bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient GPU Implementation of the McMurchie–Davidson Method for Shell-Based ERI Computations
Authors: Fujii Haruto , Ito Yasuaki , Yokogawa Nobuya , Suzuki Kanta , Tsuji Satoki , Nakano Koji , Parque Victor , Kasagi Akihiko
Published: February 2025

Quantum chemistry offers the formal machinery to derive molecular and physical properties arising from (sub)atomic interactions. However, as molecules of practical interest are largely polyatomic, contemporary approximation schemes such as the Hartree–Fock scheme are computationally expensive due to the large number of electron repulsion integrals (ERIs). Central to the Hartree–Fock method is the efficient computation of ERIs over Gaussian functions (GTO-ERIs). Here, the well-known McMurchie–Davidson method (MD) offers an elegant formalism by incrementally extending Hermite Gaussian functions and auxiliary tabulated functions. Although the MD method offers a high degree of versatility to acceleration schemes through Graphics Processing Units (GPUs), the current GPU implementations limit the practical use of supported values of the azimuthal quantum number. In this paper, we propose a generalized framework capable of computing GTO-ERIs for arbitrary azimuthal quantum numbers, provided that the intermediate terms of the MD method can be stored. Our approach benefits from extending the MD recurrence relations through shells, batches, and triple-buffering of the shared memory, and ordering similar ERIs, thus enabling the effective parallelization and use of GPU resources. Furthermore, our approach proposes four GPU implementation schemes considering the suitable mappings between Gaussian basis and CUDA blocks and threads. Our computational experiments involving the GTO-ERI computations of molecules of interest on an NVIDIA A100 Tensor Core GPU (NVIDIA, Santa Clara, CA, USA) have revealed the merits of the proposed acceleration schemes in terms of computation time, including up to a 72× improvement over our previous GPU implementation and up to a 4500× speedup compared to a naive CPU implementation, highlighting the effectiveness of our method in accelerating ERI computations for both monatomic and polyatomic molecules. Our work has the potential to explore new parallelization schemes of distinct and complex computation paths involved in ERI computation.
Automatic Correction System for Learning Activities in Remote-Access Laboratories in the Mechatronics Area
Authors: Machado Guido S. , Salgado Thiago R. M. , Ayres Florindo A. C. , Bessa Iury V. , Medeiros Renan L. P. , Lucena Vicente F.
Published: February 2025

In recent years, the educational field has evolved rapidly owing to the integration of several technologies, especially experiments in remote laboratories in the engineering area. Therefore, this article addresses the development of an innovation system for automatically correcting experiments in remote laboratories in mechatronics using digital twins, convolutional neural networks (CNNs), and generative artificial intelligence technologies. This system was designed to overcome the limitations of physical laboratories and teacher’s availability and assist in learning, enabling automatic acquisitions at any time. The digital twin captures data from the teacher’s and student’s experiments, allowing accurate comparisons to identify successes and errors. The application of CNNs serves to validate the results of the experiments through image analysis, whereas generative AI helps to identify patterns. The system was evaluated in a didactic plant, effectively correcting experiments with digital inputs and outputs. In addition, it provides students with detailed feedback on their performance, including specific errors and suggestions for improvement. With a three-layer architecture, i.e., experiments, didactics, and management, the system efficiently processes data from teachers and students, contributing to correcting experiments and optimizing teaching in remote environments.
Parallel Fin Ray Soft Gripper with Embedded Mechano-Optical Force Sensor
Authors: Navas Eduardo , Rodríguez-Nieto Daniel , Rodríguez-González Alain Antonio , Fernández Roemi
Published: February 2025

The rapid advancement in soft robotics over the past decade has driven innovation across the industrial, medical, and agricultural sectors. Among various soft robotic designs, Fin Ray-inspired soft grippers have demonstrated remarkable adaptability and efficiency in handling delicate objects. However, the integration of force sensors in soft grippers remains a significant challenge, as conventional rigid sensors compromise the inherent flexibility and compliance of soft robotic systems. This study presents a parallel soft gripper based on the Fin Ray effect, incorporating an embedded mechano-optical force sensor capable of providing linear force measurements up to 150 N. The gripper is entirely 3D printed using thermoplastic elastomers (TPEs), ensuring a cost-effective, scalable, and versatile design. The proposed sensor architecture leverages a gyroid lattice structure, yielding a near-linear response with an R2 value of 0.96 across two force regions. This study contributes to the development of sensorized soft grippers with improved force-sensing capabilities while preserving the advantages of soft robotic manipulators.
Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
Authors: Yang Mingxu , Huang Chuhua , Huang Xin , Hou Shengjin
Published: February 2025

