bluuebunny/crossref_metadata_2025 huggingface dataset:
PickT: A Decision-Making Tool for the Optimal Pickling Process Operation
Authors: Crișan Claudia Alice , Timiș Elisabeta Cristina , Vermeșan Horațiu
Published: August 2023

This research approaches knowledge gaps related to the pickling process dynamic modelling (the lack of predictability and simplicity of existing models) and answers the practical need for a software tool to facilitate the optimum process operation (by delivering estimations of the optimum corrosion inhibitor addition, optimum pickling bath lifetime, corrosion rate dynamic evolution, and material mass loss). A decision-making tool, PickT, has been developed and verified with the help of measurements from two different pickling experiments, both involving steel in hydrochloric acid. The first round of experiments lasted 336 h (each pickling batch duration was 24 h) and Cetilpyridinium bromide (CPB) was the corrosion inhibitor in additions from 8% to 12%. The collected dataset served for the tool development and first verification. The second round of experiments lasted 10 h (each batch duration was 2 h) and involved metformin hydrochloride (MET) in additions between 3.3 g/L and 10 g/L. This dataset served to test the transferability of PickT to other operating conditions in terms of corrosion inhibitor type, additions, batch duration and pickling bath lifetime magnitude. In both cases PickT results are in accordance with experimental findings. The tool advantages consist of the straightforward applicability, the low amount of field data required for reliable forecasts and the accessibility for untrained professionals from the industry.
Effects of Heat Treatment on the Physicochemical Properties and Electrochemical Behavior of Biochars for Electrocatalyst Support Applications
Authors: García-Rocha Rocío , Durón-Torres Sergio M. , Palomares-Sánchez Salvador A. , Del Rio-De Santiago Antonio , Rojas-de Soto Ivone , Escalante-García Ismailia L.
Published: August 2023

The present work reports the synthesis and the physicochemical characterization of biochar from the organic wastes of nopal (Opuntia Leucotricha), coffee grounds (Coffea arabica) and Ataulfo mango seeds (Mangifera indica) as alternative electrocatalyst supports to Vulcan XC-72 carbon black. The biochars were prepared using pyrolysis from organic wastes collected at three temperatures, 600, 750 and 900 °C, under two atmospheres, N2 and H2. The synthesized biochars were characterized using Raman spectroscopy and scanning electron microscopy (SEM) to obtain insights into their chemical structure and morphological nature, respectively, as a function of temperature and pyrolysis atmosphere. A N2 adsorption/desorption technique, two-point conductivity measurements and cyclic voltammetry (CV) were conducted to evaluate the specific surface area (SSA), electrical conductivity and double-layer capacitance, respectively, of all the biochars to estimate their physical properties as a possible alternative carbon support. The results indicated that the mango biochar demonstrated the highest properties among all the biochars, such as an electrical conductivity of 8.3 S/cm−1 at 900 °C in N2, a specific surface area of 829 m2/g at 600 °C in H2 and a capacitance of ~300 mF/g at 900 °C in N2. The nopal and coffee biochars exhibited excellent specific surface areas, up to 767 m2/g at 600 °C in N2 and 699 m2/g at 750 °C in H2, respectively; nonetheless, their electrical conductivity and capacitance were limited. Therefore, the mango biochar at 900 °C in N2 was considered a suitable alternative carbon material for electrocatalyst support. Additionally, it was possible to determine that the electrical conductivity and capacitance increased as a function of the pyrolysis temperature, while the specific surface area decreased for some biochars as the pyrolysis temperature increased. Overall, it is possible to conclude that heat treatment at a high temperature of 900 °C enhanced the biochar properties toward electrocatalyst support applications.
Cutting-Edge Analytical and Numerical Approaches to the Gilson–Pickering Equation with Plenty of Soliton Solutions
Authors: Chen Wensheng , Manafian Jalil , Mahmoud Khaled Hussein , Alsubaie Abdullah Saad , Aldurayhim Abdullah , Alkader Alabed
Published: August 2023

In this paper, the Gilson–Pickering (GP) equation with applications for wave propagation in plasma physics and crystal lattice theory is studied. The model with wave propagation in plasma physics and crystal lattice theory is explained. A collection of evolution equations from this model, containing the Fornberg–Whitham, Rosenau–Hyman, and Fuchssteiner–Fokas–Camassa–Holm equations is developed. The descriptions of new waves, crystal lattice theory, and plasma physics by applying the standard tan(ϕ/2)-expansion technique are investigated. Many alternative responses employing various formulae are achieved; each of these solutions is represented by a distinct plot. Some novel solitary wave solutions of the nonlinear GP equation are constructed utilizing the Paul–Painlevé approach. In addition, several solutions including soliton, bright soliton, and periodic wave solutions are reached using He’s variational direct technique (VDT). The superiority of the new mathematical theory over the old one is demonstrated through theorems, and an example of how to design and numerically calibrate a nonlinear model using closed-form solutions is given. In addition, the influence of changes in some important design parameters is analyzed. Our computational solutions exhibit exceptional accuracy and stability, displaying negligible errors. Furthermore, our findings unveil several unprecedented solitary wave solutions of the GP model, underscoring the significance and novelty of our study. Our research establishes a promising foundation for future investigations on incompressible fluids, facilitating the development of more efficient and accurate models for predicting fluid behavior.
Credit Risk Assessment of Heavy-Polluting Enterprises: A Wide-ℓp Penalty and Deep Learning Approach
Authors: Song Wanying , Min Jian , Yang Jianbo
Published: August 2023

