bluuebunny/crossref_metadata_2025 huggingface dataset:
Sutureless Aortic Valve Prosthesis in Redo Procedures: Single-Center Experience
Authors: Zubarevich Alina , Beltsios Eleftherios T. , Arjomandi Rad Arian , Amanov Lukman , Szczechowicz Marcin , Ruhparwar Arjang , Weymann Alexander
Published: June 2023

Background and Objectives: Sutureless aortic valve prostheses have presented favorable hemodynamic performance while facilitating minimally invasive access approaches. As the population ages, the number of patients at risk for aortic valve reoperation constantly increases. The aim of the present study is to present our single-center experience in sutureless aortic valve replacement (SU-AVR) in reoperations. Materials and Methods: The data of 18 consecutive patients who underwent SU-AVR in a reoperation between May 2020 and January 2023 were retrospectively analyzed. Results: The mean age of the patients was 67.9 ± 11.1 years; patients showed a moderate-risk profile with a median logistic EuroSCORE II of 7.8 (IQR of 3.8–32.0) %. The implantation of the Perceval S prosthesis was technically successful in all patients. The mean cardiopulmonary bypass time was 103.3 ± 50.0 min, and the cross-clamp time was 69.1 ± 38.8 min. No patients required a permanent pacemaker implantation. The postoperative gradient was 7.3 ± 2.4 mmHg, and no cases of paravalvular leakage were observed. There was one case of intraprocedural death, while the thirty-day mortality was 11%. Conclusions: Sutureless bioprosthetic valves tend to simplify the surgical procedure of a redo AVR. By maximizing the effective orifice area, sutureless valves may present an important advantage, being a safe and effective alternative not only to traditional surgical prostheses but also to transcatheter valve-in-valve approaches in select cases.
Editorial for Special Issue Foodborne Pathogens: Infections and Pathogenesis
Authors: Oleastro Mónica , Botelho Ana
Published: June 2023

Foodborne microorganisms substantially impact food safety and contribute considerably to the public health and economic burden of infectious diseases worldwide [...]
Psychosocial Factors and the Need for Multidisciplinary Support in Nutrition Counselling for Cancer Chemotherapy Patients
Authors: Koshimoto Saori , Yamazaki Tomoko , Amano Koji , Kako Jun , Arimoto Masako , Saitou Keiko , Hashizume Akiko , Takeuchi Takashi , Matsushima Eisuke
Published: June 2023

This study aims to identify the background factors and experiences of patients with cancer with eating-related problems who require nutrition counselling. Using a mixed-methods approach, this secondary analysis study was conducted on patients with head and neck, oesophageal, gastric, colorectal, or lung cancers who were receiving outpatient chemotherapy. They completed a questionnaire measuring nutrition impact symptoms, eating-related distress, and quality of life (QOL). Patients who required nutrition counselling were interviewed to identify the specific issues they experienced. We reported on nutritional status and nutrition impact symptoms in a previous study. Of the 151 participants, 42 required nutrition counselling. Background factors associated with nutrition counselling were related to the following psychosocial variables: small number of people in the household, undergoing treatment while working, low QOL, and eating-related distress. Four themes were extracted from the specific issues experienced by patients: motivation for self-management, distress from symptoms, seeking understanding and sympathy, and anxiety and confusion. The desire for nutrition counselling was attributable to ‘anxiety caused by the symptoms’ and ‘confusion about the information on eating’. Healthcare professionals should promote multidisciplinary collaboration after considering the factors associated with the required nutrition counselling to provide nutritional support.
Effect of Local Administration of Meglumine Antimoniate and Polyhexamethylene Biguanide Alone or in Combination with a Toll-like Receptor 4 Agonist for the Treatment of Papular Dermatitis due to Leishmania infantum in Dogs
Authors: Martínez-Flórez Icíar , Guerrero Maria Jose , Dalmau Annabel , Cabré Maria , Alcover Maria Magdalena , Berenguer Diana , Good Liam , Fisa Roser , Riera Cristina , Ordeix Laura , Solano-Gallego Laia
Published: June 2023

Papular dermatitis is a cutaneous manifestation of canine Leishmania infantum infection associated with mild disease. Although it is a typical presentation, nowadays, there is still no established treatment. This study evaluated the safety and clinical efficacy of local meglumine antimoniate, locally administered polyhexamethylene biguanide (PHMB) alone or PHMB in combination with a Toll-like receptor 4 agonist (TLR4a) for the treatment of papular dermatitis due to L. infantum and assessed parasitological and immunological markers in this disease. Twenty-eight dogs with papular dermatitis were divided randomly into four different groups; three of them were considered treatment groups: PHMB (n = 5), PHMB + TLR4a (n = 4), and meglumine antimoniate (n = 10)), and the remaining were considered the placebo group (n = 9), which was further subdivided into two sub-groups: diluent (n = 5) and TLR4a (n = 4). Dogs were treated locally every 12 h for four weeks. Compared to placebo, local administration of PHMB (alone or with TLR4a) showed a higher tendency towards resolution of papular dermatitis due to L. infantum infection at day 15 (χ2 = 5.78; df = 2, p = 0.06) and day 30 (χ2 = 4.; df = 2, p = 0.12), while local meglumine antimoniate administration demonstrated the fastest clinical resolution after 15 (χ2 = 12.58; df = 2, p = 0.002) and 30 days post-treatment (χ2 = 9.47; df = 2, p = 0.009). Meglumine antimoniate showed a higher tendency towards resolution at day 30 when compared with PHMB (alone or with TLR4a) (χ2 = 4.74; df = 2, p = 0.09). In conclusion, the local administration of meglumine antimoniate appears to be safe and clinically efficient for the treatment of canine papular dermatitis due to L. infantum infection.
An Insight into Citrus medica Linn.: A Systematic Review on Phytochemical Profile and Biological Activities
Authors: Benedetto Nadia , Carlucci Vittorio , Faraone Immacolata , Lela Ludovica , Ponticelli Maria , Russo Daniela , Mangieri Claudia , Tzvetkov Nikolay T. , Milella Luigi
Published: June 2023

