bluuebunny/crossref_metadata_2025 huggingface dataset:
Molecular Basis for the Differential Function of HAVCR1 Mucin Variants
Authors: Abbasi Abdolrahim , Costafreda Maria Isabel , Ballesteros Angela , Jacques Jerome , Tami Cecilia , Manangeeswaran Mohanraj , Casasnovas José M. , Kaplan Gerardo
Published: November 2024

Background/Objectives: The hepatitis A virus (HAV) cellular receptor 1 (HAVCR1) is a type I integral membrane glycoprotein discovered in monkeys and humans as a HAV receptor. HAVCR1 contains an N-terminal immunoglobulin-like variable domain (IgV) followed by a mucin-like domain (Muc), a transmembrane domain, and a cytoplasmic tail with a canonical tyrosine kinase phosphorylation site. The IgV binds phosphatidylserine on apoptotic cells, extracellular vesicles, and enveloped viruses. Insertions/deletions at position 156 (156ins/del) of the Muc were associated in humans with susceptibility to atopic, autoimmune, and infectious diseases. However, the molecular basis for the differential function of the HAVCR1 variants is not understood. Methods: We used mutagenesis, apoptotic cell binding, and signal transduction analyses to study the role of the 156ins/del in the function of HAVCR1. Results: We found that the HAVCR1 variant without insertions at position 156 (156delPMTTTV, or short-HAVCR1) bound more apoptotic cells than that containing a six amino acid insertion (156insPMTTTV, or long-HAVCR1). Furthermore, short-HAVCR1 induced stronger cell signaling and phagocytosis than long-HAVCR1. Conclusions: Our data indicated that the 156ins/del determine how the IgV is presented at the cell surface and modulate HAVCR1 binding, signaling, and phagocytosis, suggesting that variant-specific targeting could be used as therapeutic interventions to treat immune and infectious diseases.
The Design and Adaptive Control of a Parallel Chambered Pneumatic Muscle-Driven Soft Hand Robot for Grasping Rehabilitation
Authors: Zhou Zhixiong , Ai Qingsong , Li Mengnan , Meng Wei , Liu Quan , Xie Sheng Quan
Published: November 2024

The widespread application of exoskeletons driven by soft actuators in motion assistance and medical rehabilitation has proven effective for patients who struggle with precise object grasping and suffer from insufficient hand strength due to strokes or other conditions. Repetitive passive flexion/extension exercises and active grasp training are known to aid in the restoration of motor nerve function. However, conventional pneumatic artificial muscles (PAMs) used for hand rehabilitation typically allow for bending in only one direction, thereby limiting multi-degree-of-freedom movements. Moreover, establishing precise models for PAMs is challenging, making accurate control difficult to achieve. To address these challenges, we explored the design and fabrication of a bidirectionally bending PAM. The design parameters were optimized based on actual rehabilitation needs and a finite element analysis. Additionally, a dynamic model for the PAM was established using elastic strain energy and the Lagrange equation. Building on this, an adaptive position control method employing a radial basis function neural network, optimized for parameters and hidden layer nodes, was developed to enhance the accuracy of these soft PAMs in assisting patients with hand grasping. Finally, a wearable soft hand rehabilitation exoskeleton was designed, offering two modes, passive training and active grasp, aimed at helping patients regain their grasp ability.
Perspectives of Insulating Biodegradable Composites Derived from Agricultural Lignocellulosic Biomass and Fungal Mycelium: A Comprehensive Study of Thermal Conductivity and Density Characteristics
Authors: Babenko Maryna , Kononets Yevhen , Bartos Petr , Pont Ulrich , Spalek Frantisek , Zoubek Tomas , Kriz Pavel
Published: November 2024

The research suggests a production method of insulating composites created from lignocellulosic agricultural biomass with fungal mycelium as a binder agent and offers a deeper investigation of their thermophysical properties. Particularly, the samples were meticulously evaluated for density and thermal conductivity. The function was built on the suggestion by the authors regarding the thermal conductivity-weight ratio indicator. The metric was initially introduced to assess the correlation between these parameters and was also applied to qualitatively evaluate the biocomposite among other commonly used natural insulations. An applied polynomial trend analysis indicated that the most effective densities for the wheat, hemp, and flax, which were 60, 85, and 105 kg·m−3 respectively. It was determined that the optimal density for wheat and hemp composites corresponded to values of 0.28 and 0.20 W−1·kg−1·m4·K of the coefficient, respectively. These values were superior to those revealed in other common natural insulating materials, such as cork, cotton stalks, hempcrete, timber, etc. As a result, the proposed insulating material may offer numerous opportunities for application in industrial settings of civil engineering.
Portable Miniaturized IoT-Enabled Point-of-Care Device for Electrochemical Sensing of Zopiclone in Cocktails
Authors: Mejía-Méndez María Gabriela , Cifuentes-Delgado Paula C. , Gómez Sergio D. , Segura Crhistian C. , Ornelas-Soto Nancy , Osma Johann F.
Published: November 2024

