bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptive CFAR Method for SAR Ship Detection Using Intensity and Texture Feature Fusion Attention Contrast Mechanism
Authors: Li Nana , Pan Xueli , Yang Lixia , Huang Zhixiang , Wu Zhenhua , Zheng Guoqing
Published: October 2022

Due to the complexity of sea surface environments, such as speckles and side lobes of ships, ship wake, etc., the detection of ship targets in synthetic aperture radar (SAR) images is still confronted with enormous challenges, especially for small ship targets. Aiming at the key problem of ship target detection in the complex environments, the article proposes a constant false alarm rate (CFAR) algorithm for SAR ship target detection based on the attention contrast mechanism of intensity and texture feature fusion. First of all, the local feature attention contrast enhancement is performed based on the intensity dissimilarity and the texture feature difference described by local binary pattern (LBP) between ship targets and sea clutter, so as to realize the target enhancement and background suppression. Furthermore, the adaptive CFAR ship target detection method based on generalized Gamma distribution (GΓD) which can fit the clutter well by the goodness-of-fit analyses is carried out. Finally, the public datasets HRSID and LS-SSDD-v1.0 are used to verify the effectiveness of the proposed detection method. A large number of experimental results show that the proposed method can suppress clutter background and speckle noise and improve the target-to-clutter rate (TCR) significantly, and has the relative high detection rate and low false alarm rate in the complex background and multi-target marine environments.
Optimization of Bit Allocation for Spatial Multiplexing in MIMO VLC System with Smartphones
Authors: Lee Chang-Ming , Li Bo-Hung , Chiang Chang-Chin
Published: October 2022

This paper focuses on the hardware components of smartphones, namely, the use of displays and cameras in mobile devices as transmitters and receivers to establish a near-field multiple-input–multiple-output (MIMO) visible light communication (VLC) system. Based on the relationship between the grayscale values of transmitted and received signals, the physical channel responses are detected and approximated with a high-order regression to obtain the channel gain. With the constraint of bit numbers in the MIMO VLC system, an integer-type water-filling scheme was designed for bit allocation to improve transmission efficiency. The physical examinations show that bit error rate (BER) reduction can be 26.4% with Gaussian noise of 30 dB and detected channel gain compared with the equal bit allocation. The optimization of the simulation was confirmed with the bit assignments in real cases.
Improved Feature Parameter Extraction from Speech Signals Using Machine Learning Algorithm
Authors: Abdusalomov Akmalbek Bobomirzaevich , Safarov Furkat , Rakhimov Mekhriddin , Turaev Boburkhon , Whangbo Taeg Keun
Published: October 2022

Speech recognition refers to the capability of software or hardware to receive a speech signal, identify the speaker’s features in the speech signal, and recognize the speaker thereafter. In general, the speech recognition process involves three main steps: acoustic processing, feature extraction, and classification/recognition. The purpose of feature extraction is to illustrate a speech signal using a predetermined number of signal components. This is because all information in the acoustic signal is excessively cumbersome to handle, and some information is irrelevant in the identification task. This study proposes a machine learning-based approach that performs feature parameter extraction from speech signals to improve the performance of speech recognition applications in real-time smart city environments. Moreover, the principle of mapping a block of main memory to the cache is used efficiently to reduce computing time. The block size of cache memory is a parameter that strongly affects the cache performance. In particular, the implementation of such processes in real-time systems requires a high computation speed. Processing speed plays an important role in speech recognition in real-time systems. It requires the use of modern technologies and fast algorithms that increase the acceleration in extracting the feature parameters from speech signals. Problems with overclocking during the digital processing of speech signals have yet to be completely resolved. The experimental results demonstrate that the proposed method successfully extracts the signal features and achieves seamless classification performance compared to other conventional speech recognition algorithms.
AI-Based Positioning with Input Parameter Optimization in Indoor VLC Environments
Authors: Oh Sung-Hyun , Kim Jeong-Gon
Published: October 2022

Indoorlocation-based service (LBS) technology has been emerged as a major research topic in recent years. Positioning technology is essential for providing LBSs. The existing indoor positioning solutions generally use radio-frequency (RF)-based communication technologies such as Wi-Fi. However, RF-based communication technologies do not provide precise positioning owing to rapid changes in the received signal strength due to walls, obstacles, and people movement in indoor environments. Hence, this study adopts visible-light communication (VLC) for user positioning in an indoor environment. VLC is based on light-emitting diodes (LEDs) and its advantage includes high efficiency and long lifespan. In addition, this study uses a deep neural network (DNN) to improve the positioning accuracy and reduce the positioning processing time. The hyperparameters of the DNN model are optimized to improve the positioning performance. The trained DNN model is designed to yield the actual three-dimensional position of a user. The simulation results show that our optimized DNN model achieves a positioning error of 0.0898 m with a processing time of 0.5 ms, which means that the proposed method yields more precise positioning than the other methods.
A Transformer-Based Model for Super-Resolution of Anime Image
Authors: Xu Shizhuo , Dutta Vibekananda , He Xin , Matsumaru Takafumi
Published: October 2022

