bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of Paleo Diet on Body Composition, Carbohydrate and Fat Metabolism of Professional Handball Players
Authors: Pięta Aleksandra , Frączek Barbara , Wiecek Magdalena , Mazur-Kurach Paulina
Published: September 2023

The Paleo diet (PD) involves a restriction of carbohydrates and increased fat content (35% energy from carbohydrates, 35% energy from fats and 30% energy from protein). The aim of this study was to examine the effect of the PD on body composition, concentration of carbohydrates and lipids, as well as insulin, irisin, adiponectin and leptin in the blood. A total of 25 handball players were assigned to two groups: 14 in the experimental group (PD) and 11 in the control group (CD), using a PD and a rational diet, respectively. Analysis of body mass and body composition (body mass index, fat mass, lean body mass, fat-free mass, muscle mass, bone mineral content and bone mineral density), as well as blood concentration of metabolism markers (glucose, insulin, total cholesterol, HDL-cholesterol, non-HDL-cholesterol, LDL-cholesterol, triglycerides, free fatty acids, β-hydroxybutyrate, irisin, adiponectin and leptin), were determined at the beginning and after 4 and 8 weeks of nutritional intervention. Body mass was lower (p < 0.01), and adiponectin blood concentration was higher (p = 0.03) in the PD group at the end of the intervention. There were no changes (p ≥ 0.05) in body composition and blood levels of other biochemical markers in either group.
Cost Evaluation of Professional Services in a Rural Community Pharmacy: A Monocentric Exploratory Approach
Authors: Martínez Luis A. , García Cristina , Moreno Lucrecia
Published: September 2023

The increasing pressure on healthcare systems (HCSs) is a cause for concern worldwide. Rising costs, uncertainty about sustainability, and aging populations are the main issues that make it challenging to allocate scarce resources to the needs of HCSs. Clinical professional pharmacy services (PSs) have been shown to help alleviate system stress and to reach the entire population, although a cost of provision is borne. The objective of this study was to evaluate the provision costs of three PSs, a medicine-dispensing service (MDS), a multicompartmental compliance aid system service (MCAS), and a cognitive impairment screening service (CISS), in a rural community pharmacy. A cost analysis was performed using a time-driven activity-based costing model. The time dedicated to PS provision was appropriately recorded, and the corresponding expenses were extracted from the accounting records. A provision time of 4.80 min and a cost of EUR 2.24 were estimated for the MDS, while 18.33 min and EUR 8.73 were calculated for the MCAS, and 122.20 min and EUR 56.72 were calculated for the CISS. The total provision time represented 85% of the pharmacist’s effective working time. Tailored cost analysis is a useful tool for making decisions on the implementation of a PS. Larger studies including a variety of pharmacies and locations are necessary to accurately assess costs and engage in discussions on funding and remuneration.
An Interpretation of the Deep Disagreement between Plato and Protagoras from the Perspective of Contemporary Meta-Ethics and Political Epistemology
Authors: Knoll Manuel
Published: September 2023

Since the early 20th century, two new disciplines emerged in the tradition of analytic philosophy: meta-ethics and political epistemology. Nevertheless, debates on such questions go back to the ancient Greeks and, in particular, to the debates between Plato and Protagoras. This article elucidates the controversy between Plato and the influential sophist Protagoras from the perspective of contemporary meta-ethics and political epistemology. It argues that the main motivation of Plato’s philosophical endeavors is to overcome Protagoras’s skeptical claims that no moral facts and no moral knowledge applicable to political issues exist. The paper defends the thesis that there exists a deep disagreement between Protagoras and Plato on the existence of moral facts and moral knowledge. A deep disagreement is a disagreement that cannot be resolved through the use of reasons and arguments. Applying the foundationalist approach Robert J. Fogelin proposes in his seminal paper “The Logic of Deep Disagreements”, this article argues that the deep disagreement between Protagoras and Plato exists because their political thought is based on “underlying principles” that clash. While Plato’s political philosophy rests on his religious and theological convictions, the political thought of Protagoras is based on his skepticism, relativism, and agnosticism.
Studies of Interactions between Beta-Cyfluthrin and BSA Based on Fluorescence Spectrometry and Ultraviolet Degradation
Authors: Wang Xiaoyan , Wang Xuyang , Ji Rendong , Bian Haiyi , Guo Xinyue , He Ying , Chen Huichang , Abdalla Ahmed N.
Published: September 2023

