bluuebunny/crossref_metadata_2025 huggingface dataset:
Artificial Intelligence Applications in Electric Distribution Systems: Post-Pandemic Progress and Prospect
Authors: Chung Sungjoo , Zhang Ying
Published: June 2023

Advances in machine learning and artificial intelligence (AI) techniques bring new opportunities to numerous intractable tasks for operation and control in modern electric distribution systems. Nevertheless, AI applications for such grids as cyber-physical systems encounter multifaceted challenges, e.g., high requirements for the quality and quantity of training data, data efficiency, physical inconsistency, interpretability, and privacy concerns. This paper provides a systematic overview of the state-of-the-art AI methodologies in the post-pandemic era, represented by transfer learning, deep attention mechanism, graph learning, and their combination with reinforcement learning and physics-guided neural networks. Dedicated research efforts on harnessing such recent advances, including power flow, state estimation, voltage control, topology identification, and line parameter calibration, are categorized and investigated in detail. Revolving around the characteristics of distribution system operation and integration of distributed energy resources, this paper also illuminates prospects and challenges typified by the privacy, explainability, and interpretability of such AI applications in smart grids. Finally, this paper attempts to shed light on the deeper and broader prospects in the realm of smart distribution grids by interoperating them with smart building and transportation electrification
Fast Numerical Reconstruction of Integral Imaging Based on a Determined Interval Mapping
Authors: Choi Heemin , Kim Nam , Kang Hoonjong
Published: June 2023

In this paper, a fast numerical reconstruction of the integral imaging based on a determined interval mapping is proposed. To reduce the computation time, the proposed method employs the determined interval mapping instead of the use of magnification. In the numerical reconstruction procedure, the acquired elemental image array (EIA) from the 3D object is displayed. The flipped elemental image (EI)s are numerically formed by the virtual pinhole array. Then, the determined interval depending on the reconstruction plane is calculated and applied to each flipped EI. These flipped EIs are shifted to match the determined interval at the reconstruction plane and superimposed together. After this superimposed image is divided by the number of the superposition, the position error between the location of the shifted EI and the pixel position of the reconstruction plane is corrected by interpolation. As a result, the refocused image depending on the reconstruction plane can be reconstructed rapidly. From the experimental result, we confirmed that the proposed method largely decreased the computation time compared with the conventional method. In addition, we verified that the quality of the reconstruction by the proposed method is higher than the conventional method by the use of the structural similarity index method.
Improved Adaptive NDI Flight Control Law Design Based on Real-Time Aerodynamic Identification in Frequency Domain
Authors: Zhou Dapeng , Jin Zeyu , Wu Guoqiang
Published: June 2023

The traditional aircraft controller design is usually based on the off-line aerodynamic model. Due to the deviation of the off-line aerodynamic model, the flight quality is difficult to meet the requirements when the aircraft is flying in the real atmosphere. To solve this problem, this paper proposes a frequency domain identification-based improved adaptive nonlinear dynamic inversion (NDI) control method (FDI-ANDI). In this paper, an online recursive aerodynamic parameter identification method in the frequency domain is first designed, and then an adaptive dynamic inversion control method based on the online aerodynamic parameter identification results is established. Finally, aiming at the problem of the slow response speed of the NDI controller, an improved adaptive dynamic inversion control law is designed by using the method of series lead correction. Compared with the traditional control method, the adaptive dynamic inversion method based on online aerodynamic identification has stronger robustness and a faster response speed in the face of model uncertainty. The final simulation analysis shows that the method has a better control effect than the traditional control method.
Selection Criterion of Reanalysis Methods for Plane Truss Optimization
Authors: Zhao Xianzhong , Zhang Tao , Xiao Weifang
Published: June 2023

Structural reanalysis methods have been proposed to improve the efficiency of structural analysis. However, the methods are typically only applicable to their specific type of structural modification. Since the optimization process often involves multiple types of modifications, it is necessary to establish a criterion for selecting the most suitable reanalysis method for each type of modification, aiming to accelerate the optimization process. In this study, the effects of different types of structural modifications are first analyzed. A qualitative correspondence is established between different types of structural modifications and the mainstream of the reanalysis methods. Secondly, the most suitable reanalysis method for different types of structural modifications is quantitatively analyzed from the aspects of selecting efficiency indicators and clarifying accuracy requirements. Finally, in conjunction with the Structural Topology and Shape Annealing (STSA) algorithm, a criterion for selecting reanalysis methods, which are applicable to the optimization process of plane trusses, is established. To verify the validity of the selection criterion, two types of numerical examples are conducted. The results show that the proposed criterion can effectively improve the efficiency of structural computations.
ABPCaps: A Novel Capsule Network-Based Method for the Prediction of Antibacterial Peptides
Authors: Yao Lantian , Pang Yuxuan , Wan Jingting , Chung Chia-Ru , Yu Jinhan , Guan Jiahui , Leung Clement , Chiang Ying-Chih , Lee Tzong-Yi
Published: June 2023