Edge-assisted visual simultaneous localization and mapping (SLAM) is widely used in autonomous driving, robot navigation, and augmented reality for environmental perception, map construction, and real-time positioning. However, it poses significant privacy risks, as input images may contain sensitive information, and generated 3D point clouds can reconstruct original scenes. To address these concerns, this paper proposes a dual-component privacy-preserving approach for visual SLAM. First, a privacy protection method for images is proposed, which combines object detection and image inpainting to protect privacy-sensitive information in images. Second, an encryption algorithm is introduced to convert 3D point cloud data into a 3D line cloud through dimensionality enhancement. Integrated with ORB-SLAM3, the proposed method is evaluated on the Oxford Robotcar and KITTI datasets. Results demonstrate that it effectively safeguards privacy-sensitive information while ORB-SLAM3 maintains accurate pose estimation in dynamic outdoor scenes. Furthermore, the encrypted line cloud prevents unauthorized attacks on recovering the original point cloud. This approach enhances privacy protection in visual SLAM and is expected to expand its potential applications.
Investigation of the Effect of Layer Thickness on the Mechanical Properties of 316L Austenitic Stainless Steel in Additive Manufacturing with Metal Filament
Authors: Iyibilgin Osman
Published: February 2025

Additive manufacturing has revolutionized the production industry by enabling the fabrication of complex geometries. In recent years, significant advancements have been made in 3D printing using metal filament, particularly with materials such as 316L stainless steel. Known for its high strength, corrosion resistance, and ductility, 316L stainless steel is well suited for demanding applications in the medical, marine, and aerospace industries. However, secondary processes such as debinding and sintering can lead to changes in the dimensions and mechanical properties of the final product. This study investigates the effect of layer thickness on the mechanical properties of 316L stainless steel produced through additive manufacturing. Samples were produced with varying layer thicknesses (100, 200, 300, and 400 µm) and tested for tensile strength, hardness, and density. The results indicate that tensile strength increases with decreasing layer thickness. The highest tensile strength (432 MPa) and hardness (213 Hv) were observed at a layer thickness of 100 µm. Additionally, phase analyses and microstructural examinations were conducted. The primary phases identified in the samples were face-centered cubic (FCC) austenite and body-centered cubic (BCC) ferrite (δ). In this study, the manufacturing parameters with 316L filament have been optimized, and their impact on the mechanical properties has been examined.
Neural Network Method of Controllers’ Parametric Optimization with Variable Structure and Semi-Permanent Integration Based on the Computation of Second-Order Sensitivity Functions
Authors: Vladov Serhii , Scislo Lukasz , Szczepanik-Ścisło Nina , Sachenko Anatoliy , Vysotska Victoria
Published: February 2025

This article presents a method for researching processes in automatic control systems based on the operator approach for modelling the control object and the controller. Within the method framework, a system of equations has been developed that describes the relations between the control error, the reference and control action, the output coordinate and the controller and the control object operators. The traditional PI controller modification, including a switching function for adaptation to operating conditions, allows for the system’s effective control in real time. The controller optimization algorithm is based on a functional expression with weighting coefficients that take into account control errors and the control action. To train the neural network through implementing the proposed method, a multilayer architecture was used, including nonlinear activation functions and a dynamic training rate, which ensure high accuracy and accelerated convergence. The TV3-117 turboshaft engine was chosen as the research object, which allows the method to be demonstrated in practical applications in aviation technology. The experimental results showed a significant improvement in control characteristics, including a reduction in the gas-generator rotor speed parameter transient time to ≈1, which is two times faster than the traditional method, where the transient process reaches ≈0.5. The model achieved a maximum accuracy of 0.993 with 160 training epochs, minimizing the error function to 0.005. In comparison with similar approaches, the proposed method demonstrated better results in accuracy and training speed, which was confirmed by a reduction in the number of iterations by 1.36 times and an improvement in the mean square error by 1.86–6.02 times.
Upcycling Alum Sludge as a Reinforcement in PBAT Composites: A Sustainable Approach to Waste Valorisation
Authors: Sun Dongyang , Henthorn Thomas , Popescu Carmen-Mihaela , Salehiyan Reza
Published: February 2025

This study explores the valorisation of alum sludge, a byproduct of water treatment processes, as a sustainable reinforcement material in Poly(butylene adipate-co-terephthalate) (PBAT) composites. The research aims to address industrial waste challenges by developing eco-friendly composite materials while promoting circular economy principles. Alum sludge particles, classified into two size distributions (<63 µm and <250 µm), were incorporated into PBAT matrices at varying concentrations. The composites were characterised for their mechanical, thermal, crystallographic, and moisture adsorption properties; and their biodegradation behaviour was evaluated through soil burial tests over 60 days. The results revealed that the 63 µm particle size fraction exhibited superior performance compared to the 250 µm fraction, demonstrating improved mechanical properties, reduced degradation rates, and enhanced interfacial bonding. Composites with 5 wt.% alum sludge achieved a balance between reinforcement and processability, outperforming the other filler concentrations examined. This innovative approach highlights the potential of upcycling alum sludge into functional materials, advancing sustainable waste management and composite manufacturing. Furthermore, the observed variation in degradation rates suggests that these composites can be tailored for applications requiring controlled compostability.
Geochemical Survey in Mojiang Area of Yunnan Province, China: Geochemical Map and Geochemical Anomaly Map
Authors: Zhu Xianfu , Li Peiyu , Gong Qingjie , Gu Weixuan , Xu Shengchao , Yan Taotao
Published: February 2025