Image super-resolution (ISR) technology aims to enhance resolution and improve image quality. It is widely applied to various real-world applications related to image processing, especially in medical images, while relatively little appliedto anime image production. Furthermore, contemporary ISR tools are often based on convolutional neural networks (CNNs), while few methods attempt to use transformers that perform well in other advanced vision tasks. We propose a so-called anime image super-resolution (AISR) method based on the Swin Transformer in this work. The work was carried out in several stages. First, a shallow feature extraction approach was employed to facilitate the features map of the input image’s low-frequency information, which mainly approximates the distribution of detailed information in a spatial structure (shallow feature). Next, we applied deep feature extraction to extract the image semantic information (deep feature). Finally, the image reconstruction method combines shallow and deep features to upsample the feature size and performs sub-pixel convolution to obtain many feature map channels. The novelty of the proposal is the enhancement of the low-frequency information using a Gaussian filter and the introduction of different window sizes to replace the patch merging operations in the Swin Transformer. A high-quality anime dataset was constructed to curb the effects of the model robustness on the online regime. We trained our model on this dataset and tested the model quality. We implement anime image super-resolution tasks at different magnifications (2×, 4×, 8×). The results were compared numerically and graphically with those delivered by conventional convolutional neural network-based and transformer-based methods. We demonstrate the experiments numerically using standard peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), respectively. The series of experiments and ablation study showcase that our proposal outperforms others.
Health Status Assessment of Diesel Engine Valve Clearance Based on BFA-BOA-VMD Adaptive Noise Reduction and Multi-Channel Information Fusion
Authors: Liu Yangshuo , Kang Jianshe , Wen Liang , Bai Yunjie , Guo Chiming
Published: October 2022

Regarding the problem of the valve gap health status being difficult to assess due to the complex composition of the condition monitoring signal during the operation of the diesel engine, this paper proposes an adaptive noise reduction and multi-channel information fusion method for the health status assessment of diesel engine valve clearance. For the problem of missing fault information of single-channel sensors in condition monitoring, we built a diesel engine valve clearance preset simulation test bench and constructed a multi-sensor acquisition system to realize the acquisition of diesel engine multi-dimensional cylinder head signals. At the same time, for the problem of poor adaptability of most signal analysis methods, the improved butterfly optimization algorithm by the bacterial foraging algorithm was adopted to adaptively optimize the key parameter for variational mode decomposition, with discrete entropy as the fitness value. Then, to reduce the uncertainty of artificially selecting fault characteristics, the characteristic parameters with a higher recognition degree of diesel engine signal were selected through characteristic sensitivity analysis. To achieve an effective dimensionality reduction integration of multi-channel features, a stacked sparse autoencoder was used to achieve deep fusion of the multi-dimensional feature values. Finally, the feature samples were entered into the constructed one-dimensional convolutional neural network with a four-layer parameter space for training to realize the health status assessment of the diesel engine. In addition, we verified the effectiveness of the method by carrying out valve degradation simulation experiments on the diesel engine test bench. Experimental results show that, compared with other common evaluation methods, the method used in this paper has a better health state evaluation effect.
Myoelectric Control Systems for Upper Limb Wearable Robotic Exoskeletons and Exosuits—A Systematic Review
Authors: Fu Jirui , Choudhury Renoa , Hosseini Saba M. , Simpson Rylan , Park Joon-Hyuk
Published: October 2022

In recent years, myoelectric control systems have emerged for upper limb wearable robotic exoskeletons to provide movement assistance and/or to restore motor functions in people with motor disabilities and to augment human performance in able-bodied individuals. In myoelectric control, electromyographic (EMG) signals from muscles are utilized to implement control strategies in exoskeletons and exosuits, improving adaptability and human–robot interactions during various motion tasks. This paper reviews the state-of-the-art myoelectric control systems designed for upper-limb wearable robotic exoskeletons and exosuits, and highlights the key focus areas for future research directions. Here, different modalities of existing myoelectric control systems were described in detail, and their advantages and disadvantages were summarized. Furthermore, key design aspects (i.e., supported degrees of freedom, portability, and intended application scenario) and the type of experiments conducted to validate the efficacy of the proposed myoelectric controllers were also discussed. Finally, the challenges and limitations of current myoelectric control systems were analyzed, and future research directions were suggested.
Stress Monitoring Using Wearable Sensors: A Pilot Study and Stress-Predict Dataset
Authors: Iqbal Talha , Simpkin Andrew J. , Roshan Davood , Glynn Nicola , Killilea John , Walsh Jane , Molloy Gerard , Ganly Sandra , Ryman Hannah , Coen Eileen , Elahi Adnan , Wijns William , Shahzad Atif
Published: October 2022

With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the “Stress-Predict Dataset”, created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring.
Sentiment and Emotional Analysis of Risk Perception in the Herculaneum Archaeological Park during COVID-19 Pandemic
Authors: Garzia Fabio , Borghini Francesco , Bruni Alberto , Lombardi Mara , Minò Ludovica , Ramalingam Soodamani , Tricarico Giorgia
Published: October 2022