This study proposes a portable and IoT-based electrochemical point-of-care sensing device for detecting zopiclone in cocktails. The system utilizes an electrochemical laccase biosensor and a potentiostat, offering a low-cost and portable device for detecting this sedative drug in cocktails. The sensor characterization experiments demonstrated the linear behavior of the oxidation and reduction currents for each of the targeted concentrations of zopiclone, enabling their detection and quantification even when mixed with an interfering substance. The proposed system could be used for the in situ analysis of cocktails, providing a valuable tool for monitoring the presence of hypnotic drugs in various social and clinical settings. The study utilized materials and reagents, including zopiclone, lab-made lemon juice, lab-made tequila, and lab-made triple sec, all prepared with reactants obtained in Bogotá, Colombia. The potentiostat used in the system was designed to manage cyclic voltammetry measurements. The electrochemical cells’ durability and longevity were also tested and characterized, with all electrodes undergoing 200 tests and their performance degradation varying according to the molecule used. The study concludes that the proposed system offers a valuable tool for detecting and monitoring pharmaceutical substances in various interfering ingredients that build up cocktails. Further research and application of this system can help address the global concern surrounding the administration of hypnotic substances to unknowing consumers through food or drinks to enable robbery and sexual assault.
Advances in Wearable Biosensors for Healthcare: Current Trends, Applications, and Future Perspectives
Authors: Vo Dang-Khoa , Trinh Kieu The Loan
Published: November 2024

Wearable biosensors are a fast-evolving topic at the intersection of healthcare, technology, and personalized medicine. These sensors, which are frequently integrated into clothes and accessories or directly applied to the skin, provide continuous, real-time monitoring of physiological and biochemical parameters such as heart rate, glucose levels, and hydration status. Recent breakthroughs in downsizing, materials science, and wireless communication have greatly improved the functionality, comfort, and accessibility of wearable biosensors. This review examines the present status of wearable biosensor technology, with an emphasis on advances in sensor design, fabrication techniques, and data analysis algorithms. We analyze diverse applications in clinical diagnostics, chronic illness management, and fitness tracking, emphasizing their capacity to transform health monitoring and facilitate early disease diagnosis. Additionally, this review seeks to shed light on the future of wearable biosensors in healthcare and wellness by summarizing existing trends and new advancements.
Parameter Identification of a Soil Constitutive Model Based on a Hybrid Genetic Differential Evolution Algorithm
Authors: Long Lin , Li Yunyu , Yang Peiling , Tang Bo
Published: November 2024

Aiming to address the problem of selecting the parameters of a soil constitutive model in the calculation of foundation pit stability, this paper proposes a hybrid genetic differential evolution algorithm (GADE) which performs by “jumping out of local optima” with “fast convergence” based on the hybrid optimization algorithm strategy and compares the advantages and disadvantages of genetic algorithms (GAs) and differential evolution algorithms (DEs). Three typical test functions were used to evaluate the search efficiency and convergence speed of GAs, DEs, and GADE, respectively. It was found that GADE has the fastest convergence speed and can search for the global optimal solution to the problem, which highlights its excellent optimization performance. At the same time, taking the Shimao Binjiang deep foundation pit as an example, GADE was used to invert the soil modulus parameters of a CX1 measuring point and construct a finite-element model for calculation. The results showed that the simulated calculation curve and the measured displacement curve were in good agreement and the curve fitting reached 95.05%, indicating the applicability and feasibility of applying GADE to identify soil parameters.
Environmental Assessment of Calcium Sulfoaluminate Cement: A Monte Carlo Simulation in an Industrial Symbiosis Framework
Authors: Tanguler-Bayramtan Meltem , Aktas Can B. , Yaman Ismail Ozgur
Published: November 2024

Calcium sulfoaluminate (CSA) cement is recognized as an environmentally friendly alternative to Portland cement (PC) due to its lower carbon footprint and energy requirements. However, traditional CSA cement production faces significant obstacles, including the high cost and regionally constrained availability of bauxite, a key raw material. Utilizing alternative materials in the production process offers a viable approach to address these limitations. This study evaluated the environmental performance of three laboratory-synthesized CSA cements using alternative raw materials sourced through an industrial symbiosis framework. A comparative assessment with PC was conducted, focusing on energy consumption and CO2 emissions as key environmental performance indicators. The environmental impact of the CSA cements was analyzed using Monte Carlo simulations, a robust statistical approach based on data for the constituent raw materials. This method provides a practical alternative to a full life cycle assessment (LCA) when comprehensive data are not available. The results demonstrate that the CSA cements have significantly lower environmental impacts compared to PC, achieving energy savings of 13–16% and CO2 emission reductions of 35–48%. These results emphasize the potential of industrial symbiosis to enable more sustainable CSA cement production while addressing raw material constraints. In addition, this approach highlights the wider applicability of industrial symbiosis frameworks in the construction industry, contributing to a zero-waste future and supporting global climate goals.
Innovative Sports Venue Colors and Consumers’ Satisfaction Based on Multilevel Data: The Mediating Effect of Athletes’ Perceived Emotional Value
Authors: Hou Yuyang , Gou Boze , Liao Jiaping , Zhang Yujie , Huang Qian , Sun Bei
Published: November 2024

