bluuebunny/crossref_metadata_2025 huggingface dataset:
MMD-MSD: A Multimodal Multisensory Dataset in Support of Research and Technology Development for Musculoskeletal Disorders
Authors: Markova Valentina , Ganchev Todor , Filkova Silvia , Markov Miroslav
Published: April 2024

Improper sitting positions are known as the primary reason for back pain and the emergence of musculoskeletal disorders (MSDs) among individuals who spend prolonged time working with computer screens, keyboards, and mice. At the same time, it is well understood that automated technological tools can play an important role in the process of unhealthy habit alteration, so plenty of research efforts are focused on research and technology development (RTD) activities that aim to provide support for the prevention of back pain or the development of MSDs. Here, we report on creating a new resource in support of RTD activities aiming at the automated detection of improper sitting positions. It consists of multimodal multisensory recordings of 100 persons, made with a video recorder, camera, and wrist-attached sensors that capture physiological signals (PPG, EDA, skin temperature), as well as motion sensors (three-axis accelerometer). Our multimodal multisensory dataset (MMD-MSD) opens new opportunities for modeling the body stance (sitting posture and movements), physiological state (stress level, attention, emotional arousal and valence), and performance (success rate on the Stroop test) of people working with a computer. Finally, we demonstrate two use cases: improper neck posture detection from pictures, and task-specific cognitive load detection from physiological signals.
A Data-Driven Approach to Discovering Process Choreography
Authors: Hernandez-Resendiz Jaciel David , Tello-Leal Edgar , Sepúlveda Marcos
Published: April 2024

Implementing approaches based on process mining in inter-organizational collaboration environments presents challenges related to the granularity of event logs, the privacy and autonomy of business processes, and the alignment of event data generated in inter-organizational business process (IOBP) execution. Therefore, this paper proposes a complete and modular data-driven approach that implements natural language processing techniques, text similarity, and process mining techniques (discovery and conformance checking) through a set of methods and formal rules that enable analysis of the data contained in the event logs and the intra-organizational process models of the participants in the collaboration, to identify patterns that allow the discovery of the process choreography. The approach enables merging the event logs of the inter-organizational collaboration participants from the identified message interactions, enabling the automatic construction of an IOBP model. The proposed approach was evaluated using four real-life and two artificial event logs. In discovering the choreography process, average values of 0.86, 0.89, and 0.86 were obtained for relationship precision, relation recall, and relationship F-score metrics. In evaluating the quality of the built IOBP models, values of 0.95 and 1.00 were achieved for the precision and recall metrics, respectively. The performance obtained in the different scenarios is encouraging, demonstrating the ability of the approach to discover the process choreography and the construction of business process models in inter-organizational environments.
Insights into Image Understanding: Segmentation Methods for Object Recognition and Scene Classification
Authors: Mohammed Sarfaraz Ahmed , Ralescu Anca L.
Published: April 2024

Image understanding plays a pivotal role in various computer vision tasks, such as extraction of essential features from images, object detection, and segmentation. At a higher level of granularity, both semantic and instance segmentation are necessary for fully grasping a scene. In recent times, the concept of panoptic segmentation has emerged as a field of study that unifies semantic and instance segmentation. This article sheds light on the pivotal role of panoptic segmentation as a visualization tool for understanding scene components, including object detection, categorization, and precise localization of scene elements. Advancements in achieving panoptic segmentation and suggested improvements to the predicted outputs through a top-down approach are discussed. Furthermore, datasets relevant to both scene recognition and panoptic segmentation are explored to facilitate a comparative analysis. Finally, the article outlines certain promising directions in image recognition and analysis by underlining the ongoing evolution in image understanding methodologies.
Assessment of a Teaching Module for Cardiac Auscultation of Horses by Veterinary Students
Authors: Wood Alyse , Shapter Frances Marie , Stewart Allison J.
Published: April 2024

Auscultation of heart sounds is an important veterinary skill requiring an understanding of anatomy, physiology, pathophysiology and pattern recognition. This cross-sectional study was developed to evaluate a targeted, audio-visual training resource for veterinary students to improve their understanding and auscultation of common heart conditions in horses. Fourth- and fifth-year 2021 and 2022 Bachelor of Veterinary Science students at the University of Queensland (UQ) were provided the learning resource and surveyed via online pre- and post-intervention surveys. Results were quantitatively analyzed using descriptive statistics and Mann–Whitney U tests. Open-ended survey questions were qualitatively analyzed by thematic analysis and Leximancer™ Version 4 program software analysis. Over the two-year period, 231 fourth-year and 222 fifth-year veterinary students had access to the resource; 89 completed the pre-intervention survey and 57 completed the post-intervention survey. Quantitative results showed the resource helped students prepare for practicals and their perception of competency and confidence when auscultating equine cardiac sounds improved (p < 0.05). Compared to fifth-year students, fourth-year students felt less competent at identifying murmurs and arrythmias prior to accessing the learning resource (p < 0.05). Fourth-year and fifth-year students’ familiarity with detection of murmurs improved after completing the learning resource (p < 0.001). Qualitative analysis demonstrated a limited number of opportunities to practice equine cardiac auscultation throughout the veterinary degree, especially during the COVID-19 pandemic, and that integrated audio-visual resources are an effective means of teaching auscultation.
Cardiovascular Fitness and Stride Acceleration in Race-Pace Workouts for the Prediction of Performance in Thoroughbreds
Authors: Schrurs Charlotte , Dubois Guillaume , Van Erck-Westergren Emmanuelle , Gardner David S.
Published: April 2024