The emergence of drug resistance among pathogens has become a major challenge to human health on a global scale. Among them, antibiotic resistance is already a critical issue, and finding new therapeutic agents to address this problem is therefore urgent. One of the most promising alternatives to antibiotics are antibacterial peptides (ABPs), i.e., short peptides with antibacterial activity. In this study, we propose a novel ABP recognition method, called ABPCaps. It integrates a convolutional neural network (CNN), a long short-term memory (LSTM), and a new type of neural network named the capsule network. The capsule network can extract critical features automatically from both positive and negative samples, leading to superior performance of ABPCaps over all baseline models built on hand-crafted peptide descriptors. Evaluated on independent test sets, ABPCaps achieves an accuracy of 93.33% and an F1-score of 91.34%, and consistently outperforms the baseline models in other extensive experiments as well. Our study demonstrates that the proposed ABPCaps, built on the capsule network method, is a valuable addition to the current state-of-the-art in the field of ABP recognition and has significant potential for further development.
Study of Differential Evolution Variants in the Dimensional Synthesis of Four-Bar Grashof-Type Mechanisms
Authors: Rodríguez-Molina Alejandro , Villarreal-Cervantes Miguel Gabriel , Rueda-Gutiérrez Allan-Balam , Aldape-Pérez Mario , Álvarez-Piedras José David , Parra-Ocampo Miguel Fernando
Published: June 2023

Mechanisms have allowed for the automation of complex, repetitive, demanding, or dangerous tasks for humans. Among the different mechanisms, those with a closed kinematic chain are more precise and robust compared to open chain ones, which makes them suitable for many applications. One of the most widely used closed-chain alternatives is the four-bar Grashof-type mechanism, as it can generate highly nonlinear closed trajectories with a single degree of freedom. However, the dimensional synthesis of these mechanisms to generate specific trajectories is a complex task. Fortunately, computational methods known as metaheuristics can solve such problems effectively. Differential Evolution (DE) is a metaheuristic commonly used to tackle the dimensional synthesis problem. This paper presents a comparative study of the most commonly used variants of DE in solving the dimensional synthesis problem of four-bar Grashof-type mechanisms. The purpose of the study is to provide guidelines to choose the best DE alternative for solving problems of this type, as well as to support the development of DE-based algorithms that can solve more specific cases effectively. After analysis, the rand/1/exp variant was found to be the most effective in solving the dimensional synthesis problem, which was followed by best/1/bin. Based on these results, a Simple and Improved DE (SIDE) variant based on these two was proposed. The competitive performance of the SIDE with respect to the studied DE variants and in contrast to the results of algorithms used in the recent specialized literature for mechanism synthesis illustrates the usefulness of the study.
Semantic Segmentation of Packaged and Unpackaged Fresh-Cut Apples Using Deep Learning
Authors: Vadukkal Udith Krishnan Vadakkum , Palumbo Michela , Attolico Giovanni
Published: June 2023

Computer vision systems are often used in industrial quality control to offer fast, objective, non-destructive, and contactless evaluation of fruit. The senescence of fresh-cut apples is strongly related to the browning of the pulp rather than to the properties of the peel. This work addresses the identification and selection of pulp inside images of fresh-cut apples, both packaged and unpackaged; this is a critical step towards a computer vision system that is able to evaluate their quality and internal properties. A DeepLabV3+-based convolutional neural network model (CNN) has been developed for this semantic segmentation task. It has proved to be robust with respect to the similarity of colours between the peel and pulp. Its ability to separate the pulp from the peel and background has been verified on four varieties of apples: Granny Smith (greenish peel), Golden (yellowish peel), Fuji, and Pink Lady (reddish peel). The semantic segmentation achieved an accuracy greater than 99% on all these varieties. The developed approach was able to isolate regions significantly affected by the browning process on both packaged and unpackaged pieces: on these areas, the colour analysis will be studied to evaluate internal quality and senescence of packaged and unpackaged products.
A Novel Dynamic Approach for Determining Real-Time Interior Visual Comfort Exploiting Machine Learning Techniques
Authors: Tzouvaras Christos , Dimara Asimina , Papaioannou Alexios , Anagnostopoulos Christos-Nikolaos , Krinidis Stelios , Arvanitis Konstantinos , Ioannidis Dimosthenis , Tzovaras Dimitrios
Published: June 2023

The accurate assessment of visual comfort in indoor spaces is crucial for creating environments that enhance occupant well-being, productivity, and overall satisfaction. This paper presents a groundbreaking contribution to the field of visual comfort assessment in occupied buildings, addressing the existing research gap in methods for evaluating visual comfort once a building is in use while ensuring compliance with design specifications. The primary aim of this study was to introduce a pioneering approach for estimating visual comfort in indoor environments that is non-intrusive, practical, and can deliver accurate results without compromising accuracy. By incorporating mathematical visual comfort estimation into a regression model, the proposed method was evaluated and compared using real-life scenario. The experimental results demonstrated that the suggested model surpassed the mathematical model with an impressive performance improvement of 99%, requiring fewer computational resources and exhibiting a remarkable 95% faster processing time.
Asymptotic Ergodic Capacity Analysis for FSO Communication between Mobile Platforms in Maritime Environments
Authors: Han Jae Eun , Nam Sung Sik , Yoon Changseok , Hwang Duck Dong , Alouini Mohamed-Slim
Published: June 2023