Plant species are a reservoir of natural compounds that can potentially be used to treat different diseases. Citrus medica Linn. belonging to the Rutaceae family, has been used for centuries in medicine for its antioxidant, anti-inflammatory, antimicrobial, antiviral, and antihyperglycemic properties. These activities are ascribable not only to the presence of health-promoting macronutrients and micronutrients, such as carbohydrates, minerals, amino acids, and vitamins, but also to specialized metabolites, such as flavonoids (apigenin, hesperetin, hesperidin, naringin, naringenin, rutin, quercetin, and diosmin), coumarins (citropten, scoparone, and bergapten), terpenes (limonene, γ-terpinene, limonin, and nomilin), and phenolic acids (p-coumaric acid, trans-ferulic acid, and chlorogenic acid). In recent years, particular attention has been focused on the antioxidant, anti-inflammatory, antimicrobial activity, antidiabetic, anticancer, and neuroprotective activity of C. medica. However, although many studies have reported this species’ chemical and biological properties, the literature has never been analyzed via a systematic approach. For this reason, using PubMed and Scopus as databases, we performed a systematic review of C. medica’s chemical composition and biological properties to inspire new research approaches and increase its curative application.
Transcriptomic and Physiological Analyses of Two Rice Restorer Lines under Different Nitrogen Supplies Provide Novel Insights into Hybrid Rice Breeding
Authors: Qin Xiaojian , Li Xiaowei , Xiao Juan , Wu Qian , Li Yuntong , Li Cuiping , Jiang Dan , Tang Tingting , Nan Wenbin , Liang Yongshu , Zhang Hanma
Published: June 2023

Improving plant nitrogen-use efficiency (NUE) has great significance for various crops, particularly in hybrid breeding. Reducing nitrogen inputs is key to achieving sustainable rice production and mitigating environmental problems. In this study, we analyzed the transcriptomic and physiological changes in two indica restorer lines (Nanhui511 [NH511] and Minghui23 [MH23]) under high nitrogen (HN) and low nitrogen (LN) conditions. Compared to MH23, NH511 was more sensitive to different nitrogen supplies and exhibited higher nitrogen uptake and NUE under HN conditions by increasing lateral root and tiller numbers in the seedling and maturation stages, respectively. NH511 also exhibited a lower survival rate than MH23 when planted in a chlorate-containing hydroponic solution, indicating its HN uptake ability under different nitrogen-supply conditions. Transcriptomic analysis showed that NH511 has 2456 differentially expressed genes, whereas MH23 had only 266. Furthermore, these genes related to nitrogen utilization showed differential expression in NH511 under HN conditions, while the opposite was observed in MH23. Our findings revealed that NH511 could be regarded as elite rice and used for breeding high-NUE restorer lines by regulating and integrating nitrogen-utilization genes, which provides novel insights for the cultivation of high-NUE hybrid rice.
Atrazine Toxicity: The Possible Role of Natural Products for Effective Treatment
Authors: Das Srijit , Sakr Hussein , Al-Huseini Isehaq , Jetti Raghu , Al-Qasmi Sara , Sugavasi Raju , Sirasanagandla Srinivasa Rao
Published: June 2023

There are various herbicides which were used in the agriculture industry. Atrazine (ATZ) is a chlorinated triazine herbicide that consists of a ring structure, known as the triazine ring, along with a chlorine atom and five nitrogen atoms. ATZ is a water-soluble herbicide, which makes it capable of easily infiltrating into majority of the aquatic ecosystems. There are reports of toxic effects of ATZ on different systems of the body but, unfortunately, majority of these scientific reports were documented in animals. The herbicide was reported to enter the body through various routes. The toxicity of the herbicide can cause deleterious effects on the respiratory, reproductive, endocrine, central nervous system, gastrointestinal, and urinary systems of the human body. Alarmingly, few studies in industrial workers showed ATZ exposure leading to cancer. We embarked on the present review to discuss the mechanism of action of ATZ toxicity for which there is no specific antidote or drug. Evidence-based published literature on the effective use of natural products such as lycopene, curcumin, Panax ginseng, Spirulina platensis, Fucoidans, vitamin C, soyabeans, quercetin, L-carnitine, Telfairia occidentalis, vitamin E, Garcinia kola, melatonin, selenium, Isatis indigotica, polyphenols, Acacia nilotica, and Zingiber officinale were discussed in detail. In the absence of any particular allopathic drug, the present review may open the doors for future drug design involving the natural products and their active compounds.
Design and Performance Evaluation of Liquid-Cooled Heat Dissipation Structure for Lithium Battery Module
Authors: Zhao Yifei , Chen Jianhong , He Wanrong
Published: June 2023