In-training racehorse physiological data can be leveraged to further explore race-day performance prediction. To date, no large retrospective, observational study has analysed whether in-training speed and heart rate recovery can predict racehorse success. Speed (categorised as ‘slow’ to ‘fast’ according to the time taken to cover the last 600 m from a virtual finish line) and heart rate recovery (from gallop to 1 min after exercise) of flat racehorses (n = 485) of varying age, sex and type according to distance (e.g., sprinter, miler and stayer) were obtained using a fitness tracker from a single racing yard in Australia. Race-pace training sessions on turf comprised ‘fast gallop’ (n = 3418 sessions) or ‘jumpout’ (n = 1419). A posteriori racing information (n = 3810 races) for all 485 racehorses was extracted and combined with training data. Race performance was categorised as win/not-win or podium or not, each analysed by logistic regression. Colts (p < 0.001), stayers (p < 0.001) and being relatively fast over the last 600 m of a benchmark test in training (p < 0.008) were all predictive of race performance. Heart rate recovery after exercise (p = 0.21) and speed recorded at 600 m of a 1 km benchmark test in training (p = 0.94) were not predictive. In-training physiological data analytics used along with subjective experience may help trainers identify promising horses and improve decision-making.
A Static Security Region Analysis of New Power Systems Based on Improved Stochastic–Batch Gradient Pile Descent
Authors: Wu Jiahui , Zhou Yide , Wang Haiyun , Wang Weiqing
Published: April 2024

The uncertainty in the new power system has increased, leading to limitations in traditional stability analysis methods. Therefore, considering the perspective of the three-dimensional static security region (SSR), we propose a novel approach for system static stability analysis. To address the slow training speed of traditional deep learning algorithms using batch gradient descent (BGD), we introduce an improved stochastic–batch gradient descent (S-BGD) search method that combines the advantages of stochastic gradient descent (SGD) in fast training. This method ensures both speed and precision in parameter training. Moreover, to tackle the problem of getting trapped in local optima and saddle points during parameter training, we draw inspiration from kinematic theory and propose a gradient pile (GP) training method. By utilizing accumulated gradients as parameter corrections, this method effectively avoids getting stuck in local optima and saddle points, thereby enhancing precision. Finally, we apply the proposed methods to construct the static security region for the IEEE-118 new power system using its data as samples, demonstrating the effectiveness of our approach.
A Thermodynamic Model for Cryogenic Liquid Hydrogen Fuel Tanks
Authors: Choi Dongkuk , Lee Sooyong , Kim Sangwoo
Published: April 2024

Hydrogen is used as a fuel in various fields, such as aviation, space, and automobiles, due to its high specific energy. Hydrogen can be stored as a compressed gas at high pressure and as a liquid at cryogenic temperatures. In order to keep liquid hydrogen at a cryogenic temperature, the tanks for storing liquid hydrogen are required to have insulation to prevent heat leakage. When liquid hydrogen is vaporized by heat inflow, a large pressure is generated inside the tank. Therefore, a technology capable of predicting the tank pressure is required for cryogenic liquid hydrogen tanks. In this study, a thermodynamic model was developed to predict the maximum internal pressure and pressure behavior of cryogenic liquid hydrogen fuel tanks. The developed model considers the heat inflow of the tank due to heat transfer, the phase change from liquid to gas hydrogen, and the fuel consumption rate. To verify the accuracy of the proposed model, it was compared with the analyses and experimental results in the referenced literature, and the model presented good results. A cryogenic liquid hydrogen fuel tank was simulated using the proposed model, and it was confirmed that the storage time, along with conditions such as the fuel filling ratio of liquid hydrogen and the fuel consumption rate, should be considered when designing the fuel tanks. Finally, it was confirmed that the proposed thermodynamic model can be used to sufficiently predict the internal pressure and the pressure behavior of cryogenic liquid hydrogen fuel tanks.
Intelligent Agents and Causal Inference: Enhancing Decision-Making through Causal Reasoning
Authors: Vélez Bedoya Jairo Iván , González Bedia Manuel , Castillo Ossa Luis Fernando
Published: April 2024

