bluuebunny/crossref_metadata_2025 huggingface dataset:
Technological Aspects and Potential Cutaneous Application of Wine Industry By-Products
Authors: Hübner Alexandra de Almeida , de Aguiar Michelle Maria Gonçalves Barão , Demarque Daniel Pecoraro , Rosado Catarina , Velasco Maria Valéria Robles , Kikuchi Irene Satiko , Baby André Rolim , Pessoa Fabiana Vieira Lima Solino
Published: August 2023

The biomass of vinification results in up to 20% by-products (seeds, skins, pulp, and/or stems) that can be used in the production of diverse functional food, nutraceutical, pharmaceutical, and cosmetic ingredients, mainly due to their high polyphenol content. Conventional polyphenol extraction techniques are based on the use of solvents that are harmful to health and to the environment, creating a demand for sustainable complementary initiatives that mitigate part of the environmental effects and offer consumer safety. Current advances in these technologies allow for the recovery of valuable antioxidants from winemaking by-products free of hazardous solvents, biocompatible, and in compliance with international sustainable development guidelines. Nanotechnology has gained prominence in the development of green technologies to reduce or eliminate toxic agents and improve the stability and bioavailability of waste polyphenols. These efforts have led to the application of bioactive compounds from wine by-products in the development of more efficacious sunscreens, as a skin protection approach, and improvements in the antioxidant effectiveness of nanocarriers with potential use in the promotion of cutaneous health. We aimed to present different extraction and encapsulation technologies for biologically active compounds from wine by-products (Vitis vinifera L.). We also focused on a particular application of such compounds towards the development of value-added skin protection products aligned with a sustainable circular economy.
A Foreign Artist and a Russian War: Peter von Hess, a Case Study in Imperial Patronage and National Identity
Authors: Nedd Andrew M.
Published: August 2023

A number of foreign artists received the earliest commissions to represent Napoleon’s Russian Campaign of 1812 for Russian emperors. My paper is a case study of a German artist who served the Russian Imperial court. Peter von Hess trained at the Academy in Munich and served both King Ludwig I of Bavaria and Otto I of Greece. In 1839, Emperor Nicholas I commissioned the artist to complete 12 monumental canvases for the Winter Palace representing key battles that followed Napoleon’s invasion of Russia in 1812. While earlier battle paintings and portraits commissioned by Alexander I dealt only with elite officers and the emperor, Hess’s paintings elevated the common Russian as the bearers of a great sacrifice and as the true defenders of Russia. This representational shift is the product of changing ideas concerning Russia’s involvement in several alliances from 1803 to 1815 that included Austria, England, Sweden, and Prussia. In addition, over the course of Nicholas I’s reign, the concepts of “autocracy, orthodoxy, nationality” crept into representations of the Russian experience of the Napoleonic wars.
Suitability of Low-Cost Sensors for Submicron Aerosol Particle Measurement
Authors: Stoll Daniel , Kerner Maximilian , Paas Simon , Antonyuk Sergiy
Published: August 2023

The measurement and assessment of indoor air quality in terms of respirable particulate constituents is relevant, especially in light of the COVID-19 pandemic and associated infection events. To analyze indoor infectious potential and to develop customized hygiene concepts, the measurement monitoring of the anthropogenic aerosol spreading is necessary. For indoor aerosol measurements usually standard lab equipment is used. However, these devices are time-consuming, expensive and unwieldy. The idea is to replace this standard laboratory equipment with low-cost sensors widely used for monitoring fine dust (particulate matter—PM). Due to the low acquisition costs, many sensors can be used to determine the aerosol load, even in large rooms. Thus, the aim of this work is to verify the measurement capability of low-cost sensors. For this purpose, two different models of low-cost sensors are compared with established laboratory measuring instruments. The study was performed with artificially prepared NaCl aerosols with a well-defined size and morphology. In addition, the influence of the relative humidity, which can vary significantly indoors, on the measurement capability of the low-cost sensors is investigated. For this purpose, a heating stage was developed and tested. The results show a discrepancy in measurement capability between low-cost sensors and laboratory measuring instruments. This difference can be attributed to the partially different measuring method, as well as the different measuring particle size ranges. The determined measurement accuracy is nevertheless good, considering the compactness and the acquisition price of the low-cost sensors.
A Novel Sequence-to-Sequence Prediction Model for Lithium-Ion Battery Capacity Degradation Based on Improved Particle Swarm Optimization
Authors: Chen Dinghong , Zhang Weige , Zhang Caiping , Sun Bingxiang , Chen Haoze , Yang Sijia , Cong Xinwei
Published: August 2023