The geochemical maps and geochemical anomaly maps produced based on the data in the databases of the Regional Geochemistry–National Reconnaissance (RGNR) and the National Multipurpose Regional Geochemistry Survey (NMPRGS) projects have played a crucial role in China’s geochemical exploration. A geochemical survey of the Mojiang area, Yunnan Province, China, has been completed and reveals potential new regions for Ni exploration related to occurrences of serpentinite melanges. The geochemical maps and geochemical anomaly maps need to be drawn in this area. Traditional geochemical maps, heavily dependent on data quantity, are less suitable for consistent comparisons across distinct regions and elements. Here, a fixed value method is proposed to contour the Ni geochemical map on 19 levels, which is convenient for the comparison among elements. On the geochemical maps, the two known Ni deposits are located in a region with Ni surely screening risk level (on the national standard of pollution risk of heavy metals in China) and a region with Ni economic level (Ni as an associate or main economic metal on the national standard of Ni deposit in China), respectively. In addition, we have determined that the Sn and Li levels in this area are at (low or high) background levels compared to other regions. Then, the method of seven levels of classification, which is also suitable for the comparison across different areas or elements, is used to draw the geochemical anomaly maps in the Mojiang area. On the anomaly maps, the two known Ni deposits are located in the regions with Ni anomaly levels not less than four, while the anomaly areas of Sn and Li are sporadic, with anomaly levels not larger than two in this area. These consistent results with the known facts of Ni, Sn, and Li deposits in the Mojiang area not only consolidate the roles of geochemical maps and geochemical anomaly maps but also illustrate the comparison among elements in mineral exploration. Furthermore, we predicted three Ni potential regions in the Mojiang area on the geochemical survey.
Robust Sandstorm Image Restoration via Adaptive Color Correction and Saturation Line Prior-Based Dust Removal
Authors: Zhou Shan , Shi Fei , Jia Zhenhong , Wang Guoqiang , Huang Jian
Published: February 2025

Enhancing the visibility of outdoor images under sandstorm conditions remains a significant challenge in computer vision due to the complex atmospheric interference caused by dust particles. While existing image enhancement algorithms perform well in mild sandstorm scenarios, they often struggle to produce satisfactory results in more severe conditions, where residual color casts and chromatic artifacts become pronounced. These limitations highlight the need for a more robust and adaptable restoration method. In this study, we propose an advanced algorithm designed to restore sand-dust images under varying sandstorm intensities, effectively addressing the aforementioned challenges. The approach begins with a color correction step, achieved through channel compensation and color transfer techniques, which leverage the unique statistical properties of sand-dust images. To further refine the restoration, we improve the boundary constraints of the saturation line prior (SLP) by adjusting the local illumination in the atmospheric light map, enhancing the model’s robustness to environmental variations. Finally, the atmospheric scattering model is employed for comprehensive image restoration, ensuring that color correction and dust removal are optimized. Extensive experiments on real-world sandstorm images demonstrate that the proposed method performs on par with state-of-the-art (SOTA) techniques in weaker sandstorm scenarios, showing marked improvements in more severe conditions. These results highlight the potential of our approach for practical applications in outdoor image enhancement under challenging environmental conditions.
Pollutant Removal Efficiency of Pilot-Scale Horizontal Subsurface Flow Constructed Wetlands Treating Landfill Leachate
Authors: Ntountounakis Ioannis , Margaritou Ioanna-Eirini , Pervelis Ioannis , Kyrou Pavlos , Parlakidis Paraskevas , Gikas Georgios D.
Published: February 2025

Landfill leachate contains various organic and inorganic substances resulting from the decomposition of solid waste. The treatment of this complex mixture is an imperative need for environmental protection. This study used five pilot-scale horizontal subsurface flow (HSF) constructed wetland (CW) units to treat landfill leachate. The main objective was the evaluation of the performance of CW units in the removal of pollutants. The effect of porous media (gravel and zeolite), plants (common reed and cattail), and hydraulic residence time (HRT, 8 and 10 days) were investigated. Two pilot-scale CW units differed in HRT, two in porous media, and three in planting. The results showed that the planted CW units had higher organic matter (OM) and nitrogen (TKN, NH4-N) removal compared with the unplanted unit. The 10-day HRT CW unit had higher average removal rates for all pollutants compared with the CW unit with an 8-day HRT. Finally, the CW unit with zeolite (25%, by volume) in the fill material showed higher average removal rates for OM and nitrogen compared to the unit with gravel.
Generalising the Machine Tool Integrated Inverse Multilateration Method for the Ambient Thermal Error Analysis of Large Machine Tools in Industrial Environments
Authors: Egaña Fernando , Mutilba Unai , Yagüe-Fabra José A. , Chekh B. Ahmed , Lopez Susana
Published: February 2025