This paper proposes a methodology for sentiment analysis with emphasis on the emotional aspects of people visiting the Herculaneum Archaeological Park in Italy during the period of the COVID-19 pandemic. The methodology provides a valuable means of continuous feedback on perceived risk of the site. A semantic analysis on Twitter text messages provided input to the risk management team with which they could respond immediately mitigating any apparent risk and reducing the perceived risk. A two-stage approach was adopted to prune a massively large dataset from Twitter. In the first phase, a social network analysis and visualisation tool NodeXL was used to determine the most recurrent words, which was achieved using polarity. This resulted in a suitable subset. In the second phase, the subset was subjected to sentiment and emotion mapping by survey participants. This led to a hybrid approach of using automation for pruning datasets from social media and using a human approach to sentiment and emotion analysis. Whilst suffering from COVID-19, equally, people suffered due to loneliness from isolation dictated by the World Health Organisation. The work revealed that despite such conditions, people’s sentiments demonstrated a positive effect from the online discussions on the Herculaneum site.
Detecting Inference Attacks Involving Raw Sensor Data: A Case Study
Authors: Lachat Paul , Bennani Nadia , Rehn-Sonigo Veronika , Brunie Lionel , Kosch Harald
Published: October 2022

With the advent of sensors, more and more services are developed in order to provide customers with insights about their health and their appliances’ energy consumption at home. To do so, these services use new mining algorithms that create new inference channels. However, the collected sensor data can be diverted to infer personal data that customers do not consent to share. This indirect access to data that are not collected corresponds to inference attacks involving raw sensor data (IASD). Towards these new kinds of attacks, existing inference detection systems do not suit the representation requirements of these inference channels and of user knowledge. In this paper, we propose RICE-M (Raw sensor data based Inference ChannEl Model) that meets these inference channel representations. Based on RICE-M, we proposed RICE-Sy an extensible system able to detect IASDs, and evaluated its performance taking as a case study the MHEALTH dataset. As expected, detecting IASD is proven to be quadratic due to huge sensor data managed and a quickly growing amount of user knowledge. To overcome this drawback, we propose first a set of conceptual optimizations that reduces the detection complexity. Although becoming linear, as online detection time remains greater than a fixed acceptable query response limit, we propose two approaches to estimate the potential of RICE-Sy. The first one is based on partitioning strategies which aim at partitioning the knowledge of users. We observe that by considering the quantity of knowledge gained by a user as a partitioning criterion, the median detection time of RICE-Sy is reduced by 63%. The second approach is H-RICE-SY, a hybrid detection architecture built on RICE-Sy which limits the detection at query-time to users that have a high probability to be malicious. We show the limits of processing all malicious users at query-time, without impacting the query answer time. We observe that for a ratio of 30% users considered as malicious, the median online detection time stays under the acceptable time of 80 ms, for up to a total volume of 1.2 million user knowledge entities. Based on the observed growth rates, we have estimated that for 5% of user knowledge issued by malicious users, a maximum volume of approximately 8.6 million user’s information can be processed online in an acceptable time.
Real-Time Pipe and Valve Characterisation and Mapping for Autonomous Underwater Intervention Tasks
Authors: Martin-Abadal Miguel , Oliver-Codina Gabriel , Gonzalez-Cid Yolanda
Published: October 2022

Nowadays, more frequently, it is necessary to perform underwater operations such as surveying an area or inspecting and intervening on industrial infrastructures such as offshore oil and gas rigs or pipeline networks. Recently, the use of Autonomous Underwater Vehicles (AUV) has grown as a way to automate these tasks, reducing risks and execution time. One of the used sensing modalities is vision, providing RGB high-quality information in the mid to low range, making it appropriate for manipulation or detail inspection tasks. This work presents the use of a deep neural network to perform pixel-wise 3D segmentation of pipes and valves on underwater point clouds generated using a stereo pair of cameras. In addition, two novel algorithms are built to extract information from the detected instances, providing pipe vectors, gripping points, the position of structural elements such as elbows or connections, and valve type and orientation. The information extracted on spatially referenced point clouds can be unified to form an information map of an inspected area. Results show outstanding performance on the network segmentation task, achieving a mean F1-score value of 88.0% at a pixel-wise level and of 95.3% at an instance level. The information extraction algorithm also showcased excellent metrics when extracting information from pipe instances and their structural elements and good enough metrics when extracting data from valves. Finally, the neural network and information algorithms are implemented on an AUV and executed in real-time, validating that the output information stream frame rate of 0.72 fps is high enough to perform manipulation tasks and to ensure full seabed coverage during inspection tasks. The used dataset, along with a trained model and the information algorithms, are provided to the scientific community.
Wafer-Scale Room-Temperature Bonding of Smooth Au/Ti-Based Getter Layer for Vacuum Packaging
Authors: Matsumae Takashi , Kariya Shingo , Kurashima Yuichi , Thu Le Hac Huong , Higurashi Eiji , Hayase Masanori , Takagi Hideki
Published: October 2022

