bluuebunny/crossref_metadata_2025 huggingface dataset:
First-in-Class Colchicine-Based Visible Light Photoswitchable Microtubule Dynamics Disrupting Agent
Authors: Borys Filip , Tobiasz Piotr , Fabczak Hanna , Joachimiak Ewa , Krawczyk Hanna
Published: July 2023

Compounds that disrupt microtubule dynamics, such as colchicine, paclitaxel, or Vinca alkaloids, have been broadly used in biological studies and have found application in clinical anticancer medications. However, their main disadvantage is the lack of specificity towards cancerous cells, leading to severe side effects. In this paper, we report the first synthesis of 12 new visible light photoswitchable colchicine-based microtubule inhibitors AzoCols. Among the obtained compounds, two photoswitches showed light-dependent cytotoxicity in cancerous cell lines (HCT116 and MCF-7). The most promising compound displayed a nearly twofold increase in potency. Moreover, dissimilar inhibition of purified tubulin polymerisation in cell-free assay and light-dependent disruption of microtubule organisation visualised by immunofluorescence imaging sheds light on the mechanism of action as microtubule photoswitchable destabilisers. The presented results provide a foundation towards the synthesis and development of a novel class of photoswitchable colchicine-based microtubule polymerisation inhibitors.
A GSH-Activatable Theranostic Prodrug Based on Photoinduced Electron Transfer for Cancer Fluorescence Imaging and Therapy
Authors: Zhao Xuan , Zeng Xiaolong , Wu Xinyu , Shi Lihong , Zhu Sumin , Sun Wen
Published: July 2023

Traditional chemotherapeutic drugs have limitations due to their non-targeted ability toward cancer cells. Stimuli-activatable prodrugs are designed to overcome these obstacles. However, the real-time monitoring of stimuli-activatable theranostic prodrugs still poses challenges. Herein, a prodrug (Fe–SS–HCy), consisting of a ferrocene-modified hemicyanine linked via a disulfide bond, is synthesized for anticancer imaging and therapy. Before activation, the toxicity of Fe–SS–HCy is low. The fluorescence of Fe–SS–HCy is quenched by ferrocene due to photoinduced electron transfer. After being taken up by cancer cells, the intracellular GSH activates Fe–SS–HCy, which releases HCy. The fluorescence of HCy is restored and selectively accumulates in the mitochondria, which further produce reactive oxygen species (ROS) to induce cancer cell death. Thus, this “off-on” fluorogenic HCy presents a new strategy for monitoring prodrug activation in real-time and for enhancing therapeutic efficacy with reduced side effects.
Conventional Raman, SERS and TERS Studies of DNA Compounds
Authors: Safar Wafa , Azziz Aicha , Edely Mathieu , Lamy de la Chapelle Marc
Published: July 2023

DNA identification is possible by detecting its components through vibrational spectroscopy. Conventional Raman, Surface-enhanced Raman spectroscopy (SERS) and Tip-enhanced Raman spectroscopy (TERS) have shown a high capacity for the exploration of different molecules and materials (semi-conducting material, carbon nanotubes and biologicals molecules as DNA, proteins). Their applications extended to biological systems and brought significant information to this field. This review summarizes a high number of studies and research conducted with conventional Raman, SERS and TERS on every DNA component starting from the four different nucleic acids in their different forms (nucleosides, deoxyribonucleosides, deoxyribonucleotides) to their biological interaction to form one and double DNA strands. As SERS has an advantage on conventional Raman by exploiting the optical properties of metallic nanostructures to detect very small quantities of molecules, it also clarifies the DNA structure’s orientation in addition to its composition. It also clarifies the influence of different parameters, such as the presence of a spacer or a mutation in the strand on the hybridization process. TERS was shown as a relevant tool to scan DNA chemically and to provide information on its sequence.
Passive Oxide Destruction by Dense Low-Energy Radionuclide i-Analyzed by Voltammetry ii-Analyzed by Chaos
Authors: Bellanger Gilbert
Published: July 2023

The destruction of the passive oxide can be caused by the action of a radionuclide, which collides with the surface of the oxide. In this case, the β− nuclear particle produced by the decay of tritiated water is considered for corrosion, and it follows that the β− energy is absorbed first into the oxide. The penetration depth is sufficient for all the passive oxides to be destabilized. Destabilization was examined by voltammetry and by the electrochemical circuit in the passive potential. The corresponding pathway leads to the destruction of oxide. Tests carried out using a chaos data analyzer are an aid for expertise. Different behavior may occur depending on the passive potential and the β− density. The synchronization of phase space spectra and tests realized sector by sector make possible the interpretation of divergence leading to unstable oxide and oxide destruction at different passive potentials and for different β− particle densities.
PTFE-Containing Coating Obtained on Ti by Spraying and PEO Pretreatment
Authors: Mashtalyar Dmitry V. , Pleshkova Arina I. , Piatkova Mariia A. , Nadaraia Konstantine V. , Imshinetskiy Igor M. , Belov Evgeny A. , Suchkov Sergey N. , Sinebryukhov Sergey L. , Gnedenkov Sergey V.
Published: July 2023