With the rapid development of wireless communication technologies, free-space optical (FSO) communication has gained much attention in recent years. The FSO technology, which is usually used for communication in terrestrial environments, is expanding its regime to communication in maritime environments. In this study, we analyze the ergodic capacity of FSO communication between mobile platforms in maritime environments under foggy conditions, pathloss, and pointing error. More specifically, based on the moment expressions, we derive the closed-form expression of asymptotic ergodic capacity using conventional detection techniques (i.e., heterodyne and intensity modulation/direct detection). The derived analytical results are cross-verified with simulation results via Monte Carlo simulations. The results show that the combined effects of fog with pathloss and pointing error degrade the FSO performance and that it is important to apply the appropriate detection technique according to the communication environment.
Optimum Driving of Ultrasonic Cleaner Using Impedance and FFT Analysis with Validation of Image Processing of Perforated Foils
Authors: Khan Muhammad Usman , Rehman Faisal , Saleem Mohsin , Elahi Hassan , Sung Tae Hyun , Jabbar Hamid
Published: June 2023

Over the past decade, ultrasonic cleaners have been widely used in many industries. Now, this technology is finding its way into homes for vegetable, fruit, and clothes cleaning. In widely used ultrasonic cleaners, piezoelectric transducers are externally attached to the steel tank to generate ultrasonic waves inside the tank. Based on the impedance data of the piezoelectric transducers, the driving circuit was tuned to generate the required frequencies inside the cleaning tank. This paper discusses the design, development, and validation of an 800 mL tank capacity ultrasonic cleaner driven with a piezoelectric disc actuator. To achieve an optimum cleaning action without surface abrasion, several characteristics need to be considered in this complex relationship. The placement of transducers has been investigated according to the pressure distribution inside the liquid medium. The optimized ultrasonic cleaner design, along with a class-D half-bridge circuit, was developed to drive the ultrasonic transducer in the resonance frequency range. To validate the optimal design and driving frequency, the acoustic spectrum generated inside the tank was measured using a piezoelectric sensor and FFT analysis was performed. To validate the cleaning effect, a qualitative test based on aluminuim foil perforations was performed. The perforation area in the foils was quantitatively measured using image processing based on the YOLO V5 technique. The proposed image processing technique has an accuracy of 97 % in the detection of perforation areas in the aluminuim foil test.
Improved Neural Differential Distinguisher Model for Lightweight Cipher Speck
Authors: Yue Xiaoteng , Wu Wanqing
Published: June 2023

At CRYPTO 2019, Gohr proposed the neural differential distinguisher using the residual network structure in convolutional neural networks on round-reduced Speck32/64. In this paper, we construct a 7-round differential neural distinguisher for Speck32/64, which results in better than Gohr’s work. The details are as follows. Firstly, a new data format (C_r,C_r′,d_l,Cl,Cr,Cl′,Cr′) is proposed for the input data of the differential neural distinguisher, which can help the distinguisher to identify the features of the previous round of ciphertexts in the Speck algorithm. Secondly, this paper modifies the convolution layer of the residual block in the residual network, inspired by the Inception module in GoogLeNet. For Speck32/64, the experiments show that the accuracy of the 7-round differential neural distinguisher is 97.13%, which is better than the accuracy of Gohr’s distinguisher of 9.1% and also higher than the currently known accuracy of 89.63%. The experiments also show that the data format and neural network in this paper can improve the accuracy of the distinguisher by 2.38% and 2.1%, respectively. Finally, to demonstrate the effectiveness of the distinguisher in this paper, a key recovery attack is performed on 8-rounds of Speck32/64. The results show that the success rate of recovering the correct key is 92%, with no more than two incorrect bits. Finally, this paper briefly discussed the effect of the number of ciphertext pairs in a sample on the training results of the differential neural distinguisher. When the total number of ciphertext pairs is kept constant, the accuracy of the distinguisher increases with s, but it also leads to the occurrence of overfitting.
Similarities and Differences between Vestibular Migraine and Recurrent Vestibular Symptoms—Not Otherwise Specified (RVS-NOS)
Authors: Teggi Roberto , Colombo Bruno , Cangiano Iacopo , Gatti Omar , Bussi Mario , Filippi Massimo
Published: June 2023

Menière’s disease and vestibular migraine (VM) are two common inner ear disorders whose diagnoses are based on clinical history and audiometric exams. In some cases, patients have been reporting different episodes of vertigo for years but not fulfilling the Bárány Society criteria for either. These are called Recurrent Vestibular Symptoms—Not Otherwise Specified (RVS-NOS). It is still under debate if this is a single disease entity or a part of the spectrum of already established disorders. The purpose of our work was to establish similarities and differences with VM in terms of clinical history, bedside examination, and family history. We enrolled 28 patients with RVS-NOS who were followed for at least 3 years with stable diagnosis; results were compared with those of 34 subjects having a diagnosis of definite VM. The age of onset of vertigo was lower in VM than in RVS-NOS (31.2 vs. 38.4 years). As for the duration of attacks and symptoms, we detected no differences other than subjects with RVS-NOS reporting milder attacks. Cochlear accompanying symptoms were more frequently reported by VM subjects (one subject reporting tinnitus and another one reported tinnitus and fullness). Motion sickness was equally reported by subjects across two samples (around 50% for both). Bipositional long-lasting, non-paroxysmal nystagmus was the most common finding in the two groups, with no significant difference. Finally, the percentage of familial cases of migrainous headache and episodic vertigo did not differ between the two samples. In conclusion, RVS-NOS shares some common aspects with VM, including the temporal profile of attacks, motion sickness (commonly considered a migraine precursor), bedside examination, and family history. Our results are not inconsistent with the possibility that RVS-NOS may be a heterogeneous disorder, even if some of these subjects may share common pathophysiological mechanisms with VM.
Exploring Dynamic Structures in Matrix-Valued Time Series via Principal Component Analysis
Authors: Billard Lynne , Douzal-Chouakria Ahlame , Samadi S. Yaser
Published: June 2023