Pesticides play a pivotal role in modern agriculture, but their potential environmental and health impacts necessitate a comprehensive understanding of their interactions with biological molecules. Beta-cyfluthrin, a widely used synthetic pyrethroid insecticide, is known for its efficiency in pest control. However, its interaction with bovine serum albumin (BSA), a crucial transport protein in living organisms, has not been extensively studied. The interaction between beta-cyfluthrin, a prominent synthetic pyrethroid insecticide, and bovine serum albumin (BSA) was comprehensively investigated using fluorescence spectrometry. Furthermore, the influence of ultraviolet (UV) degradation on the interaction parameters was explored, enhancing our understanding of the impact of environmental conditions on this interaction. The Stern–Volmer equation was employed to determine quenching constants, revealing that the fluorescence quenching mechanism primarily involved static quenching. The temperature variations were studied, showing an increase in the binding constant with rising temperature prior to degradation, while post-UV degradation, an inverse correlation between the binding constant and temperature was observed. The thermodynamic parameters were derived through appropriate equations, unveiling the underlying reaction forces. In the absence of degradation, hydrophobic interactions dominated, whereas after UV degradation, interactions shifted to hydrogen bonding and van der Waals forces. The findings elucidate the nuanced effects of UV degradation on the interaction between beta-cyfluthrin and BSA. This study furnishes critical insights that serve as a scientific foundation for pesticide production and application strategies, accounting for the influence of UV degradation on the intricate interplay between pesticides and BSA.
Ultra-Wideband Tunable Microwave Photonic Filter Based on Thin Film Lithium Niobate
Authors: Han Mengjie , Li Jinye , Wei Chuangchuang , Liu Jianguo
Published: September 2023

In signal processing of the growing semaphore, a microwave photonic filter (MPF) is capable of dealing with high-frequency signals, offering the advantages of high bandwidth, easy tuning, and more. This paper presents an efficient tunable microwave photonic filter that features a wideband tuning capability and narrow-band filtering effect based on the lithium niobate on insulator (LNOI) material platform. A multi-mode waveguide race-track type microring resonator has been designed, along with a thermal electrode that utilizes the thermo-optical effect of lithium niobate to adjust the microring resonator. The packaged device has been tested, with a wideband tunable range of 4.7~38.2 GHz achieved. This allows for cross-band continuous tuning across the C-band to Ka-band range. When supplied with 29.1 mW of electric power, the thermal tuning efficiency reaches 9.2 pm/mW, enabling high-frequency tuning of up to 38.2 GHz. The filter possesses a high resolution, exhibiting a 3 dB bandwidth of 662 MHz.
In Vitro Culture Studies for the Mitigation of Heavy Metal Stress in Plants
Authors: Elazab Doaa , Lambardi Maurizio , Capuana Maurizio
Published: September 2023

Heavy metals are among the most common and dangerous contaminants; their action on plants, as well as the possibility for plants to effectively absorb and translocate them, have been studied for several years, mainly for exploitation in phytoremediation, an environmentally friendly and potentially effective technology proposed and studied for the recovery of contaminated soils and waters. In this work, the analysis has focused on the studies developed using in vitro techniques on the possibilities of mitigating, in plants, the stress due to the presence of heavy metals and/or improving their absorption. These objectives can be pursued with the use of different substances and organisms, which have been examined in detail. The following are therefore presented in this review: an analysis of the role of metals and metalloids; the use of several plant growth regulators, with their mechanisms of action in different physiological phases of the plant; the activity of bacteria and fungi; and the role of other effective compounds, such as ascorbic acid and glutathione.
Reality Does Not Shine, It Twinkles
Authors: Sulis William
Published: September 2023

Arguments have been made that the violation of the CHSH and similar inequalities shows that reality at the quantum level must be non-local. The derivation of Bell inequality is re-examined, and it is shown that violations of these inequalities merely demonstrate the existence of contextuality—they say nothing about the causal influences underlying such contextuality. It is argued that contextual systems do not possess enduring (propositional) properties, merely contingent properties. An example of a classical situation is presented: a two-player co-operative game, the random variables of which are consistently connected in the sense of Dzhafarov, which is contextual, and violates the CHSH inequality. In fact, it also violates the Tsirel’son bound. The key is that this system is generated, and its properties are disposed of, not determined.
Surgical Treatment of Lung Cancer in Situs Inversus Totalis—A Case Report
Authors: Wójcik Janusz , Grodzki Tomasz , Pieróg Jarosław , Wójcik Norbert , Kordykiewicz Dawid , Kiełbowski Kajetan , Morozik Maja , Brożyna Stanisław , Borowik Paulina , Wojtyś Małgorzata Edyta
Published: September 2023

Situs inversus totalis (SIT) is a congenital anomaly that involves the mirror rearrangement of the thoracic and abdominal internal organs. In this paper, we report a 56-year-old male patient with previously confirmed SIT, who was admitted to the hospital for the diagnosis and surgical treatment of non-small-cell lung cancer, located in the sixth right pulmonary segment. The patient underwent segmentectomy with mediastinal and hilar lymphadenectomy. Furthermore, we screened the PubMed and Embase databases for reports of the surgical treatment of patients with SIT and lung cancer. Articles describing inoperable and disseminated disease, as well as patients qualified for palliative treatment, were not included. Ultimately, we compared 21 articles (including the one described here). Data about the clinical condition, tumor characteristics, treatment, and histopathological examination were extracted and summarized.
Formation and Hazard Analysis of Landslide Damming Based on Multi-Source Remote Sensing Data
Authors: Shi Wei , Chen Guan , Meng Xingmin , Bian Shiqiang , Jin Jiacheng , Wu Jie , Huang Fengchun , Chong Yan
Published: September 2023