The state of health (SOH) evaluation and remaining useful life (RUL) prediction for lithium-ion batteries (LIBs) are crucial for health management. This paper proposes a novel sequence-to-sequence (Seq2Seq) prediction method for LIB capacity degradation based on the gated recurrent unit (GRU) neural network with the attention mechanism. An improved particle swarm optimization (IPSO) algorithm is developed for automatic hyperparameter search of the Seq2Seq model, which speeds up parameter convergence and avoids getting stuck in local optima. Before model training, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm decomposes the capacity degradation sequences. And the intrinsic mode function (IMF) components with the highest correlation are employed to reconstruct the sequences, reducing the influence of noise in the original data. A real-cycle-life data set under fixed operating conditions is employed to validate the superiority and effectiveness of the method. The comparison results demonstrate that the proposed model outperforms traditional GRU and RNN models. The predicted mean absolute percent error (MAPE) in SOH evaluation and RUL prediction can be as low as 0.76% and 0.24%, respectively.
WA-ResUNet: A Focused Tail Class MRI Medical Image Segmentation Algorithm
Authors: Pan Haixia , Gao Bo , Bai Wenpei , Li Bin , Li Yanan , Zhang Meng , Wang Hongqiang , Zhao Xiaoran , Chen Minghuang , Yin Cong , Kong Weiya
Published: August 2023

Medical image segmentation can effectively identify lesions in medicine, but some small and rare lesions cannot be well identified. Existing studies do not take into account the uncertainty of the occurrence of diseased tissue, and the problem of long-tailed distribution of medical data. Meanwhile, the grayscale image obtained from Magnetic Resonance Imaging (MRI) detection has problems, such as the features being difficult to extract and invalid features being difficult to distinguish. In order to solve these problems, we propose a new weighted attention ResUNet (WA-ResUNet) and a class weight formula based on the number of images contained in the class, which improves the performance of the model in the low-frequency class and the overall effect of the model by improving the degree of attention paid to the valid features and invalid ones and rebalancing the learning efficiency among the classes. We evaluated our method on an uterine MRI dataset and compared it with the ResUNet. WA-ResUNet increased Intersection over Union (IoU) in the low-frequency class (Nabothian cysts) by 21.87%, and the overall mIoU increased by more than 6.5%.
Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome
Authors: Arora Mehak , Davis Carolyn M. , Gowda Niraj R. , Foster Dennis G. , Mondal Angana , Coopersmith Craig M. , Kamaleswaran Rishikesan
Published: August 2023

Acute Respiratory Distress Syndrome (ARDS) is a severe lung injury with high mortality, primarily characterized by bilateral pulmonary opacities on chest radiographs and hypoxemia. In this work, we trained a convolutional neural network (CNN) model that can reliably identify bilateral opacities on routine chest X-ray images of critically ill patients. We propose this model as a tool to generate predictive alerts for possible ARDS cases, enabling early diagnosis. Our team created a unique dataset of 7800 single-view chest-X-ray images labeled for the presence of bilateral or unilateral pulmonary opacities, or ‘equivocal’ images, by three blinded clinicians. We used a novel training technique that enables the CNN to explicitly predict the ‘equivocal’ class using an uncertainty-aware label smoothing loss. We achieved an Area under the Receiver Operating Characteristic Curve (AUROC) of 0.82 (95% CI: 0.80, 0.85), a precision of 0.75 (95% CI: 0.73, 0.78), and a sensitivity of 0.76 (95% CI: 0.73, 0.78) on the internal test set while achieving an (AUROC) of 0.84 (95% CI: 0.81, 0.86), a precision of 0.73 (95% CI: 0.63, 0.69), and a sensitivity of 0.73 (95% CI: 0.70, 0.75) on an external validation set. Further, our results show that this approach improves the model calibration and diagnostic odds ratio of the hypothesized alert tool, making it ideal for clinical decision support systems.
Neuronavigated Cerebellar 50 Hz tACS: Attenuation of Stimulation Effects by Motor Sequence Learning
Authors: Herzog Rebecca , Bolte Christina , Radecke Jan-Ole , von Möller Kathinka , Lencer Rebekka , Tzvi Elinor , Münchau Alexander , Bäumer Tobias , Weissbach Anne
Published: August 2023

Cerebellar transcranial alternating current stimulation (tACS) is an emerging non-invasive technique that induces electric fields to modulate cerebellar function. Although the effect of cortical tACS seems to be state-dependent, the impact of concurrent motor activation and the duration of stimulation on the effects of cerebellar tACS has not yet been examined. In our study, 20 healthy subjects received neuronavigated 50 Hz cerebellar tACS for 40 s or 20 min, each during performance using a motor sequence learning task (MSL) and at rest. We measured the motor evoked potential (MEP) before and at two time points after tACS application to assess corticospinal excitability. Additionally, we investigated the online effect of tACS on MSL. Individual electric field simulations were computed to evaluate the distribution of electric fields, showing a focal electric field in the right cerebellar hemisphere with the highest intensities in lobe VIIb, VIII and IX. Corticospinal excitability was only increased after tACS was applied for 40 s or 20 min at rest, and motor activation during tACS (MSL) cancelled this effect. In addition, performance was better (shorter reaction times) for the learned sequences after 20 min of tACS, indicating more pronounced learning under 20 min of tACS compared to tACS applied only in the first 40 s.
Correction: Matysiak et al. Diagnosis of Hymenoptera Venom Allergy: State of the Art, Challenges, and Perspectives. Biomedicines 2022, 10, 2170
Authors: Matysiak Joanna , Matuszewska Eliza , Packi Kacper , Klupczyńska-Gabryszak Agnieszka
Published: August 2023