In case of using titanium alloys in equipment exposed to aggressive media (for example, sea water), it is necessary to take into account and, first of all, prevent the formation of a galvanic couple of titanium and another metal/alloy, which in most cases leads to the corrosion destruction of the latter. Another significant problem of using titanium is its low wear-resistance and poor tribological characteristics. To impart the necessary properties to titanium and its alloys, a composite coating was formed on the top of titanium. For the coating formation, a combination of the plasma electrolytic treatment and polymer spraying was used. The SEM, EDS and XRD analyses established morphological features, elemental and phase composition of the composite coatings. Contact angles and the wettability parameters of the composite coatings were investigated. An analysis of the data obtained showed that composite coatings have better protective properties than untreated material and base PEO coatings.
A Review of Flexible Acceleration Sensors Based on Piezoelectric Materials: Performance Characterization, Parametric Analysis, Frontier Technologies, and Applications
Authors: Liao Yaoyao , Yang Hong , Liao Qingwei , Si Wei , Chu Yu , Chu Xiangcheng , Qin Lei
Published: July 2023

Acceleration sensors are tools for detecting acceleration and serve purposes like fault monitoring and behavior recognition. It is extensively employed in a variety of industries, including aerospace, artificial intelligence, biology, and many more. Among these, one of the major research hotspots and challenges is the development of low-energy, self-powered, miniature, mass-produced sensors. Due to its capacity to perceive human behavior and identify errors, the flexible acceleration sensor offers a distinct advantage in the use of flexible and miniaturized sensing systems. This review analyzes the current state of piezoelectric flexible acceleration sensors’ applications in the areas of sensitive materials, processing technology, and device structure and briefly summarizes the fundamental properties of these sensors. Additionally, it ends with a prognosis for the future growth of flexible piezoelectric acceleration sensors.
Structural and Thermal Stability of CrZrON Coatings Synthesized via Reactive Magnetron Sputtering
Authors: Kim Sung-Min , Lee Sang-Yul
Published: July 2023

This research manuscript investigates the structural and thermal stability of CrZrON coatings synthesized through reactive magnetron sputtering. The coatings were deposited at different temperatures with 120 °C and 400 °C, and with varying oxygen-to-reactive gas ratios in the range of 8.3% to 25.7%. The average chemical composition, crystallographic orientation, microstructure, lattice parameter, crystallite size, and hardness of the coatings were evaluated. The results revealed that the coatings deposited at a lower temperature of 120 °C exhibited a columnar structure, while those deposited at a higher temperature of 400 °C showed a transition towards a featureless or amorphous structure. The lattice parameter and crystallite size were influenced by the deposition temperature and oxygen ratio, indicating the incorporation of oxygen into the coatings. Hardness measurements demonstrated that the coatings’ hardness decreased from 33.7 GPa to 28.6 GPa for a process temperature of 120 °C and from 32.1 GPa to 25.7 GPa for 400 °C with an increase in the oxygen ratio, primarily due to the formation of oxygen-rich compounds or oxides. Additionally, annealing experiments indicated that the coatings with featureless or amorphous structures exhibited improved thermal stability, as they maintained their structural integrity without delamination even at high annealing temperatures.
Quasi Non-Destructive Quality Assessment of Thermally Sprayed AISI 316L Coatings Using Polarization Measurements in 3.5% NaCl Gel Electrolyte
Authors: Grimm Maximilian , Kutschmann Pia , Pluta Christian , Schwabe Olga , Lindner Thomas , Lampke Thomas
Published: July 2023

There is currently a lack of suitable methods of non-destructive quality assessment of thermally sprayed coatings. Therefore, this study investigates the suitability of polarization measurements that are adapted to the special needs of thermally sprayed coatings for non-destructive quality testing. For this purpose, a gel electrolyte containing 3.5% NaCl and a measuring cell based on the three-electrode arrangement were developed to prevent the corrosion medium from infiltrating the typical microstructure of thermally sprayed coatings (pores and microcracks). The newly developed method was evaluated on AISI 316L coatings deposited by high velocity air fuel (HVAF) and atmospheric plasma spraying (APS). The polarization curves showed significant differences as a result of spraying process-related changes in the coating microstructure. Even slight differences in oxide content within the AISI 316L coating produced by APS can be detected by the new method. In order to verify the new findings, the coatings were analyzed regarding their microstructure by optical microscope, scanning electron microscope and energy dispersive X-ray spectroscopy. The measuring cell and gel electrolyte developed offer a promising opportunity to evaluate the quality of thermally sprayed coatings in a largely non-destructive manner using polarization curves.
Developing a Sustainable Online Platform for Language Learning across Europe
Authors: Mikroyannidis Alexander , Perifanou Maria , Economides Anastasios A.
Published: July 2023