Remote sensing plays an increasingly important role in the investigation of natural hazards, not only by obtaining specific data related to hazards, but also by realizing targeted research by combining with other data and/or technologies. Small-scale landslide hazard chain events occur frequently in mountainous areas with fragile geological environments and have strong destructive effects, yet have been somewhat understudied. This paper analyzes the Zhoujiaba (ZJB) landslide hazard chain that occurred in Longnan City on 18 August 2020. On the basis of the comprehensive application of multi-source remote sensing data, combined with time-series InSAR technology, electrical resistivity tomography (ERT), and numerical simulations, we studied the formation mechanism, damming characteristics, and potential outburst scenarios of this event. Our research suggests that geological structure and strong natural weathering are the preconditions for landslide development, which is eventually induced by extreme rainfall. Specific topographic conditions determine the rapid sliding and accumulation of landslide materials, and ultimately result in river damming. Our simulation results showed that a flood, rather than a debris flow, will be the result of dam outburst. When the simulated upstream inflow is 1.5 times that when the landslide occurred, 68% of the downstream village area will be flooded. The artificial spillway can effectively reduce the scale of the potential outburst flood, but there remains a risk of dam failure owing to the shallow depth. Our study of the hazard chain of a small-scale landslide using a combination of methods will provide a valuable reference for the analysis and treatment of similar hazard chains.
Correction: Xiong et al. Probabilistic Tracking of Annual Cropland Changes over Large, Complex Agricultural Landscapes Using Google Earth Engine. Remote Sens. 2022, 14, 4896
Authors: Xiong Sitian , Baltezar Priscilla , Crowley Morgan A. , Cecil Michael , Crema Stefano C. , Baldwin Eli , Cardille Jeffrey A. , Estes Lyndon
Published: September 2023

In the original publication [...]
An Improved YOLOv5s-Seg Detection and Segmentation Model for the Accurate Identification of Forest Fires Based on UAV Infrared Image
Authors: Niu Kunlong , Wang Chongyang , Xu Jianhui , Yang Chuanxun , Zhou Xia , Yang Xiankun
Published: September 2023

With the influence of climate change and human activities, the frequency and scale of forest fires have been increasing continuously, posing a significant threat to the environment and human safety. Therefore, rapid and accurate forest fire detection has become essential for effective control of forest fires. This study proposes a Forest Fire Detection and Segmentation Model (FFDSM) based on unmanned aerial vehicle (UAV) infrared images to address the problems of forest fire occlusion and the poor adaptability of traditional forest fire detection methods. The FFDSM integrates the YOLO (You Only Look Once) v5s-seg, Efficient Channel Attention (ECA), and Spatial Pyramid Pooling Fast Cross-Stage Partial Channel (SPPFCSPC) to improve the detection accuracy of forest fires of different sizes. The FFDSM enhances the detection and extraction capabilities of forest fire features, enabling the accurate segmentation of forest fires of different sizes and shapes. Furthermore, we conducted ablation and controlled experiments on different attention mechanisms, spatial pyramid pooling (SPP) modules, and fire sizes to verify the effectiveness of the added modules and the adaptability of the FFDSM model. The results of the ablation experiment show that, compared to the original YOLOv5s-seg model, the models fused with the ECA and SPPFCSPC achieve an improved accuracy, with FFDSM showing the greatest improvement. FFDSM achieves a 2.1% increase in precision, a 2.7% increase in recall, a 2.3% increase in mAP@0.5, and a 4.2% increase in mAP@0.5:0.95. The results of the controlled experiments on different attention mechanisms and SPP modules demonstrate that the ECA+SPPFCSPC model (FFDSM) performs the best, with a precision, recall, mAP@0.5, and mAP@0.5:0.95 reaching 0.959, 0.870, 0.907, and 0.711, respectively. The results of the controlled experiment on different fire sizes show that FFDSM outperforms YOLOv5s-seg for all three fire sizes, and it performs the best for small fires, with a precision, recall, mAP@0.5, and mAP@0.5:0.95 reaching 0.989, 0.938, 0.964, and 0.769, respectively, indicating its good adaptability for early forest fire detection. The results indicate that the forest fire detection model based on UAV infrared images (FFDSM) proposed in this study exhibits a high detection accuracy. It is proficient in identifying obscured fires in optical images and demonstrates good adaptability in various fire scenarios. The model effectively enables real-time detection and provides early warning of forest fires, providing valuable support for forest fire prevention and scientific decision making.
Validating a Tethered Balloon System and Optical Technologies for Marine Wildlife Detection and Tracking
Authors: Amerson Alicia , Gonzalez-Hirshfeld Ilan , Dexheimer Darielle
Published: September 2023