Time-series data are widespread and have inspired numerous research works in machine learning and data analysis fields for the classification and clustering of temporal data. While there are several clustering methods for univariate time series and a few for multivariate series, most methods are based on distance and/or dissimilarity measures that do not fully utilize the time-dependency information inherent to time-series data. To highlight the main dynamic structure of a set of multivariate time series, this study extends the use of standard variance–covariance matrices in principal component analysis to cross-autocorrelation matrices at time lags k=1,2,…. This results in “principal component time series”. Simulations and a sign language dataset are used to demonstrate the effectiveness of the proposed method and its benefits in exploring the main structural features of multiple time series.
Plants, Cells, Algae, and Cyanobacteria In Vitro and Cryobank Collections at the Institute of Plant Physiology, Russian Academy of Sciences—A Platform for Research and Production Center
Authors: Yuorieva Natalya , Sinetova Maria , Messineva Ekaterina , Kulichenko Irina , Fomenkov Artem , Vysotskaya Olga , Osipova Ekaterina , Baikalova Angela , Prudnikova Olga , Titova Maria , Nosov Alexander V. , Popova Elena
Published: June 2023

Ex situ collections of algae, cyanobacteria, and plant materials (cell cultures, hairy and adventitious root cultures, shoots, etc.) maintained in vitro or in liquid nitrogen (−196 °C, LN) are valuable sources of strains with unique ecological and biotechnological traits. Such collections play a vital role in bioresource conservation, science, and industry development but are rarely covered in publications. Here, we provide an overview of five genetic collections maintained at the Institute of Plant Physiology of the Russian Academy of Sciences (IPPRAS) since the 1950–1970s using in vitro and cryopreservation approaches. These collections represent different levels of plant organization, from individual cells (cell culture collection) to organs (hairy and adventitious root cultures, shoot apices) to in vitro plants. The total collection holdings comprise more than 430 strains of algae and cyanobacteria, over 200 potato clones, 117 cell cultures, and 50 strains of hairy and adventitious root cultures of medicinal and model plant species. The IPPRAS plant cryobank preserves in LN over 1000 specimens of in vitro cultures and seeds of wild and cultivated plants belonging to 457 species and 74 families. Several algae and plant cell culture strains have been adapted for cultivation in bioreactors from laboratory (5–20-L) to pilot (75-L) to semi-industrial (150–630-L) scale for the production of biomass with high nutritive or pharmacological value. Some of the strains with proven biological activities are currently used to produce cosmetics and food supplements. Here, we provide an overview of the current collections’ composition and major activities, their use in research, biotechnology, and commercial application. We also highlight the most interesting studies performed with collection strains and discuss strategies for the collections’ future development and exploitation in view of current trends in biotechnology and genetic resources conservation.
Evolutionary Stability of Small Molecular Regulatory Networks That Exhibit Near-Perfect Adaptation
Authors: Singhania Rajat , Tyson John J.
Published: June 2023

Large-scale protein regulatory networks, such as signal transduction systems, contain small-scale modules (‘motifs’) that carry out specific dynamical functions. Systematic characterization of the properties of small network motifs is therefore of great interest to molecular systems biologists. We simulate a generic model of three-node motifs in search of near-perfect adaptation, the property that a system responds transiently to a change in an environmental signal and then returns near-perfectly to its pre-signal state (even in the continued presence of the signal). Using an evolutionary algorithm, we search the parameter space of these generic motifs for network topologies that score well on a pre-defined measure of near-perfect adaptation. We find many high-scoring parameter sets across a variety of three-node topologies. Of all possibilities, the highest scoring topologies contain incoherent feed-forward loops (IFFLs), and these topologies are evolutionarily stable in the sense that, under ‘macro-mutations’ that alter the topology of a network, the IFFL motif is consistently maintained. Topologies that rely on negative feedback loops with buffering (NFLBs) are also high-scoring; however, they are not evolutionarily stable in the sense that, under macro-mutations, they tend to evolve an IFFL motif and may—or may not—lose the NFLB motif.
Senopathies—Diseases Associated with Cellular Senescence
Authors: Lushchak Oleh , Schosserer Markus , Grillari Johannes
Published: June 2023

Cellular senescence describes a stable cell cycle arrest state with a characteristic phenotype. Senescent cells accumulate in the human body during normal aging, limiting the lifespan and promoting aging-related, but also several non-related, pathologies. We propose to refer to all diseases whose pathogenesis or progression is associated with cellular senescence as “senopathies”. Targeting senescent cells with senolytics or senomorphics is likely to mitigate these pathologies. Examples of senopathies include cardiovascular, metabolic, musculoskeletal, liver, kidney, and lung diseases and neurodegeneration. For all these pathologies, animal studies provide clear mechanistic evidence for a connection between senescent cell accumulation and disease progression. The major persisting challenge in developing novel senotherapies is the heterogeneity of senescence phenotypes, causing a lack of universal biomarkers and difficulties in discriminating senescent from non-senescent cells.
Caffeine for Prevention of Alzheimer’s Disease: Is the A2A Adenosine Receptor Its Target?
Authors: Merighi Stefania , Travagli Alessia , Nigro Manuela , Pasquini Silvia , Cappello Martina , Contri Chiara , Varani Katia , Vincenzi Fabrizio , Borea Pier Andrea , Gessi Stefania
Published: June 2023