Currently, sports venues are endowed with the important functions of providing sports venues and facilities to the urban public, watching sports events, publicizing the mass sports culture, etc. An in-depth study of how to enhance the attractiveness of sports venue buildings and the competitiveness of the industry to stimulate the public’s potential for sports consumption is necessary. Based on the perspective of color psychology, this study constructs a three-level structural equation model based on the nested data obtained from the questionnaire survey to explore the relationship between managers’ innovative sports venue colors, athletes’ perceived emotional value, and consumers’ sports venue satisfaction. The results show that innovative sports venue colors have a significant positive effect on consumers’ satisfaction with sports venues, and the perceived emotional value has a positive mediating role in the effect of innovative sports venue colors on consumers’ satisfaction with sports venues. This study aims to provide a basis for sports venue managers or event organizers to improve athlete satisfaction, optimize consumer experience, stimulate residents’ motivation to attend games, and strengthen the profitability of the venues.
Workability and Mechanical Properties of PVA Fiber-Limestone Fine Cementitious Composite
Authors: Xie Weiliang , Chen Jiajian , Chen Tianxiang , Guan Garfield Xianzhang
Published: November 2024

Cement-based materials are the most widely used building materials and have two main problems: low flexural/tensile strength and low sustainability. To solve these two problems at the same time, the strategy of the utilization of fillers as cement paste replacement and utilization of fiber was proposed. Mixes with varying PVA fiber and LF were produced for workability and mechanical properties measurement and analysis. The results showed that the addition of PVA fibers reduced the flowability and bonding, while the addition of LF similarly reduced the flowability but enhanced its bonding. Both effects on strength showed an increase and then a decrease. An analysis of microstructure and chemical composition demonstrated that the addition of PVA fiber and/or LF first decreased the porosity, and a further addition increased the porosity. The mixes with 0.2% fiber content showed fracture failure mode, while the mixes with 0.4–0.6% fiber content showed the pulling out of failure mode. A mix with 0.2% fiber content and 10% LF content exhibited concurrently good workability and mechanical properties.
Numerical Study on Welding Residual Stress and Microstructure in Gas Metal Arc Welding Square Tube–Plate Y-Shaped Joints
Authors: Yan Zhaoru , Ju Jinsan
Published: November 2024

Welding residual stresses significantly influence the mechanical behavior of hollow section joints, especially in the pivotal connection zones of steel structures employed in construction. The research object of this study is the Q355 steel square tube–plate Y-joint welded using Gas Metal Arc Welding (GMAW) with CO2 Shielding. The thermodynamic sequence coupling method was employed to simulate the temperature field, microstructure distribution, and welding residual stresses in square tube–plate Y-joints. Based on the monitored temperature field data and the cross-sectional dimensions of the weld pool, this study calibrated the finite element model. Subsequently, the calibrated finite element model was employed to analyze the influence of microstructural phase transformations and welding sequences on the welding residual stresses in square tube–plate Y-joints. The research findings indicate that the peak transverse welding residual stresses in the branch pipes of the four joint zones were lower when considering the phase transformation effect than when not accounting for it in the calculations. There was no significant difference in the transverse and longitudinal welding residual stresses on the surface of branch pipes under the three welding sequences. However, there were certain differences in the microstructural content of the weld zones under the three welding sequences, with the martensite content in the third welding sequence being significantly lower than that in the other two sequences.
Black Soybean Seed Coat Extract Suppresses Gut Tumorigenesis by Augmenting the Production of Gut Microbiota-Derived Short-Chain Fatty Acids
Authors: Shimizu Yasuyuki , Hirano Shunta , Salah Mohammed , Hoshi Namiko , Yamashita Yoko , Fukumoto Takeshi , Mukumoto Naritoshi , Nakaoka Ai , Ishihara Takeaki , Miyawaki Daisuke , Ashida Hitoshi , Sasaki Ryohei
Published: November 2024

Background: Proanthocyanidins (PACs) from black soybean seed coat have antioxidant and anti-tumorigenic properties. We investigated the anti-tumor properties and mechanisms of action of PACs on colorectal cancer (CRC). Methods: We fed the APCmin/+ mice, which are highly susceptible to spontaneous intestinal adenoma formation, diets supplemented with or without PACs for 7 weeks and assessed adverse effects, the number and size of intestinal polyps, and the expression of pro- and anti-proliferative proteins in the intestine. The mouse gut microbiome composition was analyzed, and the concentrations of gut short-chain fatty acids (SCFAs) were quantified. We also compared CRC incidence in Tamba in Japan, where black soybean is consumed frequently, with that in the rest of Japan. Results: The number and size of intestinal polyps notably decreased in the PAC-fed mice. Compared with control mice, the PAC-fed mice showed lower expression of proliferation markers proliferating cell nuclear antigen and β catenin and a higher expression of the anti-inflammatory protein oligomeric mucus gel-forming. PAC supplementation increased the prevalence and concentrations of beneficial gut microbes and SCFAs, respectively. Conclusions: Diet supplemented with black soybean-derived PACs could prevent CRC development in mice through gut microbiome remodeling. Regions consuming black soybeans have low CRC incidence. Notably, the incidence of CRC, breast cancer, and liver cancer was significantly lower in Tamba than in the rest of Hyogo Prefecture or Japan. Future studies should delineate the mechanisms underlying the CRC-protective effects of PACs. Nevertheless, our results demonstrate the potential of including PACs in dietary recommendations for cancer prevention.
Acute Myeloid Leukemia: Diagnosis and Evaluation by Flow Cytometry
Authors: Ally Feras , Chen Xueyan
Published: November 2024