This study examines the incorporation of causal inference methods into intelligent entities and examines the benefits of utilizing causal reasoning to improve decision-making procedures. This study entails conducting an experimental evaluation within a video game setting to evaluate the performance of three separate agent types: ExplorerBOT, GuardBOT, and CausalBOT. The ExplorerBOT utilizes a stochastic path selection technique for task completion, whereas the GuardBOT remains immobile yet exhibits exceptional proficiency in identifying and neutralizing other bots. On the other hand, the CausalBOT utilizes sophisticated causal inference methods to examine the underlying factors contributing to the failures noticed in the task completion of the ExplorerBOT. The aforementioned feature allows CausalBOT to make informed decisions by selecting paths that have a greater likelihood of achieving success. The main purpose of these experiments is to assess and compare the effectiveness of two distinct bots, namely ExplorerBOT and CausalBOT, in accomplishing their respective objectives. To facilitate comparison, two iterations of the ExplorerBOT are utilized. The initial iteration is predicated exclusively on stochastic path selection and necessitates a more profound understanding of the variables that impact the achievement of tasks. On the other hand, the second version integrates an algorithm for informed search. In contrast, CausalBOT employs causal inference techniques to discover the underlying causes of failures exhibited by ExplorerBOTs and collect pertinent data. Through the process of discerning the fundamental causal mechanisms, CausalBOT is able to make well-informed decisions by selecting pathways that maximize the probability of successfully completing a given job. The utilization of this approach greatly boosts the decision-making powers of CausalBOT, hence enabling it to effectively adapt and overcome problems in a more efficient manner when compared to alternative agents.
Estimating the Duration of Construction Works Using Fuzzy Modeling to Assess the Impact of Risk Factors
Authors: Ladnykh Irene A. , Ibadov Nabi
Published: April 2024

One of the most pressing issues in the implementation of construction projects is the extension of planned deadlines, significantly impacting project costs. This situation often arises due to inaccurate estimation of construction durations, which rely on normative values without accounting for factors hindering construction progress. Consequently, this article aims to develop an innovative approach for assessing construction durations, considering specific risk factors and their influence on construction activities. Given the difficulty of determining risk factors and their effects during the design phase using classical probability theory, characterized by unknown probability distributions, it is highlighted that this scenario represents planning and implementation under conditions of non-statistical uncertainty. Therefore, the article proposes an approach utilizing elements of fuzzy set theory, particularly fuzzy rules and linguistic variables, to determine delays in individual construction tasks. The proposed approach involves estimating extensions of construction timelines based on a specified probability level of occurrence for risk events and their impact. Additionally, the article provides a theoretical description of the proposed approach and practical calculation examples, demonstrating that the authors’ approach significantly enhances the accuracy of construction timeline forecasts, providing more reliable data for project planning and management.
Repair of Rat Calvarial Critical-Sized Defects Using Heparin-Conjugated Fibrin Hydrogel Containing BMP-2 and Adipose-Derived Pericytes
Authors: Kudaibergen Gulshakhar , Mukhlis Sholpan , Mukhambetova Ainur , Issabekova Assel , Sekenova Aliya , Sarsenova Madina , Temirzhan Abay , Baidarbekov Murat , Umbayev Baurzhan , Ogay Vyacheslav
Published: April 2024

The repair of critical-sized calvarial defects is a challenging problem for orthopedic surgery. One of the promising strategies of bone bioengineering to enhance the efficacy of large bone defect regeneration is the combined delivery of stem cells with osteoinductive factors within polymer carriers. The purpose of the research was to study the regenerative effects of heparin-conjugated fibrin (HCF) hydrogel containing bone morphogenetic protein 2 (BMP-2) and adipose-derived pericytes (ADPs) in a rat critical-sized calvarial defect model. In vitro analysis revealed that the HCF hydrogel was able to control the BMP-2 release and induce alkaline phosphatase (ALP) activity in neonatal rat osteoblasts. In addition, it was found that eluted BMP-2 significantly induced the osteogenic differentiation of ADPs. It was characterized by the increased ALP activity, osteocalcin expression and calcium deposits in ADPs. In vivo studies have shown that both HCF hydrogel with BMP-2 and HCF hydrogel with pericytes are able to significantly increase the regeneration of critical-sized calvarial defects in comparison with the control group. Nevertheless, the greatest regenerative effect was found after the co-delivery of ADPs and BMP-2 into a critical-sized calvarial defect. Thus, our findings suggest that the combined delivery of ADPs and BMP-2 in HCF hydrogel holds promise to be applied as an alternative biopolymer for the critical-sized bone defect restoration.
Comparison of Depressive Symptoms between International and Domestic Students in a Japanese University: Pre- and Post-COVID-19 Pandemic
Authors: Shiratori Yuki , Ogawa Takafumi , Ota Miho , Sodeyama Noriko , Arai Tetsuaki , Tachikawa Hirokazu
Published: April 2024