Alzheimer’s disease (AD) is the most prevalent kind of dementia with roughly 135 million cases expected in the world by 2050. Unfortunately, current medications for the treatment of AD can only relieve symptoms but they do not act as disease-modifying agents that can stop the course of AD. Caffeine is one of the most widely used drugs in the world today, and a number of clinical studies suggest that drinking coffee may be good for health, especially in the fight against neurodegenerative conditions such as AD. Experimental works conducted “in vivo” and “in vitro” provide intriguing evidence that caffeine exerts its neuroprotective effects by antagonistically binding to A2A receptors (A2ARs), a subset of GPCRs that are triggered by the endogenous nucleoside adenosine. This review provides a summary of the scientific data supporting the critical role that A2ARs play in memory loss and cognitive decline, as well as the evidence supporting the protective benefits against neurodegeneration that may be attained by caffeine’s antagonistic action on these receptors. They are a novel and fascinating target for regulating and enhancing synaptic activity, achieving symptomatic and potentially disease-modifying effects, and protecting against neurodegeneration.
Molecularly Imprinted Polymer-Based Biomimetic Systems for Sensing Environmental Contaminants, Biomarkers, and Bioimaging Applications
Authors: Ramajayam Kalaipriya , Ganesan Selvaganapathy , Ramesh Purnimajayasree , Beena Maya , Kokulnathan Thangavelu , Palaniappan Arunkumar
Published: June 2023

Molecularly imprinted polymers (MIPs), a biomimetic artificial receptor system inspired by the human body’s antibody-antigen reactions, have gained significant attraction in the area of sensor development applications, especially in the areas of medical, pharmaceutical, food quality control, and the environment. MIPs are found to enhance the sensitivity and specificity of typical optical and electrochemical sensors severalfold with their precise binding to the analytes of choice. In this review, different polymerization chemistries, strategies used in the synthesis of MIPs, and various factors influencing the imprinting parameters to achieve high-performing MIPs are explained in depth. This review also highlights the recent developments in the field, such as MIP-based nanocomposites through nanoscale imprinting, MIP-based thin layers through surface imprinting, and other latest advancements in the sensor field. Furthermore, the role of MIPs in enhancing the sensitivity and specificity of sensors, especially optical and electrochemical sensors, is elaborated. In the later part of the review, applications of MIP-based optical and electrochemical sensors for the detection of biomarkers, enzymes, bacteria, viruses, and various emerging micropollutants like pharmaceutical drugs, pesticides, and heavy metal ions are discussed in detail. Finally, MIP’s role in bioimaging applications is elucidated with a critical assessment of the future research directions for MIP-based biomimetic systems.
Biomimetic Scaffolds for Tendon Tissue Regeneration
Authors: Huang Lvxing , Chen Le , Chen Hengyi , Wang Manju , Jin Letian , Zhou Shenghai , Gao Lexin , Li Ruwei , Li Quan , Wang Hanchang , Zhang Can , Wang Junjuan
Published: June 2023

Tendon tissue connects muscle to bone and plays crucial roles in stress transfer. Tendon injury remains a significant clinical challenge due to its complicated biological structure and poor self-healing capacity. The treatments for tendon injury have advanced significantly with the development of technology, including the use of sophisticated biomaterials, bioactive growth factors, and numerous stem cells. Among these, biomaterials that the mimic extracellular matrix (ECM) of tendon tissue would provide a resembling microenvironment to improve efficacy in tendon repair and regeneration. In this review, we will begin with a description of the constituents and structural features of tendon tissue, followed by a focus on the available biomimetic scaffolds of natural or synthetic origin for tendon tissue engineering. Finally, we will discuss novel strategies and present challenges in tendon regeneration and repair.
Respiratory Rate Estimation during Walking and Running Using Breathing Sounds Recorded with a Microphone
Authors: Romano Chiara , Nicolò Andrea , Innocenti Lorenzo , Bravi Marco , Miccinilli Sandra , Sterzi Silvia , Sacchetti Massimo , Schena Emiliano , Massaroni Carlo
Published: June 2023