In the original article [...]
Towards Artificial Intelligence Applications in Next Generation Cytopathology
Authors: Giarnieri Enrico , Scardapane Simone
Published: August 2023

Over the last 20 years we have seen an increase in techniques in the field of computational pathology and machine learning, improving our ability to analyze and interpret imaging. Neural networks, in particular, have been used for more than thirty years, starting with the computer assisted smear test using early generation models. Today, advanced machine learning, working on large image data sets, has been shown to perform classification, detection, and segmentation with remarkable accuracy and generalization in several domains. Deep learning algorithms, as a branch of machine learning, are thus attracting attention in digital pathology and cytopathology, providing feasible solutions for accurate and efficient cytological diagnoses, ranging from efficient cell counts to automatic classification of anomalous cells and queries over large clinical databases. The integration of machine learning with related next-generation technologies powered by AI, such as augmented/virtual reality, metaverse, and computational linguistic models are a focus of interest in health care digitalization, to support education, diagnosis, and therapy. In this work we will consider how all these innovations can help cytopathology to go beyond the microscope and to undergo a hyper-digitalized transformation. We also discuss specific challenges to their applications in the field, notably, the requirement for large-scale cytopathology datasets, the necessity of new protocols for sharing information, and the need for further technological training for pathologists.
Comparison of Autografts and Biodegradable 3D-Printed Composite Scaffolds with Osteoconductive Properties for Tissue Regeneration in Bone Tuberculosis
Authors: Vinogradova Tatiana I. , Serdobintsev Mikhail S. , Korzhikova-Vlakh Evgenia G. , Korzhikov-Vlakh Viktor A. , Kaftyrev Alexander S. , Blum Natalya M. , Semenova Natalya Yu. , Esmedlyaeva Dilyara S. , Dyakova Marina E. , Nashchekina Yulia A. , Dogonadze Marine Z. , Zabolotnykh Natalia V. , Yablonsky Petr K.
Published: August 2023

Tuberculosis remains one of the major health problems worldwide. Besides the lungs, tuberculosis affects other organs, including bones and joints. In the case of bone tuberculosis, current treatment protocols include necrectomy in combination with conventional anti-tuberculosis therapy, followed by reconstruction of the resulting bone defects. In this study, we compared autografting and implantation with a biodegradable composite scaffold for bone-defect regeneration in a tuberculosis rabbit model. Porous three-dimensional composite materials were prepared by 3D printing and consisted of poly(ε-caprolactone) filled with nanocrystalline cellulose modified with poly(glutamic acid). In addition, rabbit mesenchymal stem cells were adhered to the surface of the composite scaffolds. The developed tuberculosis model was verified by immunological subcutaneous test, real-time polymerase chain reaction, biochemical markers and histomorphological study. Infected animals were randomly divided into three groups, representing the infection control and two experimental groups subjected to necrectomy, anti-tuberculosis treatment, and plastic surgery using autografts or 3D-composite scaffolds. The lifetime observation of the experimental animals and analysis of various biochemical markers at different time periods allowed the comparison of the state of the animals between the groups. Micro-computed tomography and histomorphological analysis enabled the evaluation of osteogenesis, inflammation and cellular changes between the groups, respectively.
Effective Quantum Graph Models of Some Nonequilateral Graphyne Materials
Authors: de Oliveira César R. , Rocha Vinícius L.
Published: August 2023

It is shown that it is possible to adapt the quantum graph model of graphene to some types of nonequilateral graphynes considered in the literature; we also discuss the corresponding nanotubes. The proposed models are, in fact, effective models and are obtained through selected boundary conditions and an ad hoc prescription. We analytically recover some results from the literature, in particular, the presence of Dirac cones for α-, β- and (6,6,12)-graphynes; for γ-graphyne, our model presents a band gap (according to the literature), but only for a range of parameters, with a transition at a certain point with quadratic touch and then the presence of Dirac cones.
Ratiometric Detection of Zn2+ Using DNAzyme-Based Bioluminescence Resonance Energy Transfer Sensors
Authors: Wu Yuting , Lewis Whitney , Wai Jing Luen , Xiong Mengyi , Zheng Jiao , Yang Zhenglin , Gordon Chloe , Lu Ying , New Siu Yee , Zhang Xiao-Bing , Lu Yi
Published: August 2023

While fluorescent sensors have been developed for monitoring metal ions in health and diseases, they are limited by the requirement of an excitation light source that can lead to photobleaching and a high autofluorescence background. To address these issues, bioluminescence resonance energy transfer (BRET)-based protein or small molecule sensors have been developed; however, most of them are not highly selective nor generalizable to different metal ions. Taking advantage of the high selectivity and generalizability of DNAzymes, we report herein DNAzyme-based ratiometric sensors for Zn2+ based on BRET. The 8-17 DNAzyme was labeled with luciferase and Cy3. The proximity between luciferase and Cy3 permitted BRET when coelenterazine, the substrate for luciferase, was introduced. Adding samples containing Zn2+ resulted in a cleavage of the substrate strand, causing dehybridization of the DNAzyme construct, thus increasing the distance between Cy3 and luciferase and changing the BRET signals. Using these sensors, we detected Zn2+ in serum samples and achieved Zn2+ detection with a smartphone camera. Moreover, since the BRET pair is not the component that determines the selectivity of the sensors, this sensing platform has the potential to be adapted for the detection of other metal ions with other metal-dependent DNAzymes.
Fabrication and Characterization of Hybrid and Tunable ZnO@Ag Flexible Thin Films Used as SERS Substrates
Authors: Brezeștean Ioana Andreea , Marconi Daniel , Dina Nicoleta Elena , Suciu Maria , Colniță Alia
Published: August 2023