Background: The COVID-19 pandemic led to significant lifestyle changes and uncertainties, triggering a secondary wave of mental health issues in society. University students are especially susceptible to mental disorders. International students are considered more vulnerable due to limited emotional and financial support from their families and difficulties accessing community support. Hence, we conducted a longitudinal analysis to compare depressive symptoms among international students before and during the pandemic. Methods: Data from depression screenings conducted at the University of Tsukuba in 2019 and 2020 were utilized. We included all students enrolled in 2019 who underwent health check-ups in both 2019 and 2020. Participants completed the Patient Health Questionnaire-9 (PHQ-9), enabling a comparison of item scores between domestic and international students. Psychopathological network analysis was employed to examine relationships among the items. Results: Prior to the pandemic, international students generally exhibited relatively good mental health compared with domestic students. During the 2020 pandemic, no significant difference was observed, but international students tended to demonstrate better mental health. However, network analysis revealed intergroup variations in the relationships among PHQ-9 items, with concentration problems and suicidal thoughts being more prominent among international students. Conclusion: This study’s findings suggest that young people studying abroad experience mental health crises similar to their domestic counterparts. Nevertheless, the patterns of these crises may differ between the two groups.
Incorporating Evidence-Based Gamification and Machine Learning to Assess Preschool Executive Function: A Feasibility Study
Authors: Eng Cassondra M. , Tsegai-Moore Aria , Fisher Anna V.
Published: April 2024

Computerized assessments and digital games have become more prevalent in childhood, necessitating a systematic investigation of the effects of gamified executive function assessments on performance and engagement. This study examined the feasibility of incorporating gamification and a machine learning algorithm that adapts task difficulty to individual children’s performance into a traditional executive function task (i.e., Flanker Task) with children ages 3–5. The results demonstrated that performance on a gamified version of the Flanker Task was associated with performance on the traditional version of the task and standardized academic achievement outcomes. Furthermore, gamification grounded in learning science and developmental psychology theories applied to a traditional executive function measure increased children’s task enjoyment while preserving psychometric properties of the Flanker Task. Overall, this feasibility study indicates that gamification and adaptive machine learning algorithms can be successfully incorporated into executive function assessments with young children to increase enjoyment and reduce data loss with developmentally appropriate and intentional practices.
Incident Cancer Risk in Patients with Incident Type 2 Diabetes Mellitus in Hungary (Part 1)
Authors: Abonyi-Tóth Zsolt , Rokszin György , Fábián Ibolya , Kiss Zoltán , Jermendy György , Kempler Péter , Lengyel Csaba , Wittmann István , Molnár Gergő A. , Sütő Gábor
Published: April 2024

(1) Background: Patients with type 2 diabetes mellitus (T2DM) are at higher risk of cancer but how these two diseases associate is still debated. The goal of this study was the assessment of the overall incidence of cancer among patients with newly diagnosed T2DM in Hungary. (2) Methods: A nationwide, retrospective, longitudinal study was performed using a Hungarian database. After exclusion of cases of age < 18 years, with gestational diabetes, with polycystic ovary syndrome, and with type 1 and prevalent type 2 diabetes mellitus, the incident T2DM (approx. 50,000 cases yearly) and for comparison, the diabetes-free Hungarian adult population (approx. 7,000,000 cases yearly) was included in the study. The primary endpoints were the overall and site-specific incidence and annual percentage change of the incidence of cancer in both populations. (3) Results: The overall incidence of cancer in patients amounted to 29.4/1000 and 6.6/1000 with or without T2DM, respectively, and the OR (95%CI) of cancer of the T2DM group was 4.32 (4.14–4.53), p < 0.0001. The risk of having cancer was age dependent. The incidence of cancer was declining in the non-diabetic but was unchanged in the T2DM population. The average lag time of diagnosing cancer after the detection of T2DM was 3.86 months. (4) Conclusions: Incident T2DM is associated with a significantly higher overall risk of incident cancer, with a reverse correlation of age. Newly registered T2DM patients were suggested to be screened for cancer within 6 months.
Recent Progress in Treatment for HER2-Positive Advanced Gastric Cancer
Authors: Kawakami Takeshi , Yamazaki Kentaro
Published: April 2024

Human epidermal receptor (HER) 2-positive advanced gastric cancer is one of the major subtypes of gastric cancer, accounting for ~20% of all cases. Although combination therapy with trastuzumab and chemotherapy provides meaningful survival benefit, clinical trials targeting HER2 have failed to demonstrate clinical benefits in first- or subsequent-line treatment. Trastuzumab deruxtecan, an antibody–drug conjugate, has shown positive results even in later-line treatment and has become new standard treatment. In first-line therapy, combination therapy with pembrolizumab and trastuzumab plus chemotherapy demonstrated a dramatic response rate. Therefore, the FDA rapidly approved it without waiting for the results of survival time. The emergence of combination therapy including immunotherapy with HER2-targeting agents and the development of HER2 targeting agents with or without immunotherapy have been advancing for treating HER2-positive gastric cancer. In this review, we will discuss the current status of treatment development and future perspectives for HER2-positive gastric cancer.
Drivers Underlying Metastasis and Relapse in Medulloblastoma and Targeting Strategies
Authors: Holmberg Karl O. , Borgenvik Anna , Zhao Miao , Giraud Géraldine , Swartling Fredrik J.
Published: April 2024