Emerging evidence suggests that respiratory frequency (fR) is a valid marker of physical effort. This has stimulated interest in developing devices that allow athletes and exercise practitioners to monitor this vital sign. The numerous technical challenges posed by breathing monitoring in sporting scenarios (e.g., motion artifacts) require careful consideration of the variety of sensors potentially suitable for this purpose. Despite being less prone to motion artifacts than other sensors (e.g., strain sensors), microphone sensors have received limited attention so far. This paper proposes the use of a microphone embedded in a facemask for estimating fR from breath sounds during walking and running. fR was estimated in the time domain as the time elapsed between consecutive exhalation events retrieved from breathing sounds every 30 s. Data were collected from ten healthy subjects (both males and females) at rest and during walking (at 3 km/h and 6 km/h) and running (at 9 km/h and 12 km/h) activities. The reference respiratory signal was recorded with an orifice flowmeter. The mean absolute error (MAE), the mean of differences (MOD), and the limits of agreements (LOAs) were computed separately for each condition. Relatively good agreement was found between the proposed system and the reference system, with MAE and MOD values increasing with the increase in exercise intensity and ambient noise up to a maximum of 3.8 bpm (breaths per minute) and −2.0 bpm, respectively, during running at 12 km/h. When considering all the conditions together, we found an MAE of 1.7 bpm and an MOD ± LOAs of −0.24 ± 5.07 bpm. These findings suggest that microphone sensors can be considered among the suitable options for estimating fR during exercise.
Combining Unimanual and Bimanual Therapies for Children with Hemiparesis: Is There an Optimal Delivery Schedule?
Authors: Au Ka Lai K. , Knitter Julie L. , Morrow-McGinty Susan , Campos Talita C. , Carmel Jason B. , Friel Kathleen M.
Published: June 2023

Constraint-induced movement therapy (CIMT) and bimanual therapy (BT) are among the most effective hand therapies for children with unilateral cerebral palsy (uCP). Since they train different aspects of hand use, they likely have synergistic effects. The aim of this study was to examine the efficacy of different combinations of mCIMT and BT in an intensive occupational therapy program for children with uCP. Children (n = 35) participated in intensive modified CIMT (mCIMT) and BT, 6 weeks, 5 days/week, 6 h/day. During the first 2 weeks, children wore a mitt over the less-affected hand and engaged in functional and play activities with the affected hand. Starting in week 3, bimanual play and functional activities were added progressively, 1 hour/week. This intervention was compared to two different schedules of block interventions: (1) 3 weeks of mCIMT followed by 3 weeks of BT, and (2) 3 weeks of BT followed by 3 weeks of mCIMT. Hand function was tested before, after, and two months after therapy with the Assisting Hand Assessment (AHA), Pediatric Evaluation of Disability Inventory (PEDI), and Canadian Occupational Performance Measure (COPM). All three groups of children improved in functional independence (PEDI; p < 0.031), goal performance (COPM Performance; p < 0.0001) and satisfaction (COPM Satisfaction; p < 0.0001), which persisted two months post-intervention. All groups showed similar amounts of improvement, indicating that the delivery schedule for mCIMT and BT does not significantly impact the outcomes.
Preclinical Evaluation of a Microwave-Based Accessory Device for Colonoscopy in an In Vivo Porcine Model with Colorectal Polyps
Authors: Garrido Alejandra , Guardiola Marta , Neira Luz María , Sont Roberto , Córdova Henry , Cuatrecasas Miriam , Flisikowski Krzysztof , Troya Joel , Sanahuja Josep , Winogrodzki Thomas , Belda Ignasi , Meining Alexander , Fernández-Esparrach Glòria
Published: June 2023

Background and Aims: Colonoscopy is currently the most effective way of detecting colorectal cancer and removing polyps, but it has some drawbacks and can miss up to 22% of polyps. Microwave imaging has the potential to provide a 360° view of the colon and addresses some of the limitations of conventional colonoscopy. This study evaluates the feasibility of a microwave-based colonoscopy in an in vivo porcine model. Methods: A prototype device with microwave antennas attached to a conventional endoscope was tested on four healthy pigs and three gene-targeted pigs with mutations in the adenomatous polyposis coli gene. The first four animals were used to evaluate safety and maneuverability and compatibility with endoscopic tools. The ability to detect polyps was tested in a series of three gene-targeted pigs. Results: the microwave-based device did not affect endoscopic vision or cause any adverse events such as deep mural injuries. The microwave system was stable during the procedures, and the detection algorithm showed a maximum detection signal for adenomas compared with healthy mucosa. Conclusions: Microwave-based colonoscopy is feasible and safe in a preclinical model, and it has the potential to improve polyp detection. Further investigations are required to assess the device’s efficacy in humans.
Effect of Capping Ligands for the Synthesis of Gold Nanoparticles and on the Catalytic Performance for the Oxidation of 5-Hydroxymethyl-2-furfural
Authors: Liuzzi Francesca , Ventimiglia Alessia , Allegri Alessandro , Rodríguez-Aguado Elena , Cecilia Juan Antonio , Rivalta Ivan , Dimitratos Nikolaos , Albonetti Stefania
Published: June 2023

Different series of Au on carbon catalysts were prepared via sol-immobilization to investigate the role of polymers (polyvinylpyrrolidone PVP, polyethylene glycol PEG and polyvinyl alcohol PVA), employed as gold nanoparticle (NP) stabilizers, on catalyst properties and on catalytic activity. The synthesized materials were widely characterized with several techniques (DLS, XRD, TEM and XPS) and used as catalysts in the 5-hydroxymethylfurfural (HMF) oxidation to produce 2,5-furandicarboxylic acid (FDCA). The obtained results clearly demonstrated the PVA leading to the formation of smaller and more active NPs. On the contrary, polyethylene glycol was shown to affect gold exposure and, as a consequence, to reduce the catalyst activity due to steric effects while PVP-based catalysts presented bigger and more covered Au NPs. The investigation on the reusability of the catalysts demonstrated the presence of a significant deactivation on all prepared materials, but the presence of higher amounts of polymer seems to have a positive effect on catalyst stability even if associated with lower reactivity. Computational studies have provided interesting information on the NP-polymer interactions and consequently on the catalytic activities.
Preparation of Mn-Co-MCM-41 Molecular Sieve with Thermosensitive Template and Its Degradation Performance for Rhodamine B
Authors: Peng Wenju , Cai Lixia , Lu Yani , Zhang Yaoyao
Published: June 2023