The interactions between marine wildlife and marine energy devices are not well understood, leading to regulatory delays for device deployments and testing. Technologies that enable marine wildlife observations can help to fill data gaps and reduce uncertainties about animal–device interactions. A validation test conducted in Galveston Bay near La Porte, Texas, in December 2022 used a technology package consisting of a tethered balloon system and three independent sensor systems, including three-band visible, eight-band multispectral, and single-band thermal to detect three marine-mammal-shaped surrogates. The field campaign aimed to provide an initial step to evaluating the use of the TBS and the effectiveness of the sensor suite for marine wildlife observations and detection. From 2 December to 7 December 2022, 6 flights were conducted under varying altitudes and environmental conditions resulting in the collection of 5454 images. A subset of the images was classified and analyzed with two collection criteria including Beaufort wind force scale and TBS altitude to assess a range of observations of a surrogate from near-shore to offshore based on pixel count. The results of this validation test demonstrate the potential for using TBSs and imaging sensors for marine wildlife observations and offer valuable information for further development and application of this technology for marine energy and other blue economy sectors.
A Method to Suppress Interferences Based on Secondary Compensation with QPC-FDA-MIMO Radar
Authors: Zhang Yiqun , Liao Guisheng , Xu Jingwei , Zhang Xuepan , Lan Lan
Published: September 2023

The traditional phased array radar is unable to distinguish mainlobe deceptive interference from the true target when they share an identical angle. To address this issue, a method is developed to suppress the interference in the joint transmit–receive–Doppler frequency domain, using secondary compensation and quadratic phase code (QPC) with the frequency diversity array multiple-input multiple-output (FDA-MIMO) radar. The QPC technique is employed by encoding the transmitted pulses in various spatial channels, which extends the degrees-of-freedom (DOFs) of the system. To distinguish and suppress the false targets behind the true target by at least one pulse, Doppler compensation is performed and Doppler filters are employed. To distinguish the fast-generated false targets, which are located in the same pulse with the true target, the spatial frequency compensation is implemented, which equivalently relocates the mainlobe interference to the sidelobes. Via the singular spectrum analysis (SSA) technique and the data-dependent beamforming, the interferences within the ambiguity region are suppressed. Simulation results verify the effectiveness of the method.
Estimation and Mapping of Soil Organic Matter Content Using a Stacking Ensemble Learning Model Based on Hyperspectral Images
Authors: Wu Menghong , Dou Sen , Lin Nan , Jiang Ranzhe , Zhu Bingxue
Published: September 2023

Fast and accurate SOM estimation and spatial mapping are significant for cultivated land planning and management, crop growth monitoring, and soil carbon pool estimation. It is a key problem to construct a fast and efficient estimation model based on hyperspectral remote sensing image data to realize the inversion mapping of SOM in large areas. In order to solve the problem that the estimation accuracy is not high due to the influence of hyperspectral image quality and soil sample quantity during the estimation model construction, this study explored a method for constructing an estimation model of SOM contents based on a new stacking ensemble learning algorithm and hyperspectral images. Surface soil samples in Huangzhong County of Qinghai Province were collected, and their ZY1-02D hyperspectral remote sensing images were investigated. As input data, a feature band dataset was constructed using the Pearson correlation coefficient and successive projections algorithm. Based on the dataset, a new SOM estimation model under the stacking ensemble learning framework combined with heterogeneous models was developed by optimizing the combination of base and meta-learners. Finally, the spatial distribution map of SOM was plotted based on the result of the model over the study area. The result suggested that the input data quality of the estimation model is improved by constructing a feature band dataset. The multi-class ensemble learning estimation model with the combination strategy of the base and meta-learners has better predictive effects and stability than the single-algorithm and single-level ensemble models with homogeneous learners. The coefficient of determination is 0.829, the residual prediction deviation is 2.85, and the predictive set root mean square error is 1.953. The results can provide new ideas for estimating SOM content using hyperspectral images and ensemble learning algorithms, and serve as a reference for mapping large-scale SOM spatial distribution using space-borne hyperspectral images.
A Hybrid Approach for Alluring Ads Phishing Attack Detection Using Machine Learning
Authors: Shaukat Muhammad Waqas , Amin Rashid , Muslam Muhana Magboul Ali , Alshehri Asma Hassan , Xie Jiang
Published: September 2023

Phishing attacks are evolving with more sophisticated techniques, posing significant threats. Considering the potential of machine-learning-based approaches, our research presents a similar modern approach for web phishing detection by applying powerful machine learning algorithms. An efficient layered classification model is proposed to detect websites based on their URL structure, text, and image features. Previously, similar studies have used machine learning techniques for URL features with a limited dataset. In our research, we have used a large dataset of 20,000 website URLs, and 22 salient features from each URL are extracted to prepare a comprehensive dataset. Along with this, another dataset containing website text is also prepared for NLP-based text evaluation. It is seen that many phishing websites contain text as images, and to handle this, the text from images is extracted to classify it as spam or legitimate. The experimental evaluation demonstrated efficient and accurate phishing detection. Our layered classification model uses support vector machine (SVM), XGBoost, random forest, multilayer perceptron, linear regression, decision tree, naïve Bayes, and SVC algorithms. The performance evaluation revealed that the XGBoost algorithm outperformed other applied models with maximum accuracy and precision of 94% in the training phase and 91% in the testing phase. Multilayer perceptron also worked well with an accuracy of 91% in the testing phase. The accuracy results for random forest and decision tree were 91% and 90%, respectively. Logistic regression and SVM algorithms were used in the text-based classification, and the accuracy was found to be 87% and 88%, respectively. With these precision values, the models classified phishing and legitimate websites very well, based on URL, text, and image features. This research contributes to early detection of sophisticated phishing attacks, enhancing internet user security.
Modeling of Ammunition Dynamic Pressure Measurement Chain in Ballistic Tests
Authors: Felix Caio Bittencourt Cardoso , Medeiros Khrissy Aracélly Reis , Barbosa Carlos Roberto Hall
Published: September 2023