This study demonstrates room-temperature bonding using a getter layer for the vacuum packaging of microsystems. A thick Ti layer covered with an Au layer is utilized as a getter layer because it can absorb gas molecules in the package. Additionally, smooth Au surfaces can form direct bonds for hermetic sealing at room temperature. Direct bonding using a getter layer can simplify the vacuum packaging process; however, typical getter layers are rough in bonding formation. This study demonstrates two fabrication techniques for smooth getter layers. In the first approach, the Au/Ti layer is bonded to an Au layer on a smooth SiO2 template, and the Au/SiO2 interface is mechanically exfoliated. Although the root-mean-square roughness was reduced from 2.00 to 0.98 nm, the surface was still extremely rough for direct bonding. In the second approach, an Au/Ti/Au multilayer on a smooth SiO2 template is bonded with a packaging substrate, and the Au/SiO2 interface is exfoliated. The transferred Au/Ti/Au getter layer has a smooth surface with the root-mean-square roughness of 0.54 nm and could form wafer-scale direct bonding at room temperature. We believe that the second approach would allow a simple packaging process using direct bonding of the getter layer.
Development, Validation and Application of a Novel UHPLC-UV Method for the Simultaneous Determination of Valsartan and Nifedipine in the New Formulation of Self-Nanoemulsifying Drug Delivery Systems
Authors: Rashid Md Abdur , Bilani Mohammad , Shazly Gamal , Kazi Mohsin
Published: October 2022

Our objective is to develop a robust method to quantify valsartan (VAL) and nifedipine (NIF) in developed a self-nanoemulsifying drug delivery formulation. An in-house ultra-high-performance liquid chromatography (UHPLC UV) method has been developed and validated for the simultaneous determination of VAL and NIF. The UV detection was performed isocratically, with a mobile phase comprised of acetonitrile, methanol and ammonium formate in the ratios of (15:45:40% v/v) and a flow rate of 0.35 mL/min at 236 nm. The correlation coefficients (R2) of this linear regression were 0.9984 for NIF and 0.9997 for VAL, respectively. The limit of detection (LOD) for NIF as 3.78 ppm, whereas the limit of detection (LOD) for VAL was 1.56 ppm. The limit of quantification (LOQ) of NIF and VAL were 11.47 ppm and 4.73 ppm, respectively. This method was effectively utilized to determine the amount of drug entrapped and drug loading efficiency percentage of the NIF and VAL self-nanoemulsifying formulations.
Evaluation of Compressive Geophysical Prospecting Method for the Identification of the Abandoned Goaf at the Tengzhou Section of China’s Mu Shi Expressway
Authors: Zhang Shukun , Jiang Peng , Lu Lu , Wang Shuai , Wang Haohao
Published: October 2022

Subsidence deformation of abandoned goafs can induce cracking, distortion and even collapse of surface buildings (structures), and thus, subsidence deformation poses a great threat. Accurate detection of the abandoned goaf location and overburden morphology is an important prerequisite for stability evaluation and scientific management of surface buildings (structures), and effective detection methods are bottlenecks for accurate detection. Taking the abandoned goaf in the Tengzhou section of China’s Mu Shi expressway as an engineering example, step-by-step detection, traditional detection and combination methods are used to determine the location of the underlying abandoned goaf and overburden morphology. First, we conduct disaster investigation on the expressway and surface within the affected area of the abandoned goaf and initially determine the detection area. Then, according to the principle that the detection range can be examined step-by-step from large to small, the high-density resistivity method is used for detection, and the high-resolution seismic method is further selected to analyze the target area. Then, based on the results of the resistivity method, the position of the abandoned goaf is evaluated with the high-resolution seismic method, and the distributions of the overburden subsidence, the water-filled fractured zone and the caving zone (the three belts) are determined. Finally, boreholes are drilled deep into the bottom of the abandoned goaf at specific locations and the distributions of the abandoned goaf and three belts are verified and corrected with drilling data, acoustic detection and borehole TV imaging technology, thereby providing accurate data on abandoned goafs for highway stability evaluation.
Gravitational Intelligent Decision-Making Model at the Fuzzy Front End with Extrinsic Idea Integration by the K-Means Algorithm
Authors: Kurt Onur Emre , Vayvay Ozalp
Published: October 2022

If the dynamic fuzziness of the Front End (FE) part of New Product Development (NPD) cannot be treated in a timely manner, fuzziness accumulates over other parts of NPD hence NPD can result in costly mistakes. The authors tried to remedy this strategically critical problem by implementing mainstream theoretical/methodological approaches, but they found inherent weaknesses of each. The purpose of this study is to bring an objective and intelligent decision-making model to FE so as to lessen fuzziness of it. Model quantizes ideas based on pillars, and re-clusters them with every new idea addition thanks to combining non-mainstream approaches like K-means, distance-based algorithm with gravitational theory inspiration, an accumulation of idea and an exponential function. Study showed that fuzziness of FE can be lessened by quantizing, and objectively managing. The founded core reasons of fuzziness can guide practitioners and authors for better understanding and coping with fuzziness of FE; moreover, the model can be used by companies. Introducing an objective and intelligent decision-making model working like a human brain to FE is a unique idea that has not been tried ever before.
Quantitative Approaches for Exploring the Influence of Education as Positional Good for Economic Outcomes
Authors: Aldea Anamaria Beatrice , Zamfir Ana-Maria , Davidescu Adriana AnaMaria
Published: October 2022