Effective credit risk assessment of heavy-polluting enterprises can achieve a balance between environmental and economic benefits. It requires the consideration of risk indicators for both the carbon information dimension and the compliance dimension. However, as the feature dimensions of the model continue to increase, so does the irrelevant feature or noise. Therefore, we investigate the use of non-integers for regularization from high-dimensional data under the conditions of a large number of irrelevant features. In this paper, a novel Wide-ℓp Penalty and Deep Learning (WPDL) method for credit risk assessment is proposed, which could provide a sparse solution. The Wide-ℓp Penalty component allows feature selection using a linear model with an ℓp Penalty regularization mechanism, where 0 < p ≤ 2. The deep component is a DNN that can generalize indicator features from the credit risk data. The experimental results show that the minimum prediction error occurs at a non-integer ℓp Penalty. Furthermore, the WPDL outperforms other models such as KNN, DT, RF, SVM, MLP, DNN, Gradient Boosting, and Bagging.
Mathematical Modeling of Toxoplasmosis in Cats with Two Time Delays under Environmental Effects
Authors: Sultana Sharmin , González-Parra Gilberto , Arenas Abraham J.
Published: August 2023

In this paper, we construct a more realistic mathematical model to study toxoplasmosis dynamics. The model considers two discrete time delays. The first delay is related to the latent phase, which is the time lag between when a susceptible cat has effective contact with an oocyst and when it begins to produce oocysts. The second discrete time delay is the time that elapses from when the oocysts become present in the environment to when they are able to infect. The main aim in this paper is to find the conditions under which the toxoplasmosis can disappear from the cat population and to study whether the time delays can affect the qualitative properties of the model. Thus, we investigate the impact of the combination of two discrete time delays on the toxoplasmosis dynamics. Using dynamical systems theory, we are able to find the basic reproduction number R0d that determines the global long-term dynamics of the toxoplasmosis. We prove that, if R0d<1, the toxoplasmosis will be eradicated and that the toxoplasmosis-free equilibrium is globally stable. We design a Lyapunov function in order to prove the global stability of the toxoplasmosis-free equilibrium. We also prove that, if the threshold parameter R0d is greater than one, then there is only one toxoplasmosis-endemic equilibrium point, but the stability of this point is not theoretically proven. However, we obtained partial theoretical results and performed numerical simulations that suggest that, if R0d>1, then the toxoplasmosis-endemic equilibrium point is globally stable. In addition, other numerical simulations were performed in order to help to support the theoretical stability results.
Anatomical Study of the Ventral Upper Arm Muscles with a Case Report of the Accessory Coracobrachialis Muscle
Authors: Vrzgula Marko , Mihalik Jozef , Vicen Martin , Hvizdošová Natália , Hodorová Ingrid
Published: August 2023

Background and Objectives: The muscles in the upper arm are categorized into two groups: ventral muscles, which include the biceps brachii, coracobrachialis, and brachialis, and dorsal muscles comprising the triceps brachii and anconeus. These muscles are positioned in a way that they contribute to movements at the shoulder and elbow joints. Given the importance of the upper arm muscles for various reasons, they need to be well-known by medical professionals. Ventral upper arm muscles exhibit various topographical and morphological variations. Understanding these variations is critical from both anatomical and clinical standpoints. Therefore, our aim was to conduct an anatomical study focusing on these muscles and potentially identify ventral upper arm muscle variations that could contribute to the broader understanding of this area. For this anatomical study, 32 upper limbs obtained from 16 adult cadavers were dissected. Case report: During our anatomical survey, an accessory coracobrachialis muscle in the left upper extremity of one cadaver was discovered. This additional muscle was located anterior to the classical coracobrachialis muscle and measured 162 mm in length. It originated from the distal anterior surface of the coracoid process and was inserted into the middle third of the humeral shaft. The accessory muscle was supplied by the musculocutaneous nerve. No apparent anatomic variations were observed in the other upper arm muscles in any of the cadavers. Conclusions: Gaining insight into the ventral upper arm muscle variations holds vital significance in both anatomy and clinical practice, as they can influence surgical approaches, rehabilitation strategies, and the interpretation of imaging studies. Based on the morphological characteristics of the accessory coracobrachialis muscle discovered in our case, we hypothesize that it could have caused an atypical palpable mass in the medial brachial area, adjacent to the short head of the biceps brachii.
Structural Optimization Design of Microfluidic Chips Based on Fast Sequence Pair Algorithm
Authors: Wu Chuang , Sun Jiju , Almuaalemi Haithm Yahya Mohammed , Sohan A. S. M. Muhtasim Fuad , Yin Binfeng
Published: August 2023