In this paper, we present a sustainable approach for addressing the language skills gap among EU citizens, which significantly hinders their mobility across the EU and their participation in education, in training, as well as in youth programmes. Our approach is based on the sustainable design of the OpenLang Network platform, which provides an open and collaborative online learning environment for language learners and teachers across Europe, and addresses the limitations of existing computer-assisted language learning approaches. The OpenLang Network platform is bringing together educators and Erasmus+ mobility participants to improve their language skills and cultural knowledge. To this end, the OpenLang Network platform offers a collection of multilingual Open Educational Resources and language learning services. The paper presents the results from the user evaluation of the platform, which has been conducted with members of its community of language teachers and learners. A mixed methods approach has been adopted in order to collect and analyse both qualitative and quantitative data from users about the sustainable design of the OpenLang Network platform, as well as to measure the user satisfaction levels of the platform’s language learning services. According to the user evaluation results, the platform offers a sustainable online environment and a positive user experience for language learning. The user evaluation has also helped us identify a set of best practices and challenges associated with the long-term sustainability of an online language learning community.
A Deep Learning Network with Aggregation Residual Transformation for Human Activity Recognition Using Inertial and Stretch Sensors
Authors: Mekruksavanich Sakorn , Jitpattanakul Anuchit
Published: July 2023

With the rise of artificial intelligence, sensor-based human activity recognition (S-HAR) is increasingly being employed in healthcare monitoring for the elderly, fitness tracking, and patient rehabilitation using smart devices. Inertial sensors have been commonly used for S-HAR, but wearable devices have been demanding more comfort and flexibility in recent years. Consequently, there has been an effort to incorporate stretch sensors into S-HAR with the advancement of flexible electronics technology. This paper presents a deep learning network model, utilizing aggregation residual transformation, that can efficiently extract spatial–temporal features and perform activity classification. The efficacy of the suggested model was assessed using the w-HAR dataset, which included both inertial and stretch sensor data. This dataset was used to train and test five fundamental deep learning models (CNN, LSTM, BiLSTM, GRU, and BiGRU), along with the proposed model. The primary objective of the w-HAR investigations was to determine the feasibility of utilizing stretch sensors for recognizing human actions. Additionally, this study aimed to explore the effectiveness of combining data from both inertial and stretch sensors in S-HAR. The results clearly demonstrate the effectiveness of the proposed approach in enhancing HAR using inertial and stretch sensors. The deep learning model we presented achieved an impressive accuracy of 97.68%. Notably, our method outperformed existing approaches and demonstrated excellent generalization capabilities.
Microstructure and Properties of Semi-Solid 7075 Aluminum Alloy Processed with an Enclosed Cooling Slope Channel
Authors: Li Zhaoqiang , Li Yongkun , Zhou Rongfeng , Xie Lingzhi , Wang Qiansi , Zhang Lingzhi , Ji Qiang , Xu Bin
Published: July 2023

In this study, an enclosed cooling slope channel (ECSC) was used to produce a semi-solid slurry of the 7075 aluminum alloy. The effects of the pouring temperature and the rate of cooling water on the microstructure of the semi-solid slurry were studied. The microstructure, solidification behavior, mechanical properties, and fracture mechanism of rheological squeeze casting (Rheo-SC) and liquid squeeze casting (LSC) samples were compared. The results indicate that lowering the pouring temperature and increasing the rate of cooling water can refine the crystals of the semi-solid slurry. The best process is a pouring temperature of 670 °C and a rate of cooling water of 200 L/h. The microstructure of the LSC samples was made up of coarse dendritic crystals, but the microstructure of the Rheo-SC samples was made up of almost spherical primary α1-Al and refined secondary α2-Al under this method. The ultimate tensile strength, yield strength, and elongation of the Rheo-SC samples were 238 MPa, 151 MPa, and 5.2%, respectively, which were 10%, 10.5%, and 44.4% higher than those of the LSC sample. The key factor contributing to the increased performance of the Rheo-SC samples is the combination of decreased casting flaws, strengthened grain refinement, and improved segregation.
Investigation of the Microstructure and Mechanical Properties in Friction Stir Welded Dissimilar Aluminium Alloy Joints via Sampling Direction
Authors: Mabuwa Sipokazi , Msomi Velaphi
Published: July 2023

This research study investigates the influence of sampling direction on the microstructure and mechanical properties of dissimilar joints formed by friction stir welding (FSW). The specimens were cut in two directions: perpendicular (transverse) and parallel (longitudinal) to the FSW joint. The tests conducted included X-ray diffraction (XRD), macrostructure, microstructure, tensile, microhardness, and fractography analysis. Different phases were noted in the XRD patterns and explained, with the aluminum phase being the dominating one. The results further showed that the transverse dissimilar joint exhibited higher microhardness compared to the longitudinal dissimilar joint, which is consistent with the respective grain sizes. Moreover, the ultimate tensile strength of the longitudinal joint exceeded that of the transverse joints, showing a substantial 47% increase. Similarly, the elongation of the joints followed a similar trend, with the longitudinal joint displaying a significant 41% increase in elongation compared to the transverse joint. Fractographic analysis revealed ductile fracture behaviour in all joints.
The Use of Contrast-Enhanced Sonography for Therapy Monitoring of Metastatic Lymph Nodes: A Systematic Review
Authors: Rink Maximilian , Jung Ernst-Michael , Künzel Julian
Published: July 2023