This study expands on prior research by generalising the machine tool integrated inverse multilateration methodology to evaluate ambient thermal effects on medium- and large-sized machine tools in industrial environments. This method integrates an absolute distance measurement device into the machine tool spindle, enabling an automated and robust multilateration scheme without requiring controlled environments, expensive thermal instruments, or specialised artifacts. Tests were conducted using a LEICA AT960™ laser tracker and wide-angle retro-reflectors (both from Hexagon Manufacturing Intelligence, Stockholm, Sweden) across two machine architectures, THERA™ (gantry type) and ZERO™ (bed type), building on earlier work with the ARION G™ (bridge type), all of them MTs manufactured by Zayer (Vitoria, Spain). Sequential experiments in varying ambient conditions demonstrated the reliability of the machine tool integrated inverse multilateration approach over extended periods, showing strong correlations between the measured errors and temperature variations. The results were validated using a first-order mathematical model and finite element method simulations, confirming thermal error evolution as a function of ambient temperature changes. This method’s adaptability to diverse machine architectures and industrial conditions highlights its potential for characterising and mitigating thermal errors in large machine tools. This work underscores the method’s effectiveness and utility for advancing thermal error analysis in practical manufacturing settings.
Exploring Vision Transformers and Convolution Neural Networks for the Thermal Image Classification of Volcanic Activity
Authors: Nunnari Giuseppe , Calvari Sonia
Published: February 2025

This paper addresses the classification of images depicting the eruptive activity of Mount Etna that were captured by a network of ground-based thermal cameras. This study aimed to evaluate the performance of Vision Transformers (ViTs), such as the Swin Transformer, compared with Convolutional Neural Networks (CNNs), including AlexNet and ShuffleNet. A dataset of 3000 images, evenly distributed across six classes, was utilized for training and testing. The results indicate that for this specific application, the performance advantage of Vision Transformers over CNNs was marginal, likely due to the nature of the classification task. While the Transformer-based models, like the Swin Transformer, demonstrated a slightly improved accuracy for certain complex classes, the CNN-based models, such as AlexNet and ShuffleNet, exhibited superior computational efficiency, particularly in terms of the classification speed. These findings highlight the suitability of CNNs for real-time volcanic activity monitoring. Additionally, this paper provides a comprehensive review of the various CNN and Vision Transformer architectures, offering insights into their strengths and limitations in the context of volcanic activity classification.
Design and Implementation of an Indoor Localization System Based on RSSI in IEEE 802.11ax
Authors: Gaona Juárez Roberto , García-Barrientos Abel , Acosta-Elias Jesus , Stevens-Navarro Enrique , Galván César G. , Palavicini Alessio , Monroy Cruz Ernesto
Published: February 2025

This article describes the design, implementation, and evaluation of an indoor localization system based on Received Signal Strength Indicator (RSSI) measurements in wireless sensor networks. While the majority of the literature uses the IEEE 802.15 standard for this type of system, all of the measurements in this study were performed using a test bench operating under the IEEE 802.11ax standard in the 2.4 GHz band. RSSI is widely used due to its simplicity and availability; however, its accuracy is limited by signal attenuation, electromagnetic interference, and environmental variability. To mitigate these limitations, the present work proposes the implementation of advanced techniques, including weighted averages and positioning algorithms such as Min–Max, Maximum Likelihood, and trilateration, aiming to achieve an accuracy of 2 m in controlled conditions. The design also included a specialized test bench to calculate the coordinates and estimate the location of unknown nodes using anchor node positioning. This approach combines the simplicity of RSSI with optimized algorithms, providing a robust and practical solution for indoor localization. The results validate the system’s effectiveness and highlight its potential for future applications in real-world environments, opening new possibilities for optimizing wireless sensor networks and addressing the current challenges in localization systems.
Unlocking the Hidden Microbiome of Food: The Role of Metagenomics in Analyzing Fresh Produce, Poultry, and Meat
Authors: Muñoz-Martinez Tania Isabel , Rodríguez-Hernández Bianca , Rodríguez-Montaño Milagros , Alfau Jessica , Reyes Claudia , Fernandez Yumeris , Ramos Rommel T. , De Los Santos Edian F. Franco , Maroto-Martín Luis Orlando
Published: February 2025

The increasing complexity of food safety concerns, driven by the rising risks of contamination from pathogens, chemical hazards, and environmental influences, has highlighted the need for more effective detection and prevention strategies. Metagenomics, a powerful molecular tool, is transforming the food industry by providing a comprehensive understanding of the microbial communities in fresh produce, poultry, and meat. Advances in microbial detection techniques, such as shotgun sequencing, metabarcoding, and long-read sequencing technologies, have led to faster and more accurate pathogen detection, reducing the risk of outbreaks and ensuring public health protection. Despite its promise, several challenges remain in implementing metagenomics on a broader scale, including the high cost of sequencing technologies, the complexity of analyzing large datasets, and the lack of standardized procedures across platforms. These limitations hinder its adoption, particularly for smaller operations or in regions with fewer resources. This review examines the applications of metagenomics in food safety, focusing on its impact on meat, poultry, and fresh produce, while discussing the obstacles to its widespread use and potential solutions to overcome these barriers.
Framing the Virtual: New Technologies and Immersive Exhibitions
Authors: Lawhead Emily , Mondloch Kate
Published: February 2025