The market for microfluidic chips is experiencing significant growth; however, their development is hindered by a complex design process and low efficiency. Enhancing microfluidic chips’ design quality and efficiency has emerged as an integral approach to foster their advancement. Currently, the existing structural design schemes lack careful consideration regarding the impact of chip area, microchannel length, and the number of intersections on chip design. This inadequacy leads to redundant chip structures resulting from the separation of layout and wiring design. This study proposes a structural optimization method for microfluidic chips to address these issues utilizing a simulated annealing algorithm. The simulated annealing algorithm generates an initial solution in advance using the fast sequence pair algorithm. Subsequently, an improved simulated annealing algorithm is employed to obtain the optimal solution for the device layout. During the wiring stage, an advanced wiring method is used to designate the high wiring area, thereby increasing the success rate of microfluidic chip wiring. Furthermore, the connection between layout and routing is reinforced through an improved layout adjustment method, which reduces the length of microchannels and the number of intersections. Finally, the effectiveness of the structural optimization approach is validated through six sets of test cases, successfully achieving the objective of enhancing the design quality of microfluidic chips.
Evolutionary Algorithm-Based Crystal Structure Prediction of CuxZnyOz Ternary Oxides
Authors: Kuklin Mikhail S. , Karttunen Antti J.
Published: August 2023

Binary zinc(II) oxide (ZnO) and copper(II) oxide (CuO) are used in a number of applications, including optoelectronic and semiconductor applications. However, no crystal structures have been reported for ternary Cu-Zn-O oxides. In that context, we investigated the structural characteristics and thermodynamics of CuxZnyOz ternary oxides to map their experimental feasibility. We combined evolutionary crystal structure prediction and quantum chemical methods to investigate potential CuxZnyOz ternary oxides. The USPEX algorithm and density functional theory were used to screen over 4000 crystal structures with different stoichiometries. When comparing compositions with non-magnetic CuI ions, magnetic CuII ions, and mixed CuI-CuII compositions, the magnetic Cu2Zn2O4 system is thermodynamically the most favorable. At ambient pressures, the thermodynamically most favorable ternary crystal structure is still 2.8 kJ/mol per atom higher in Gibbs free energy compared to experimentally known binary phases. The results suggest that thermodynamics of the hypothetical CuxZnyOz ternary oxides should also be evaluated at high pressures. The predicted ternary materials are indirect band gap semiconductors.
Chitosan and Its Carboxymethyl-Based Membranes Produced by Crosslinking with Magnesium Phytate
Authors: Zając Adam , Sąsiadek Wojciech , Dymińska Lucyna , Ropuszyńska-Robak Paulina , Hanuza Jerzy , Ptak Maciej , Smółka Szymon , Lisiecki Radosław , Skrzypczak Katarzyna
Published: August 2023

Membranes produced by crosslinking chitosan with magnesium phytate were prepared using highly deacetylated chitosan and its N-carboxymethyl, O-carboxymethyl and N,O-carboxymethyl derivatives. The conditions of the membrane production were described. IR, Raman, electron absorption and emission spectra were measured and analyzed for all the substrates. It was found that O-carboxymethyl chitosan derivative is the most effectively crosslinked by magnesium phytate, and the films formed on this substrate exhibit good mechanical parameters of strength, resistance and stability. Strong O–H···O hydrogen bonds proved to be responsible for an effective crosslinking process. Newly discovered membrane types produced from chitosan and magnesium phytate were characterized as morphologically homogenous and uniform by scanning electron microscopy (SEM) and IR measurements. Due to their good covering properties, they do not have pores or channels and are proposed as packaging materials.
Study on the Degradation of Methylene Blue by Cu-Doped SnSe
Authors: Fan Li , Zhu Hongliang , Wang Kaili , Liu Hao , Hu Weina , Xu Xin , Yan Shancheng
Published: August 2023

Treatment of organic wastewater is still a difficult problem to solve. In this paper, Cu-doped SnSe powder was synthesized by a convenient and efficient hydrothermal method. Meanwhile, the degradation effect of different doping concentrations of SnSe on methylene blue was investigated. It was found that at low doping concentrations, the degradation effect on methylene blue was not obvious because Cu was dissolved in the lattice of the SnSe matrix at low concentrations. As the doping concentration increased, SnSe changed from a layered structure to a nanocluster structure with reduced particle size, and a mixed phase of SnSe and Cu2SnSe4 appeared. In fact, the degradation effect on methylene blue was significantly enhanced, and we found that the catalytic degradation effect on methylene blue was best at a doping concentration of 10 wt.%.
Natural and Synthetic Anticancer Epidrugs Targeting the Epigenetic Integrator UHRF1
Authors: Ashraf Waseem , Ahmad Tanveer , Reynoird Nicolas , Hamiche Ali , Mély Yves , Bronner Christian , Mousli Marc
Published: August 2023