Education is one of the most important drivers for development and wellbeing both at the level of the society and at an individual level. Recognising the key role of education for social development, economic growth and individual wellbeing, education expansion has become an important objective for educational systems across the world. Education influences distribution of economic outcomes, making people pursue more education in order to obtain higher rewards. While expansion of education accelerates, new theories treat education as a positional good. From this perspective, due to its positional character, returns to education are affected in situations of skills imbalances characterised by a supply of graduates that surpasses the demand of the labour market. This paper employs this new perspective and explores the influence of education on economic outcomes in Romania. The authors present and discuss the use of traditional and new quantitative methods in order to shed light on the positional character of education. Our findings show that, in the case of Romania, the expansion of education did not reach the point at which education can be considered a positional good. The application of such methods is useful to inform a data-driven governance system targeting a better match between the supply and demand for education and skills.
The Impossible, the Unlikely, and the Probable Nudges: A Classification for the Design of Your Next Nudge
Authors: Karlsen Randi , Andersen Anders
Published: October 2022

Nudging provides a way to gently influence people to change behavior towards a desired goal, e.g., by moving towards a healthier or more environmentally friendly lifestyle. Personalized and context-aware digital nudging (named smart nudging) can be a powerful tool for efficient nudging by tailoring nudges to the current situation of each individual user. However, designing smart nudges is challenging, as different users may need different supports to improve their behavior. Determining the next nudge for a specific user must be done based on the user’s current situation, abilities, and potential for improvement. In this paper, we focus on the challenge of designing the next nudge by presenting a novel classification of nudges that distinguishes between (i) nudges that are impossible for the user to follow, (ii) nudges that are unlikely to be followed, and (iii) probable nudges that the user can follow. The classification is tailored to individual users based on user profiles, current situations, and knowledge of previous behaviors. This paper describes steps in the nudge design process and a novel set of principles for designing smart nudges.
Safety after BBIBP-CorV (Sinopharm) COVID-19 Vaccine in Adolescents Aged 10–17 Years in Thailand
Authors: Thonginnetra Saraiorn , Tawinprai Kriangkrai , Niemsorn Krongkwan , Promsena Pathariya , Tandhansakul Manunya , Kasemlawan Naruporn , Ruangkijpaisal Natthanan , Banomyong Narin , Phattraprayoon Nanthida , Rangkakulnuwat Pisuttikan , Vanichsetakul Preeda , Ungtrakul Teerapat , Wittayasak Kasiruck , Thonwirak Nawarat , Soonklang Kamonwan , Mahanonda Nithi
Published: October 2022

Coronavirus disease 2019 affected child health and impacted learning because of the resulting onsite school closures. This prospective cohort study included children aged 10–17 who received two 4 µg doses of BBIBP-CorV administered intramuscularly 21–28 days apart. To assess vaccine safety, 36,808 participants were then followed with paper- and web-based online questionnaire surveys that captured local and systemic reactogenicities following vaccine administration on days 1, 7, and 30. Among participants, 76% (27,880) reported reactogenicity within the first 24 h and 7 days following the first dose. Half (51.41%) of participants experienced pain at the injection site; the majority of cases were mild in severity. Injection site tenderness (37.93%) was another common local reaction. Fatigue (37.89%), myalgia (33.56%), and headache (26.76%) were the most common systemic reactions. On days 2–7 after the first dose, 25.85% of participants experienced adverse reactions. Following the second dose, reactogenicity was 7.6% and 1.09% within 24 h and between days 2–7. The majority of reactions were of mild to moderate severity. We report that two doses of the BBIBP-CorV caused mild to moderate side effects in adolescents in Thailand. The findings confirm the vaccine’s safety profile in this age group.
COVID-19 Vaccination Trends and Side Effects among Egyptian Hemodialysis Patients: A Multicenter Survey Study
Authors: Nassar Mohammed Kamal , Salem Karem Mohamed , Elgamal Mohamed , Abdel-Gawad Sara M. , Tharwat Samar
Published: October 2022

(1) Background: Vaccination may be a key intervention to prevent infection in chronic hemodialysis (CHD) patients. This study aimed to determine the COVID-19 vaccination status in Egyptian CHD patients and to analyze the safety and detailed side effect profile of the COVID-19 vaccine among these patients. (2) Methods: This survey-based study was conducted on 670 end-stage renal disease (ESRD) patients on CHD from 3 December 2021 to 5 February 2022. Subjects were asked about sociodemographic characteristics, clinical and therapeutic data, in addition to their COVID-19 vaccination status. If the subject had been vaccinated, we inquired about the type of vaccine and the side effects that occurred within a few days after administration of the first and second dose of the COVID-19 vaccine. Additionally, subjects were asked about the onset of side effects (days from vaccination), timing of maximum symptoms, intensity of symptoms and their effect on activity and need for medical attention. (3) Results: The study included 670 CHD patients with a mean age of 50.79 years; 58.1% were females. The vast majority (614; 91.6%) of the studied patients received two doses of the vaccine. Side effects were more commonly reported after the first dose than the second dose. The main side effects reported were generalized weakness/fatigue (56%), headache (43.8%) and fever (40.4%), and sore arm/pain was also reported (29.3%). Adverse events mostly occurred within one day after vaccination and the maximum symptoms usually happened on the second day. The median duration of symptoms was 3 days with a maximum duration up to 5 days. The univariate logistic regression analysis showed that male gender (OR 1.848; (95% CI, 1.242–2.749), p = 0.002), age (OR 0.981; (95% CI, 0.969–0.993), p = 0.003), smoking (OR 6.067; (95% CI, 3.514–10.475), p < 0.001), duration since starting HD (OR 0.998; (95% CI, 0.998–0.999), p < 0.001), associated comorbidities (OR 2.202; (95% CI, 1.478–3.281), p < 0.001) and prior COVID-19 infection (OR 3.318; (95% CI, 1.952–5.642), p < 0.001) were the main determinants of adverse events related to COVID-19 vaccination. (4) Conclusions: our preliminary findings support the favorable short-term safety profile of the COVID-19 vaccine among CHD patients, and hence can reassure both clinicians and patients, as well as further promote COVID-19 vaccine administration among these patients.
Adverse Reactions after the Third Dose of the BNT162b2 mRNA COVID-19 Vaccine among Medical School Residents in a Regional Reference University Hospital in Italy
Authors: Rahmani Alborz , Dini Guglielmo , Montecucco Alfredo , Orsi Andrea , Sticchi Laura , Domnich Alexander , Bruzzone Bianca , Pellegrini Luca , Manca Alessia , Ogliastro Matilde , Kusznir Vitturi Bruno , Zacconi Sonia , Debarbieri Nicoletta , Icardi Giancarlo , Durando Paolo
Published: October 2022