Metastatic cervical lymph nodes are a frequent finding in head and neck squamous cell carcinoma (HNSCC). If a non-surgical approach is primarily chosen, a therapy response evaluation of the primary tumor and the affected lymph nodes is necessary in the follow-up. Supplementary contrast-enhanced ultrasound (CEUS) can be used to precisely visualize the microcirculation of the target lesion in the neck, whereby malignant and benign findings differ in their uptake behavior. The same applies to many other solid tumors. For various tumor entities, it has already been shown that therapy monitoring is possible through regular contrast-enhanced sonography of the primary tumor or the affected lymph nodes. Thus, in some cases, maybe in the future, a change in therapy strategy can be achieved at an early stage in the case of non-response or, in the case of therapy success, a de-escalation of subsequent (surgical) measures can be achieved. In this paper, a systematic review of the available studies and a discussion of the potential of therapy monitoring by means of CEUS in HNSCC are presented.
Ethnobotanical Research on Dye Plants Used by the Baiyi Indigenous Peoples’ from Heqing County, Dali, Yunnan, China
Authors: Yang Rong , Pei Shengji , Xie Yuying , Yan Xiuxiang , Inta Angkhana , Yang Lixin
Published: July 2023

With the advantage of being eco-friendly, plant dyes have been noticed by textile practitioners and the public. However, as a result of the rapid advancements in industrial manufacturing, the traditional knowledge of plant dyes is dying, which demands heightened attention and protection. To document this traditional knowledge in the Baiyi community, semi-structured interviews were conducted with 288 informants from four villages (Five stars village, Hedong village, Nanpo village, and Shang’ eping village) of the Liuhe Yi Nationality Township, Heqing County, Dali Prefecture. Based on the ethnobotanical investigation of plant dyes, there were 11 plant species from 11 genera in 10 families that have been used as dyes. The Baiyi Indigenous peoples mainly extract the dye from the roots, leaves, fruits, etc., of the herbaceous plants. Through quantitative analysis, the frequency of use (f value) and cultural importance indices (CI value) of Viburnum cylindricum are the highest. The optimal conditions for dyeing cotton fabric with V. cylindricum were found to be a pH of 5, dyeing for 30 min at 60 °C by an orthogonal array design. As for the dyeing properties, biomordants provided better properties when used in dyeing cotton fabric with V. cylindricum compared with metallic mordants. This study reveals the great potential of the application of plant dyes in the Baiyi Indigenous peoples community; it will be beneficial to the economic development of ethnic areas, the inheritance of ethnic culture and the protection of biodiversity.
Response of the Coastal Phytoplankton Community to the Runoff from Small Rivers in the Northeastern Black Sea
Authors: Sergeeva Valentina M. , Mosharov Sergey A. , Shulga Natalia A. , Kremenetskiy Viacheslav V. , Khlebopashev Pavel V. , Matorin Dmitry N.
Published: July 2023

River runoff is an important source of nutrients as well as suspended and dissolved organic matter that in coastal zones and on the shelf are transformed due to local production cycles. River runoff affects the hydrological regime, salinity, temperature, and irradiance in river–seawater mixing zone. Our study focuses on the response of phytoplankton to the impact of small Caucasian rivers in the Northeastern (NE) Black Sea, as one of the most sensitive components of marine ecosystems with respect to the changes in abiotic factors. The leading role of marine species of diatoms, dinoflagellates, and coccolithophores in the structure and functioning when impacted by runoff from small rivers is demonstrated in comparison to the freshwater community. Variability of the taxonomic composition and quantitative and productive characteristics of marine phytoplankton communities impacted by small rivers were comparable to or exceed the seasonal and interannual variability on the NE Black Sea shelf. This indicates the significant role of runoff from small Caucasian rivers in maintaining of a high production level of phytoplankton overall and of the coccolithophore Emiliania huxleyi in particular in the coastal zone.
Survivors from a Pliocene Climatic Catastrophe: Gyrodactylus (Platyhelminthes, Monogenea) Parasites of the Relict Fishes in the Central Asian Internal Drainage Basin of Mongolia
Authors: Lebedeva Daria , Ziętara Marek , Mendsaikhan Bud , Ermolenko Alexey , Lumme Jaakko
Published: July 2023