In 2017, the Hirshhorn Museum and Sculpture Garden organized a major retrospective of Kusama Yayoi’s Infinity Mirror Rooms that enjoyed a six-venue tour of North America [...]
Early Detection of Failing Lead-Acid Automotive Batteries Using the Detrended Cross-Correlation Analysis Coefficient
Authors: Murari Thiago B. , Costa Roberto C. da , Pereira Hernane B. de B. , Monteiro Roberto L. S. , Moret Marcelo A.
Published: February 2025

This work introduces a model for lead-acid battery health monitoring in automobiles, focusing on detecting degradation before complete failure. With the proliferation of electronic modules and increasing power demands in vehicles, along with enhanced sensor data availability, this study aims to investigate battery lifespan. Dead batteries often lead to customer dissatisfaction and additional expenses due to inadequate diagnosis. This study seeks to enhance predictive diagnostics and provide drivers with timely warnings about battery health. The proposed method employs the Detrended Cross-Correlation Analysis Coefficient for end-of-life detection by analyzing the cross-correlation of voltage signals from batteries in different states of health. The results demonstrate that batteries with a good state of health exhibit a coefficient consistently within the statistically significant cross-correlation zone across all time scales, indicating a strong correlation with reference batteries over extended time scales. In contrast, batteries with a deteriorated state of health compute a coefficient below 0.3, often falling within the non-significant cross-correlation zone, confirming a clear decline in correlation. The method effectively distinguishes batteries nearing the end of their useful life, offering a low-computational-cost alternative for real-time battery monitoring in automotive applications.
The Sensitivity of Heatwave Climatology to Input Gridded Datasets: A Case Study of Ukraine
Authors: Skrynyk Oleg , Aguilar Enric , Cimolai Caterina
Published: February 2025

In this research, based on a case study of Ukraine, we (1) examined the sensitivity of heatwave (HW) climatology to input gridded data and (2) statistically compared HW metrics (such as duration, intensity, etc.) calculated from the gridded data against similar results derived from high-quality station time series. For the first task, we used a mini statistical ensemble of gridded datasets of the daily maximum air temperature (TX). The ensemble included the following: ClimUAd and E-OBS (Ukrainian and European observation-based gridded data, respectively), reanalyzes ERA5, ERA5-Land, NOAA-CIRES 20CR V2c and V3, and NCEP-NCAR R1. For the second task, the same gridded data were used along with 178 quality-controlled and homogenized TX station time series from Ukraine. HWs and their metrics were defined according to the approach summarized by Perkins and Alexander (2013). All calculations were performed for the period 1950–2014. Our results showed that, depending on the gridded dataset, the calculated values of HW metrics might differ significantly. Even after averaging over the study period and the territory of Ukraine, the ranges between the max and min values of HW metrics remain large. For instance, the spread in HW number per year may be up to six events. However, the differences in the trend slopes of HW metrics are less pronounced. In addition, the comparison of HW calculations derived using gridded and station data showed that E-OBS, ERA5, and ERA5-Land provide similar verification statistics. The evaluation statistics for 20CRV3 are worse compared to E-OBS, ERA5, and ERA5-Land, but significantly better than for 20CRV2c and NCEP-NCAR R1. Our findings can aid in selecting gridded datasets for calculating reliable HW climatology and, consequently, contribute to developing climate adaptation strategies for extreme temperature events in Ukraine, its neighboring countries, and potentially across Europe.
Indoor Radon Monitoring in Residential Areas in the Vicinity of Coal Mining Operations in the Mpumalanga Province, South Africa
Authors: Moshupya Paballo M. , Mohuba Seeke C. , Abiye Tamiru A. , Korir Ian , Nhleko Sifiso
Published: February 2025

Coal mining and combustion have the potential to increase exposure to radon, a form of radioactive gas recognized as one of the major contributors to lung cancer incidents. In South Africa, coal is used as the primary energy source for producing electricity and for heating, predominantly in informal settlements and township communities. Most of the existing coal-fired power plants are found in the Mpumalanga province. This paper presents long-term radon (222Rn) measurements in dwellings surrounding coal mining centres in the Mpumalanga province and evaluates their contributions to indoor radon exposures. The indoor radon measurements were conducted using solid-state nuclear track detectors and were performed during warm and cold seasons. It was found that the overall indoor radon activity concentrations ranged between 21 Bq/m3 and 145 Bq/m3, with a mean value of 40 Bq/m3. In all the measured dwellings, the levels were below the WHO reference level of 100 Bq/m3 and 300 Bq/m3 reference level recommended by the IAEA and ICRP, with the exception of one dwelling that was poorly ventilated. The results reveal that individuals residing in the surveyed homes are not exposed to radon levels higher than the WHO, ICRP, and IAEA reference levels. The main source influencing indoor radon activity concentrations was found to be primarily the concentration of uranium found in the geological formations in the area, with ventilation being an additional contributing factor of radon levels in dwellings. To maintain good air quality in homes, it is recommended that household occupants should keep their dwellings well ventilated to keep indoor radon levels as low as possible.
Artificial Intelligence-Driven Wireless Sensing for Health Management
Authors: Toruner Merih Deniz , Shi Victoria , Sollee John , Hsu Wen-Chi , Yu Guangdi , Dai Yu-wei , Merlo Christian , Suresh Karthik , Jiao Zhicheng , Wang Xuyu , Mao Shiwen , Bai Harrison
Published: February 2025

