detection method is proposed to provide real-time assessment of particle weights so as to promptly identify degeneracy and improve computational efficiency. (2) An adaptive crossover and mutation strategy is proposed to reallocate low-weight particles and maintain particle diversity to improve modeling and RUL forecasting accuracy. The effectiveness of the AI-PF framework is validated through systematic evaluations carried out using benchmark models and well-recognized battery datasets.
Progress in 3D Printing Applications for the Management of Orbital Disorders: A Systematic Review
Authors: Michelutti Luca , Tel Alessandro , Robiony Massimo , Sembronio Salvatore , Nocini Riccardo , Agosti Edoardo , Ius Tamara , Gagliano Caterina , Zeppieri Marco
Published: December 2024

Introduction: 3D printing technology has gained considerable interest in the domain of orbital illnesses owing to its capacity to transform diagnosis, surgery planning, and treatment. This systematic review seeks to deliver a thorough examination of the contemporary applications of 3D printing in the treatment of ocular problems, encompassing tumors, injuries, and congenital defects. This systematic review of recent studies has examined the application of patient-specific 3D-printed models for preoperative planning, personalized implants, and prosthetics. Methods: This systematic review was conducted according to the PRISMA guidelines. The PICOS is “What are the current advances and applications of 3D printing for the management of orbital pathology?” The databases analyzed for the research phase are MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), ClinicalTrials.gov, ScienceDirect, Scopus, CINAHL, and Web of Science. Results: Out of 314 studies found in the literature, only 12 met the inclusion and exclusion criteria. From the included studies, it is evident that 3D printing can be a useful technology for the management of trauma and oncological pathologies of the orbital region. Discussion: 3D printing proves to be very useful mainly for the purpose of improving the preoperative planning of a surgical procedure, allowing for better preparation by the surgical team and a reduction in operative time and complications. Conclusions: 3D printing has proven to be an outstanding tool in the management of orbit pathology. Comparing the advantages and disadvantages of such technology, the former far outweigh the latter.
Comprehensive Analysis of Cardiovascular Diseases: Symptoms, Diagnosis, and AI Innovations
Authors: Khan Muhammad Raheel , Haider Zunaib Maqsood , Hussain Jawad , Malik Farhan Hameed , Talib Irsa , Abdullah Saad
Published: December 2024

Cardiovascular diseases are some of the underlying reasons contributing to the relentless rise in mortality rates across the globe. In this regard, there is a genuine need to integrate advanced technologies into the medical realm to detect such diseases accurately. Moreover, numerous academic studies have been published using AI-based methodologies because of their enhanced accuracy in detecting heart conditions. This research extensively delineates the different heart conditions, e.g., coronary artery disease, arrhythmia, atherosclerosis, mitral valve prolapse/mitral regurgitation, and myocardial infarction, and their underlying reasons and symptoms and subsequently introduces AI-based detection methodologies for precisely classifying such diseases. The review shows that the incorporation of artificial intelligence in detecting heart diseases exhibits enhanced accuracies along with a plethora of other benefits, like improved diagnostic accuracy, early detection and prevention, reduction in diagnostic errors, faster diagnosis, personalized treatment schedules, optimized monitoring and predictive analysis, improved efficiency, and scalability. Furthermore, the review also indicates the conspicuous disparities between the results generated by previous algorithms and the latest ones, paving the way for medical researchers to ascertain the accuracy of these results through comparative analysis with the practical conditions of patients. In conclusion, AI in heart disease detection holds paramount significance and transformative potential to greatly enhance patient outcomes, mitigate healthcare expenditure, and amplify the speed of diagnosis.
Advanced Dynamic Centre of Pressure Diagnostics with Smart Insoles: Comparison of Diabetic and Healthy Persons for Diagnosing Diabetic Peripheral Neuropathy
Authors: Fuss Franz Konstantin , Tan Adin Ming , Weizman Yehuda
Published: December 2024