Cancer is one of the leading causes of death worldwide, and its incidence and mortality are increasing each year. Improved therapeutic strategies against cancer have progressed, but remain insufficient to invert this trend. Along with several other risk factors, abnormal genetic and epigenetic regulations play a critical role in the initiation of cellular transformation, as well as tumorigenesis. The epigenetic regulator UHRF1 (ubiquitin-like, containing PHD and RING finger domains 1) is a multidomain protein with oncogenic abilities overexpressed in most cancers. Through the coordination of its multiple domains and other epigenetic key players, UHRF1 regulates DNA methylation and histone modifications. This well-coordinated dialogue leads to the silencing of tumor-suppressor genes (TSGs) and facilitates tumor cells’ resistance toward anticancer drugs, ultimately promoting apoptosis escape and uncontrolled proliferation. Several studies have shown that the downregulation of UHRF1 with natural compounds in tumor cells induces the reactivation of various TSGs, inhibits cell growth, and promotes apoptosis. In this review, we discuss the underlying mechanisms and the potential of various natural and synthetic compounds that can inhibit/minimize UHRF1’s oncogenic activities and/or its expression.
Macrophage Activity under Hyperglycemia: A Study of the Effect of Resveratrol and 3H-1,2-Dithiole-3-thione on Potential Polarization
Authors: Manríquez-Núñez Josué , Mora Ofelia , Villarroya Francesc , Reynoso-Camacho Rosalía , Pérez-Ramírez Iza Fernanda , Ramos-Gómez Minerva
Published: August 2023

Currently, research is focused on bioactive compounds with the potential to promote macrophage polarization with the aim of reducing the development of inflammatory-related diseases. However, the effect of bioactive compounds under oxidative-stress-induced hyperglycemia on macrophage polarization has been scarcely investigated. RAW 264.7 macrophages were incubated under standard (SG) or high glucose (HG) conditions and stimulated with lipopolysaccharide (LPS) (10, 60 and 100 ng/mL) to monitor macrophage polarization after resveratrol (RSV) or 3H-1,2-dithiole-3-thione (D3T) supplementation (2.5, 5, 10 and 20 µM). Under SG and HG conditions without LPS stimulation, RSV significantly decreased macrophage viability at the highest concentration (20 µM), whereas D3T had no or low effect. LPS stimulation at 60 and 100 ng/mL, under SG and HG conditions, increased significantly macrophage viability. Both RSV and D3T significantly decreased NO production in LPS-stimulated macrophages under HG condition, whereas only D3T increased GSH levels at 100 ng/mL and normalized MDA values at 60 ng/mL of LPS under HG condition. Under 60 ng/mL LPS stimulation and HG, mRNA IL-1 and IL-6 were higher. Interestingly, RSV decreased pro-inflammatory interleukins; meanwhile, D3T increased Arg1 and IL-10 relative expression. Overall, our results indicate that hyperglycemia plays a fundamental role in the modulation of macrophage-induced inflammation in response to bioactive compounds.
A Microporous Zn(bdc)(ted)0.5 with Super High Ethane Uptake for Efficient Selective Adsorption and Separation of Light Hydrocarbons
Authors: Xu Feng , Wu Yilu , Wu Juan , Lv Daofei , Yan Jian , Wang Xun , Chen Xin , Liu Zewei , Peng Junjie
Published: August 2023

Separating light hydrocarbons (C2H6, C3H8, and C4H10) from CH4 is challenging but important for natural gas upgrading. A microporous metal-organic framework, Zn(bdc)(ted)0.5, based on terephthalic acid (bdc) and 1,4-diazabicyclo[2.2.2]octane (ted) ligands, is synthesized and characterized through various techniques, including powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and porosity analysis. The adsorption isotherms of light hydrocarbons on the material are measured and the isosteric adsorption heats of CH4, C2H6, C3H8, and C4H10 are calculated. The prediction of C2–4/C1 adsorption selectivities is accomplished using ideal adsorbed solution theory (IAST). The results indicate that the material exhibits exceptional characteristics, including a Brunauer-Emmett-Teller (BET) surface area of 1904 m2/g and a pore volume of 0.73 cm3/g. Notably, the material demonstrates remarkable C2H6 adsorption capacities (4.9 mmol/g), while CH4 uptake remains minimal at 0.4 mmol/g at 298 K and 100 kPa. These findings surpass those of most reported MOFs, highlighting the material’s outstanding performance. The isosteric adsorption heats of C2H6, C3H8, and C4H10 on the Zn(bdc)(ted)0.5 are higher than CH4, suggesting a stronger interaction between C2H6, C3H8, and C4H10 molecules and Zn(bdc)(ted)0.5. The molecular simulation reveals that Zn(bdc)(ted)0.5 prefers to adsorb hydrocarbon molecules with richer C-H bonds and larger polarizability, which results in a stronger dispersion force generated by an adsorbent-adsorbate induced polarization effect. Therefore, the selectivity of C4H10/CH4 is up to 180 at 100 kPa, C3H8/CH4 selectivity is 67, and the selectivity of C2H6/CH4 is 13, showing a great potential for separating C2–4 over methane.
Modulation of NRF2/KEAP1-Mediated Oxidative Stress for Cancer Treatment by Natural Products Using Pharmacophore-Based Screening, Molecular Docking, and Molecular Dynamics Studies
Authors: Alzain Abdulrahim A. , Mukhtar Rua M. , Abdelmoniem Nihal , Shoaib Tagyedeen H. , Osman Wadah , Alsulaimany Marwa , Aljohani Ahmed K. B. , Almadani Sara A. , Alsaadi Baiaan H. , Althubyani Maryam M. , Mohamed Shaimaa G. A. , Mohamed Gamal A. , Ibrahim Sabrin R. M.
Published: August 2023