(1) Background: With technological advancements, the integration of wireless sensing and artificial intelligence (AI) has significant potential for real-time monitoring and intervention. Wireless sensing devices have been applied to various medical areas for early diagnosis, monitoring, and treatment response. This review focuses on the latest advancements in wireless, AI-incorporated methods applied to clinical medicine. (2) Methods: We conducted a comprehensive search in PubMed, IEEEXplore, Embase, and Scopus for articles that describe AI-incorporated wireless sensing devices for clinical applications. We analyzed the strengths and limitations within their respective medical domains, highlighting the value of wireless sensing in precision medicine, and synthesized the literature to provide areas for future work. (3) Results: We identified 10,691 articles and selected 34 that met our inclusion criteria, focusing on real-world validation of wireless sensing. The findings indicate that these technologies demonstrate significant potential in improving diagnosis, treatment monitoring, and disease prevention. Notably, the use of acoustic signals, channel state information, and radar emerged as leading techniques, showing promising results in detecting physiological changes without invasive procedures. (4) Conclusions: This review highlights the role of wireless sensing in clinical care and suggests a growing trend towards integrating these technologies into routine healthcare, particularly patient monitoring and diagnostic support.
Cetuximab and Paclitaxel Drug Response in Head and Neck Tumor Stem Cells
Authors: Serafim Júnior Vilson , Fernandes Glaucia Maria de Mendonça , Tedeschi Bianca Barbério Bogdan , Cuzziol Caroline Izak , Castanhole-Nunes Márcia Maria Urbanin , Ferreira Letícia Antunes Muniz , Rodrigues Gabriela Helena , de Oliveira-Cucolo Juliana Garcia , Pavarino Érika Cristina , Goloni-Bertollo Eny Maria
Published: February 2025

Head and neck cancer (HNC) is one of the most common types of cancer in the world, characterized by resistance to conventional therapies and an unfavorable prognosis due to the presence of tumor stem cells (TSCs). TSCs are cell subpopulations with high potential for invasion, migration, and metastasis, being responsible for the initiation and dissemination of cancer. This study aimed to evaluate the efficacy of treatments with cetuximab and paclitaxel, alone and in combination, in TSCs from oral cavity (SCC-28) and hypopharynx (FADU) cancer cell lines. In addition, the influence of the gene and protein expression of EGFR, NTRK2 (TRKB), KRAS, and HIF-1α on the response to treatments was investigated. TSCs were identified based on ALDH staining, and cell viability assays (MTS) indicated that both TSCs and non-TSCs showed resistance to cetuximab monotherapy, while paclitaxel, either alone or in combination with cetuximab, was more effective in reducing cell viability. Real-time PCR and Western blot analysis revealed increased expression of KRAS and HIF-1α in TSCs, suggesting their possible association with treatment resistance. The results of this study point to specific molecular factors that influence therapeutic responses in HNC, with an emphasis on the efficacy of drug combinations to overcome TSC resistance. The identification of these molecular mechanisms may provide guidelines for the development of more targeted and effective therapies against HNC, improving clinical management and patient prognoses.
Unveiling the Important Role of Gut Microbiota and Diet in Multiple Sclerosis
Authors: Džidić Krivić Amina , Begagić Emir , Hadžić Semir , Bećirović Amir , Bećirović Emir , Hibić Harisa , Tandir Lihić Lejla , Kadić Vukas Samra , Bečulić Hakija , Kasapović Tarik , Pojskić Mirza
Published: February 2025

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS), characterized by neurodegeneration, axonal damage, demyelination, and inflammation. Recently, gut dysbiosis has been linked to MS and other autoimmune conditions. Namely, gut microbiota has a vital role in regulating immune function by influencing immune cell development, cytokine production, and intestinal barrier integrity. While balanced microbiota fosters immune tolerance, dysbiosis disrupts immune regulation, damages intestinal permeability, and heightens the risk of autoimmune diseases. The critical factor in shaping the gut microbiota and modulating immune response is diet. Research shows that high-fat diets rich in saturated fats are associated with disease progression. Conversely, diets rich in fruits, yogurt, and legumes may lower the risk of MS onset and progression. Specific dietary interventions, such as the Mediterranean diet (MD) and ketogenic diet, have shown potential to reduce inflammation, support neuroprotection, and promote CNS repair. Probiotics, by restoring microbial balance, may also help mitigate immune dysfunction noted in MS. Personalized dietary strategies targeting the gut microbiota hold promise for managing MS by modulating immune responses and slowing disease progression. Optimizing nutrient intake and adopting anti-inflammatory diets could improve disease control and quality of life. Understanding gut-immune interactions is essential for developing tailored nutritional therapies for MS patients.
Hypoglossal Nerve Palsy Following Cervical Spine Surgery—Two Case Reports and a Systematic Review of the Literature
Authors: Hellquist Felicia , El-Hajj Victor Gabriel , Buwaider Ali , Edström Erik , Elmi-Terander Adrian
Published: February 2025