We investigated the Gyrodactylus ectoparasites on relict fishes in the isolated endorheic Central Asian Internal drainage basin in Mongolia (The Hollow) and placed them into the global phylogenetic framework based on internal transcribed spacer regions of the nuclear ribosomal DNA (ITS). Much of the rich Pliocene lacustrine ichthyofauna is extinct. We sampled five riverine survivors: Altai osmans Oreoleuciscus humilis and O. potanini (Leuciscidae), Mongolian grayling Thymallus brevirostris (Salmonidae), and stone loaches Barbatula conilobus and B. cobdonensis (Nemacheilidae). We found eight species of the subgenus Gyrodactylus (Limnonephrotus) and four of G. (Gyrodactylus). Nine species were identified as taxa described earlier, and three were described as new. The endemic Mongolian grayling carried four species, only one of wageneri group typical to salmonids (Gyrodactylus radimi sp. nov.), two of nemachili group (G. zavkhanensis sp. nov., G. pseudonemachili Ergens and Bychowsky, 1967), and G. amurensis Akhmerov, 1952 of subgenus G. (Gyrodactylus). G. pseudonemachili was also found on osman and loach. A parasite clade typical for Nemacheilidae was overrepresented by five species (G. tayshirensis sp. nov. on Barbatula conilobus, G. mongolicus Ergens and Dulmaa, 1970, G. nemachili Bychowsky, 1936). Relaxed host specificity mentioned already by Ergens and Dulmaa was evident. In the updated global ITS phylogenies of the two freshwater-restricted subgenera, the parasites from the Mongolian relict populations assumed positions concordant with a hypothesis of multiple ancient introductions from the Euro-Siberian fauna, strong rarefaction and three cases of endemic divergence.
Digital Engineering Methods in Practical Use during Mechatronic Design Processes
Authors: Gerschütz Benjamin , Sauer Christopher , Kormann Andreas , Nicklas Simon J. , Goetz Stefan , Roppel Matthias , Tremmel Stephan , Paetzold-Byhain Kristin , Wartzack Sandro
Published: July 2023

This work aims to evaluate the current state of research on the use of artificial intelligence, deep learning, digitalization, and Data Mining in product development, mainly in the mechanical and mechatronic domain. These methods, collectively referred to as “digital engineering”, have the potential to disrupt the way products are developed and improve the efficiency of the product development process. However, their integration into current product development processes is not yet widespread. This work presents a novel consolidated view of the current state of research on digital engineering in product development by a literature review. This includes discussing the methods of digital engineering, introducing a product development process, and presenting results classified by their individual area of application. The work concludes with an evaluation of the literature analysis results and a discussion of future research potentials.
Paediatric Spitzoid Neoplasms: 10-Year Retrospective Study Characterizing Histological, Clinical, Dermoscopic Presentation and FISH Test Results
Authors: Herzum Astrid , Occella Corrado , Vellone Valerio Gaetano , Gariazzo Lodovica , Pastorino Carlotta , Ferro Jacopo , Sementa Angela , Mazzocco Katia , Vercellino Nadia , Viglizzo Gianmaria
Published: July 2023

Introduction: Spitzoid lesions are a wide tumour class comprising Spitz nevus (SN), atypical Spitz tumour (AST) and Spitz melanoma (SM). Materials and Methods: We conducted a single-centre-based retrospective survey on all histologically diagnosed spitzoid lesions of paediatric patients (1–18 years) of the last 10 years (2012–2022). Histopathological reports and electronic records of patients were used to retrieve relevant data regarding patients’ features, clinical and dermatoscopical aspects of lesions when recorded, and FISH tests when present. Results: Of 255 lesions, 82% were histologically benign, 17% atypical, 1% malignant. Clinically, 100% of SM were large (≥6 mm) and raised; AST were mainly large (63%), raised (98%), pink (95%). Small (≤5 mm), pigmented, flat lesions correlated with benign histology (respectively 90%, 97%, 98% SN) (p < 0.0001). Dermatoscopical patterns were analysed in 100 patients: starburst pattern correlated with benign histology (26% SN (p = 0.004)), while multicomponent pattern correlated with atypical/malignant lesions (56% AST, 50% SM (p = 0.0052)). Eighty-five lesions were subjected to fluorescence in situ hybridization (FISH): 34 (71% AST; 29% SN) were FISH-positive; 51 (63% SN; 37% AST) were FISH-negative (p = 0.0038). Discussion: This study confirmed predominant benign histology (82%) of paediatric spitzoid lesions, thus detecting 17% AST and 1% SM, highlighting the need for caution in handling spitzoid lesions. Conclusion: Until AST are considered potentially malignant proliferations and no reliable criteria are identified to distinguish them, the authors suggest a prudent approach, especially in children.
The Impact of Heart Rate Variability Monitoring on Preventing Severe Cardiovascular Events
Authors: Turcu Ana-Maria , Ilie Adina Carmen , Ștefăniu Ramona , Țăranu Sabinne Marie , Sandu Ioana Alexandra , Alexa-Stratulat Teodora , Pîslaru Anca Iuliana , Alexa Ioana Dana
Published: July 2023