Flexible substrates have known increased popularity over rigid ones due to their use in surface-enhanced Raman scattering (SERS). They provide irregular surfaces, ideal for in situ sensing. In this context, we report the SERS performance of hybrid ZnO@Ag thin films deposited by magnetron sputtering (MS) on flexible, thermoplastic substrates. This physical deposition method is acknowledged for obtaining high-quality and reproducible ZnO films that can be embedded in (bio)sensing devices with various applications. Three types of thermoplastic-based, commercially available substrates with different glass transition temperatures (Tg) were chosen for the variation in flexibility, transparency, and thickness. Zeonor® (Tg = 136 °C, thickness of 188 μm) and two types of Topas (Topas®: Tg = 142 °C, thickness of 176 μm; Topas2: Tg = 78 °C, thickness of 140 μm) thermoplastic sheets are nonpolar and amorphous cyclo-olefin polymer (COP) and cyclo-olefin copolymers (COC), respectively. Their thicknesses and different values of Tg can greatly affect the topographical and roughness properties of films with small thicknesses and, thus, can greatly influence the enhancement of the Raman signal. The ZnO films deposited on top of Zeonor® or Topas® have identical morphological properties, as shown by the scanning electron microscopy (SEM) characterization. Subsequently, by using the MS technique, we tuned the thickness of the deposited silver (Ag) films in the range of 7–30 nm to assess the growth influence on the morphology and the SERS signal amplification of the substrates with and without the ZnO intermediate layer. The SEM analysis showed that the Ag atoms migrated both into the interstitial areas, filling the voids between the ZnO granular structures, and over the latter, forming, in this case, isolated Ag clusters. SERS analysis performed on the ZnO-Ag hybrid films using crystal violet (CV) molecule revealed a limit of detection (LOD) of 10−7 M in the case of 15 nm thick Ag/Zeonor® interlayer films ZnO and relative standard deviation (RSD) below 10%.
Brain Volumes and Cognition in Patients with Sickle Cell Anaemia: A Systematic Review and Meta-Analysis
Authors: Hamdule Shifa , Kirkham Fenella J.
Published: August 2023

Cognitive decline is a major problem in paediatric and adult patients with sickle cell anaemia (SCA) and affects the quality of life. Multiple studies investigating the association between quantitative and qualitative neuroimaging findings and cognition have had mixed results. Hence, the aetiology of cognitive decline in this population is not clearly understood. Several studies have established cerebral atrophy in SCA children as well as adults, but the relationship between cognition and brain volumes remains unclear. The purpose of this systematic review was therefore to evaluate the literature on regional brain volumes and their association with cognitive outcomes. We also meta-analysed studies which compared regional brain volumes between patients and controls. Studies report that patients with SCA tend to have lower grey matter volumes, including total subcortical volumes in childhood as compared to controls, which stabilise in young adulthood and may be subjected to decline with age in older adulthood. White matter volumes remain stable in children but are subjected to reduced volumes in young adulthood. Age and haemoglobin are better predictors of cognitive outcomes as compared to regional brain volumes.
An Optimized Dip Coating Approach for Metallic, Dielectric, and Semiconducting Nanomaterial-Based Optical Thin Film Fabrication
Authors: Sarkar Arnab Kumar , Sarmah Devabrata , Baruah Sunandan , Datta Pranayee
Published: August 2023

The field of optical thin films has garnered significant attention due to their potential applications in visible light communication, optical sensing, and imaging. Among the various fabrication methods available, conventional layer-by-layer (LBL) dip coating is less sophisticated and more economical. Nevertheless, this approach frequently encounters deficiencies in the precise control of the growth of thin films. This work aimed at properly comprehending the growth conditions associated with the LBL dip coating process and optimizing the conditions to obtain the best thin film growth for different materials: metallic (Ag), semiconducting (ZnO), and insulating (SiO2). The optimization of the conditions for surface functionalization with 3-aminopropyltriethoxysilane (APTES) together with other parameters like dipping time, drying time, the number of dipping–drying cycles, and the timing of the intermediate APTES layers led to the controlled growth of thin films. Atomic force microscopy and scanning electron microscopy revealed even deposition in the case of ZnO and SiO2 from the very beginning, while with Ag NPs, the growth of the thin film was observed to be uneven and gradually became smooth as the number of layers increased, and a smooth layer could be observed after over 100 layers of dipping.
Characterization of Liquid Adsorption Layers Formed from Aqueous Polymer–Surfactant Solutions—Significant Contributions by Boris A. Noskov
Authors: Milyaeva Olga , Bykov Alexey , Miller Reinhard
Published: August 2023