Medulloblastomas comprise a molecularly diverse set of malignant pediatric brain tumors in which patients are stratified according to different prognostic risk groups that span from very good to very poor. Metastasis at diagnosis is most often a marker of poor prognosis and the relapse incidence is higher in these children. Medulloblastoma relapse is almost always fatal and recurring cells have, apart from resistance to standard of care, acquired genetic and epigenetic changes that correlate with an increased dormancy state, cell state reprogramming and immune escape. Here, we review means to carefully study metastasis and relapse in preclinical models, in light of recently described molecular subgroups. We will exemplify how therapy resistance develops at the cellular level, in a specific niche or from therapy-induced secondary mutations. We further describe underlying molecular mechanisms on how tumors acquire the ability to promote leptomeningeal dissemination and discuss how they can establish therapy-resistant cell clones. Finally, we describe some of the ongoing clinical trials of high-risk medulloblastoma and suggest or discuss more individualized treatments that could be of benefit to specific subgroups.
Antiretroviral Drug Repositioning for Glioblastoma
Authors: Rivas Sarah R. , Mendez Valdez Mynor J. , Chandar Jay S. , Desgraves Jelisah F. , Lu Victor M. , Ampie Leo , Singh Eric B. , Seetharam Deepa , Ramsoomair Christian K. , Hudson Anna , Ingle Shreya M. , Govindarajan Vaidya , Doucet-O’Hare Tara T. , DeMarino Catherine , Heiss John D. , Nath Avindra , Shah Ashish H.
Published: April 2024

Outcomes for glioblastoma (GBM) remain poor despite standard-of-care treatments including surgical resection, radiation, and chemotherapy. Intratumoral heterogeneity contributes to treatment resistance and poor prognosis, thus demanding novel therapeutic approaches. Drug repositioning studies on antiretroviral therapy (ART) have shown promising potent antineoplastic effects in multiple cancers; however, its efficacy in GBM remains unclear. To better understand the pleiotropic anticancer effects of ART on GBM, we conducted a comprehensive drug repurposing analysis of ART in GBM to highlight its utility in translational neuro-oncology. To uncover the anticancer role of ART in GBM, we conducted a comprehensive bioinformatic and in vitro screen of antiretrovirals against glioblastoma. Using the DepMap repository and reversal of gene expression score, we conducted an unbiased screen of 16 antiretrovirals in 40 glioma cell lines to identify promising candidates for GBM drug repositioning. We utilized patient-derived neurospheres and glioma cell lines to assess neurosphere viability, proliferation, and stemness. Our in silico screen revealed that several ART drugs including reverse transcriptase inhibitors (RTIs) and protease inhibitors (PIs) demonstrated marked anti-glioma activity with the capability of reversing the GBM disease signature. RTIs effectively decreased cell viability, GBM stem cell markers, and proliferation. Our study provides mechanistic and functional insight into the utility of ART repurposing for malignant gliomas, which supports the current literature. Given their safety profile, preclinical efficacy, and neuropenetrance, ARTs may be a promising adjuvant treatment for GBM.
A Critical Review on Recent Advancements in Aluminium-Based Metal Matrix Composites
Authors: Kar Amlan , Sharma Aditya , Kumar Sachin
Published: April 2024

Aluminum matrix composites (AMCs) have garnered significant attention across various industrial sectors owing to their remarkable properties compared to conventional engineering materials. These include low density, high strength-to-weight ratio, excellent corrosion resistance, enhanced wear resistance, and favorable high-temperature properties. These materials find extensive applications in the military, automotive, and aerospace industries. AMCs are manufactured using diverse processing techniques, tailored to their specific classifications. Over three decades of intensive research have yielded numerous scientific revelations regarding the internal and extrinsic influences of ceramic reinforcement on the mechanical, thermomechanical, tribological, and physical characteristics of AMCs. In recent times, AMCs have witnessed a surge in usage across high-tech structural and functional domains, encompassing sports and recreation, automotive, aerospace, defense, and thermal management applications. Notably, studies on particle-reinforced cast AMCs originated in India during the 1970s, attained industrial maturity in developed nations, and are now progressively penetrating the mainstream materials arena. This study provides a comprehensive understanding of AMC material systems, encompassing processing, microstructure, characteristics, and applications, with the latest advancements in the field.
Possibility of Phase Transformation of Al2O3 by a Laser: A Review
Authors: Matijošius Tadas , Padgurskas Juozas , Bikulčius Gedvidas
Published: April 2024

Aluminum (Al) components of high quality often require an optimal ratio of lightness and favorable mechanical properties. In order to improve the physical-mechanical properties of Al, an aluminum oxide (Al2O3) film is usually formed on the surface of Al, which itself is characterized by high strength, hardness, corrosion resistance, and other technical properties. Unfortunately, depending on the conditions, the oxide film may be formed from different crystal phases on the Al surface, which are not always of desirable quality, i.e., the α-Al2O3 phase. The present review demonstrates that the properties of the Al2O3 film may be improved by Al processing with a laser beam according to the scheme: Al (Al alloy) → electrochemical anodizing → treatment with laser irradiation → α-Al2O3. Both Al substrate and the anodizing electrolyte affect the phase transformation of anodic Al2O3. Laser irradiation of the Al2O3 surface leads to high heating and cooling rates, which may promote the formation of a highly crystalline α-Al2O3 phase on anodic Al2O3.
Further Insight into the Manganese(II) 2,2′-Bipyridine-1,1′-dioxide Homoleptic Complex: Single-Crystal X-ray Structure Determination of the Perchlorate Salt and DFT Calculations
Authors: Castro Jesús , Ferraro Valentina , Bortoluzzi Marco
Published: April 2024