The current global resource shortage and environmental pollution are becoming increasingly serious, and the development of the new energy vehicle industry has become one of the important issues of the times. In this paper, a nickel–cobalt lithium manganate (NCM) battery for a pure electric vehicle is taken as the research object, a heat dissipation design simulation is carried out using COMSOL software, and a charging heat generation model of the battery pack is established. Combined with the related research on the thermal management technology of the lithium-ion battery, five liquid-cooled temperature control models are designed for thermal management, and their temperature control simulation and effect analysis are carried out. Finally, the performance evaluation system of the thermal management scheme of the lithium-ion battery pack is established based on the analytic network process (ANP) and system dynamics (SD), and the performance of the above five thermal management design models is comprehensively scored and analyzed. The results show that liquid-cooled Models 1 (86.7075) and 5 (89.1055) have the highest overall scores, meeting both the temperature control requirements and the overall thermal management performance, and it is recommended to apply the working condition settings for which they are evaluated as Level I.
Research on Assembly Method of Threaded Fasteners Based on Visual and Force Information
Authors: Zhou Yibang , Wang Xiaoyong , Zhang Lanzhu
Published: June 2023

Threaded fastening operations are widely used in assembly and are typically time-consuming and costly. In low-volume, high-value manufacturing, fastening operations are carried out manually by skilled workers. The existing approaches are found to be less flexible and robust for performing assembly in a less structured industrial environment. This paper introduces a novel algorithm for detecting the position and orientation of threaded holes and a new method for tightening bolts. First, the elliptic arc fitting method and the three-point method are used to estimate the initial position and orientation of the threaded hole, and the force impact caused by switching from the free space to the constrained space during bolt tightening is solved. Second, by monitoring the deformation of passive compliance, the position information is introduced into the control process to better control the radial force between the bolt and the threaded hole in the tightening process. The constant force controller and orientation compliance controller are designed according to the adaptive control theory. A series of experiments are carried out. The results show that the proposed method can estimate the initial position and orientation of an M24 bolt with an average position error of 0.36 mm, 0.43 mm and 0.46 mm and an orientation error of 0.65°, 0.46° and 0.59°, and it can tighten the bolt with a success rate of 98.5%.
Ataulfo Mango (Mangifera indica L.) Peel Extract as a Potential Natural Antioxidant in Ground Beef
Authors: Zafra Ciprián Dalia I. , Nevárez Moorillón Guadalupe V. , Soto Simental Sergio , Guzmán Pantoja Ludmila E. , López Hernández Luis H. , Santiago Castro Joaquín T. , Villalobos Delgado Luz H.
Published: June 2023

Total phenolic content (TPC), total flavonoid content (TFC), antioxidant and antimicrobial in vitro activity of ethanolic (EE) and hydroethanolic (HE) extracts of mango peel Ataulfo were evaluated. The highest TPC, TFC and antioxidant capacity were as shown in EE. Ethanolic extract was incorporated into ground beef at 1000 (BBEA) and 2000 mg/kg meat (BEEB) concentrations and then compared with 250 mg of sodium ascorbate/kg meat (ASC) along with a control (without antioxidant). The effects of adding EE on instrumental colour, metmyoglobin content, thiobarbituric acid reactive substances (TBARS), pH, microbial load and sensorial analysis of ground beef were evaluated for 11 days at 4 °C. BEEB added in raw ground beef improved colour stability while the lipid oxidation in raw and raw-cooked ground beef was inhibited with a greater antioxidant effect than ASC and a similar overall acceptability score (cooked ground beef). However, incorporating BEEB into ground beef did not show significant antimicrobial activity. Therefore, mango peel extracts could potentially be used as a natural antioxidant in ground beef.
Robust Cascade MRAC for a Hybrid Grid-Connected Renewable Energy System
Authors: Travieso-Torres Juan Carlos , Ricaldi-Morales Abdiel , Véliz-Tejo Adolfo , Leiva-Silva Felipe
Published: June 2023

Hybrid grid-connected renewable energy systems have gained significant importance in sustainably responding to an increased electrical energy demand. These are time-varying nonlinear dynamical plants, where the value of their parameters depends on changing weather conditions and the alternating grid voltage with randomly fluctuating amplitude. This paper proposes a robust cascade MRAC for nonlinear plants representing a class of these systems, which includes n renewable energy converts and a DC/AC single-phase full bridge inverter. The proposal reduces commissioning time by avoiding linearization and knowledge of the plant parameters. Moreover, it includes specific formulas for tuning the controller parameters that decrease their adjustments based on trial and error. Finally, it uses a direct adaptive method with adaptive laws having σθ modification and an inner loop at least five times faster than the outer loop. The proposition validation includes the theoretical stability proof based on the Lyapunov stability method and Barbalat’s Lemma. Furthermore, it presents comparative simulation results with quoted cascade PI controllers for a monophasic system, including two renewable energy sources and injection. Both techniques effectively track setpoint changes of the energy sources’ currents and direct current bus voltage, showing the proposal similar or reduced ripple. At the same time, both ensure robustness against decreased photovoltage panels irradiance, increased fuel cells voltage, and grid voltage amplitude random fluctuations. However, the proposal does these things while avoiding prior linearization and unknowing the plant parameters.
Effects of Superheated Surface on the Deposition Behavior of Na2SO4 in Supercritical Water
Authors: Zhu Qiao , Li Zhe , Song Yafei , Tong Yujun , Yang Tao , Cheng Zhenmin
Published: June 2023