The recent emergence of new variants of concern (VOCs) of SARS-CoV-2 and the uncertain duration of protection provided by the primary immunization cycle have highlighted the need for COVID-19 booster vaccinations. However, only a few studies have assessed the safety and reactogenicity profile of mRNA booster doses. Therefore, we conducted an online survey with the aim of assessing the adverse reaction profile in the 7 days following a third dose of the BNT162b2 vaccine in a population of resident physicians who had already been investigated after the primary vaccination. Among the 512 resident physicians (female = 53.2%, mean age = 29.8 years) invited to participate in the survey, 222 completed the survey (56.5% female, mean age of 29.9 years), with an average time from second to third dose of 8.6 months. The most common adverse reactions were local pain (88.3%), fatigue (58.1%), muscle/joint pain (44.1%), and headache (38.3%), all subsiding in 48–72 h. While the local reaction rate was similar to that following the first two doses, the systemic reactions were considerably less common and milder compared to the second vaccination. Nonetheless, over one third (36.1%) of participants reported interference with their normal activities. These results complement our previous findings and could aid occupational and public health professionals in the counselling of vaccinees.
Utilization of Mao (Antidesma thwaitesianum Muell. Arg.) Pomace Meal to Substitute Rice Bran on Feed Utilization and Rumen Fermentation in Tropical Beef Cattle
Authors: Gunun Nirawan , Khejornsart Pichad , Polyorach Sineenart , Kaewpila Chatchai , Kimprasit Thachawech , Sanjun Ittipol , Cherdthong Anusorn , Wanapat Metha , Gunun Pongsatorn
Published: October 2022

This experiment was conducted to investigate the effects of replacing rice bran with mao pomace meal on feed intake, digestibility, and rumen fermentation in beef cattle. Four crossbred (50% Brahman × 50% Thai native) beef cattle with an initial body weight of 195 ± 13 kg and 16 months of age were used in a 4 × 4 Latin square design. The dietary treatments included four levels of RB replacement with mao pomace meal at 0, 33, 67, and 100% in concentrate diets. Rice straw was used as a roughage source, fed ad libitum. Replacement of mao pomace meal with rice bran did not affect (p > 0.05) the intakes of concentrate, rice straw, and total dry matter intake. Ether extract intake decreased linearly when increasing the levels of mao pomace meal (p < 0.01). The experimental diets had no effect (p > 0.05) on the digestibility of fiber and crude protein, while dry matter, organic matter, and ether extract digestibility decreased linearly in the group of mao pomace meal replacing rice bran (p < 0.05). Increasing levels of mao pomace meal in concentrate diets did not alter rumen pH, ammonia–nitrogen, or total volatile fatty acid concentration (p > 0.05). The proportion of propionate increased linearly (p < 0.05), whereas acetate and the acetate to propionate ratio decreased linearly (p < 0.05) when replacing rice bran with mao pomace meal. Moreover, the proportion of propionate was greatest, while acetate was lowest when mao pomace meal was included at 100% in the concentrate diet. In conclusion, the replacement of rice bran with mao pomace meal in a diet could enhance the efficiency of rumen fermentation. Nonetheless, it reduced the digestion of nutrients in tropical beef cattle.
O exercício do horror: modelos de teatralidade trágica nos Excerpta Declamationum de Calpúrnio Flaco e nas Declamationes Minores de Pseudo-Quintiliano
Authors:   , Pontes Jefferson da Silva
Published: October 2022