The use of piezoelectric transducers for internal dynamic pressure measurements in ammunition testing provides a significant advantage in the development and performance analysis of weapons and ammunition. Knowledge of the electrical characteristics of the dynamic pressure measurement chain, which includes the piezoelectric transducer and the charge amplifier, is a relevant condition for the design of interior ballistics pressure measurement systems. Thus, this study aims to characterize and model a piezoelectric transducer and its associated charge amplifier. First, the piezoelectric transducer was characterized using impedance analysis and modeled using a least squares curve-fitting tool, according to the Butterworth–Van Dyke model. Next, the charge amplifier was characterized through response analysis based on known inputs and modeled using LTSpice simulation techniques and the least squares curve-fit tool. Consequently, a measurement chain model is presented and simulated for two cases with different impulse signals. The first impulse signal was obtained from an interior ballistics computer simulation, and in the second case, it was considered the negative step signal characteristic of the calibration of piezoelectric transducers by means of dead weight. From the simulations, it was possible to verify the effectiveness of the model, which provided results with a low error in relation to the original pressure curve, and its applicability is demonstrated by the result of the simulation of the pressure variation in the calibration, where the attenuation of the signal can be visualized as the characteristic of the input curve changes.
Novel Contactless Sensors for Food, Beverage and Packaging Evaluation
Authors: Gonzalez Viejo Claudia , Torrico Damir D. , Fuentes Sigfredo
Published: September 2023

The use of traditional methods to evaluate food, beverages, and packaging tends to be time-consuming, labour-intensive, and usually involves high costs due to the need for expensive equipment, regular refill of consumables, skilled personnel and, in the case of sensory evaluation, incentives or payments involved for participants recruitment and/or panelists training and participation [...]
The Denoising Method for Transformer Partial Discharge Based on the Whale VMD Algorithm Combined with Adaptive Filtering and Wavelet Thresholding
Authors: Wu Zhongdong , Zhang Zhuo , Zheng Li , Yan Tianfeng , Tang Chunyang
Published: September 2023

Partial discharge (PD) is the primary factor causing insulation degradation in transformers. However, the collected signals of partial discharge are often contaminated with significant noise. This makes it difficult to extract the PD signal and hinders subsequent signal analysis and processing. This paper proposes a denoising method for transformer partial discharge based on the Whale VMD algorithm combined with adaptive filtering and wavelet thresholding (WVNW). First, the WOA is used to optimize the important parameters of the VMD. The selected mode components from the VMD decomposition are then subjected to preliminary denoising based on the kurtosis criterion. The reconstructed signal is further denoised using the Adaptive Filter (NLMS) algorithm to remove narrowband interference noise. Finally, the residual white noise is eliminated using the Wavelet Thresholding algorithm. In simulation experiments and practical measurements, the proposed method is compared quantitatively with previous methods, VMD-WT, and EMD-WT, based on metrics such as SNR, RMSE, NCC, and NRR. The results indicate that the WVNW method effectively suppresses noise interference and restores the original PD signal waveform with high waveform similarity while preserving a significant amount of local discharge signal features.
6D Object Pose Estimation Based on Cross-Modality Feature Fusion
Authors: Jiang Meng , Zhang Liming , Wang Xiaohua , Li Shuang , Jiao Yijie
Published: September 2023

The 6D pose estimation using RGBD images plays a pivotal role in robotics applications. At present, after obtaining the RGB and depth modality information, most methods directly concatenate them without considering information interactions. This leads to the low accuracy of 6D pose estimation in occlusion and illumination changes. To solve this problem, we propose a new method to fuse RGB and depth modality features. Our method effectively uses individual information contained within each RGBD image modality and fully integrates cross-modality interactive information. Specifically, we transform depth images into point clouds, applying the PointNet++ network to extract point cloud features; RGB image features are extracted by CNNs and attention mechanisms are added to obtain context information within the single modality; then, we propose a cross-modality feature fusion module (CFFM) to obtain the cross-modality information, and introduce a feature contribution weight training module (CWTM) to allocate the different contributions of the two modalities to the target task. Finally, the result of 6D object pose estimation is obtained by the final cross-modality fusion feature. By enabling information interactions within and between modalities, the integration of the two modalities is maximized. Furthermore, considering the contribution of each modality enhances the overall robustness of the model. Our experiments indicate that the accuracy rate of our method on the LineMOD dataset can reach 96.9%, on average, using the ADD (-S) metric, while on the YCB-Video dataset, it can reach 94.7% using the ADD-S AUC metric and 96.5% using the ADD-S score (<2 cm) metric.
Enhancing IoT Security through a Green and Sustainable Federated Learning Platform: Leveraging Efficient Encryption and the Quondam Signature Algorithm
Authors: Aljrees Turki , Kumar Ankit , Singh Kamred Udham , Singh Teekam
Published: September 2023