Oxidative stress plays a significant role in the development of cancer. Inhibiting the protein-protein interaction (PPI) between Keap1 and Nrf2 offers a promising strategy to activate the Nrf2 antioxidant pathway, which is normally suppressed by the binding of Keap1 to Nrf2. This study aimed to identify natural compounds capable of targeting the kelch domain of KEAP1 using structure-based drug design methods. A pharmacophore model was constructed based on the KEAP1-inhibitor complex, leading to the selection of 6178 compounds that matched the model. Subsequently, docking and MM/GBSA analyses were conducted, resulting in the identification of 10 compounds with superior binding energies compared to the reference compound. From these, three compounds (ZINC000002123788, ZINC000002111341, and ZINC000002125904) were chosen for further investigation. Ligand–residue interaction analysis revealed specific interactions between these compounds and key residues, indicating their stability within the binding site. ADMET analysis confirmed that the selected compounds possessed desirable drug-like properties. Furthermore, molecular dynamics simulations were performed, demonstrating the stability of the ligand–protein complexes over a 100 ns duration. These findings underscore the potential of the selected natural compounds as agents targeting KEAP1 and provide valuable insights for future experimental studies.
Development of Blueberry-Derived Extracellular Nanovesicles for Immunomodulatory Therapy
Authors: Nguyen Tuong Ngoc-Gia , Pham Cuong Viet , Chowdhury Rocky , Patel Shweta , Jaysawal Satendra Kumar , Hou Yingchun , Xu Huo , Jia Lee , Duan Andrew , Tran Phuong Ha-Lien , Duan Wei
Published: August 2023

Over the past decade, there has been a significant expansion in the development of plant-derived extracellular nanovesicles (EVs) as an effective drug delivery system for precision therapy. However, the lack of effective methods for the isolation and characterization of plant EVs hampers progress in the field. To solve a challenge related to systemic separation and characterization in the plant-derived EV field, herein, we report the development of a simple 3D inner filter-based method that allows the extraction of apoplastic fluid (AF) from blueberry, facilitating EV isolation as well as effective downstream applications. Class I chitinase (PR-3) was found in blueberry-derived EVs (BENVs). As Class I chitinase is expressed in a wide range of plants, it could serve as a universal marker for plant-derived EVs. Significantly, the BENVs exhibit not only higher drug loading capacity than that reported for other EVs but also possess the ability to modulate the release of the proinflammatory cytokine IL-8 and total glutathione in response to oxidative stress. Therefore, the BENV is a promising edible multifunctional nano-bio-platform for future immunomodulatory therapies.
Optimizing Absorption for Intranasal Delivery of Drugs Targeting the Central Nervous System Using Alkylsaccharide Permeation Enhancers
Authors: Madden Stuart , Carrazana Enrique , Rabinowicz Adrian L.
Published: August 2023

Intranasal delivery of drugs offers several potential benefits related to ease of delivery, rapid onset, and patient experience, which may be of particular relevance to patients with central nervous system (CNS) conditions who experience acute events. Intranasal formulations must be adapted to address anatomical and physiological characteristics of the nasal cavity, including restricted dose volume, limited surface area, and barriers to mucosal absorption, in addition to constraints on the absorption window due to mucociliary clearance. Development of an effective formulation may utilize strategies including the addition of excipients to address the physicochemical properties of the drug within the constraints of nasal delivery. Dodecyl maltoside (DDM) and tetradecyl maltoside are alkylsaccharide permeation enhancers with well-established safety profiles, and studies have demonstrated transiently improved absorption and favorable bioavailability of several compounds in preclinical and clinical trials. Dodecyl maltoside is a component of three US Food and Drug Administration (FDA)–approved intranasal medications: diazepam for the treatment of seizure cluster in epilepsy, nalmefene for the treatment of acute opioid overdose, and sumatriptan for the treatment of migraine. Another drug product with DDM as an excipient is currently under FDA review, and numerous investigational drugs are in early-stage development. Here, we review factors related to the delivery of intranasal drugs and the role of alkylsaccharide permeation enhancers in the context of approved and future intranasal formulations of drugs for CNS conditions.
Current Challenges to the Protection of (Neo)pagans’ Religious Freedom in the Baltic States
Authors: Stasulane Anita
Published: August 2023

In the late 1980s, the activities of (neo)pagan groups were renewed in Estonia, Latvia, and Lithuania. Today, the (neo)pagans have taken a stable place among the new religious movements in the Baltic states. The social inclusion of Baltic (neo)pagans is made difficult by the state authorities, which have applied to them the same kinds of legal regulations as for unregistered “non-traditional” religious communities. Currently, (neo)pagan attempts to be included in social life come up against the state authorities’ desire to closely control religious organisations. Despite common features, differences determined by both historical and political conditions can be observed: in Latvia and Lithuania, the (neo)pagan groups maintain that they are entitled to the same legal status as “traditional denominations” or “traditional religious association”; in Estonia, (neo)pagans are involved in discussions about religious freedom, religious education, and the preservation of historical sacred sites. Even though (neo)pagan groups in the Baltics have not gained the same legal status as the Christian denominations and their social inclusion is problematic, they have not been excluded from social life, as their activities have roots in the Latvian, Lithuanian, and Estonian traditional way of life, which is valued highly nowadays. In this article, qualitative comparative analysis is used as a methodological tool, which helps to explain changes in contexts and settings of the Baltic states.
Path Following and Collision Avoidance of a Ribbon-Fin Propelled Underwater Biomimetic Vehicle-Manipulator System
Authors: He Yanbing , Dong Xiang , Wang Yu , Wang Shuo
Published: August 2023