Rhodamine B (RhB) in dyes is widely used in various industries, but it poses a great threat to the natural environment and human health. In this work, a series of thermosensitive polymer materials, PNxDy, with controllable morphology and particle size were prepared by free radical polymerization using N-isopropylacrylamide and N,N-dimethylacrylamide as monomers. Then, by using PNxDy as a template, bimetallic Mn- and Co-doped MCM-41 molecular sieves with good morphology and properties were prepared by the microwave-assisted hydrothermal method. The effects of a series of thermosensitive templates on the morphology and properties of the Mn-Co-MCM-41 molecular sieve were investigated. The results demonstrated that the Mn-Co-MCM-41 by PN100D4 as a templating agent showed the best mesoporous ordering and the most regular material morphology with 2 nm nanoparticles. In addition, the molecular sieve with the best structure was selected for the RhB degradation experiments. The Mn-Co-MCM-41 with PN100D4 as the template showed regular morphology and uniform pore channels. It was applied as a catalyst for the degradation of RhB by potassium monopersulfate (PMS). The degradation rate of RhB could reach 98% with a 20 min reaction by Mn-Co-MCM-41 (PN100D4). Meanwhile, the degradation rate could be maintained at 91% after being reused six times. The bimetallic-doped Mn-Co-MCM-41 molecular sieves prepared using the thermosensitive material PN100D4 as a template have good catalytic performance and can be effectively reused.
Genetic Variations in IL-1β, TNF-α, and TGF-β Associated with the Severity of Chronic Cervical Spondylitis in Patients
Authors: Yadav Shashi Ranjan Mani , Goyal Bela , Mamgain Garima , Kothari Ashish , Kumar Sandeep , Saha Sarama , Naithani Manisha , Mirza Anissa Atif , Kumar Raj , Arora Rajnish
Published: June 2023

Chronic cervical spondylitis (CCS), a degenerative disorder of the spine, is known for causing disability among old and young people. Single-nucleotide polymorphisms (SNPs) in various cytokine genes have demonstrated an impactful association with several inflammatory disorders. In the present study, we have investigated the SNPs and allelic distribution of the three most prevalent cytokines genes, IL-1β (-511C/T), TNF-α (-308G/A), and TGF-β (-509C/T), along with serum levels of these cytokines in 252 subjects. SNPs were analyzed using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), and digested fragments were separated and visualized using agarose gel electrophoresis and Native Polyacrylamide gel electrophoresis (PAGE). The serum cytokine levels were analyzed with a flow cytometer using a customized multiplex bead-based assay. It was observed that these SNPs did not reflect the susceptibility to CCS but were associated with susceptibility to CCS. We found a significant association between the C/C and G/G genotypes and the C and G alleles of IL-1β and TNF-α, respectively, suggesting a lower risk of CCS. The frequency distribution of risk alleles (-511T) and (-308A) were simultaneously higher in CCS compared to the control, reflecting the susceptibility to CCS. TGF-β showed a significant association with disease susceptibility, along with a significant correlation between age and the chronicity of CCS. The serum cytokine levels were significantly different in CCS and controls.
Study of Factors Affecting the Copper Ore Leaching Process
Authors: Koizhanova Aigul , Kenzhaliyev Bagdaulet , Magomedov David , Kamalov Emil , Yerdenova Mariya , Bakrayeva Akbota , Abdyldayev Nurgali
Published: June 2023

This paper provides an overview of hydrometallurgical copper extraction studies in which liquid extraction technology has been used with four copper deposits of different compositions. The sulfuric acid consumption rate and copper extraction efficiency, which are dependent on the initial content and forms of calcium compounds and other impurities in ore samples, were calculated, and the results are presented herein. It was established that during the leaching process, silicate compounds of alkaline earth metals, in addition to calcium and magnesium carbonate compounds, would affect the levels of sulfuric acid consumption, thereby actively lowering the acidity of the environment. Moreover, these compounds could partially sorb copper ions from sulfuric acid leaching solutions. Thus, the analysis of waste ore samples showed that residual copper is mainly contained in the form of complex silicate complexes. The presence of divalent iron compounds in the composition from one of the deposits also allowed us to perform a biochemical leaching experiment with preliminary oxidation using an Acidithiobacillus ferrooxidans bacterial culture adapted to the ore composition. The use of this biochemical method in the copper leaching process resulted in a significant reduction in sulfuric acid consumption, by 40%, and a copper recovery rate of 87.2%.
Unbalanced Web Phishing Classification through Deep Reinforcement Learning
Authors: Maci Antonio , Santorsola Alessandro , Coscia Antonio , Iannacone Andrea
Published: June 2023