This research paper introduces a novel paradigm that synergizes innovative algorithms, namely efficient data encryption, the Quondam Signature Algorithm (QSA), and federated learning, to effectively counteract random attacks targeting Internet of Things (IoT) systems. The incorporation of federated learning not only fosters continuous learning but also upholds data privacy, bolsters security measures, and provides a robust defence mechanism against evolving threats. The Quondam Signature Algorithm (QSA) emerges as a formidable solution, adept at mitigating vulnerabilities linked to man-in-the-middle attacks. Remarkably, the QSA algorithm achieves noteworthy cost savings in IoT communication by optimizing communication bit requirements. By seamlessly integrating federated learning, IoT systems attain the ability to harmoniously aggregate and analyse data from an array of devices while zealously guarding data privacy. The decentralized approach of federated learning orchestrates local machine-learning model training on individual devices, subsequently amalgamating these models into a global one. Such a mechanism not only nurtures data privacy but also empowers the system to harness diverse data sources, enhancing its analytical capabilities. A thorough comparative analysis scrutinizes varied cost-in-communication schemes, meticulously weighing both encryption and federated learning facets. The proposed approach shines by virtue of its optimization of time complexity through the synergy of offline phase computations and online phase signature generation, hinged on an elliptic curve digital signature algorithm-based online/offline scheme. In contrast, the Slow Block Move (SBM) scheme lags behind, necessitating over 25 rounds, 1500 signature generations, and an equal number of verifications. The proposed scheme, fortified by its marriage of federated learning and efficient encryption techniques, emerges as an embodiment of improved efficiency and reduced communication costs. The culmination of this research underscores the intrinsic benefits of the proposed approach: marked reduction in communication costs, elevated analytical prowess, and heightened resilience against the spectrum of attacks that IoT systems confront.
Image-Based Ship Detection Using Deep Variational Information Bottleneck
Authors: Ngo Duc-Dat , Vo Van-Linh , Nguyen Tri , Nguyen Manh-Hung , Le My-Ha
Published: September 2023

Image-based ship detection is a critical function in maritime security. However, lacking high-quality training datasets makes it challenging to train a robust supervision deep learning model. Conventional methods use data augmentation to increase training samples. This approach is not robust because the data augmentation may not present a complex background or occlusion well. This paper proposes to use an information bottleneck and a reparameterization trick to address the challenge. The information bottleneck learns features that focus only on the object and eliminate all backgrounds. It helps to avoid background variance. In addition, the reparameterization introduces uncertainty during the training phase. It helps to learn more robust detectors. Comprehensive experiments show that the proposed method outperforms conventional methods on Seaship datasets, especially when the number of training samples is small. In addition, this paper discusses how to integrate the information bottleneck and the reparameterization into well-known object detection frameworks efficiently.
Treatment of Diabetes Mellitus by Acupuncture: Dynamics of Blood Glucose Level and Its Mathematical Modelling
Authors: Šimat Marija , Janković Makek Mateja , Mičetić Maja
Published: September 2023

The aim of this research is to present the effects of acupuncture treatment on morning blood glucose level (BGL) in type 2 diabetes mellitus (T2DM) patients, and to describe them by a predictive model. The morning BGL is measured after overnight fasting during a three-month long acupuncture treatment for two persons diagnosed with T2DM and is compared with the BGL of two persons in similar health conditions taking only metformin-based drugs. It is shown that the morning BGL is highly affected by each single acupuncture treatment and by the number of the already applied treatments. Significant lowering of BGL after each treatment is observed, as well as an overall BGL lowering effect, which is the result of the repeated acupuncture. The observed BGL reduction was found to be maintained during a follow-up performed a year after the acupuncture. The measured BGL dynamics curves are analyzed and described by a model. This model describes well all of the key features of the measured BGL dynamics and provides personal parameters that describe the BGL regulation. The model is used to simulate BGL regulation by acupuncture performed with different frequencies. It can be used generally to predict the effects of acupuncture on BGL and to optimize the time between two treatments. The results will enable a better understanding of acupuncture application in diabetes, and a prediction of its effects in diabetes treatment.
Impacts of Air Pollution and Thermal Discomfort in Hospitalizations for Multiple Sclerosis in Sao Paulo, Brazil
Authors: Diniz Fernanda Rodrigues , Gonçalves Fábio L. T. , Zilli Vieira Carolina Letícia , Piacenti-Silva Marina
Published: September 2023