A retórica mantém uma relação axiomática com o gênero dramático trágico. No contexto imperial de Roma, as declamações e os dramas de Sêneca, o Jovem são exemplos profícuos dessa vinculação intergenérica. Enquanto a dramaturgia senequiana parece ter se servido de elementos intrínsecos da prática escolar na ornamentação dos discursos e na construção dos caráteres dos personagens, a retórica escolar dá-nos indícios de que se valeu não apenas dos mitos trágicos para a elaboração de alguns temas, mas também de certos personagens e suas características típicas. Esta tese propõe mapear o avizinhamento entre declamação e drama trágico com a finalidade de averiguar uma possível circulação de histórias, personagens típicos e de mitemas na composição das querelas declamatórias, uma vez que se notam certos modelos de teatralidade trágica das obras senequianas nas Declamationes Minores de Pseudo-Quintiliano e nos Excerpta Declamationum de Calpúrnio Flaco. Para a realização deste estudo, buscaram-se os mitos e princípios filosóficos utilizados na composição das declamações a partir de um recorte que nos ajudou a identificar não apenas mecanismos de construção textual, mas também certa circularidade de temas que podem dar face e voz à herança filosófica e mito-poética romana. Este trabalho, regido por uma análise literária, foi conduzido por meio das controvérsias calpurnianas e de 13 “Declamações Menores”, que apresentam alguma semelhança temática com aquelas de Calpúrnio Flaco. Além destes corpora, valemo- nos também de algumas das “Cartas a Lucílio” (Ad Lucilium epistulae morales) e das tragédias senequianas “As Fenícias” (Phoenissae), “Agamêmnon” (Agamemnon), “As Troianas” (Troades), “Édipo” (Oedipus), “Fedra” (Phaedra), “Hércules Furioso” (Hercules Furens) e “Tiestes” (Thyestes). O estudo das declamações com o teatro senequiano nos permite demonstrar que, dadas as qualidades literárias das declamações, não é coincidência que elas tenham influenciado e sido influenciadas pelo drama latino.
Secukinumab in the Therapy of Psoriatic Arthritis
Authors: Рахматов А.Б. , Расулова Н.А.
Published: October 2022

Проведение клинико-лабораторных исследований позволило у 64 из 158 (40,5%) пациентов с псориазом установить наличие суставного синдрома с определенной клинической симптоматикой, интенсивность которой была оценена по индексу CASPAR. Особенности клинических проявлений псориатического артрита и данные по экспрессии провоспалительных цитокинов (TNF-α и IL-17) явились основанием для применения генно-инженерных биологических препаратов, в частности секукинумаба, являющегося блокатором IL-17. На курс лечения псориатического артрита понадобилось провести в среднем 10–12 инъекций секукинумаба (доза инъекции препарата составляла 300 мг). Отмечена целесообразность и эффективность применения секукинумаба именно при раннем псориатическом артрите, предупреждающего развитие в дальнейшем инвалидизирующих форм заболевания.

Clinical and laboratory investigation made it possible to establish the of joint syndrome with special clinical symptomatology in 64 of 158 (40.5%) patients with psoriasis, the intensity of which was estimated according to CASPAR index. The features of clinical manifestations of psoriatic arthritis and data on the expression of proinflammatory cytokines (TNF-α and IL-17) were the grounds for using genetically engineered biological drugs, in particular secukinumab, which is an IL-17-blocker. An average of 10–12 injections of secukinumab (an injection dose of the drug was 300 mg) were needed for a course of treatment of psoriatic arthritis. The appropriateness and effectiveness of secukinumab use in early psoriatic arthritis was noted, preventing the development of further disabling forms of the disease.
The Effect of Surface Roughness on Push-Out Force in Aluminum Outer Cage Bushings
Authors: GÜNEY Murat , KABOĞLU Cihan
Published: October 2022

Bilyalama yöntemi parçalar üzerinde pürüzlülüğü arttırmak, atık gerilmeleri önlemek, yorulma ve çatlak oluşumu gibi durumları engellemek için yapılan bir işlemdir. Çaplama işlemi ise süspansiyon sistemini oluşturan parçalardan birisi olan burçların üretimi sonrasında oluşan ölçü farklılıklarını engellemek ve burcu uygun ölçüye getirmek için uygulanan bir yöntemdir. Bu çalışmada, alüminyum dış kafese sahip olan burçlarda bilyalama ve çaplama işlemleri farklı sıralamalar ve farklı parametreler ile gerçekleştirilerek yüzey pürüzlülükleri farklı burçlar elde edilmiştir. Elde edilen burçlar ile gerçekleştirilen montaj işlemleri sonrasında, salıncak üzerindeki çıkma yükleri incelenerek kayıt altına alınmıştır. Artan yüzey pürüzlülüğüne bağlı olarak burçların yuvalarında kayma esnasında karşılaşacağı sürtünme kuvvetleri arttığı için çıkma yüklerinde artış beklenmektedir. Çıkma yükleri incelendiğinde artan yüzey pürüzlülüğünün çıkma yüklerini arttırdığı gözlemlenmiştir.
Experimental Investigation of the Circular Footing Bearing Capacity Rested on Microgrid Reinforced Soil
Authors: YEŞİLTEPE Ömer , KAHRAMAN Mehmet Şerif , TÜREDİ Yakup , ÖRNEK Murat
Published: October 2022