In many modern technologies, surface-active compounds, such as surfactants, polymers, proteins, particles and their mixtures, are essential components. They change the dynamic and equilibrium properties of the inherent interfaces, which is mostly visible in foams and emulsions. The interfacial dilational visco-elasticity is probably the most informative quantity due to its direct interrelation to the equation of state of the corresponding interfacial layers as well as the mechanisms governing the interfacial molecular dynamics. The scientific field of interfacial visco-elasticity, although quite young, has been inspired by the pioneering work of Marangoni, Levich, Lucassen, Lucassen-Reynders, Hansen, van den Tempel and Krotov, and during the last decades, also significantly by Boris Noskov. His contributions to the theoretical foundation and experimental analysis of polymer and mixed surfactant–polymer interfacial layers in particular are essential.
Revealing the Genetic Code Symmetries through Computations Involving Fibonacci-like Sequences and Their Properties
Authors: Négadi Tidjani
Published: August 2023

In this work, we present a new way of studying the mathematical structure of the genetic code. This study relies on the use of mathematical computations involving five Fibonacci-like sequences; a few of their “seeds” or “initial conditions” are chosen according to the chemical and physical data of the three amino acids serine, arginine and leucine, playing a prominent role in a recent symmetry classification scheme of the genetic code. It appears that these mathematical sequences, of the same kind as the famous Fibonacci series, apart from their usual recurrence relations, are highly intertwined by many useful linear relationships. Using these sequences and also various sums or linear combinations of them, we derive several physical and chemical quantities of interest, such as the number of total coding codons, 61, obeying various degeneracy patterns, the detailed number of H/CNOS atoms and the integer molecular mass (or nucleon number), in the side chains of the coded amino acids and also in various degeneracy patterns, in agreement with those described in the literature. We also discover, as a by-product, an accurate description of the very chemical structure of the four ribonucleotides uridine monophosphate (UMP), cytidine monophosphate (CMP), adenosine monophosphate (AMP) and guanosine monophosphate (GMP), the building blocks of RNA whose groupings, in three units, constitute the triplet codons. In summary, we find a full mathematical and chemical connection with the “ideal sextet’s classification scheme”, which we alluded to above, as well as with others—notably, the Findley–Findley–McGlynn and Rumer’s symmetrical classifications.
A Systematic Review of the Use of mHealth in Oral Health Education among Older Adults
Authors: Chau Reinhard Chun Wang , Thu Khaing Myat , Chaurasia Akhilanand , Hsung Richard Tai Chiu , Lam Walter Yu-Hang
Published: August 2023

Oral diseases are largely preventable. However, as the number of older adults is expected to increase, along with the high cost and various barriers to seeking continuous professional care, a sustainable approach is needed to assist older adults in maintaining their oral health. Mobile health (mHealth) technologies may facilitate oral disease prevention and management through oral health education. This review aims to provide an overview of existing evidence on using mHealth to promote oral health through education among older adults. A literature search was performed across five electronic databases. A total of five studies were identified, which provided low to moderate evidence to support using mHealth among older adults. The selected studies showed that mHealth could improve oral health management, oral health behavior, and oral health knowledge among older adults. However, more quality studies regarding using mHealth technologies in oral health management, oral health behavior, and oral health knowledge among older adults are needed.
Geometric Insights into the Multivariate Gaussian Distribution and Its Entropy and Mutual Information
Authors: Jwo Dah-Jing , Cho Ta-Shun , Biswal Amita
Published: August 2023

In this paper, we provide geometric insights with visualization into the multivariate Gaussian distribution and its entropy and mutual information. In order to develop the multivariate Gaussian distribution with entropy and mutual information, several significant methodologies are presented through the discussion, supported by illustrations, both technically and statistically. The paper examines broad measurements of structure for the Gaussian distributions, which show that they can be described in terms of the information theory between the given covariance matrix and correlated random variables (in terms of relative entropy). The content obtained allows readers to better perceive concepts, comprehend techniques, and properly execute software programs for future study on the topic’s science and implementations. It also helps readers grasp the themes’ fundamental concepts to study the application of multivariate sets of data in Gaussian distribution. The simulation results also convey the behavior of different elliptical interpretations based on the multivariate Gaussian distribution with entropy for real-world applications in our daily lives, including information coding, nonlinear signal detection, etc. Involving the relative entropy and mutual information as well as the potential correlated covariance analysis, a wide range of information is addressed, including basic application concerns as well as clinical diagnostics to detect the multi-disease effects.
Preserving Microbial Biodiversity: The Case of Food-Associated Microorganisms
Authors: Paramithiotis Spiros , Dimopoulou Maria
Published: August 2023