The increase in the incidence of cardiovascular diseases worldwide raises concerns about the urgent need to increase definite measures for the self-determination of different parameters, especially those defining cardiac function. Heart rate variability (HRV) is a non-invasive method used to evaluate autonomic nervous system modulation on the cardiac sinus node, thus describing the oscillations between consecutive electrocardiogram R-R intervals. These fluctuations are undetectable except when using specialized devices, with ECG Holter monitoring considered the gold standard. HRV is considered an independent biomarker for measuring cardiovascular risk and for screening the occurrence of both acute and chronic heart diseases. Also, it can be an important predictive factor of frailty or neurocognitive disorders, like anxiety and depression. An increased HRV is correlated with rest, exercise, and good recovery, while a decreased HRV is an effect of stress or illness. Until now, ECG Holter monitoring has been considered the gold standard for determining HRV, but the recent decade has led to an accelerated development of technology using numerous devices that were created specifically for the pre-hospital self-monitoring of health statuses. The new generation of devices is based on the use of photoplethysmography, which involves the determination of blood changes at the level of blood vessels. These devices provide additional information about heart rate (HR), blood pressure (BP), peripheral oxygen saturation (SpO2), step counting, physical activity, and sleep monitoring. The most common devices that have this technique are smartwatches (used on a large scale) and chest strap monitors. Therefore, the use of technology and the self-monitoring of heart rate and heart rate variability can be an important first step in screening cardiovascular pathology and reducing the pressure on medical services in a hospital. The use of telemedicine can be an alternative, especially among elderly patients who are associated with walking disorders, frailty, or neurocognitive disorders.
AM3F-FlowNet: Attention-Based Multi-Scale Multi-Branch Flow Network
Authors: Fu Chenghao , Yang Wenzhong , Chen Danny , Wei Fuyuan
Published: July 2023

Micro-expressions are the small, brief facial expression changes that humans momentarily show during emotional experiences, and their data annotation is complicated, which leads to the scarcity of micro-expression data. To extract salient and distinguishing features from a limited dataset, we propose an attention-based multi-scale, multi-modal, multi-branch flow network to thoroughly learn the motion information of micro-expressions by exploiting the attention mechanism and the complementary properties between different optical flow information. First, we extract optical flow information (horizontal optical flow, vertical optical flow, and optical strain) based on the onset and apex frames of micro-expression videos, and each branch learns one kind of optical flow information separately. Second, we propose a multi-scale fusion module to extract more prosperous and more stable feature expressions using spatial attention to focus on locally important information at each scale. Then, we design a multi-optical flow feature reweighting module to adaptively select features for each optical flow separately by channel attention. Finally, to better integrate the information of the three branches and to alleviate the problem of uneven distribution of micro-expression samples, we introduce a logarithmically adjusted prior knowledge weighting loss. This loss function weights the prediction scores of samples from different categories to mitigate the negative impact of category imbalance during the classification process. The effectiveness of the proposed model is demonstrated through extensive experiments and feature visualization on three benchmark datasets (CASMEII, SAMM, and SMIC), and its performance is comparable to that of state-of-the-art methods.
Fair Max–Min Diversity Maximization in Streaming and Sliding-Window Models
Authors: Wang Yanhao , Fabbri Francesco , Mathioudakis Michael , Li Jia
Published: July 2023

Diversity maximization is a fundamental problem with broad applications in data summarization, web search, and recommender systems. Given a set X of n elements, the problem asks for a subset S of k≪n elements with maximum diversity, as quantified by the dissimilarities among the elements in S. In this paper, we study diversity maximization with fairness constraints in streaming and sliding-window models. Specifically, we focus on the max–min diversity maximization problem, which selects a subset S that maximizes the minimum distance (dissimilarity) between any pair of distinct elements within it. Assuming that the set X is partitioned into m disjoint groups by a specific sensitive attribute, e.g., sex or race, ensuring fairness requires that the selected subset S contains ki elements from each group i∈[m]. Although diversity maximization has been extensively studied, existing algorithms for fair max–min diversity maximization are inefficient for data streams. To address the problem, we first design efficient approximation algorithms for this problem in the (insert-only) streaming model, where data arrive one element at a time, and a solution should be computed based on the elements observed in one pass. Furthermore, we propose approximation algorithms for this problem in the sliding-window model, where only the latest w elements in the stream are considered for computation to capture the recency of the data. Experimental results on real-world and synthetic datasets show that our algorithms provide solutions of comparable quality to the state-of-the-art offline algorithms while running several orders of magnitude faster in the streaming and sliding-window settings.
The Degradation and Aging of Biological Systems as a Process of Information Loss and Entropy Increase
Authors: Aristov Vladimir V. , Karnaukhov Alexey V. , Buchelnikov Anatoly S. , Levchenko Vladimir F. , Nechipurenko Yury D.
Published: July 2023

The problem of the degradation and aging of bioorganisms is herein considered from the viewpoint of statistical physics. Two typical timescales in biological systems—the time of metabolic processes and the time of the life cycle—are used. A kinetic equation describing the small timescales of the systems’ characteristic processes in is proposed. Maintaining a biosystem in a time-stable state requires a constant inflow of negative entropy (negentropy). Ratios are proposed to evaluate the aging and degradation of systems in terms of entropy. As an example, the aging of the epithelium is studied. The connection of our approach to the information theory of aging is discussed, as well as theoretical constructions related to the concept of cooperon and its changing with time.
Illegal Community Detection in Bitcoin Transaction Networks
Authors: Kamuhanda Dany , Cui Mengtian , Tessone Claudio J.
Published: July 2023