The reduced solubility of inorganic salts in supercritical water has a significant impact on the stable operation of desalination facilities as it may lead to surface fouling due to salt deposition. In this study, the solubility of Na2SO4 was experimentally determined to be 0.04–15.34 mmol/kg water at 23–25 MPa and 390–420 °C. To investigate the precipitation behavior of Na2SO4 in supercritical water, a reactor with a heating bar was designed and the deposition effect of salt on the superheated surface in an autoclave was tested at a temperature of 390 °C and a pressure of 23 MPa. Then, the deposition mechanism of salt in the autoclave was analyzed and the temperature field in the reactor was simulated using CFD commercial software. The experimental results showed that Na2SO4 was present on both the heating rod and the bottom of the autoclave with a loose salt layer. The simulation results indicated that the temperature near the heating rod was significantly higher than the bulk fluid temperature and it provided the temperature condition where the inorganic salt preferentially nucleated and precipitated. Nickel foam was chosen as the porous media carrier to investigate the selective precipitation of salt on different superheated surfaces. The results showed that nickel foam could collect a large amount of salt on the heating rod and change the state of the compact salt layer on the kettle bottom wall, providing a technical choice for salt recovery under supercritical conditions.
Assimilation and Inversion of Ionospheric Electron Density Data Using Lightning Whistlers
Authors: Xiang Tian , Liu Moran , He Shimin , Zhou Chen
Published: June 2023

The data assimilation algorithm is a common algorithm in space weather research. In this paper, the time-frequency information in the dispersion spectrum of lightning whistlers received by the ZH-1 satellite is used as the observed value, and the international reference ionospheric model serves as the background model to construct the calculation model of the propagation time of lightning whistlers in the ionosphere. Kalman filtering is adopted to assimilate the electron density distribution along the propagation path of lightning whistlers. The results show that the situation where the electron density of the background model deviates greatly from the true value is significantly improved through data assimilation. The electron density after assimilation is in good agreement with the true value, which effectively helps realize the process of using observed values to correct the background value. On this basis, the influence of the frequency difference on the assimilation inversion effect is studied, and the results show that the assimilation effect is worse when the frequency difference between frequency points is less than 1 kHz.
First Retrievals of Surface and Atmospheric Properties Using EnMAP Measurements over Antarctica
Authors: Kokhanovsky Alexander A. , Brell Maximillian , Segl Karl , Bianchini Giovanni , Lanconelli Christian , Lupi Angelo , Petkov Boyan , Picard Ghislain , Arnaud Laurent , Stone Robert S. , Chabrillat Sabine
Published: June 2023

The paper presents the first retrievals of clean snow properties using spaceborne hyperspectral observations via the Environmental Mapping and Analysis Program (EnMAP). The location close to the Concordia station at the Dome C Plateau (Antarctica) was selected. At this location, the atmospheric effects (except molecular light scattering and absorption) are weak, and the simplified atmospheric correction scheme could be applied. The ice grain size, snow specific surface area, and snow spectral and broadband albedos were retrieved using single-view EnMAP measurements. In addition, we propose a technique to retrieve trace gas concentrations (e.g., water vapor and ozone) from EnMAP observations over the snow surfaces. A close correspondence of satellite and ground-measured parameters was found.
Estimating Three-Dimensional Distribution of Leaf Area Using Airborne LiDAR in Deciduous Broad-Leaved Forest
Authors: Awaya Yoshio , Araki Kazuho
Published: June 2023

We examined the performance of airborne light detection and ranging (LiDAR) data obtained in 2011 for leaf area estimation in deciduous broad-leaved forest using the Beer–Lambert law in Takayama, Gifu, Japan. We estimated leaf area index (LAI, allometry-LAI) and vertical leaf area density (LAD) using field survey data by applying allometric equations to estimate leaf-area of trees and a Weibull distribution equation to estimate vertical leaf distribution. We then estimated extinction coefficients (Ke) of LiDAR data for three height layers from the ground to the canopy top using the vertical LAD and vertical laser pulse distribution. The estimated PAI (LiDAR-PAI) using the Beer–Lambert law and Ke, when treating the canopies as three height layers, showed a significant linear relationship with allometry-LAI (p < 0.001). However, LiDAR-PAI when treating the canopies as single layer saturated at a PAI of six. It was similar to the lesser PAI estimation by hemispherical photography or relative photosynthetic photon flux density which treated the canopy as a single layer, compared to LAI measurements by litter traps. It is therefore important to allocate distinct Ke values to each of the multiple height layers for an accurate estimation of PAI and vertical PAD when applying the Beer–Lambert law to airborne LiDAR data.
Semi-Coprime Array with Staggered Beam-Steering of Sub-Arrays
Authors: Khan Waseem , Shahid Saleem , Iqbal Waleed , Rana Ahsan Sarwar , Zahra Hijab , Alathbah Moath , Abbas Syed Muzahir
Published: June 2023