The homoleptic cationic complex formed by reacting suitable manganese(II) salts with 2,2′-bipyridine-1,1′-dioxide (bipyO2) has been subjected to several studies in the past because of its peculiar absorption and electrochemical features. Here, the first single-crystal X-ray structure determination of a [Mn(bipyO2)3]2+ salt is reported, where the charge of the cation is balanced by perchlorate anions. The hydrated salt [Mn(bipyO2)3](ClO4)2 crystallizes in the monoclinic system (P21/n space group) and the asymmetric unit contains three cationic complexes and six perchlorate anions. The environment of the manganese(II) ions is best described as octahedral, with scarce variations among the three cations in the asymmetric unit. The bipyO2 ligands exhibit κ2 coordination mode, forming seven-membered metallacycles. The X-ray outcomes have been used as the starting point for DFT and TDDFT calculations, aimed to elucidate the charge transfer origin of the noticeable absorption in the visible range. The MLCT nature is confirmed by the hole and electron distributions associated with the spin-allowed transitions. DFT calculations on the related manganese(III) complex indicate that the geometry of [Mn(bipyO2)3]2+ changes only slightly upon oxidation, in agreement with the reversible electrochemical behaviour experimentally observed.
Polarization Coupling between Ferroelectric Liquids and Ferroelectric Solids: Effects of the Fringing Field Profile
Authors: Marni Stefano , Barboza Raouf , Oluwajoba Ayomide S. , Zamboni Riccardo , Lucchetti Liana
Published: April 2024

Recent experiments devoted to characterizing the behavior of sessile ferroelectric liquid droplets on ferroelectric solid substrates have shown the existence of a droplet electromechanical Rayleigh-like instability. The instability is induced by the bulk polarization of the ferroelectric fluid, which couples to the polarization of the underlying substrate through its fringing field and solid–fluid interface coupling. With the aim of characterizing this phenomenon, namely the coupling between the polarizations of a fluid and a solid material, we studied the behavior of ferroelectric liquid droplets confined between two solid substrates, arranged in different configurations, realized to generate fringing fields with different profiles. The results show that the features of the droplets instability are indeed affected by the specific fringing field shape in a way dominated by the minimization of the electrostatic energy associated with the bulk polarization of the ferroelectric fluid.
Modification and Stabilization of Collapsible Loess Using Diammonium Phosphate Solution
Authors: Ying Chengjuan , Huang Lingxia , Chen Haiming , Zhang Yadong , Yao Duoxi
Published: April 2024

The collapsible loess will rapidly soften and lose its bearing capacity when soaked in water. Under a mild condition (20 °C), the biomimetic inorganic agent, diammonium phosphate (DAP), reacts with calcite in the collapsible loess, producing a stronger bonding material, hydroxyapatite (HAP), to modify and stabilize the soil. Uniaxial compression, permeability tests, and morphological analysis using X-ray diffraction and scanning electron microscopy equipped with an energy X-ray dispersive system were used to assess the effectiveness of DAP stabilization on the collapsible loess. The results indicated that HAP improved the inter-particle bonding within the loess, filled the pores within particles, reduced the permeability, and consequently mitigated the collapsibility of the loess. The compressive strength of the DAP-treated loess increased as DAP concentration increased. Following 28 days of curing, the compressive strength of the loess treated with a 3.0 mol/L DAP solution was six times greater than that of the untreated group. DAP’s reinforcement effect on the loess was superior to that of cement. The compressive strength of the DAP-treated loess was about double that of the cement-treated loess and the permeability coefficient was reduced by more than 50% at equivalent solid content. Furthermore, DAP generated 82% fewer carbon emissions compared to Portland cement. Considering eco-friendly and sustainable development, DAP offers a more competitive alternative for modification and stabilization of loess.
Beyond the Beach: Multi-Parameter Interpretation of Shore Deterioration in a Caribbean Reef System
Authors: de Almeida Laura R. , Ávila-Mosqueda S. Valery , Mendoza Edgar , van Tussenbroek Brigitta I. , Silva Rodolfo
Published: April 2024