Community detection is widely used in social networks to uncover groups of related vertices (nodes). In cryptocurrency transaction networks, community detection can help identify users that are most related to known illegal users. However, there are challenges in applying community detection in cryptocurrency transaction networks: (1) the use of pseudonymous addresses that are not directly linked to personal information make it difficult to interpret the detected communities; (2) on Bitcoin, a user usually owns multiple Bitcoin addresses, and nodes in transaction networks do not always represent users. Existing works on cluster analysis on Bitcoin transaction networks focus on addressing the later using different heuristics to cluster addresses that are controlled by the same user. This research focuses on illegal community detection containing one or more illegal Bitcoin addresses. We first investigate the structure of Bitcoin transaction networks and suitable community detection methods, then collect a set of illegal addresses and use them to label the detected communities. The results show that 0.06% of communities from daily transaction networks contain one or more illegal addresses when 2,313,344 illegal addresses are used to label the communities. The results also show that distance-based clustering methods and other methods depending on them, such as network representation learning, are not suitable for Bitcoin transaction networks while community quality optimization and label-propagation-based methods are the most suitable.
Management Discourse Analysis of High- and Low-Efficacy Schools: A Comparative Study of Factors Influencing School Performance
Authors: García-Jiménez Jesús , Lucas-Oliva Inés , Rodríguez-Santero Javier , Torres-Gordillo Juan-Jesús
Published: July 2023

Offering an efficient, egalitarian, and quality education is an agreed-upon goal in society that aims to guarantee upwards social mobility. For this reason, the objectives of this article are to determine how Andalusian primary schools with high and low efficiency rate their own performance, and to study in depth the factors that favour or hinder academic performance in these schools. To this end, 50 interviews were conducted with school management teams based on the ESCALA test scores. Analysis of the informants’ discourse on educational performance shows that high-performing schools are evaluated in terms of learning standards, while low-performing schools are compared with other schools that are considered similar. It is concluded that low-performing schools perform much more poorly than high-performing schools, failing to provide quality and equal education to the whole school population and perpetuating social divides. Additionally, both types of schools overlook intrinsic factors that have a negative impact on academic performance. It is necessary for them to adopt a self-critical attitude that allows them to identify room for improvement and demand necessary support.
Preservice Special Education Teachers’ Perceptions of Field Experience with English-Language Learner Students
Authors: Adams David L. , Hord Casey
Published: July 2023

In the United States, students are increasingly diverse in their academic and linguistic abilities. The specialization of teaching professionals has been the primary method used in order to respond to this increase. Programs for these professionals have largely been siloed along the dichotomous groupings of students identified in law, namely, disabled/nondisabled or English proficient/nonproficient. However, the reality of students is much more complex and interdisciplinary approaches present an opportunity for meeting the realistic needs of all students. This study used a one-time, open-ended survey to gather the perceptions of four preservice special education teachers who worked collaboratively with students identified as English-Language Learners (ELL) and their teacher. These perceptions were analyzed in light of recent literature calling for increased collaboration among professionals in special and bilingual education.
Security and Privacy Issues in Software-Defined Networking (SDN): A Systematic Literature Review
Authors: Farooq Muhammad Shoaib , Riaz Shamyla , Alvi Atif
Published: July 2023

Software-defined network (SDNs) have fundamentally changed network infrastructure by decoupling the data plane and the control plane. This architectural shift rejuvenates the network layer by granting the re-programmability and centralized management of networks which brings about exciting challenges. Although an SDN seems to be a secured network when compared to conventional networks, it is still vulnerable and faces rigorous deployment challenges. Moreover, the bifurcation of data and control planes also opens up new security problems. This systematic literature review (SLR) has formalized the problem by identifying the potential attack scenarios and highlighting the possible vulnerabilities. Eighty-six articles have been selected carefully to formulize the SLR. In this SLR, we have identified major security attacks on SDN planes, including the application plane, control plane, and data plane. Moreover, this research also identifies the approaches used by industry experts and researchers to develop security solutions for SDN planes. In this research, we have introduced an attack taxonomy and proposed a collaborative security model after comprehensively identifying security attacks on SDN planes. Lastly, research gaps, challenges, and future directions are discussed for the deployment of secure SDNs.
Discrete-Time Incremental Backstepping Control with Extended Kalman Filter for UAVs
Authors: Liu Yanju , Duan Chengyu , Liu Lei , Cao Lijia
Published: July 2023

In this study, a discrete-time incremental backstepping (DTIBS) controller with an extended Kalman filter (EKF) is proposed for unmanned aerial vehicles (UAVs) with unknown actuator dynamics. The Taylor series and an approximate discrete method are employed, transforming the second-order continuous-time nonlinear system into a discrete-time nonlinear plant with an incremental input form. The incremental control laws are designed using the incremental nonlinear dynamic inversion (INDI) method and the time-delay control (TDC) method. The TDC is introduced to design the control law, eliminating the need for prior knowledge of the control effectiveness matrix involving some unknown aerodynamic coefficients. In addition, the airflow angle and body rotation rate are selected as key system states, and the EKF is used to design a state estimator to estimate the local state of the small unmanned aerial vehicle closed-loop flight control system under strong noise conditions. The effectiveness of the DTIBS control method with EKF is verified through numerical simulation. The results show that the proposed method can effectively estimate the state under the typical noise characteristics of low-cost sensors, and the closed-loop control systems has good tracking performance and can quickly and effectively track sudden commands.
Application of Auto-Regulative Sparse Variational Mode Decomposition in Mechanical Fault Diagnosis
Authors: Li Huipeng , Zhou Fengxing , Xu Bo , Yan Baokang , Zhou Fengqi
Published: July 2023