Zayıf zeminlerde inşa edilen yapıların temel tasarımı oldukça önemlidir. Bu tür zeminlerde yüzeysel temeller yetersiz kalıyorsa ya bir zemin iyileştirme yöntemi uygulanmalı ya da uygun görülen bir derin temel sistemi tercih edilmelidir. Zayıf zeminlere geosentetik donatılar yerleştirilerek zeminin taşıma gücü bakımından iyileştirilmesi mümkün olmaktadır. Bu çalışmada daire ve kare kesitlerde hazırlanmış mikrogrid donatı yerleştirilerek güçlendirilmiş gevşek kum zemine yerleştirilen daire temelin eksenel kuvvet altındaki davranışını gözlemlemek için 31 adet laboratuvar deneyi yapılmıştır. Donatının derinliği, donatıların genişliği/çapı, donatıların geometrileri değiştirilerek taşıma gücü bakımından optimum donatı parametreleri yapılan deneylerle araştırılmıştır. Deneyler sonucunda mikrogrid donatıların daire temelin taşıma gücünü arttırdığı tespit edilmiştir. Ayrıca mikrogrid donatıların optimum parametreleri belirlenmiştir.
Sürdürülebilir Enerjiye Dayalı Kablosuz Güç Transferi Teknolojisinin Araştırma ve Analizi
Authors: EREL Mehmet Zahid
Published: October 2022

Kablosuz güç aktarımı (WPT), özellikle son yıllarda sürdürülebilir enerji tabanlı uygulamalarda ilgi çeken bir konu olmuştur. Bunun nedeni hem sosyal hem de politik nedenlerden kaynaklanan enerji talebindeki artıştır. Kablolara ihtiyaç duymadan sağlam ve esnek çözümler sunan WPT teknolojisi, bu makale kapsamında sürdürülebilir enerji uygulamalarında kapsamlı bir şekilde incelenmektedir. Uygulamalar güneş enerjisi ve rüzgâr enerjisi olmak üzere ikiye ayrılmaktadır. Burada, diğer kablosuz şarj teknolojileri arasında endüktif ve mikrodalga kablosuz şarj teknolojileri öne çıkmaktadır. WPT teknolojisinin sürdürülebilir enerji uygulamalarında yararları ve zararları da bu makalede değerlendirilmektedir. Ayrıca, güneş enerjisi uygulamalarında gelecek vaat eden bir teknoloji olarak endüktif güç transferinin tasarımı ve analizi sunulmaktadır. Bu makalenin aynı zamanda sürdürülebilir enerjiye dayalı WPT teknolojisi ile ilgilenen araştırmacılara ışık tutması beklenmektedir.
Geri Döngü Akışlı Ultrasonik Reaktörde Ag+ ile Escherichia coli Dezenfeksiyonu
Authors: KARAER ÖZMEN Fadime
Published: October 2022

After the recent pandemic process, disinfection/sterilization methods used for the control of infectious diseases have gained even more importance. The alternative disinfection studies like ultrasound (US) have been developing due to the various shortcomings of microbial inactivation processes such as chlorine application, ozone and ultraviolet radiation, which are widely applied for the prevention of epidemic diseases caused by pathogenic microorganisms. Within the scope of this study, disinfection treatments were carried out in recycled flow ultrasonic reactors operated at 22kHz, 36kHz and 833kHz ultrasonic frequencies using Escherichia coli bacteria indicated fecal contamination in the water sources. In this article, the combined usage of silver ions (0.1mM, 0.01 mM and 0.005 mM Ag+) and US (22 kHz, 36 kHz and 833 kHz ultrasonic frequencies) were mainly investigated using initial Escherichia coli bacteria concentration of 1x104 CFU/mL. As a result of the study, Escherichia coli was inactivated obtaining 4-log reduction with 22 kHz ultrasonic frequency with the minimum Ag+ concentrations. To conclude, the disinfection was effectively achieved with US and Ag+ methods, and higher Escherichia coli inactivation rate were obtained in US-Ag+ dual processes with faster decontamination time and lower Ag+ concentration due to their synergistic effects.
Ultrasonic Fabrication Of ZnO/Chitosan Nano Permeable Microspheres
Authors: ÜNLÜ Özlem , AKSU Mecit
Published: October 2022

Sonokimyasal reaksiyonlarda, sıvıdaki ultrasonik dalgaların ve kabarcıkların etkileşimi, kimyasal aktivitenin başlaması veya artması gibi birçok olaya neden olabilir. Nano malzemelerin sentezinde akustik kavitasyon sırasında oluşan H ve OH radikalleri bir çok yükseltgenme ve indirgemeleri tetikler. Bu çalışmada, sonokimyasal oluşum ile katalitik ve biyomateryal çekirdek/kabuk morfolojisinin sentezi ve ultrasonik dalga yoğunluğunun biyomalzeme boyutuna etkisi araştırılmıştır. Çekirdek kabuk morfolojisine sahip mikroküreler hazırlamak için yeni bir tek adımlı ultrasonik yöntem geliştirildi. Mikrokürelerin boyutunu ve morfolojisini belirlemek için optik mikroskop, Taramalı Elektron mikroskobu (SEM) kullanıldı. Yüzey morfolojisi, Atomik Kuvvet Mikroskobu (AFM) kullanılarak yeniden incelendi. Sonikasyon süresi 18 dk olarak ayarlandığında istenen mikroküreler elde edildi. Mikrokürelerin kabuğu biyolojik olarak parçalanabilen kitosandır ve çekirdek ZnO nanoparçacıklarıdır. Çekirdek kabuk morfolojisine sahip mikrokürelerin sentezi için geliştirilen reaksiyon yöntemi hızlı ve düşük maliyetlidir.