With recent technological advances and significant progress in understanding the pathogenesis of acute myeloid leukemia (AML), the updated fifth edition WHO Classification (WHO-HAEM5) and the newly introduced International Consensus Classification (ICC), as well as the European LeukemiaNet (ELN) recommendations in 2022, require the integration of immunophenotypic, cytogenetic, and molecular data, alongside clinical and morphologic findings, for accurate diagnosis, prognostication, and guiding therapeutic strategies in AML. Flow cytometry offers rapid and sensitive immunophenotyping through a multiparametric approach and is a pivotal laboratory tool for the classification of AML, identification of therapeutic targets, and monitoring of measurable residual disease (MRD) post therapy. The association of immunophenotypic features and recurrent genetic abnormalities has been recognized and applied in informing further diagnostic evaluation and immediate therapeutic decision-making. Recently, the evolving role of machine learning models in assisting flow cytometric data analysis for the automated diagnosis and prediction of underlying genetic alterations has been illustrated.
Feasibility of Non-Invasive Sentinel Lymph Node Identification in Early-Stage NSCLC Through Ultrasound Guided Intra-Tumoral Injection of 99mTc-Nanocolloid and Iodinated Contrast Agent During Navigation Bronchoscopy
Authors: ter Woerds Desi K. M. , Verhoeven Roel L. J. , Aarntzen Erik H. J. G. , van der Heijden Erik H. F. M.
Published: November 2024

Background: As the first sentinel lymph nodes (SLN) in lung cancer are most likely to harbor metastasis, their non-invasive identification could have a significant role in future treatments. We investigated the feasibility of adding an SLN procedure to a diagnostic navigation bronchoscopy. Methods: Thirty-one patients were included for injection of 99mTc-nanocolloid and an iodinated contrast agent intra-/peritumorally and assessment of tracer dissipation via SPECT and CBCT imaging. Injections were performed endobronchially using a multi-modal catheter (Pioneer Plus), combining radial ultrasound and an angulated retractable needle to place injections under fluoroscopy and real-time ultrasound. Results: The injection of an imaging tracer was feasible in all cases using the catheter. Ultrasound visualized 29/30 tumors, and tracer injection was performed in 100% of patients. An SLN was subsequently identified in 10 out of 31 cases (32.3%) via SPECT/CT imaging. Iodinated contrast agent injection under CBCT imaging prior to 99mTc nanocolloid injection visualized dissipation pathways and enabled needle relocation for subsequent 99mTc-nanocolloid injection. Conclusions: Performing imaging tracer injections with a multi-modal catheter provided safe and local depot placement immediately following diagnostic navigation bronchoscopy. SPECT/CT imaging using 99mTc-nanocolloid showed inconsistent results for SLN identification.
Low-Dose Eribulin Promotes NK Cell-Mediated Therapeutic Efficacy in Bladder Cancer
Authors: Hassouneh Zaineb , Noel Onika D. V. , Ji Niannian , Kim Michelle E. , Svatek Jordan , Svatek Robert S. , Risinger April L. , Mukherjee Neelam
Published: November 2024

Despite its immunogenic nature, bladder cancer (BCa) responds sub-optimally to FDA-approved immunotherapy. Background/Objectives: We have previously shown that natural killer (NK) cells are major contributors to overall patient survival in BCa. In our efforts to identify clinically approved agents that enhance NK cell activation, we identified eribulin, a microtubule destabilizer primarily used in breast cancer. Ongoing clinical trials are investigating the potential integration of eribulin into the standard of care in BCa; however, the mechanistic rationale for these trials remains unclear. Methods: Here, we explore the effects of low-dose eribulin on direct NK cell activation in vitro, including on primary patient samples, and in vivo utilizing multiple murine models. Flow cytometry and RNA sequencing were employed to identify the mechanism of NK cell activation by eribulin, which was associated with increased migration and cytotoxicity of NK cells against BCa cells. Results: We found that localized eribulin instillation significantly reduces bladder tumor burden and improves survival in primary BCa in an NK cell-dependent manner. Importantly, eribulin promoted the shift of patient-derived intratumoral NK cells towards an anti-tumor CD49a+ CD103+ NK subset (ieILC1-like) while diminishing the dysfunctional NR4A2-expressing CD49a− NK subset. Moreover, it decreased the overall expression of exhaustion markers on NK cells, a pattern replicated in our murine models. Conclusions: These findings are paradigm-shifting given that chemotherapy is traditionally considered immunosuppressive. Our study reveals the novel effect of low-dose eribulin chemotherapy in inhibiting bladder tumor growth by enhancing anti-tumor NK cell immunity, challenging previous assumptions and opening new therapeutic approaches to improve antitumor immunity.
LUCAT1-Mediated Competing Endogenous RNA (ceRNA) Network in Triple-Negative Breast Cancer
Authors: Verma Deepak , Siddharth Sumit , Yende Ashutosh S. , Wu Qitong , Sharma Dipali
Published: November 2024