Throughout the Caribbean region, coastal areas are of vital importance for national incomes from the tourism industry. However, accelerated coastal development has impacted the coastal ecosystems, including the beaches, and deterioration of the shore results from cumulative impacts on both marine and coastal ecosystems. It is essential to identify the areas that need special attention for targeted management plans and actions, especially in areas with high anthropogenic pressure. This research proposes an integrated assessment of the conservation state of shore and coastal ecosystems in the Puerto Morelos National Reef Park (PNAPM) in the Mexican Caribbean, through the spatial monitoring of key parameters. A Geographic Information System (GIS) was employed to analyze the land use on the shore, foredune condition, morphological characteristics of the beaches, shoreline evolution, and the condition of coral reefs and seagrass meadows. The analysis identified the most critical areas in relation to shore deterioration and priority areas for the preservation of ecosystems. The spatial data obtained for the PNAPM can serve as a basis for ongoing shore monitoring, and targeted management actions through the designation of areas that require either preservation or ecosystem restoration practices. This methodology can be applied to other reef systems in the Caribbean.
Novel Entropy for Enhanced Thermal Imaging and Uncertainty Quantification
Authors: Ayunts Hrach , Grigoryan Artyom , Agaian Sos
Published: April 2024

This paper addresses the critical need for precise thermal modeling in electronics, where temperature significantly impacts system reliability. We emphasize the necessity of accurate temperature measurement and uncertainty quantification in thermal imaging, a vital tool across multiple industries. Current mathematical models and uncertainty measures, such as Rényi and Shannon entropies, are inadequate for the detailed informational content required in thermal images. Our work introduces a novel entropy that effectively captures the informational content of thermal images by combining local and global data, surpassing existing metrics. Validated by rigorous experimentation, this method enhances thermal images’ reliability and information preservation. We also present two enhancement frameworks that integrate an optimized genetic algorithm and image fusion techniques, improving image quality by reducing artifacts and enhancing contrast. These advancements offer significant contributions to thermal imaging and uncertainty quantification, with broad applications in various sectors.
Fast Fusion Clustering via Double Random Projection
Authors: Wang Hongni , Li Na , Zhou Yanqiu , Yan Jingxin , Jiang Bei , Kong Linglong , Yan Xiaodong
Published: April 2024

In unsupervised learning, clustering is a common starting point for data processing. The convex or concave fusion clustering method is a novel approach that is more stable and accurate than traditional methods such as k-means and hierarchical clustering. However, the optimization algorithm used with this method can be slowed down significantly by the complexity of the fusion penalty, which increases the computational burden. This paper introduces a random projection ADMM algorithm based on the Bernoulli distribution and develops a double random projection ADMM method for high-dimensional fusion clustering. These new approaches significantly outperform the classical ADMM algorithm due to their ability to significantly increase computational speed by reducing complexity and improving clustering accuracy by using multiple random projections under a new evaluation criterion. We also demonstrate the convergence of our new algorithm and test its performance on both simulated and real data examples.
Application of Recurrence Plot Analysis to Examine Dynamics of Biological Molecules on the Example of Aggregation of Seed Mucilage Components
Authors: Sionkowski Piotr , Kruszewska Natalia , Kreitschitz Agnieszka , Gorb Stanislav N. , Domino Krzysztof
Published: April 2024

The goal of the research is to describe the aggregation process inside the mucilage produced by plant seeds using molecular dynamics (MD) combined with time series algorithmic analysis based on the recurrence plots. The studied biological molecules model is seed mucilage composed of three main polysaccharides, i.e. pectins, hemicellulose, and cellulose. The modeling of biological molecules is based on the assumption that a classical–quantum passage underlies the aggregation process in the mucilage, resulting from non-covalent interactions, as they affect the macroscopic properties of the system. The applied recurrence plot approach is an important tool for time series analysis and data mining dedicated to analyzing time series data originating from complex, chaotic systems. In the current research, we demonstrated that advanced algorithmic analysis of seed mucilage data can reveal some features of the dynamics of the system, namely temperature-dependent regions with different dynamics of increments of a number of hydrogen bonds and regions of stable oscillation of increments of a number of hydrophobic–polar interactions. Henceforth, we pave the path for automatic data-mining methods for the analysis of biological molecules with the intermediate step of the application of recurrence plot analysis, as the generalization of recurrence plot applications to other (biological molecules) datasets is straightforward.
Chip-Based Electronic System for Quantum Key Distribution
Authors: Zhang Siyuan , Mao Wei , Luo Shaobo , Sun Shihai
Published: April 2024

Quantum Key Distribution (QKD) has garnered significant attention due to its unconditional security based on the fundamental principles of quantum mechanics. While QKD has been demonstrated by various groups and commercial QKD products are available, the development of a fully chip-based QKD system, aimed at reducing costs, size, and power consumption, remains a significant technological challenge. Most researchers focus on the optical aspects, leaving the integration of the electronic components largely unexplored. In this paper, we present the design of a fully integrated electrical control chip for QKD applications. The chip, fabricated using 28 nm CMOS technology, comprises five main modules: an ARM processor for digital signal processing, delay cells for timing synchronization, ADC for sampling analog signals from monitors, OPAMP for signal amplification, and DAC for generating the required voltage for phase or intensity modulators. According to the simulations, the minimum delay is 11ps, the open-loop gain of the operational amplifier is 86.2 dB, the sampling rate of the ADC reaches 50 MHz, and the DAC achieves a high rate of 100 MHz. To the best of our knowledge, this marks the first design and evaluation of a fully integrated driver chip for QKD, holding the potential to significantly enhance QKD system performance. Thus, we believe our work could inspire future investigations toward the development of more efficient and reliable QKD systems.
CCTFv2: Modeling Cyber Competitions
Authors: Qolomany Basheer , Calay Tristan J. , Hossain Liaquat , Mulahuwaish Aos , Bou Abdo Jacques
Published: April 2024