Web phishing is a form of cybercrime aimed at tricking people into visiting malicious URLs to exfiltrate sensitive data. Since the structure of a malicious URL evolves over time, phishing detection mechanisms that can adapt to such variations are paramount. Furthermore, web phishing detection is an unbalanced classification task, as legitimate URLs outnumber malicious ones in real-life cases. Deep learning (DL) has emerged as a promising technique to minimize concept drift to enhance web phishing detection. Deep reinforcement learning (DRL) combines DL with reinforcement learning (RL); that is, a sequential decision-making paradigm in which the problem to be addressed is expressed as a Markov decision process (MDP). Recent studies have proposed an ad hoc MDP formulation to tackle unbalanced classification tasks called the imbalanced classification Markov decision process (ICMDP). In this paper, we exploit the ICMDP to present a double deep Q-Network (DDQN)-based classifier to address the unbalanced web phishing classification problem. The proposed algorithm is evaluated on a Mendeley web phishing dataset, from which three different data imbalance scenarios are generated. Despite a significant training time, it results in better geometric mean, index of balanced accuracy, F1 score, and area under the ROC curve than other DL-based classifiers combined with data-level sampling techniques in all test cases.
Editorial: Application of Shape Memory Alloys
Authors: Manfredi Luigi
Published: June 2023

In the ambit of smart materials, shape memory alloys (SMA) have emerged as a remarkable class of materials capable of undergoing significant shape changes when stimulated by external factors such as mechanical, magnetic, thermal, or electrical forces [...]
Factors Associated with Long-Term Prostate Cancer Survival after Palliative Radiotherapy to a Bone Metastasis and Contemporary Palliative Systemic Therapy: A Retrospective, Population-Based Study
Authors: Venugopal Bindu , Shahhat Shaheer , Beck James , Hanumanthappa Nikesh , Ong Aldrich D. , Dubey Arbind , Koul Rashmi , Bashir Bashir , Chowdhury Amitava , Sivananthan Gokulan , Kim Julian Oliver
Published: June 2023

Background: Radiation therapy (RT) is an established palliative treatment for bone metastases; however, little is known about post-radiation survival and factors which impact it. The aim of this study was to assess a population-based sample of metastatic prostate cancer patients receiving palliative radiation therapy to bone metastases and contemporary palliative systemic therapy and identify factors that impact long-term survival. Materials/methods: This retrospective, population-based, cohort study assessed all prostate cancer patients receiving palliative RT for bone metastases at a Canadian provincial Cancer program during a contemporary time period. Baseline patient, disease, and treatment characteristics were extracted from the provincial medical physics databases and the electronic medical record. Post-RT Survival intervals were defined as the time interval from the first fraction of palliative RT to death from any cause or date of the last known follow-up. The median survival of the cohort was used to dichotomize the cohort into short- and long-term survivors following RT. Univariable and multivariable hazard regression analyses were performed to identify variables associated with post-RT survival. Results: From 1 January 2018 until 31 December 2019, 545 palliative RT courses for bone metastases were delivered to n = 274 metastatic prostate cancer patients with a median age of 76 yrs (Interquartile range (IQR) 39–83) and a median follow-up of 10.6 months (range 0.2 to 47.9). The median survival of the cohort was 10.6 months (IQR 3.5–25 months). The ECOG performance status of the whole cohort was ≤2 in n = 200 (73%) and 3–4 in n = 67 (24.5%). The most commonly treated sites of bone metastasis were the pelvis and lower extremities n = 130 (47.4%), skull and spine n = 114 (41.6%), and chest and upper extremities n = 30 (10.9%). Most patients had CHAARTED high volume disease n = 239 (87.2%). On multivariable hazard regression analysis, an ECOG performance status of 3–4 (p = 0.02), CHAARTED high volume disease burden (p = 0.023), and non-receipt of systemic therapy (p = 0.006) were significantly associated with worse post-RT survival. Conclusion: Amongst metastatic prostate cancer patients treated with palliative radiotherapy to bone metastases and modern palliative systemic therapies, ECOG performance status, CHAARTED metastatic disease burden, and type of first-line palliative systemic therapy were significantly associated with post-RT survival durations.
The Role of mTOR Inhibitors after Liver Transplantation for Hepatocellular Carcinoma
Authors: Todeschini Letizia , Cristin Luca , Martinino Alessandro , Mattia Amelia , Agnes Salvatore , Giovinazzo Francesco
Published: June 2023

Liver transplantation is a treatment option for nonresectable patients with early-stage HCC, with more significant advantages when Milan criteria are fulfilled. An immunosuppressive regimen is required to reduce the risk of graft rejection after transplantation, and CNIs represent the drugs of choice in this setting. However, their inhibitory effect on T-cell activity accounts for a higher risk of tumour regrowth. mTOR inhibitors (mTORi) have been introduced as an alternative immunosuppressive approach to conventional CNI-based regimens to address both immunosuppression and cancer control. The PI3K-AKT-mTOR signalling pathway regulates protein translation, cell growth, and metabolism, and the pathway is frequently deregulated in human tumours. Several studies have suggested the role of mTORi in reducing HCC progression after LT, accounting for a lower recurrence rate. Furthermore, mTOR immunosuppression controls the renal damage associated with CNI exposure. Conversion to mTOR inhibitors is associated with stabilizing and recovering renal dysfunction, suggesting an essential renoprotective effect. Limitations in this therapeutic approach are related to their negative impact on lipid and glucose metabolism as well as on proteinuria development and wound healing. This review aims to summarize the roles of mTORi in managing patients with HCC undergoing LT. Strategies to overcome common adverse effects are also proposed.