Breast cancer is a heterogeneous disease comprising multiple molecularly distinct subtypes with varied prevalence, prognostics, and treatment strategies. Among them, triple-negative breast cancer, though the least prevalent, is the most aggressive subtype, with limited therapeutic options. Recent emergence of competing endogenous RNA (ceRNA) networks has highlighted how long noncoding RNAs (lncRNAs), microRNAs (miRs), and mRNA orchestrate a complex interplay meticulously modulating mRNA functionality. Focusing on TNBC, this study aimed to construct a ceRNA network using differentially expressed lncRNAs, miRs, and mRNAs. We queried the differentially expressed lncRNAs (DElncRNAs) between TNBC and luminal samples and found 389 upregulated and 386 downregulated lncRNAs, including novel transcripts in TNBC. DElncRNAs were further evaluated for their clinical, functional, and mechanistic relevance to TNBCs using the lnc2cancer 3.0 database, which presented LUCAT1 (lung cancer-associated transcript 1) as a putative node. Next, the ceRNA network (lncRNA–miRNA–mRNA) of LUCAT1 was established. Several miRNA–mRNA connections of LUCAT1 implicated in regulating stemness (LUCAT1-miR-375-Yap1, LUCAT1-miR181-5p-Wnt, LUCAT1-miR-199a-5p-ZEB1), apoptosis (LUCAT1-miR-181c-5p-Bcl2), drug efflux (LUCAT1-miR-200c-ABCB1, LRP1, MRP5, MDR1), and sheddase activities (LUCAT1-miR-493-5p-ADAM10) were identified, indicating an intricate regulatory mechanism of LUCAT1 in TNBC. Indeed, LUCAT1 silencing led to mitigated cell growth, migration, and stem-like features in TNBC. This work sheds light on the LUCAT1 ceRNA network in TNBC and implies its involvement in TNBC growth and progression.
A GNN-Based QSPR Model for Surfactant Properties
Authors: Ham Seokgyun , Wang Xin , Zhang Hongwei , Lattimer Brian , Qiao Rui
Published: November 2024

Surfactants are among the most versatile molecules in the chemical industry because they can self-assemble in bulk solutions and at interfaces. Predicting the properties of surfactant solutions, such as their critical micelle concentration (CMC), limiting surface tension (γcmc), and maximal packing density (Γmax) at water–air interfaces, is essential to their rational design. However, the relationship between surfactant structure and these properties is complex and difficult to predict theoretically. Here, we develop a graph neural network (GNN)-based quantitative structure–property relationship (QSPR) model to predict the CMC, γcmc, and Γmax. Ninety-two surfactant data points, encompassing all types of surfactants—anionic, cationic, zwitterionic, and nonionic—are fed into the model, covering a temperature range of [20–30 °C], which contributes to its generalization across all surfactant types. We show that our models have high accuracy (R2 = 0.87 on average in tests) in predicting the three parameters across all types of surfactants. The effectiveness of the QSPR model in capturing the variation of CMC, γcmc, and Γmax with molecular design parameters are carefully assessed. The curated dataset, developed model, and critical assessment of the developed model will contribute to the development of improved surfactants QSPR models and facilitate their rational design for diverse applications.
The Conundrum Challenges for Research Software in Open Science
Authors: Gomez-Diaz Teresa , Recio Tomas
Published: November 2024

In the context of Open Science, the importance of Borgman’s conundrum challenges that have been initially formulated concerning the difficulties to share Research Data is well known: which Research Data might be shared, by whom, with whom, under what conditions, why, and to what effects. We have recently reviewed the concepts of Research Software and Research Data, concluding with new formulations for their definitions, and proposing answers to these conundrum challenges for Research Data. In the present work we extend the consideration of the Borgman’s conundrum challenges to Research Software, providing answers to these questions in this new context. Moreover, we complete the initial list of questions/answers, by asking how and where the Research Software may be shared. Our approach begins by recalling the main issues involved in the Research Software definition, and its production context in the research environment, from the Open Science perspective. Then we address the conundrum challenges for Research Software by exploring the potential similarities and differences regarding our answers for these questions in the case of Research Data. We conclude emphasizing the usefulness of the followed methodology, exploiting the parallelism between Research Software and Research Data in the Open Science environment.
The Immediate Effect of a Single Treatment of Neuromuscular Electrical Stimulation with the StimaWELL 120MTRS System on Multifidus Stiffness in Patients with Chronic Low Back Pain
Authors: Wolfe Daniel , Dover Geoffrey , Boily Mathieu , Fortin Maryse
Published: November 2024

Background/Objectives: Individuals with chronic low back pain (CLBP) have altered lumbar multifidus stiffness properties compared to healthy controls. Although neuromuscular electrical stimulation (NMES) application to the multifidus might affect stiffness, this has never been investigated. The aims of this study were to examine the effect of a single NMES treatment on multifidus stiffness and pain intensity in CLBP patients. Methods: 30 participants (13 male, 17 female) were randomized to one of two intervention (‘phasic’ and ‘combined’) protocols with the StimaWELL 120MTRS system. Multifidus stiffness at L4 and L5 was measured via shear-wave elastography (SWE) at rest and in standing prior to, and 15 min after, a 20 min NMES treatment. Pain intensity was measured pre- and post-treatment with the numerical pain rating scale (NPRS). Results: There were significant increases in resting shear modulus at right L4 (p = 0.001) and bilaterally at L5 (p = 0.017; p = 0.020) in the ‘combined’ intervention group, and a significant between-group difference at right L4 (p < 0.001). There were significant decreases in standing shear modulus at right L4 (p = 0.015) and left L5 (p = 0.036) in the ‘combined’ intervention group, and a significant between-group difference at left L5 (p = 0.016). Both groups experienced significant decreases in pain intensity (MD combined group = 1.12, 95% CI [0.34, 1.90], p = 0.011) (MD phasic group = 1.42, 95% CI [0.68, 2.16], p = 0.001). Conclusions: There were multiple significant changes in multifidus stiffness in the combined group, but not in the phasic group. Both groups experienced significant decreases in low back pain intensity.
Digital Condylar Parameter Assessment Using Cadiax® 2 and Modjaw®
Authors: Buduru Smaranda , Hafidi Sara , Almășan Oana , Manziuc Manuela , Tăut Manuela , Buduru Rareș , Nechita Vlad-Ionuț , Kui Andreea , Chisnoiu Andreea , Bacali Cecilia
Published: November 2024