The preservation of microbial diversity is an issue not properly addressed, considering their role in shaping Earth into a habitable planet and their contribution to human well-being. The disturbance of their natural habitats triggers responses, which are reflected in the modification of microecosystem composition and metabolic activities. This is also the case with food-related microecosystems; changes in the growing environment, recorded as agricultural practices and manufacturing or storage conditions, result in similar alterations in the residing microcommunity. In fact, the principle aim of food microbiology is to favor the growth of health-promoting microorganisms and restrict the development of the ones that may negatively affect the quality of food or even cause infection or intoxication. Therefore, the current perspective is one-sided, disregarding issues of general interest, such as the preservation of actual biodiversity. The aim of the present article is to present the current food microbiology perspective, which is based on the different roles of food-related microbiota and highlight the need to move from an anthropocentric to a microbe-centric perception.
Ecosystem-Based Adaptation Practices of Smallholder Farmers in the Oti Basin, Togo: Probing Their Effectiveness and Co-Benefits
Authors: Kissi Abravi Essenam , Villamor Grace B. , Abbey Georges Abbevi
Published: August 2023

The ecosystem-based adaptation (EbA) strategy is considered an effective approach to address the impact of climate change while ensuring the continued provision of ecosystem services on which farming depends. However, understanding the EbA’s effectiveness for smallholder farmers in the Savannah region remains limited. The focus of this study is to explore the EbA practices that have been implemented by farming communities in the Savannah region of Togo. The study aims to evaluate the effectiveness of these practices and the perceived co-benefits reported by 425 smallholder farmers who participated in the survey. Our findings show that five practices, namely agroforestry, crop rotation, grass hedge/stone bunds, in-field water drainage channel, and intercropping, were practiced mainly by smallholder farmers and perceived as effective in reducing their vulnerability to climate risks. In addition, the benefits observed were linked to all five EbA practices. As a result, we can determine the suitable combination of EbA practices that fulfil the requirements of smallholder farmers, including co-benefits such as food security, adaptation advantages, and ecosystem service provisions. Such findings provide insights for developing integrated agriculture and climate change policies suitable for weather-induced disaster-prone areas such as the Savannah region.
Improving the Learning of Superheterodyne Demodulation of Frequency-Division Multiplexing Signals via the Educational Software Tool DOSHER
Authors: Pardo-Quiles Domingo , Rodríguez José-Víctor , Juan-Llácer Leandro , Morales-Sánchez Juan , Rodríguez-Rodríguez Ignacio
Published: August 2023

The knowledge of signal demodulation processes using superheterodyne receivers is of great importance in the field of telecommunications. Superheterodyne receivers receive the current focus of many scientists in a wide variety of applications. This topic is part of the syllabus of the Communication Theory course at the School of Telecommunications of the Polytechnic University of Cartagena, Spain. The authors found that the academic performance of the students had not been entirely satisfactory in recent years. This situation was aggravated during the COVID-19 lockdown. Students had to reinforce their knowledge independently at home, despite the support provided by teachers. To the best of the authors’ knowledge, there is a noticeable lack of educational tools in this area; of those that are available, they exhibit a mismatch with the specific needs of this subject. This manuscript shows how the design of the educational software tool DOSHER, tailored to enhance the understanding of superheterodyne receivers, successfully alleviated the aforementioned drawbacks. DOSHER was designed, developed, and applied during the 2020–2021 academic year (during the COVID-19 lockdown). The results show that students were not only very satisfied with its use, but they also improved their marks. Analysis of students’ academic performance in the year of DOSHER implementation showed an average improvement in their marks of between 9–12% compared with previous years. In 2021–2022, when DOSHER was fully operational from the start, the improvement in terms of pass rate (31%) at the first mid-term was significant compared to previous years (<20%).
Enhancing Privacy-Preserving Intrusion Detection through Federated Learning
Authors: Alazab Ammar , Khraisat Ansam , Singh Sarabjot , Jan Tony
Published: August 2023

Detecting anomalies, intrusions, and security threats in the network (including Internet of Things) traffic necessitates the processing of large volumes of sensitive data, which raises concerns about privacy and security. Federated learning, a distributed machine learning approach, enables multiple parties to collaboratively train a shared model while preserving data decentralization and privacy. In a federated learning environment, instead of training and evaluating the model on a single machine, each client learns a local model with the same structure but is trained on different local datasets. These local models are then communicated to an aggregation server that employs federated averaging to aggregate them and produce an optimized global model. This approach offers significant benefits for developing efficient and effective intrusion detection system (IDS) solutions. In this research, we investigated the effectiveness of federated learning for IDSs and compared it with that of traditional deep learning models. Our findings demonstrate that federated learning, by utilizing random client selection, achieved higher accuracy and lower loss compared to deep learning, particularly in scenarios emphasizing data privacy and security. Our experiments highlight the capability of federated learning to create global models without sharing sensitive data, thereby mitigating the risks associated with data breaches or leakage. The results suggest that federated averaging in federated learning has the potential to revolutionize the development of IDS solutions, thus making them more secure, efficient, and effective.
The Driving Factors of Italy’s CO2 Emissions Based on the STIRPAT Model: ARDL, FMOLS, DOLS, and CCR Approaches
Authors: Pattak Dulal Chandra , Tahrim Farian , Salehi Mahdi , Voumik Liton Chandra , Akter Salma , Ridwan Mohammad , Sadowska Beata , Zimon Grzegorz
Published: August 2023