This paper addresses the problem of path following and dynamic obstacle avoidance for an underwater biomimetic vehicle-manipulator system (UBVMS). Firstly, the general kinematic and dynamic models of underwater vehicles are presented; then, a nonlinear model predictive control (NMPC) scheme is employed to track a reference path and collision avoidance simultaneously. Moreover, to minimize the tracking error and for a higher degree of robustness, improved extended state observers are used to estimate model uncertainties and disturbances to be fed into the NMPC prediction model. On top of this, the proposed method also considers the uncertainty of the state estimator, while combining a priori estimation of the Kalman filter to reasonably predict the position of dynamic obstacles during short periods. Finally, simulations and experimental results are carried out to assess the validity of the proposed method in case of disturbances and constraints.
Damage Monitoring of Braided Composites Using CNT Yarn Sensor Based on Artificial Fish Swarm Algorithm
Authors: Wang Hongxia , Jia Yungang , Jia Minrui , Pei Xiaoyuan , Wan Zhenkai
Published: August 2023

This study aims to enable intelligent structural health monitoring of internal damage in aerospace structural components, providing a crucial means of assuring safety and reliability in the aerospace field. To address the limitations and assumptions of traditional monitoring methods, carbon nanotube (CNT) yarn sensors are used as key elements. These sensors are woven with carbon fiber yarns using a three-dimensional six-way braiding process and cured with resin composites. To optimize the sensor configuration, an artificial fish swarm algorithm (AFSA) is introduced, simulating the foraging behavior of fish to determine the best position and number of CNT yarn sensors. Experimental simulations are conducted on 3D braided composites of varying sizes, including penetration hole damage, line damage, and folded wire-mounted damage, to analyze the changes in the resistance data of carbon nanosensors within the damaged material. The results demonstrate that the optimized configuration of CNT yarn sensors based on AFSA is suitable for damage monitoring in 3D woven composites. The experimental positioning errors range from 0.224 to 0.510 mm, with all error values being less than 1 mm, thus achieving minimum sensor coverage for a maximum area. This result not only effectively reduces the cost of the monitoring system, but also improves the accuracy and reliability of the monitoring process.
An Improved Performance Radar Sensor for K-Band Automotive Radars
Authors: Abd El-Hameed Anwer S. , Ouf Eman G. , Elboushi Ayman , Seliem Asmaa G. , Izumi Yuta
Published: August 2023

This paper presents a new radar sensor configuration of a planar grid antenna array (PGAA) for automotive ultra-wideband (UWB) radar applications. For system realisation, the MIMO concept is adopted. The proposed antenna is designed to operate over the 24 GHz frequency band. It is based on split-ring resonator (SRR) elements to enhance the operating bandwidth and increase the antenna gain, leading to a better-performing radar system. The PGAA consists of thirty-one radiating elements, in which each element excitation is obtained using a common transmission line centre fed by a 50 Ω coaxial probe. By introducing a superstrate dielectric layer at a distance of λ/2 from the top of the antenna array, the PGAA gain and impedance bandwidth are further improved. The gain is improved by 2.7 dB to reach 16.5 dBi at 24 GHz, and the impedance bandwidth is enhanced to 9.3 GHz (37.7%). The measured impedance bandwidth of the proposed antenna array ranges from 20 GHz to 29.3 GHz for a reflection coefficient (S11) of less than −10 dB. The proposed antenna is validated for automotive applications.
Spectrum-Weighted Fusion Cooperative Detection Algorithm Based on Double Thresholds for Underwater Acoustic Networks
Authors: Zhang Jing , Lin Liyuan , Zhang Rui
Published: August 2023

Spectrum-sensing technology is crucial for the development of underwater acoustic communication networks and plays a key role in detecting spectrum holes and channel occupancy. Energy detection technology, as the fundamental spectrum sensing technology in cognitive radio, has reached a mature level of development. Its application in hydroacoustic communications can significantly enhance the utilization of the hydroacoustic spectrum. However, due to the complexity of the hydroacoustic channel compared with that of the radio channel, the traditional double-threshold energy detection technique faces challenges such as fixed threshold values and limited flexibility. To address this, we propose a model for the hydroacoustic channel that incorporates a weight factor based on the signal-to-noise ratio in the algorithm. This allows for adaptive threshold values based on the user’s signal-to-noise environment, reducing false detection rates and improving overall detection performance. Through simulation experiments and comparisons, our proposed signal-to-noise weighted collaborative spectrum-sensing technique demonstrates superior detection performance compared with other spectrum-sensing techniques.
Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches
Authors: Avazov Kuldoshbay , Jamil Muhammad Kafeel , Muminov Bahodir , Abdusalomov Akmalbek Bobomirzaevich , Cho Young-Im
Published: August 2023