Background: The main aim of this research was to assess the reliability of two systems designed specifically for condylar movement recording using condylar slope and Bennett angle information. The objectives were to evaluate the validity of two subsequent null hypotheses: (1) there is no significant difference between the measurements of condylar slope and Bennett angle taken at T0 (initial) and T1 (after one week) using the same equipment; (2) there is no notable difference in the values of the condylar slope and Bennett angle measurements obtained using Modjaw and Cadiax 2. Methods: An observational, descriptive, and prospective study was conducted with a selected group of 25 individuals (13 females and 12 males) aged between 22 and 27. Results: The results of Cadiax 2 and Modjaw showed excellent measurement repeatability for both parameters, with intraclass correlation coefficients (ICC) above 0.90, indicating excellent reliability between T0 and T1, both at 3 mm and 5 mm of displacement. Modjaw had an overall average value relatively higher than Cadiax 2, even though Modjaw’s condylar slope at 5mm had a significantly lower average value (37.4 ± 6.31) with an interval of 24.5–48.1, which was lower than Cadiax 2 (48.4 ± 10.6) with an interval of 30.5–68.5. Regarding the primary aim, it can be stated that both Modjaw and Cadiax 2 demonstrated excellent repeatability on their own, demonstrating robust reliability since there was no discernible difference between the T0 and T1 measurements. On the contrary, analyses of the two devices’ measured values for the secondary aim showed a considerable difference. Conclusions: Even though each device is reliable on its own, the absolute values that are obtained are different. Technological differences between the systems may account for these variations.
A Fast Obstacle Detection Algorithm Based on 3D LiDAR and Multiple Depth Cameras for Unmanned Ground Vehicles
Authors: Pang Fenglin , Chen Yutian , Luo Yan , Lv Zigui , Sun Xuefei , Xu Xiaobin , Luo Minzhou
Published: November 2024

With the advancement of technology, unmanned ground vehicles (UGVs) have shown increasing application value in various tasks, such as food delivery and cleaning. A key capability of UGVs is obstacle detection, which is essential for avoiding collisions during movement. Current mainstream methods use point cloud information from onboard sensors, such as light detection and ranging (LiDAR) and depth cameras, for obstacle perception. However, the substantial volume of point clouds generated by these sensors, coupled with the presence of noise, poses significant challenges for efficient obstacle detection. Therefore, this paper presents a fast obstacle detection algorithm designed to ensure the safe operation of UGVs. Building on multi-sensor point cloud fusion, an efficient ground segmentation algorithm based on multi-plane fitting and plane combination is proposed in order to prevent them from being considered as obstacles. Additionally, instead of point cloud clustering, a vertical projection method is used to count the distribution of the potential obstacle points through converting the point cloud to a 2D polar coordinate system. Points in the fan-shaped area with a density lower than a certain threshold will be considered as noise. To verify the effectiveness of the proposed algorithm, a cleaning UGV equipped with one LiDAR sensor and four depth cameras is used to test the performance of obstacle detection in various environments. Several experiments have demonstrated the effectiveness and real-time capability of the proposed algorithm. The experimental results show that the proposed algorithm achieves an over 90% detection rate within a 20 m sensing area and has an average processing time of just 14.1 ms per frame.
Grid Matrix-Based Ground Risk Map Generation for Unmanned Aerial Vehicles in Urban Environments
Authors: Zhu Yuanjun , Zhang Xuejun , Li Yan , Liu Yang , Ma Jianxiang
Published: November 2024

As a novel mode of urban air mobility (UAM), unmanned aerial vehicles (UAVs) pose a great amount of risk to ground people. Assessing ground risk and mitigation effects correctly is a focused issue. This paper proposes a grid-based risk matrix framework for assessing the ground risk associated with two types of UAVs, namely fixed-wing and quadrotor. The framework has a three-stage structure of “intrinsic risk assessment—mitigation effect—final map generation”. First, the intrinsic risk to ground populations caused by potential UAV crashes is quantified. Second, the mitigation effects are measured by establishing a mathematical model with a focus on the ground sheltering and parachute systems. Finally, a modular approach is presented for generating a ground risk map of UAVs, aiming to effectively characterize the effects of each influencing factor on the failure process of UAVs. The framework facilitates the modular analysis and quantification of the impact of diverse risk factors on UAV ground risk. It also provides a new perspective for analyzing ground risk mitigation measures, such as ground sheltering and UAV parachute systems. A case study experiment on a realistic urban environment in Shenzhen shows that the risk map generated by the presented framework can accurately characterize the distribution of ground risk posed by various UAVs.
Novel Energy-Aware 3D UAV Path Planning and Collision Avoidance Using Receding Horizon and Optimization-Based Control
Authors: Ahmed Gamil , Sheltami Tarek
Published: November 2024