Background/Objectives: Hypoglossal nerve palsy (HNP) is a rare complication after cervical spine surgery and is reported after both anterior and posterior approaches. It often presents with dysarthria, dysphagia, and hoarseness. We present a systematic review of the literature and two cases of patients presenting with confirmed HNP after anterior cervical spine surgery. Methods: Two retrospective case reports and a systematic review of the literature were presented. The electronic databases PubMed and Web of Science were systematically searched from inception. Results: In total, 17 cases of HNP were reported in the literature, including the two hereby presented. Ten cases involved the anterior approach and seven the posterior approach. The reported risk of HNP following cervical spine surgery varied between 0.01% and 2.5% depending on the procedure. The main etiology was mechanical compression of the nerve. Most of the cases recovered within a few months with conservative treatment. In some cases, permanent hypoglossal injury with persistent symptoms was reported. In both of the current cases, the symptoms gradually improved and completely resolved after a few months. Conclusions: HNP is a rare complication after cervical spine surgery. The causes of hypoglossal palsy are multifactorial, but mechanical injury is the most common. A thorough understanding of the nerve’s anatomy is essential to minimize the risk of injury during anesthesia, patient positioning, and surgery. Understanding the underlying mechanisms contributing to HNP post-cervical spine surgery enables the implementation of preventive measures to mitigate its occurrence.
Psychological Fatigue or Satisfaction? The Impact of Online Office App Use on Job Performance: Differentiating Work-Related and Non-Work-Related Contexts
Authors: Zhang Huichuan , Di He , Liu Mingzheng , An Jiaji
Published: February 2025

Media psychological fatigue, a common negative behavior among employees using online office applications (apps), has a significant impact on job satisfaction and performance. This study explores the influence of online office app use on job performance, differentiating between work-related and non-work-related contexts, based on the Uses and Gratifications Theory and the Stimulus–Organism–Response Theory. Data were collected from 418 employees in 11 enterprises in mainland China through random sampling. Harman’s single-factor test was utilized to evaluate common method bias. Regression analysis, bootstrap tests, and structural equation modeling (SEM) were used to analyze variable relationships and mediating effects. The results showed that work-related online office app use did not cause psychological fatigue or satisfaction, non-work-related app use enhanced job performance by improving job satisfaction, media psychological fatigue negatively affected job satisfaction and performance, psychological fatigue partially mediated the relationship between app use and job performance, and job satisfaction was essential for alleviating work-related fatigue and facilitating non-work-related use. This research emphasizes the dual influence of online office app use on employee perceptions and performance. Enterprises should focus on work-related app functions and foster a positive social environment with entertainment elements to encourage non-work-related use, reducing psychological fatigue and enhancing job satisfaction and performance.
Longitudinal CSF Tumor Cell Enumeration and Mutational Analysis as a Driver for Leptomeningeal Disease Management
Authors: Tripathy Arushi , Corkos Pericles , Blouw Barbara , Montgomery Deondra A. , Moore Melissa , Hedrick Marc H. , Youssef Michael , Kumthekar Priya U.
Published: February 2025

Background: Leptomeningeal disease (LMD) is challenging to diagnose and monitor given the poor sensitivity of current gold-standard diagnostics. Cerebrospinal fluid tumor cells (CSF-TCs) have been studied as a biomarker for disease management because oncogene amplification of the primary, metastatic, and CNS metastatic tumors can be heterogeneous. The CNSide platform enumerates CSF-TCs and analyzes oncogene expression via immunocytochemistry (ICC), fluorescent in situ hybridization (FISH), and next-generation sequencing (NGS). We report the utility of this combined enumerative and mutational testing for LMD diagnosis and disease monitoring. Methods: A multicenter, retrospective analysis of commercially ordered assays from two health systems between January 2020 and July 2023 included 613 tests on 218 individual patients with suspected or confirmed LMD. To date, this is the largest cohort of patients in LMD literature evaluated using CSF-TCs. Results: CSF-TCs were detected in 67% (412/613) of samples. The most analyzed cancer types were breast (n = 105) and lung (n = 65). In lung cancer, anaplastic lymphoma kinase (ALK) was detected in 14% (17/118), and c-MET was detected in 61% (78/128). In breast cancer, HER2 was detected in 39% (65/168), and estrogen receptor (ER) was detected in 26% (44/168). Sixty-six patients underwent 2+ longitudinal CSF draws; among these, there were 58 flips in oncogene detection over time, and 30% (20/66) of patients had at least one biomarker change in the CSF. Conclusions: Longitudinal combined ICC/FISH/NGS CSF testing demonstrates a wide range in CSF-TC enumeration, which may be correlated with clinical course, and furthermore identifies actionable tumor markers that frequently fluctuate over time. Utilization of this platform would enable timely, personalized LMD-specific chemotherapy.
Skim Milk Culture of Lactobacillus johnsonii SBT0309 Increases Intestinal Alkaline Phosphatase Activity and Inhibits Lipopolysaccharide-Induced Interleukin-8 Production in Intestinal Epithelial Cells
Authors: Kawano Michio , Arai Toshinobu , Kabuki Toshihide
Published: February 2025