Multiple sclerosis (MS) is an autoimmune, neurological, and demyelinating disease of unknown etiology. Neuroinflammation caused by the disease has been associated with air pollution as well as bioclimatic conditions. The aim of this study was to investigate the impacts of air pollution and human thermal discomfort on hospitalizations for multiple sclerosis in Sao Paulo, Brazil, from 2008 to 2015. Generalized Additive Model for Location Scale and Shape (GAMLSS) with Zero Inflated Poisson was used to relate multiple sclerosis hospitalizations in three age groups (less than 30 years old, between 30 and 50 years old, and more than 50 years old) and gender (female and male) with atmospheric pollutants PM10, SO2, NO2, NO, and NOx and thermal discomfort. The results showed that the exposure to an increase of 1 µg/m3 in SO2 concentration is highly associated with a 10% increase of the risk of MS hospitalization (95% CI: 2–21%) in female patients and a 7.5% (95% CI: 1.5–16%) increase in male patients. PM10 and NO were associated with increased MS risk only for female patients, mainly aged between 30 and 50 years old (2% and 1% increase in hospitalizations, respectively). The cold discomfort was also associated with MS hospitalization, mainly in males (2% increase in hospitalizations; 95% IC: 1–3%). These results are important, since there are few studies that relate air pollution and thermal discomfort with hospitalizations for multiple sclerosis in Brazil.
Evaluation of Empirical Separation Efficiency Theories for Uniflow Cyclones for Different Particle Types and Experimental Verification
Authors: Klapper Vinzenz , Luzi Giovanni , Loekman Soebiakto , Delgado Antonio
Published: September 2023

Cyclones serve as essential devices in various industries for the removal of particulate matter from gases and liquids, contributing to improved equipment efficiency and longevity by mitigating the wear and damage caused by dust and small particles. Uniflow cyclones offer improved accessibility due to their predominantly horizontal orientation. This characteristic enhances the ease of maintenance and operation. This study focuses on investigating the collection efficiency of uniflow centrifugal cyclones for oil mist and fine dust particles ranging from 0.5µm to 29 µm in diameter. The investigation is based on the specific vane angles βv of a swirl inducer from 0∘ to 60∘ at a flow rate V˙ of 130 Ls−1. The measured collection efficiencies are compared with theoretical efficiencies calculated using six different empirical approaches. The different results for oil and fine dust particles are discussed. Comparison of the experimental results with the empirical models demonstrated that certain models closely matched the observed separation efficiencies for different aerosols and vane angles βv (respectively, their induced radial velocities V). Through a systematic examination, this research aims to provide more insight into the validity of empirical approaches for different particle types and compositions using a uniflow-cyclonic system.
Adsorption of Phenols from Aqueous Solution with A pH-Sensitive Surfactant-Modified Bentonite
Authors: Cui Xiangfen , Liao Jingmei , Liu Huaying , Tang Wei , Tie Cheng , Tian Senlin , Li Yingjie
Published: September 2023

The presence of organic pollutants in wastewater remains a prominent environmental concern due to the related ecological and health hazards. In response, this study employs an adsorptive methodology to address the removal of phenol and catechol, utilizing an organo-bentonite material modified with a pH-responsive switchable surfactant, dodecyldimethylamine oxide (C12DAO). The synthesized organo-bentonite (C12DAO-Bt) manifests commendable thermostability resulting from thermogravimetric analyses. The adsorption capacities of C12DAO-Bt concerning phenol and catechol intensify with the augmentation of the C12DAO/bentonite mass ratio. The utmost adsorption capacities of 150C12DAO-Bt, deduced through a pseudo-second-order kinetic model, stand at 5.72 mg·g−1 for phenol and 5.55 mg·g−1 for catechol, respectively. Subject to modification by a pH-responsive surfactant, conditions leaning towards weakly acidic and neutral conditions (pH = 6~7) are conducive to the adsorption of phenolic compounds. Conversely, alkaline conditions (pH = 8~9) facilitate the dissociation of adsorbates from adsorbents. The augmentation of cationic strength within the examined scope incites the adsorption procedure while impeding the desorption efficacy. In the case of cationic species with comparable ionic strengths, Na+ exhibited a superior effect on the adsorption–desorption dynamics of phenol, while Ca2+ exerts a more pronounced effect on those of catechol. Moreover, even following five consecutive acid–base regulation cycles, C12DAO-Bt retains a relatively high adsorption capacity and desorption efficacy, which underscores its exceptional regenerative capacity for removing phenolic compounds from wastewater.
Microwave-Assisted Semisynthesis and Leishmanicidal Activity of Some Phenolic Constituents from Lichens
Authors: Castañeta Grover , Villagomez Rodrigo , Salamanca Efrain , Canaviri-Paz Pamela , Bravo José A. , Vila José L. , Bárcenas-Pérez Daniela , Cheel José , Sepúlveda Beatriz , Giménez Alberto , Areche Carlos
Published: September 2023

Leishmaniasis is considered one of the most untreated tropical diseases in the world. In this study, we investigated the in vitro leishmanicidal activity and cytotoxicity of various isolated lichen substances, including atranorin (1), usnic acid (2), gyrophoric acid (3), salazinic acid (4), galbinic acid (5), and parietin (6), and some semi-synthetic imine derivatives of usnic acid (7, 8, 9) and atranorin (10, 11, 12, 13). Imine condensation reactions with hydrazine and several amines were assisted by microwave heating, an efficient and eco-friendly energy source. The most interesting result was obtained for compound 2, which has high leishmanicidal activity but also high cytotoxicity. This cytotoxicity was mitigated in its derivative, 9, with better selectivity and high antileishmanic activity. This result may indicate that the usnic acid derivative (9) obtained using condensation with two cyclohexylamine groups is a promising lead compound for the discovery of new semisynthetic antiparasitic drugs.
Beyond Staircasing Effect: Robust Image Smoothing via ℓ0 Gradient Minimization and Novel Gradient Constraints
Authors: Matsuoka Ryo , Okuda Masahiro
Published: September 2023