A split-aperture array (SAA) is an array of sensors or antenna elements in which the array is split into two or more sub-arrays (SAs). Recently proposed SAAs, namely coprime and semi-coprime arrays, offer to attain a small half-power beamwidth (HPBW) with a small number of elements, compared to most conventional unified-aperture arrays, at the cost of reduced peak-to-side-lobe ratio (PSLR). To reduce HPBW and increase PSLR, non-uniform inter-element spacing and excitation amplitudes have proven helpful. However, all the existing arrays and beam-formers suffer increased HPBW, degraded PSLR or both when the main beam is steered away from the broadside. In this paper, we propose staggered beam-steering of SAs, a novel technique for decreasing HPBW. In this technique, we steer the main beams of the SAs of a semi-coprime array to angles slightly different from the desired steering angle. In conjunction with staggered beam-steering of SAs, we have utilized Chebyshev weights to suppress the side lobes. The results show that the beam-widening effect of Chebyshev weights can be mitigated considerably by staggered beam-steering of the SAs. Ultimately, the unified beam-pattern of the whole array offers HPBW and PSLR better than the existing SAAs, uniform and non-uniform linear arrays, especially when the desired steering angle is away from the broadside direction.
Surface Electromyography-Driven Parameters for Representing Muscle Mass and Strength
Authors: Sung Joo Hye , Baek Seol-Hee , Park Jin-Woo , Rho Jeong Hwa , Kim Byung-Jo
Published: June 2023

The need for developing a simple and effective assessment tool for muscle mass has been increasing in a rapidly aging society. This study aimed to evaluate the feasibility of the surface electromyography (sEMG) parameters for estimating muscle mass. Overall, 212 healthy volunteers participated in this study. Maximal voluntary contraction (MVC) strength and root mean square (RMS) values of motor unit potentials from surface electrodes on each muscle (biceps brachii, triceps brachii, biceps femoris, rectus femoris) during isometric exercises of elbow flexion (EF), elbow extension (EE), knee flexion (KF), knee extension (KE) were acquired. New variables (MeanRMS, MaxRMS, and RatioRMS) were calculated from RMS values according to each exercise. Bioimpedance analysis (BIA) was performed to determine the segmental lean mass (SLM), segmental fat mass (SFM), and appendicular skeletal muscle mass (ASM). Muscle thicknesses were measured using ultrasonography (US). sEMG parameters showed positive correlations with MVC strength, SLM, ASM, and muscle thickness measured by US, but showed negative correlations with SFM. An equation was developed for ASM: ASM = −26.04 + 20.345 × Height + 0.178 × weight − 2.065 × (1, if female; 0, if male) + 0.327 × RatioRMS(KF) + 0.965 × MeanRMS(EE) (SEE = 1.167, adjusted R2 = 0.934). sEMG parameters in controlled conditions may represent overall muscle strength and muscle mass in healthy individuals.
A Cost-Driven Method for Deep-Learning-Based Hardware Trojan Detection
Authors: Dong Chen , Yao Yinan , Xu Yi , Liu Ximeng , Wang Yan , Zhang Hao , Xu Li
Published: June 2023

The Cyber-Physical System and even the Metaverse will become the second space in which human beings live. While bringing convenience to human beings, it also brings many security threats. These threats may come from software or hardware. There has been a lot of research on managing malware, and there are many mature commercial products, such as antivirus software, firewalls, etc. In stark contrast, the research community on governing malicious hardware is still in its infancy. Chips are the core component of hardware, and hardware Trojans are the primary and complex security issue faced by chips. Detection of hardware Trojans is the first step for dealing with malicious circuits. Due to the limitation of the golden chip and the computational consumption, the existing traditional detection methods are not applicable to very large-scale integration. The performances of traditional machine-learning-based methods depend on the accuracy of the multi-feature representation, and most of the methods may lead to instability because of the difficulty of extracting features manually. In this paper, employing deep learning, a multiscale detection model for automatic feature extraction is proposed. The model is called MHTtext and provides two strategies to balance the accuracy and computational consumption. After selecting a strategy according to the actual situations and requirements, the MHTtext generates the corresponding path sentences from the netlist and employs TextCNN for identification. Further, it can also obtain non-repeated hardware Trojan component information to improve its stability performance. Moreover, a new evaluation metric is established to intuitively measure the model’s effectiveness and balance: the stabilization efficiency index (SEI). In the experimental results for the benchmark netlists, the average accuracy (ACC) in the TextCNN of the global strategy is as high as 99.26%, and one of its stabilization efficiency index values ranks first with a score of 71.21 in all comparison classifiers. The local strategy also achieved an excellent effect, according to the SEI. The results show that the proposed MHTtext model has high stability, flexibility, and accuracy, in general.
Terrain Characterization via Machine vs. Deep Learning Using Remote Sensing
Authors: Ewing Jordan , Oommen Thomas , Thomas Jobin , Kasaragod Anush , Dobson Richard , Brooks Colin , Jayakumar Paramsothy , Cole Michael , Ersal Tulga
Published: June 2023