Fire incidents occurring onboard ships cause significant consequences that result in substantial effects. Fires on ships can have extensive and severe wide-ranging impacts on matters such as the safety of the crew, cargo, the environment, finances, reputation, etc. Therefore, timely detection of fires is essential for quick responses and powerful mitigation. The study in this research paper presents a fire detection technique based on YOLOv7 (You Only Look Once version 7), incorporating improved deep learning algorithms. The YOLOv7 architecture, with an improved E-ELAN (extended efficient layer aggregation network) as its backbone, serves as the basis of our fire detection system. Its enhanced feature fusion technique makes it superior to all its predecessors. To train the model, we collected 4622 images of various ship scenarios and performed data augmentation techniques such as rotation, horizontal and vertical flips, and scaling. Our model, through rigorous evaluation, showcases enhanced capabilities of fire recognition to improve maritime safety. The proposed strategy successfully achieves an accuracy of 93% in detecting fires to minimize catastrophic incidents. Objects having visual similarities to fire may lead to false prediction and detection by the model, but this can be controlled by expanding the dataset. However, our model can be utilized as a real-time fire detector in challenging environments and for small-object detection. Advancements in deep learning models hold the potential to enhance safety measures, and our proposed model in this paper exhibits this potential. Experimental results proved that the proposed method can be used successfully for the protection of ships and in monitoring fires in ship port areas. Finally, we compared the performance of our method with those of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.
A Malicious Code Detection Method Based on Stacked Depthwise Separable Convolutions and Attention Mechanism
Authors: Huang Hong , Du Rui , Wang Zhaolian , Li Xin , Yuan Guotao
Published: August 2023

To address the challenges of weak model generalization and limited model capacity adaptation in traditional malware detection methods, this article presents a novel malware detection approach based on stacked depthwise separable convolutions and self-attention, termed CoAtNet. This method combines the strengths of the self-attention module’s robust model adaptation and the convolutional networks’ powerful generalization abilities. The initial step involves transforming the malicious code into grayscale images. These images are subsequently processed using a detection model that employs stacked depthwise separable convolutions and an attention mechanism. This model effectively recognizes and classifies the images, automatically extracting essential features from malicious software images. The effectiveness of the method was validated through comparative experiments using both the Malimg dataset and the augmented Blended+ dataset. The approach’s performance was evaluated against popular models, including XceptionNet, EfficientNetB0, ResNet50, VGG16, DenseNet169, and InceptionResNetV2. The experimental results highlight that the model surpasses other malware detection models in terms of accuracy and generalization ability. In conclusion, the proposed method addresses the limitations of traditional malware detection approaches by leveraging stacked depthwise separable convolutions and self-attention. Comprehensive experiments demonstrate its superior performance compared to existing models. This research contributes to advancing the field of malware detection and provides a promising solution for enhanced accuracy and robustness.
New Trends in Emotion Recognition Using Image Analysis by Neural Networks, a Systematic Review
Authors: Cîrneanu Andrada-Livia , Popescu Dan , Iordache Dragoș
Published: August 2023

Facial emotion recognition (FER) is a computer vision process aimed at detecting and classifying human emotional expressions. FER systems are currently used in a vast range of applications from areas such as education, healthcare, or public safety; therefore, detection and recognition accuracies are very important. Similar to any computer vision task based on image analyses, FER solutions are also suitable for integration with artificial intelligence solutions represented by different neural network varieties, especially deep neural networks that have shown great potential in the last years due to their feature extraction capabilities and computational efficiency over large datasets. In this context, this paper reviews the latest developments in the FER area, with a focus on recent neural network models that implement specific facial image analysis algorithms to detect and recognize facial emotions. This paper’s scope is to present from historical and conceptual perspectives the evolution of the neural network architectures that proved significant results in the FER area. This paper endorses convolutional neural network (CNN)-based architectures against other neural network architectures, such as recurrent neural networks or generative adversarial networks, highlighting the key elements and performance of each architecture, and the advantages and limitations of the proposed models in the analyzed papers. Additionally, this paper presents the available datasets that are currently used for emotion recognition from facial expressions and micro-expressions. The usage of FER systems is also highlighted in various domains such as healthcare, education, security, or social IoT. Finally, open issues and future possible developments in the FER area are identified.
Deep Learning for Counting People from UWB Channel Impulse Response Signals
Authors: Lee Gun , An Subin , Jang Byung-Jun , Lee Soochahn
Published: August 2023