As the sustainability of the environment is a very much concerning issue for developed countries, the drive of the paper is to reveal the effects of nuclear, environment-friendly, and non-friendly energy, population, and GDP on CO2 emission for Italy, a developed country. Using the extended Stochastic Regression on Population, Affluence, and Technology (STIRPAT) framework, the yearly data from 1972 to 2021 are analyzed in this paper through an Autoregressive Distributed Lag (ARDL) framework. The reliability of the study is also examined by employing Fully Modified Ordinary Least Square (FMOLS), Dynamic Ordinary Least Square (DOLS), and Canonical Cointegration Regression (CCR) estimators and also the Granger causality method which is used to see the directional relationship among the indicators. The investigation confirms the findings of previous studies by showing that in the longer period, rising Italian GDP and non-green energy by 1% can lead to higher CO2 emissions by 8.08% and 1.505%, respectively, while rising alternative and nuclear energy by 1% can lead to falling in CO2 emission by 0.624%. Although population and green energy adversely influence the upsurge of CO2, they seem insignificant. Robustness tests confirm these longer-period impacts. This analysis may be helpful in planning and developing strategies for future financial funding in the energy sector in Italy, which is essential if the country is to achieve its goals of sustainable development.
Mechanism of Low-Frequency Oscillation When Electric Multiple Units Pass Neutral Zone, and Suppression Method
Authors: Sun Jixing , Zhang Kun , Liu Jiyong , Hu Kaixuan , Huo Jindong , Yan Shengchun , Zhang Yan
Published: August 2023

This article addresses the problem of the contact voltage increase caused by the low-frequency oscillation of the train-grid system in the phase-separation process of EMUs. The article establishes the EMU-contact line-traction substation model, reveals the mechanism of low-frequency oscillation, and ascertains the relationship between the phase angle when the pantograph leaves the line, and low-frequency oscillations. Methods to suppress overvoltage during the low-frequency oscillation are proposed. The research indicated that a significant voltage amplitude was observed in the neutral zone, when the phase angle of the pantograph to the contact line separation power supply fell within the range of 60–90° and 240–270°. The maximum voltage amplitude reached 69.75 kV, and there was an occurrence of low-frequency oscillation in the neutral zone, where electrical phase separation takes place. During this oscillation, the voltage of the contact network in the neutral zone mainly operated at one-third of the power frequency (16.7 Hz). However, after installing an RC suppression device in the neutral zone, when low-frequency oscillation occurred, the absolute value of the peak voltage dropped below 37 kV as soon as the EMU entered electric phase separation. Furthermore, compared to situations without a connected suppression device, there was nearly a 30% reduction in the absolute value of the peak voltage. The study provides a basis for the design of the neutral zone of the contact line, and the selection of high-voltage equipment for the EMU.
Heat and Mass Transfer Issues in Mini-Gaps
Authors: Piasecka Magdalena
Published: August 2023

The transfer of large heat fluxes is one of the most significant issues in modern technology [...]
Tailoring NiMo-Based Catalysts for Production of Low-Viscosity Sustainable Hydrocarbon Bases for Drilling Muds from Secondary Gas Oils
Authors: Iusovskii Aleksei , Boldushevskii Roman , Mozhaev Aleksandr , Shmelkova Olga , Pavlycheva Elizaveta , Koklyukhin Aleksandr , Nikulshin Pavel
Published: August 2023

This article presents the prospect of using the process of deep hydrodesulfurization and hydrodearomatization of secondary gas oils using highly active NiMo catalysts to obtain hydrocarbon bases for drilling fluids. Catalysts were synthesized using PMo heteropolyanions, citric acid, and diethylene glycol on alumina carriers with different pore volumes. This study showed that the concentration of the impregnating solution affects the composition and morphology of the active phase particles of the prepared catalyst, while the textural characteristics of the carrier influence the physicochemical properties and catalytic activity of the NiMo/Al2O3 catalysts. The catalyst that was synthesized using a carrier with the largest pore volume and an effective diameter of more than 7 nm exhibited the highest activity. It was demonstrated that the use of such a catalyst allows for the procurement of hydrocarbon bases for drilling fluids from mixtures of secondary gas oils at a hydrogen pressure of 15–20 MPa. This study has practical significance for the development of sustainable and economically efficient methods for the utilization of low-quality petroleum gas oils to produce high-margin environmentally friendly non-fuel petroleum products, as well as contributes to the development of economically efficient technologies for the utilization of petroleum raw materials.
A Review of the Evaluation of Building Energy Code Compliance in the United States
Authors: Xie Yulong , Tyler Matthew , Huckett Jennifer , Bartlett Rosemarie , Chen Yan , Salcido Victor , Mendon Vrushali , Rosenberg Michael
Published: August 2023