Background/Objectives: Intestinal alkaline phosphatase (IAP) is an enzyme expressed in the intestinal brush border, which may exert anti-inflammatory effects by detoxifying lipopolysaccharides (LPSs), thereby preventing metabolic disorders. Various food components have been reported to influence IAP activity. However, few studies have evaluated the effects of fermented milk on IAP activity. In this study, we aimed to investigate fermented milk with high IAP-activating capacity and investigate its effect. Methods: We screened a skim milk culture (SC), a fermented milk model, using differentiated Caco-2 cells. We investigated the effect of SC on IAP activity and gene expression in the Drosophila midgut. Quantitative PCR and immunoblot assays were conducted to examine gene and protein levels. Results: Among the SC samples from different lactic acid bacteria or bifidobacteria, the SC of Lactobacillus johnsonii SBT0309 (LJ0309 SC) demonstrated a particularly strong capacity to activate IAP in Caco-2 cells, demonstrated by significantly increased IAP gene expression and protein levels in Caco-2 cells. Additionally, LJ0309 SC inhibited increased secretion of IL-8 in LPS-stimulated Caco-2 cells. Finally, in Drosophila melanogaster fed LJ0309 SC, we observed an increase in both IAP activity and gene expression in the midgut. Conclusions: LJ0309 SC increased IAP activity and gene expression in both Caco-2 cells and the Drosophila midgut, and inhibited the inflammatory response in LPS-stimulated Caco-2 cells. Although further in vivo studies are required, LJ0309 SC might help to ameliorate LPS-induced inflammation and disease via IAP activation.
Cardiological Findings in Children and Adolescents Before and After Guanfacine Treatment for Attention Deficit and Hyperactivity Disorder
Authors: Yükcü Bekir , Önal Bedia Sultan , Çobanoğlu Osmanlı Cansu , Tonkaz Gülsüm Yitik , Şahin Berkan
Published: February 2025

Objective: This study evaluates the short-term cardiovascular effects of guanfacine treatment in children and adolescents with attention deficit/hyperactivity disorder (ADHD). The treatment’s impact on novel electrocardiographic parameters was also investigated. Methods: In a retrospective study conducted between January 2023 and June 2024, 37 patients aged 6–18 years with ADHD underwent baseline and follow-up cardiac evaluations including electrocardiography (ECG) and blood pressure measurements. Novel ECG markers (QRS-T angle, QT dispersion, QTc dispersion, Tp-e interval, Tp-e dispersion, Tp-e/QT ratio, and Tp-e/QTc ratio) were analyzed alongside standard parameters such as heart rate, QT, and corrected QT (QTc) intervals. Guanfacine was initiated at 1 mg and titrated weekly until an optimal clinical response was achieved. Results: Guanfacine treatment led to a significant reduction in heart rate (−12.3 bpm; p < 0.001) and P wave axis (−12.3°; p = 0.038) and an increase in QT interval (+16.8 ms; p = 0.014). However, no significant changes were observed in blood pressure, QTc duration, or the novel ECG parameters. Importantly, the absence of any changes in these advanced markers supports the cardiovascular safety of guanfacine. Two patients experienced side effects (bradycardia and hypotension), leading to treatment discontinuation. ANCOVA analysis indicated that the duration between ECG evaluations significantly influenced the QT interval changes, emphasizing the importance of timing when monitoring cardiovascular effects. Conclusions: Guanfacine demonstrated minor, statistically significant effects on the selected cardiac parameters without clinically meaningful changes to or adverse impacts on the novel ECG markers investigated. As extended-release guanfacine has only been available in Türkiye for the management of ADHD for approximately two years, studies evaluating its clinical efficacy and side effects are critical for clinicians working in this field.
Foundations of Pediatric Lifestyle Medicine
Authors: Gaínza-Lein Marina
Published: February 2025

Background: Lifestyle medicine utilizes therapeutic interventions to prevent, treat, and reverse chronic diseases by promoting healthy habits. While extensively studied in adults, its application in pediatrics remains underexplored. Given that many chronic diseases originate in early life, establishing the foundations of Pediatric Lifestyle Medicine is essential. Methods: This paper presents a comprehensive literature review and clinical insights to assess the evidence supporting Pediatric Lifestyle Medicine and provide age-appropriate, evidence-based guidelines for children and adolescents. Results: Pediatric Lifestyle Medicine is an evidence-based healthcare discipline focused on promoting and maintaining children’s health by encouraging healthy habits from an early age. This approach prevents chronic diseases, supports physical and emotional well-being, and fosters long-term quality of life. Findings show that applying its principles in childhood can prevent obesity, improve mental health, and aid in disease management, while also reducing the risk of adult-onset conditions and benefiting planetary health. Pediatric Lifestyle Medicine is based on six pillars: preventive nutrition, physical activity, sleep, stress management, positive social connections, and risk prevention, the last of which includes toxin avoidance and other childhood-specific risks, such as accident prevention and screen overuse. Conclusions: Pediatric Lifestyle Medicine provides a cost-effective, evidence-based framework for improving childhood health and preventing chronic conditions. Integrating these principles into pediatric care can foster lifelong health benefits, emphasizing the need for further research and implementation in medical education.