Cyber competitions are usually team activities, where team performance not only depends on the members’ abilities but also on team collaboration. This seems intuitive, especially given that team formation is a well-studied discipline in competitive sports and project management, but unfortunately, team performance and team formation strategies are rarely studied in the context of cybersecurity and cyber competitions. Since cyber competitions are becoming more prevalent and organized, this gap becomes an opportunity to formalize the study of team performance in the context of cyber competitions. This work follows a cross-validating two-approach methodology. The first is the computational modeling of cyber competitions using Agent-Based Modeling. Team members are modeled, in NetLogo, as collaborating agents competing over a network in a red team/blue team match. Members’ abilities, team interaction and network properties are parametrized (inputs), and the match score is reported as output. The second approach is grounded in the literature of team performance (not in the context of cyber competitions), where a theoretical framework is built in accordance with the literature. The results of the first approach are used to build a causal inference model using Structural Equation Modeling. Upon comparing the causal inference model to the theoretical model, they showed high resemblance, and this cross-validated both approaches. Two main findings are deduced: first, the body of literature studying teams remains valid and applicable in the context of cyber competitions. Second, coaches and researchers can test new team strategies computationally and achieve precise performance predictions. The targeted gap used methodology and findings which are novel to the study of cyber competitions.
Cascade Residual Multiscale Convolution and Mamba-Structured UNet for Advanced Brain Tumor Image Segmentation
Authors: Zhou Rui , Wang Ju , Xia Guijiang , Xing Jingyang , Shen Hongming , Shen Xiaoyan
Published: April 2024

In brain imaging segmentation, precise tumor delineation is crucial for diagnosis and treatment planning. Traditional approaches include convolutional neural networks (CNNs), which struggle with processing sequential data, and transformer models that face limitations in maintaining computational efficiency with large-scale data. This study introduces MambaBTS: a model that synergizes the strengths of CNNs and transformers, is inspired by the Mamba architecture, and integrates cascade residual multi-scale convolutional kernels. The model employs a mixed loss function that blends dice loss with cross-entropy to refine segmentation accuracy effectively. This novel approach reduces computational complexity, enhances the receptive field, and demonstrates superior performance for accurately segmenting brain tumors in MRI images. Experiments on the MICCAI BraTS 2019 dataset show that MambaBTS achieves dice coefficients of 0.8450 for the whole tumor (WT), 0.8606 for the tumor core (TC), and 0.7796 for the enhancing tumor (ET) and outperforms existing models in terms of accuracy, computational efficiency, and parameter efficiency. These results underscore the model’s potential to offer a balanced, efficient, and effective segmentation method, overcoming the constraints of existing models and promising significant improvements in clinical diagnostics and planning.
On the Dimensions of Hermitian Subfield Subcodes from Higher-Degree Places
Authors: El Khalfaoui Sabira , Nagy Gábor P.
Published: April 2024

The focus of our research is the examination of Hermitian curves over finite fields, specifically concentrating on places of degree three and their role in constructing Hermitian codes. We begin by studying the structure of the Riemann–Roch space associated with these degree-three places, aiming to determine essential characteristics such as the basis. The investigation then turns to Hermitian codes, where we analyze both functional and differential codes of degree-three places, focusing on their parameters and automorphisms. In addition, we explore the study of subfield subcodes and trace codes, determining their structure by giving lower bounds for their dimensions. This presents a complex problem in coding theory. Based on numerical experiments, we formulate a conjecture for the dimension of some subfield subcodes of Hermitian codes. Our comprehensive exploration seeks to deepen the understanding of Hermitian codes and their associated subfield subcodes related to degree-three places, thus contributing to the advancement of algebraic coding theory and code-based cryptography.
The Inverse of Exact Renormalization Group Flows as Statistical Inference
Authors: Berman David S. , Klinger Marc S.
Published: April 2024

We build on the view of the Exact Renormalization Group (ERG) as an instantiation of Optimal Transport described by a functional convection–diffusion equation. We provide a new information-theoretic perspective for understanding the ERG through the intermediary of Bayesian Statistical Inference. This connection is facilitated by the Dynamical Bayesian Inference scheme, which encodes Bayesian inference in the form of a one-parameter family of probability distributions solving an integro-differential equation derived from Bayes’ law. In this note, we demonstrate how the Dynamical Bayesian Inference equation is, itself, equivalent to a diffusion equation, which we dub Bayesian Diffusion. By identifying the features that define Bayesian Diffusion and mapping them onto the features that define the ERG, we obtain a dictionary outlining how renormalization can be understood as the inverse of statistical inference.