Unmanned Aerial Vehicles (UAVs) have gained significant popularity in recent years thanks to their agility, mobility, and cost-effectiveness. However, UAV navigation presents several challenges, particularly in path planning, which requires determining an optimal route while avoiding obstacles and adhering to various constraints. Another critical challenge is the limited flight time imposed by the onboard battery. This paper introduces a novel approach for energy-efficient three-dimensional online path planning for UAV formations operating in complex environments. We formulate the path planning problem as a minimization optimization problem, and employ Mixed-Integer Linear Programming (MILP) to achieve optimal solutions. The cost function is designed to minimize energy consumption while considering the inter-collision and intra-collision avoidance constraints within a limited detection range. To achieve this, an optimization approach incorporating Receding Horizon Control (RHC) is applied. The entire path is divided into segments or sub-paths, with constraints used to avoid collisions with obstacles and other members of the fleet. The proposed optimization approach enables fast navigation through dense environments and ensures a collision-free path for all UAVs. A path-smoothing strategy is proposed to further reduce energy consumption caused by sharp turns. The results demonstrate the effectiveness and accuracy of the proposed approach in dense environments with high risk of collision. We compared our proposed approach against recent works, and the results illustrate that the proposed approach outperforms others in terms of UAV formation, number of collisions, and partial path generation time.
Drones in Precision Agriculture: A Comprehensive Review of Applications, Technologies, and Challenges
Authors: Guebsi Ridha , Mami Sonia , Chokmani Karem
Published: November 2024

In the face of growing challenges in modern agriculture, such as climate change, sustainable resource management, and food security, drones are emerging as essential tools for transforming precision agriculture. This systematic review, based on an in-depth analysis of recent scientific literature (2020–2024), provides a comprehensive synthesis of current drone applications in the agricultural sector, primarily focusing on studies from this period while including a few notable exceptions of particular interest. Our study examines in detail the technological advancements in drone systems, including innovative aerial platforms, cutting-edge multispectral and hyperspectral sensors, and advanced navigation and communication systems. We analyze diagnostic applications, such as crop monitoring and multispectral mapping, as well as interventional applications like precision spraying and drone-assisted seeding. The integration of artificial intelligence and IoTs in analyzing drone-collected data is highlighted, demonstrating significant improvements in early disease detection, yield estimation, and irrigation management. Specific case studies illustrate the effectiveness of drones in various crops, from viticulture to cereal cultivation. Despite these advancements, we identify several obstacles to widespread drone adoption, including regulatory, technological, and socio-economic challenges. This study particularly emphasizes the need to harmonize regulations on beyond visual line of sight (BVLOS) flights and improve economic accessibility for small-scale farmers. This review also identifies key opportunities for future research, including the use of drone swarms, improved energy autonomy, and the development of more sophisticated decision-support systems integrating drone data. In conclusion, we underscore the transformative potential of drones as a key technology for more sustainable, productive, and resilient agriculture in the face of global challenges in the 21st century, while highlighting the need for an integrated approach combining technological innovation, adapted policies, and farmer training.
Advancing Social Equity in Urban UAV Logistics: Insights from the Academic Literature and Social Media
Authors: Zhang Dong , Yang Perry Pei-Ju , Tsou Jin-Yeu
Published: November 2024

In recent years, the rapid growth of e-commerce and on-demand delivery services has placed a significant strain on urban logistics systems. Technological advances such as unmanned aerial vehicle (UAV)-based logistics systems have thus emerged as promising solutions in urban environments and are increasingly being piloted worldwide. However, the implementation of UAV logistics risks exacerbating social inequities, particularly in marginalized communities that may disproportionately bear the noise and safety risks. To mitigate these risks, it is crucial to integrate social equity considerations into urban UAV logistics. This study explores social equity factors through a systematic literature review and social media analysis of Xiaohongshu (the Little Red Book), a popular Chinese social media platform known for its extensive user base and active discussions on social issues. This literature review involves a full-text examination, while latent Dirichlet allocation (LDA) topic modeling is used to analyze social media comment datasets. Each method identifies social equity factors and separately assesses their relative importance, resulting in the final identification of 24 key factors that provide a holistic view of public sentiment and academic discourse. The findings reveal a divide between academic concerns around systemic risks and a public focus on immediate needs. By synthesizing these insights, this study provides a social equity landscape for urban UAV logistics and actionable references for policymakers and stakeholders.
Multi-Rotor Drone-Based Thermal Target Tracking with Track Segment Association for Search and Rescue Missions
Authors: Yeom Seokwon
Published: November 2024