The variational mode decomposition (VMD) method has been widely applied in the field of mechanical fault diagnosis as an excellent non-recursive signal processing tool. The performance of VMD depends on its inherent prior parameters. Searching for the key parameters of VMD using intelligent optimization algorithms poses challenges for the internal essence and fitness function selection of intelligent optimization algorithm. Moreover, the computational complexity of optimization is high. Meanwhile, such methods are not competitive in evaluating orthogonality between intrinsic mode functions and the reconstruction error of the signal as a joint indictor for the termination of decomposition. Therefore, this paper proposes a new auto-regulative sparse variational mode decomposition method (ASparse–VMD) to achieve accurate feature extraction. The regularization term of the VMD handles sparsification by constructing an L2-norm with a damping coefficient ε, and mode number K is set adaptively in a recursive manner to ensure appropriateness. The penalty parameter α is dynamically selected according to the number of modes and sampling frequency. The update step τ of the VMD algorithm is set using the signal-to-noise ratio to ensure the singleness and orthogonality of the modal components and suppress mode aliasing. The experimental results of the simulation signal and measured signal demonstrate the effectiveness of the proposed strategies for improving the inherent defects of VMD. Extensive comparisons with state-of-the-art methods show that the proposed algorithm is more effective and practical for hybrid feature extraction in mechanical faults.
Car Price Quotes Driven by Data-Comprehensive Predictions Grounded in Deep Learning Techniques
Authors: Dutulescu Andreea , Catruna Andy , Ruseti Stefan , Iorga Denis , Ghita Vladimir , Neagu Laurentiu-Marian , Dascalu Mihai
Published: July 2023

The used car market has a high global economic importance, with more than 35 million cars sold yearly. Accurately predicting prices is a crucial task for both buyers and sellers to facilitate informed decisions in terms of opportunities or potential problems. Although various machine learning techniques have been applied to create robust prediction models, a comprehensive approach has yet to be studied. This research introduced two datasets from different markets, one with over 300,000 entries from Germany to serve as a training basis for deep prediction models and a second dataset from Romania containing more than 15,000 car quotes used mainly to observe local traits. As such, we included extensive cross-market analyses by comparing the emerging Romanian market versus one of the world’s largest and most developed car markets, Germany. Our study used several neural network architectures that captured complex relationships between car model features, individual add-ons, and visual features to predict used car prices accurately. Our models achieved a high R2 score exceeding 0.95 on both datasets, indicating their effectiveness in estimating used car prices. Moreover, we experimented with advanced convolutional architectures to predict car prices based solely on visual features extracted from car images. This approach exhibited transfer-learning capabilities, leading to improved prediction accuracy, especially since the Romanian training dataset was limited. Our experiments highlighted the most important factors influencing the price, while our findings have practical implications for buyers and sellers in assessing the value of vehicles. At the same time, the insights gained from this study enable informed decision making and provide valuable guidance in the used car market.
Transformer-Based Global PointPillars 3D Object Detection Method
Authors: Zhang Lin , Meng Hua , Yan Yunbing , Xu Xiaowei
Published: July 2023

The PointPillars algorithm can detect vehicles, pedestrians, and cyclists on the road, and is widely used in the field of environmental awareness in autonomous driving. However, its feature encoding network only uses a minimalist PointNet network for feature extraction of point cloud information, which does not consider the global context information of the point cloud, and the local structure features are not sufficiently extracted, and these feature losses can seriously affect the performance of the object detection network. To address this problem, this paper proposes an improved PointPillars algorithm named TGPP: Transformer-based Global PointPillars. After the point cloud is divided into several pillars, global context features and local structure features are extracted through a multi-head attention mechanism, so that the point cloud after feature coding has global context features and local structure features; the two-dimensional pseudo-image generated by this feature is used for feature learning using a two-dimensional convolutional neural network. Finally, the SSD detection head is used to achieve 3D object detection. It is demonstrated that the TGPP achieves an average accuracy improvement of 2.64% in the KITTI test set.
Adsorption Air-Conditioning for Automotive Applications: A Critical Review
Authors: Vasta Salvatore
Published: July 2023

The automotive industry seeks innovative solutions for energy-efficient and sustainable vehicles. Conventional air-conditioning systems consume significant energy and have environmental impacts. Adsorption technology offers a promising alternative with higher energy efficiency and reduced emissions. This review analyzes recent advancements in adsorption air-conditioning for automotive applications, including material selection and system integration. It discusses the benefits, challenges, environmental impact, and waste heat recovery potential of adsorption systems. This review explores emerging trends like hybridization with vapour-compression (VC) systems to optimize energy efficiency and sustainability. By evaluating the existing literature, this review provides insights into thermally driven technologies for automotive air-conditioning.