Terrain traversability is critical for developing Go/No-Go maps for ground vehicles, which significantly impact a mission’s success. To predict the mobility of terrain, one must understand the soil characteristics. In-situ measurements performed in the field are the current method of collecting this information, which is time-consuming, costly, and can be lethal for military operations. This paper investigates an alternative approach using thermal, multispectral, and hyperspectral remote sensing from an unmanned aerial vehicle (UAV) platform. Remotely sensed data combined with machine learning (linear, ridge, lasso, partial least squares (PLS), support vector machines (SVM), and k nearest neighbors (KNN)) and deep learning (multi-layer perceptron (MLP) and convolutional neural network (CNN)) are used to perform a comparative study to estimate the soil properties, such as the soil moisture and terrain strength, used to generate prediction maps of these terrain characteristics. This study found that deep learning outperformed machine learning. Specifically, a multi-layer perceptron performed the best for predicting the percent moisture content (R2/RMSE = 0.97/1.55) and the soil strength (in PSI), as measured by a cone penetrometer for the averaged 0–6” (CP06) (R2/RMSE = 0.95/67) and 0–12” depth (CP12) (R2/RMSE = 0.92/94). A Polaris MRZR vehicle was used to test the application of these prediction maps for mobility purposes, and correlations were observed between the CP06 and the rear wheel slip and the CP12 and the vehicle speed. Thus, this study demonstrates the potential of a more rapid, cost-efficient, and safer approach to predict terrain properties for mobility mapping using remote sensing data with machine and deep learning algorithms.
Publisher’s Note: Sclerosis—A New Open Access Journal
Authors: Craciun Ioana
Published: June 2023

Sclerosis is a medical condition characterized by the hardening of tissues and can affect organs, the nervous system, and the vascular system [...]
Exergo–Economic and Parametric Analysis of Waste Heat Recovery from Taji Gas Turbines Power Plant Using Rankine Cycle and Organic Rankine Cycle
Authors: Kareem Alaa Fadhel , Akroot Abdulrazzak , Abdul Wahhab Hasanain A. , Talal Wadah , Ghazal Rabeea M. , Alfaris Ali
Published: June 2023

This study focused on exergo–conomic and parametric analysis for Taji station in Baghdad. This station was chosen to reduce the emission of waste gases that pollute the environment, as it is located in a residential area, and to increase the production of electric power, since for a long time, Iraq has been a country that has suffered from a shortage of electricity. The main objective of this work is to integrate the Taji gas turbine’s power plant, which is in Baghdad, with the Rankine cycle and organic Rankine cycle to verify waste heat recovery to produce extra electricity and reduce emissions into the environment. Thermodynamic and exergoeconomic assessment of the combined Brayton cycle–Rankine cycle/Organic Rankin cycle (GSO CC) system, considering the three objective functions of the First- and Second-Law efficiencies and the total cost rates of the system, were applied. According to the findings, 258.2 MW of power is produced from the GSO CC system, whereas 167.3 MW of power is created for the Brayton cycle (BC) under the optimum operating conditions. It was demonstrated that the overall energy and exergy efficiencies, respectively, are 44.37% and 42.84% for the GSO CC system, while they are 28.74% and 27.75%, respectively, for the Brayton cycle. The findings indicate that the combustion chamber has the highest exergy degradation rate. The exergo–economic factor for the entire cycle is 37%, demonstrating that the cost of exergy destruction exceeds the cost of capital investment. Moreover, the cost of the energy produced by the GSO CC system is USD 9.03/MWh, whereas it is USD 8.24/MWh for BC. The results also indicate that the network of the GSO CC system decreases as the pressure ratio increases. Nonetheless, the GSO CC system’s efficiencies and costs increase with a rise in the pressure ratio until they reach a maximum and then decrease with further pressure ratio increases. The increase in the gas turbine inlet temperature and isentropic efficiency of the air compressor and gas turbine enhances the thermodynamic performance of the system; however, a further increase in these parameters increases the overall cost rates.
Valorization of Face Masks Produced during COVID-19 Pandemic through Hydrothermal Carbonization (HTC): A Preliminary Study
Authors: Farru Gianluigi , Libra Judy A. , Ro Kyoung S. , Cannas Carla , Cara Claudio , Muntoni Aldo , Piredda Martina , Cappai Giovanna
Published: June 2023