Multi-rotor drones have expanded their range of applications, one of which being search and rescue (SAR) missions using infrared thermal imaging. This paper addresses thermal target tracking with track segment association (TSA) for SAR missions. Three types of associations including TSA are developed with an interacting multiple model (IMM) approach. During multiple-target tracking, tracks are initialized, maintained, and terminated. There are three different associations in track maintenance: measurement–track association, track–track association for tracks that exist at the same time (track association and fusion), and track–track association for tracks that exist at separate times (TSA). Measurement–track association selects the statistically nearest measurement and updates the track with the measurement through the IMM filter. Track association and fusion fuses redundant tracks for the same target that are spatially separated. TSA connects tracks that have become broken and separated over time. This process is accomplished through the selection of candidate track pairs, backward IMM filtering, association testing, and an assignment rule. In the experiments, a drone was equipped with an infrared thermal imaging camera, and two thermal videos were captured of three people in a non-visible environment. These three hikers were located close together and occluded by each other or other obstacles in the mountains. The drone was allowed to move arbitrarily. The tracking results were evaluated by the average total track life, average mean track life, and average track purity. The track segment association improved the average mean track life of each video by 99.8% and 250%, respectively.
Machine Learning-Based Environment-Aware GNSS Integrity Monitoring for Urban Air Mobility
Authors: Isik Oguz Kagan , Petrunin Ivan , Tsourdos Antonios
Published: November 2024

The increasing deployment of unmanned aerial vehicles (UAVs) in urban air mobility (UAM) necessitates robust Global Navigation Satellite System (GNSS) integrity monitoring that can adapt to the complexities of urban environments. The traditional integrity monitoring approaches struggle with the unique challenges posed by urban settings, such as frequent signal blockages, multipath reflections, and Non-Line-of-Sight (NLoS) receptions. This study introduces a novel machine learning-based GNSS integrity monitoring framework that incorporates environment recognition to create environment-specific error models. Using a comprehensive Hardware-in-the-Loop (HIL) simulation setup, extensive data were generated for suburban, urban, and urban canyon environments to train and validate the models. The proposed Natural Gradient Boosting Protection Level (NGB-PL) method, leveraging the uncertainty prediction capabilities of the NGB algorithm, demonstrated superior performance in estimating protection levels compared to the classical methods. The results indicated that environment-specific models significantly enhanced both accuracy and system availability, particularly in challenging urban scenarios. The integration of environment recognition into the integrity monitoring framework allows the dynamic adaptation to varying environmental conditions, thus substantially improving the reliability and safety of UAV operations in urban air mobility applications. This research offers a novel protection level (PL) estimation method and a framework tailored to GNSS integrity monitoring for UAM, which enhances the availability with narrower PL bound gaps without yielding higher integrity risks.
Differential Impacts of Environmental, Social, and Governance News Sentiment on Corporate Financial Performance in the Global Market: An Analysis of Dynamic Industries Using Advanced Natural Language Processing Models
Authors: Kim Minjoong , Kang Jinseong , Jeon Insoo , Lee Juyeon , Park Jungwon , Youm Seulgi , Jeong Jonghee , Woo Jiyoung , Moon Jihoon
Published: November 2024

This study examines how sentiment analysis of environmental, social, and governance (ESG) news affects the financial performance of companies in innovative sectors such as mobility, technology, and renewable energy. Using approximately 9828 general ESG articles from Google News and approximately 140,000 company-specific ESG articles, we performed term frequency-inverse document frequency (TF-IDF) analysis to identify key ESG-related terms and visualize their materiality across industries. We then applied models such as bidirectional encoder representations from transformers (BERT), the robustly optimized BERT pretraining approach (RoBERTa), and big bidirectional encoder representations from transformers (BigBird) for multiclass sentiment analysis, and distilled BERT (DistilBERT), a lite BERT (ALBERT), tiny BERT (TinyBERT), and efficiently learning an encoder that classifies token replacements accurately (ELECTRA) for positive and negative sentiment identification. Sentiment analysis results were correlated with profitability, cash flow, and stability indicators over a three-year period (2019–2021). ESG ratings from Morgan Stanley Capital International (MSCI), a prominent provider that evaluates companies’ sustainability practices, further enriched our analysis. The results suggest that sentiment impacts financial performance differently across industries; for example, positive sentiment correlates with financial success in mobility and renewable energy, while consumer goods often show positive sentiment even with low environmental ESG scores. The study highlights the need for industry-specific ESG strategies, especially in dynamic sectors, and suggests future research directions to improve the accuracy of ESG sentiment analysis.
Design of an Accurate, Planar, Resonant Microwave Sensor for Testing a Wide Range of Liquid Samples
Authors: Agarwal Smriti , Garg Manoj Chandra
Published: November 2024

In this paper, an inductively coupled capacitively loaded ring resonator (IC-CLRR)-based microwave resonant sensor has been proposed for the accurate identification of any unknown liquid sample and its permittivity estimation. The key element of this work is the sensor’s wide range capability towards the non-invasive testing of liquids covering a wide dielectric range of liquid samples, i.e., εr = 2 to 80. The proposed microwave sensor is etched over the FR-4 substrate and is excited by the microstrip line through inductive coupling. The placement of an unknown liquid sample in close proximity to the sensor alters its natural resonant frequency due to a change in effective inductance and capacitance as per the dielectric property of the liquid sample. Further, a mathematical formulation using curve fitting has also been derived. The measurement results show a good accuracy in estimating the permittivity and, thus, the unknown liquid identification capability of the designed sensor with a very low error (nearly 5%). This sensor design is simple to fabricate, cost-friendly, and small in size.