In this paper, we propose robust image-smoothing methods based on ℓ0 gradient minimization with novel gradient constraints to effectively suppress pseudo-edges. Simultaneously minimizing the ℓ0 gradient, i.e., the number of nonzero gradients in an image, and the ℓ2 data fidelity results in a smooth image. However, this optimization often leads to undesirable artifacts, such as pseudo-edges, known as the “staircasing effect”, and halos, which become more visible in image enhancement tasks, like detail enhancement and tone mapping. To address these issues, we introduce two types of gradient constraints: box and ball. These constraints are applied using a reference image (e.g., the input image is used as a reference for image smoothing) to suppress pseudo-edges in homogeneous regions and the blurring effect around strong edges. We also present an ℓ0 gradient minimization problem based on the box-/ball-type gradient constraints using an alternating direction method of multipliers (ADMM). Experimental results on important applications of ℓ0 gradient minimization demonstrate the advantages of our proposed methods compared to existing ℓ0 gradient-based approaches.
Dry Sanitation Technologies: Developing a Simplified Multi-Criteria Decision Analysis Tool
Authors: Santos Margarida Fidélis , Castro Carolina Pires , Matos Rita Ventura , Alves Liliana , Matos José Saldanha
Published: September 2023

Safely managed sanitation is indispensable for societies to ensure public health, environmental protection, and economic and social development. This could be achieved, in large areas of the world, through dry sanitation systems. Dry sanitation systems are especially used in water-scarce regions and low-income households. In dense peri-urban areas, the achievement of safe sanitation necessitates a comprehensive fecal sludge management (FSM) service chain, surpassing the mere provision of latrines. This research introduces an automatic Multi-Criteria Decision Analysis (MCDA) approach, which focuses on the particular interface/storage stage of the FSM service chain. The tool aims to support the decision-making process and may be especially useful in the early stages of sanitation planning as it compares different technologies with potential application in low-income countries. It includes different criteria and parameters for the Social, Economic, Technical, and Environmental dimensions of dry sanitation options (SETEds), being adaptable to different contexts and to different priorities. The main key strengths of the tool were found to be its minimal data requirements and ability to customize operation and maintenance cost parameters. These features are particularly relevant in data-scarce contexts, where traditional models may lead to unreliable recommendations or lack of solution ownership by users. The tool was applied to the Ambriz case study, a coastal town in Northern Angola, in West Africa. The obtained results are analyzed and show the tool’s application provides technology recommendations aligned with the site and community characteristics.
Exploring Citizens’ Adoption of Sustainable Innovations Implemented by Cities and Municipalities in Germany
Authors: Schaefer Cindy , Stelter Aida , Godefroid Marie , Niehaves Björn
Published: September 2023

Sustainable innovations, such as sustainable products or services, are becoming increasingly relevant for society and thrive on societies’ knowledge and usage. But this is where many cities/municipalities are experiencing disharmony: citizens often do not know that these sustainable innovations exist and, therefore, do not use them. Our study starts at this point: To address this disharmony in Germany, the government has established the German Sustainability Award, which honors cities/municipalities with outstanding sustainable innovations. We qualitatively surveyed the citizens and administrations of these cities about their knowledge and use in their city/municipality (using eight categories) under the theoretical lens of Rogers’ (1960/2003) Diffusion of Innovation Theory (DOI). In 29 interviews in six cities/municipalities, we were able to match sustainable innovations based on DOI. In particular, we determined that few sustainable innovations were only found in specific categories (such as economy (EC)), while others existed in all (such as climate and environment (CE) or education (ED)), and, e.g., city/municipality’s size did not matter at all.
Symptoms of Anxiety and Depression in Polish Population in the Context of the War in Ukraine: Analysis of Risk Factors and Practical Implications
Authors: Kasierska Maria , Suwalska Julia , Łojko Dorota , Jakubiak-Głowacka Marta , Tobis Sławomir , Suwalska Aleksandra
Published: September 2023

The aim of the study was to assess the intensity of depressive and anxiety symptoms in those indirectly affected by war in Ukraine and to identify a group of people at particular risk of developing these symptoms. The study encompassed 72 Poles (60 women and 12 men). The measurements were carried out at three time points: (1) in the first month after the outbreak of Russia’s war against Ukraine, (2) in the second month and (3) after six months of the conflict. During the first and second month, the symptom severity of generalized anxiety (GAD-7) was 9.8 ± 5.2 and 7.0 ± 5.6, state anxiety (STAI-X1) was 48.2 ± 10.4 and 45.2 ± 13.9 and depression (BDI) was 10.4 ± 7.5 and 15.4 ± 12.7. After six months, the symptom severity was statistically significantly lower. Greater symptom severity was observed in women, people with low income, those without a job, those who did not have good relationships with people they were close to, those with sleep problems and those who frequently followed the news and talked about the war. This study indicates that in a crisis situation, mental health screening and the identification of people whose condition requires specialized interventions are necessary.