The use of higher frequency bands compared to other wireless communication protocols enhances the capability of accurately determining locations from ultra-wideband (UWB) signals. It can also be used to estimate the number of people in a room based on the waveform of the channel impulse response (CIR) from UWB transceivers. In this paper, we apply deep neural networks to UWB CIR signals for the purpose of estimating the number of people in a room. We especially focus on empirically investigating the various network architectures for classification from single UWB CIR data, as well as from various ensemble configurations. We present our processes for acquiring and preprocessing CIR data, our designs of the different network architectures and ensembles that were applied, and the comparative experimental evaluations. We demonstrate that deep neural networks can accurately classify the number of people within a Line of Sight (LoS), thereby achieving an 99% performance and efficiency with respect to both memory size and FLOPs (Floating Point Operations Per Second).
Remote Health Monitoring Systems for Elderly People: A Survey
Authors: Ahmed Salman , Irfan Saad , Kiran Nasira , Masood Nayyer , Anjum Nadeem , Ramzan Naeem
Published: August 2023

This paper addresses the growing demand for healthcare systems, particularly among the elderly population. The need for these systems arises from the desire to enable patients and seniors to live independently in their homes without relying heavily on their families or caretakers. To achieve substantial improvements in healthcare, it is essential to ensure the continuous development and availability of information technologies tailored explicitly for patients and elderly individuals. The primary objective of this study is to comprehensively review the latest remote health monitoring systems, with a specific focus on those designed for older adults. To facilitate a comprehensive understanding, we categorize these remote monitoring systems and provide an overview of their general architectures. Additionally, we emphasize the standards utilized in their development and highlight the challenges encountered throughout the developmental processes. Moreover, this paper identifies several potential areas for future research, which promise further advancements in remote health monitoring systems. Addressing these research gaps can drive progress and innovation, ultimately enhancing the quality of healthcare services available to elderly individuals. This, in turn, empowers them to lead more independent and fulfilling lives while enjoying the comforts and familiarity of their own homes. By acknowledging the importance of healthcare systems for the elderly and recognizing the role of information technologies, we can address the evolving needs of this population. Through ongoing research and development, we can continue to enhance remote health monitoring systems, ensuring they remain effective, efficient, and responsive to the unique requirements of elderly individuals.
Time to Treatment and Risk Factors for Unsuccessful Treatment Outcomes among People Who Started Second-Line Treatment for Rifampicin-Resistant or Multi-Drug-Resistant Tuberculosis in the Kyrgyz Republic, 2021
Authors: Kyrbashov Bolot , Kulzhabaeva Aizat , Kadyrov Abdullaat , Toktogonova Atyrkul , Timire Collins , Satyanarayana Srinath , Istamov Kylychbek
Published: August 2023

The Kyrgyz Republic is a high-burden country for rifampicin resistant/multi-drug resistant tuberculosis (RR/MDR-TB). TB control efforts rely on early diagnosis and initiation of people on effective regimens. We studied the interval from diagnosis of RR-TB to starting treatment and risk factors for unsuccessful outcomes among people who started RR/MDR-TB treatment in 2021. We conducted a cohort study using country-wide programme data and used binomial regression to determine associations between unsuccessful outcomes and predictor variables. Of the 535 people included in the study, three-quarters were in the age category 18–59 years, and 68% had past history of TB. The median (IQR) time from onset of TB symptoms to diagnosis was 30 (11–62) days, 1 (0–4) days from diagnosis to starting treatment, and 35 (24–65) days from starting treatment to receipt of second-line drug susceptibility test (SL-DST) results. Overall, 136 (25%) had unsuccessful outcomes. Risk factors for unsuccessful outcomes were being homeless, fluroquinolone resistance, having unknown HIV status, past TB treatment, male gender and being unemployed. Treatment outcomes and the interval from diagnosis to starting treatment were commendable. Further reductions in unsuccessful outcomes by be achieved through ensuring timely diagnosis and access to SL-DSTs and by reducing the proportion of people who are lost to follow-up.
Improvement in the Surveillance System for Livestock Diseases and Antimicrobial Use Following Operational Research Studies in Sierra Leone January–March 2023
Authors: Konteh Samuel Alie , Bangura (Turay) Fatmata Isatu , Leno Amara , Satyanarayana Srinath , Nair Divya , Bah Mohamed Alpha , Saidu Salam , Sellu-Sallu David , Gborie Sahr Raymond , Kamara Sorie Mohamed , Jalloh Amadu Tejan , Kanu Joseph Sam , Kamara Kadijatu Nabie , Moiwo Matilda Mattu , Dsani Esther , Nantima Noelina
Published: August 2023

In Sierra Leone, two operational research (OR) studies in 2019 and 2021 showed deficiencies in the data being captured by the Integrated Animal Disease Surveillance and Reporting (IADSR) system. This third OR study was conducted in 2023 to assess whether the second OR study’s results and recommendations were disseminated with the key stakeholders, the uptake of the recommendations, improvements in data capture in the IADSR system, and to describe the data on livestock disease and antimicrobial use. In 2022, on seven occasions, the authors of the second OR study disseminated the study’s findings. Of the four recommendations, the one on improving laboratory infrastructure for confirmation of animal disease was not implemented. The district animal health weekly surveillance reports received through the IADSR system were sustained at 88% between the second (2021) and third (2023) studies. In both studies, the proportion of sick animals receiving antibiotics (25%) remained the same, but the use of “critically important antimicrobials for veterinary use” declined from 77% (in 2021) to 69% (in 2023). The IADSR system has improved considerably in providing information on animal health and antibiotic use, and sequential OR studies have played a key role in its improvement.