Although diabetic polyneuropathy (DPN) has a very high prevalence among people with diabetes, gait analysis using cyclograms is very limited, and cyclogram research, in general, is limited to standard measures available in software packages. In this study, cyclograms (movements of the centre of pressure, COP, on and between the plantar surfaces) of diabetics and healthy individuals recorded with a smart insole were compared in terms of geometry and balance index, BI. The latter was calculated as the summed product of standard deviations of cyclogram markers, i.e., start/end points, turning points, and intersection points of the COP. The geometry was assessed by the positions of, and distances between, these points, and the distance ratios (14 parameters in total). The BI of healthy and diabetic individuals differed significantly. Of the fifteen parameters (including the BI), three were suitable as classifiers to predict DPN, namely two distances and their ratio, with false negatives ranging from 1.8 to 12.5%, and false positives ranging from 2.9 to 7.1%. The standard metric of the cyclogram provided by the software packages failed as a classifier. While the BI captures both DPN-related balance and other balance disorders, the changing geometry of the cyclogram in diabetics appears to be DPN-specific.
Integrating Physical and Biochemical Cues for Muscle Engineering: Scaffolds and Graft Durability
Authors: Yousefi Farbod , Foster Lauren Ann , Selim Omar A. , Zhao Chunfeng
Published: December 2024

Muscle stem cells (MuSCs) are essential for skeletal muscle regeneration, influenced by a complex interplay of mechanical, biochemical, and molecular cues. Properties of the extracellular matrix (ECM) such as stiffness and alignment guide stem cell fate through mechanosensitive pathways, where forces like shear stress translate into biochemical signals, affecting cell behavior. Aging introduces senescence which disrupts the MuSC niche, leading to reduced regenerative capacity via epigenetic alterations and metabolic shifts. Transplantation further challenges MuSC viability, often resulting in fibrosis driven by dysregulated fibro-adipogenic progenitors (FAPs). Addressing these issues, scaffold designs integrated with pharmacotherapy emulate ECM environments, providing cues that enhance graft functionality and endurance. These scaffolds facilitate the synergy between mechanotransduction and intracellular signaling, optimizing MuSC proliferation and differentiation. Innovations utilizing human pluripotent stem cell-derived myogenic progenitors and exosome-mediated delivery exploit bioactive properties for targeted repair. Additionally, 3D-printed and electrospun scaffolds with adjustable biomechanical traits tackle scalability in treating volumetric muscle loss. Advanced techniques like single-cell RNA sequencing and high-resolution imaging unravel muscle repair mechanisms, offering precise mapping of cellular interactions. Collectively, this interdisciplinary approach fortifies tissue graft durability and MuSC maintenance, propelling therapeutic strategies for muscle injuries and degenerative diseases.
Antimicrobial Peptides from Frogs of the Glandirana Genus
Authors: Harris Frederick , Phoenix David A. , Dennison Sarah R.
Published: December 2024

Glandirana is a genus of frogs that includes G. rugosa, G. emeljanovi, G. minima, G. tientaiensis, G. susurra, G. nakamurai and G. reliquia. These frogs produce antimicrobial peptides (AMPs), which are endogenous antibiotics that possess antibacterial, antifungal, antiviral and anti-endotoxin activity and help keep the hosts free from infections. In these activities, microbial death is promoted by membranolytic mechanisms that are mediated by the cationic charge and amphiphilic α-helical structures of these peptides. In general, these peptides are selective for microbes, showing low levels of hemolytic and cytotoxic activity, as well as possessing other biological activities, including anticancer, antioxidative and insulinotrophic action. In this review, a brief overview of AMPs with a focus on those from amphibians is provided, along with the phylogeny and nomenclature of frogs and AMPs from the Glandirana genus. This review then provides a comprehensive, in-depth description of the antimicrobial and other biological activities of all AMPs produced by known frogs of the Glandirana for the period 1994 to 2024. This description includes a detailed discussion of the structure/function relationships and mechanisms involved in the membrane interactions that drive these biological activities, with comparisons between AMPs from the same frog and between frogs across the genus. Based on their biological properties, AMPs from frogs of the Glandirana genus have been proposed for investigation as potential therapeutic agents, such as in the treatment of cancers and diabetes, as well as antimicrobial agents in areas, including crop protection, the food industry and oral hygiene.