Building energy codes are essential tools for achieving energy efficiency in buildings. However, the full energy savings potential of these codes can only be realized if buildings are constructed in compliance with them. Therefore, evaluating building energy code compliance is crucial in bridging the gap between the energy efficiency requirements set by energy codes and the actualized energy savings achieved. An energy code compliance evaluation serves as a mechanism to assess construction practices, evaluate the effectiveness of code enforcement, identify gaps in compliance, and guide strategies for improvement through training and education. Conducting code compliance evaluation activities involves field studies that require careful design and significant resources. Historically, more emphasis has been placed on developing and adopting building energy codes, while efforts to evaluate compliance have been relatively limited and lacking consistent approaches. The passage of the 2009 American Recovery and Reinvestment Act (ARRA), which mandated that states create plans for achieving 90% compliance within eight years, stimulated the need for an energy code compliance evaluation. As a result, federal, state, and local governments, and utilities have invested in the development of methodologies and tools for code compliance evaluation studies. This paper reviews the code compliance evaluation studies conducted in the United States over the past three decades. It describes and compares the methodologies and metrics used to assess building energy code compliance, summarizes the general elements and steps involved in the evaluation process, and discusses common issues in these studies. Over time, code compliance evaluation methodologies have evolved from isolated development within individual states, regions, and utilities, to widely accepted protocols applicable across different states and local jurisdictions. There has been a transition in compliance metrics, shifting from historical compliance rates to energy-consumption-oriented approaches.
Engineering-Scale Integrated Energy System Data Projection Demonstration via the Dynamic Energy Transport and Integration Laboratory
Authors: Yoshiura Ramon , Creasman Sarah , Epiney Aaron
Published: August 2023

The objective of this study is to demonstrate and validate the Dynamic Energy Transport and Integration Laboratory (DETAIL) preliminary scaling analysis using Modelica language system-code Dymola. The DETAIL preliminary scaling analysis includes a multisystem integral scaling package between thermal-storage and hydrogen-electrolysis systems. To construct the system of scaled equations, dynamical system scaling (DSS) was applied to all governing laws and closure relations associated with the selected integral system. The existing Dymola thermal-energy distribution system (TEDS) facility and high-temperature steam electrolysis (HTSE) facility models in the Idaho National Laboratory HYBRID repository were used to simulate a test case and a corresponding scaled case for integrated system HYBRID demonstration and validation. The DSS projected data based on the test-case simulations and determined scaling ratios were generated and compared with scaled case simulations. The preliminary scaling analysis performance was evaluated, and scaling distortions were investigated based on data magnitude, sequence, and similarity. The results indicated a necessity to change the normalization method for thermal storage generating optimal operating conditions of 261 kW power and mass flow rate of 6.42 kg/s and the possibility of reselecting governing laws for hydrogen electrolysis to improve scaling predictive properties. To enhance system-scaling similarity for TEDS and HTSE, the requirement for scaling validation via physical-facility demonstration was identified.
Potential Domestic Energy System Vulnerabilities from Major Exports of Green Hydrogen: A Case Study of Australia
Authors: Curtis Andrew J. , McLellan Benjamin C.
Published: August 2023

Australia has clear aspirations to become a major global exporter of hydrogen as a replacement for fossil fuels and as part of the drive to reduce CO2 emissions, as set out in the National Hydrogen Strategy released in 2019 jointly by the federal and state governments. In 2021, the Australian Energy Market Operator specified a grid forecast scenario for the first time entitled “hydrogen superpower”. Not only does Australia hope to capitalise on the emerging demand for zero-carbon hydrogen in places like Japan and South Korea by establishing a new export industry, but it also needs to mitigate the built-in carbon risk of its export revenue from coal and LNG as major customers, such as Japan and South Korea, move to decarbonise their energy systems. This places hydrogen at the nexus of energy, climate change mitigation and economic growth, with implications for energy security. Much of the published literature on this topic concentrates on the details of what being a major hydrogen exporter will look like and what steps will need to be taken to achieve it. However, there appears to be a gap in the study of the implications for Australia’s domestic energy system in terms of energy security and export economic vulnerability. The objective of this paper is to develop a conceptual framework for the implications of becoming a major hydrogen exporter on Australia’s energy system. Various green hydrogen export scenarios for Australia were compared, and the most recent and comprehensive was selected as the basis for further examination for domestic energy system impacts. In this scenario, 248.5 GW of new renewable electricity generation capacity was estimated to be required by 2050 to produce the additional 867 TWh required for an electrolyser output of 2088 PJ of green hydrogen for export, which will comprise 55.9% of Australia’s total electricity demand at that time. The characteristics of comparative export-oriented resources and their interactions with the domestic economy and energy system are then examined through the lens of the resource curse hypothesis, and the LNG and aluminium industries. These existing resource export frameworks are reviewed for applicability of specific factors to export-oriented green hydrogen production, with applicable factors then compiled into a novel conceptual framework for exporter domestic implications from large-scale exports of green hydrogen. The green hydrogen export superpower (2050) scenario is then quantitatively assessed using the established indicators for energy exporter vulnerability and domestic energy security, comparing it to Australia’s 2019 energy exports profile. This assessment finds that in almost all factors, exporter vulnerability is reduced, and domestic energy security is enhanced by the transition from fossil fuel exports to green hydrogen, with the exception of an increase in exposure of the domestic energy system to international market forces.