The COVID-19 pandemic has led to the increased use of disposable face masks worldwide, resulting in a surge of potentially infectious waste. This waste must be safely managed and disposed of to prevent the spread of the virus. To address this issue, a preliminary study explored the use of hydrothermal carbonization (HTC) as a potential method for converting surgical mask waste into value-added carbonaceous materials. The HTC treatments were conducted at 220 °C for 3 h with or without the addition of acetic acid. The resulting hydrochar was characterized using several techniques, including thermogravimetric analysis (TGA), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and N2-physisorption analyzers. The study found that the masks formed a melt with reduced mass (−15%) and volume (up to −75%) under the applied conditions. The carbon content and higher heating value (HHV) of the produced hydrochars were higher than those of the original masks (+5%). Furthermore, when acetic acid was added during the HTC experiment, a new crystal phase, terephthalic acid, was produced. This acid is a precursor in surgical mask production. The study suggests that hydrothermal carbonization could potentially achieve sanitization and volume reduction in non-renewable and non-biodegradable surgical masks while also producing a solid fuel or a raw material for terephthalic acid production. This approach offers an innovative and sustainable solution to manage the waste generated by the increased use of disposable face masks during the pandemic.
An Approach to Understanding the Hydration of Cement-Based Composites Reinforced with Untreated Natural Fibers
Authors: Llorens Joan , Julián Fernando , Gifra Ester , Espinach Francesc X. , Soler Jordi , Chamorro Miquel Àngel
Published: June 2023

The use of untreated natural fibers to reinforce cementitious composites improves their environmental friendliness, resulting in a more sustainable material. Moreover, the influence of the untreated natural fibers on the hydration process of Portland cement composites presents some uncertainties. According to the literature, the most usual tests to analyze the degree of hydration of cement composites are the differential thermal and thermogravimetric analyses (TGA/dTGA). Several authors propose to analyze data methods to establish the degree of hydration of cement composites. This paper presents the TGA/dTGA test carried out on mortar samples with and without fibers at age 2, 3, 7, 14, and 28 days. The degree of hydration was calculated according to Bhatty’s method. To characterize the raw materials, the quantitative chemical was determined using scanning electron microscopy and energy dispersive X-ray spectroscopy (SEM–EDX). The main findings of this study were that the presence of untreated natural hemp fibers in the OPC composites increased the hydration degree by 9%. The presence of fibers affected the formation of several components. Thus, their presence increased the formation of monosulphate, reduced portlandite, did not affect ettringite, and increased the formation of calcite, thereby improving the sustainable footprint due to the increased CO2 fixation.
A Monitoring Method for Corporate Environmental Performance Based on Data Fusion in China under the Double Carbon Target
Authors: Mu Youying , Duan Chengzhuo , Li Xin , Wu Yongbo
Published: June 2023

The production and operation of corporates have a significant impact on the environment, and it is crucial for corporates to operate in an environmentally friendly manner, especially in the context of the China double carbon target. Corporate environmental performance refers to the degree of impact on the environment and the degree of contribution to environmental protection by corporates in their business activities. Our study conducted an assessment and early warning system for corporate environmental performance by monitoring seven typical corporate environmental performance variables, including the green asset ratio (Gra), the proportion of environmentally friendly products (Pefp), and cash flow for environmental protection to total assets ratio (ECF), of 2718 non-financial listed corporates in China’s A-share market. The dataset comprised empirical data from the CSMAR database and multi-scale measurements collected by us. Among data-driven monitoring methods, deep learning is widely applied due to its powerful automatic feature extraction abilities. However, multi-time scale data is often encountered in industrial ecology-related data, as the different underlying physical quantities of various data result in inconsistent sampling rates. Multi-time scale data are incomplete and asymmetrical, making it difficult for traditional models to use directly for corporate ecological monitoring. In this article, an improved CNN-LSTM monitoring model based on data fusion is proposed to address this issue. This method employs unified vectorization processing to transform incomplete multi-time scale data into uniform complete data. An end-to-end diagnostic model is constructed to simultaneously optimize feature extraction and monitoring. In a multi-time scale corporate monitoring model, CNN can mine hidden features of data, while LSTM can further capture the time dependence of underlying time series. Compared to manual feature extraction that relies on prior knowledge, the proposed model can learn more effective data features. The effectiveness of the method has been demonstrated through empirical data experiments, which is beneficial for corporates in the context of double carbon emissions, providing a method for regulating corporate ecological indicators.
A Fuzzy-Based Analysis of the Mediating Factors Affecting Sustainable Purchase Intentions of Smartphones: The Case of Two Brands in Two Asian Countries
Authors: Moslehpour Massoud , Faez Sahand E. P. , Gupta Brij B. , Arya Varsha
Published: June 2023

Given their functionality, all smartphone brands are the same. Their similarities notwithstanding, they supply the same product at different prices in the same market. Strangely enough, the consumers do comply and willingly pay such price premiums. This study examines the mediation effect of price premium and brand preference on the causal impact of brand equity on sustainable purchase intention. The novelty of this study is in transforming the initial measures in a 5-point Likert scale into continuous values through a fuzzification and defuzzification process. Brand equity comprises three factors: brand awareness, perceived quality, and prestige value. Standardized questionnaire collected data in two countries (Taiwan and Indonesia) on two brands of smartphones (iPhone and HTC). Overall, 404 questionnaires were distributed in Taiwan, and 434 questionnaires were distributed in Indonesia. The data were analyzed by applying a structural equation model after conducting an exploratory and confirmatory factors analysis. In order to improve the estimations’ accuracy, the initial measures in a 5-point Likert scale were transformed into continuous values through a fuzzification and defuzzification process. The former consisted of assigning triangular fuzzy numbers, and the latter entailed assigning a center of gravity to each triangular fuzzy number and then extracting a random number from a normal distribution function based on the center of gravity. According to the results, price premium and brand preference exhibited significant mediation effects, with price premium having stronger effects than brand preference. Furthermore, the mediation effect was strongest for perceived quality and weakest for perceived prestige value.
Blockchain-Enabled Communication Framework for Secure and Trustworthy Internet of Vehicles
Authors: Biswas Manju , Das Debashis , Banerjee Sourav , Mukherjee Amrit , AL-Numay Waleed , Biswas Utpal , Zhang Yudong
Published: June 2023

The emerging field of the Internet of Vehicles (IoV) has garnered significant attention due to its potential to revolutionize transportation and mobility. IoV enables the development of innovative services and applications that can enhance the efficiency, safety, and sustainability of transportation systems. However, ensuring secure and reliable communication among different components of an IoV system poses a critical challenge. This study proposes a blockchain-based communication framework for secure and trustworthy IoV applications. The framework utilizes blockchain technology’s decentralization and security features to create secure communication channels between IoV system components, including vehicles, infrastructure, and service providers. An identity management system is also integrated into the framework to authenticate and authorize users and devices, thereby preventing unauthorized access and data breaches. To assess the proposed framework’s effectiveness, real-world IoV scenarios were used to conduct experiments, and the results demonstrate that the framework can provide secure and trustworthy communication for IoV applications. The proposed blockchain-enabled communication framework provides a promising solution for addressing security and trust challenges in IoV communication systems.
Experiences from a School–University Partnership Climate and Sustainability Education Project in England: The Value of Citizen Science and Practical STEM Approaches
Authors: Batchelder Meryl , Swinney Mark , O’Hara Tess , Goddard Alethea , Lewis Elizabeth , Cox Jo , Fowler Hayley J.
Published: June 2023

Extracurricular citizen science and hands-on STEM (science, technology, engineering, and maths) projects can ensure that climate and sustainability education is not just superficial but truly transformative. Through working under the guidance of academic partners, young people can develop a deep appreciation for climate science and, most importantly, become aware of the real-world sustainable solutions being developed to address the consequences of our changing climate. We present the experiences of four stakeholders from one case study involving middle school students (9–13 years) and their teachers working on a climate change and sustainable drainage system (SuDS) project with academics from Newcastle University and the Schools Engagement programme of the Royal Society. We found that all stakeholders considered it to be a successful project with significant benefits, including engaging learning in the pupils, a challenge for the teachers, and meaningful data collection for the academic team. The funding organisation thought the positive interactions that they try to encourage between students and STEM partners were evidenced in this project perfectly. Young people are future decision makers; this type of project can enable them to develop essential skills and an understanding of a range of STEM careers. Subsequent developments may include refining the way similar projects are run.
Investigation the Relationship between Mental Toughness and Courage Levels of Sports Sciences Faculty Students for Sustainable Performance
Authors: Toros Turhan , Ogras Emre Bulent , Okan İlyas , Temel Cenk , Keskin Muzaffer Toprak , Korkmaz Cihat , Uluoz Eren
Published: June 2023

This research aimed to explore any links between mental toughness and courage in sports among students enrolled in the faculty of sports science, employing a relational screening model with a sample consisting of 340 university students who were selected using a convenience sampling method. Research data were collected through a Personal Information Form, the Sports Mental Toughness Questionnaire (SMTQ), and the Sport Courage Scale (SCS). Data analyses included independent groups T-test, One-Way ANOVA, and Pearson’s product-moment correlation coefficient. Significant differences were detected in the SMTQ and SCS, as well as in the total scale scores, based on the gender variable, which is one of the demographic variables. SMTQ and SCS subdimensions and total scale scores differed significantly according to the university department. Furthermore, the study identified significant differences in the SMTQ and the SCS subdimensions and total scale scores based on the academic year variable. However, no significant relationship was found between age and SMTQ and SCS subdimensions and total scale scores. The findings of this study indicate a significant and positive correlation between the SMTQ and SCS subdimensions and the total scale scores. Therefore, it can be concluded that as the mental toughness levels of the students in sports science increase, their courage levels also increase.
Proposal of a Methodology for Prediction of Indoor PM2.5 Concentration Using Sensor-Based Residential Environments Monitoring Data and Time-Divided Multiple Linear Regression Model
Authors: Park Shin-Young , Yoon Dan-Ki , Park Si-Hyun , Jeon Jung-In , Lee Jung-Mi , Yang Won-Ho , Cho Yong-Sung , Kwon Jaymin , Lee Cheol-Min
Published: June 2023

This study aims to propose an indoor air quality prediction method that can be easily utilized and reflects temporal characteristics using indoor and outdoor input data measured near the indoor target point as input to calculate indoor PM2.5 concentration through a multiple linear regression model. The atmospheric conditions and air pollution detected in one-minute intervals using sensor-based monitoring equipment (Dust Mon, Sentry Co Ltd., Seoul, Korea) inside and outside houses from May 2019 to April 2021 were used to develop the prediction model. By dividing the multiple linear regression model into one-hour increments, we attempted to overcome the limitation of not representing the multiple linear regression model’s characteristics over time and limited input variables. The multiple linear regression (MLR) model classified by time unit showed an improvement in explanatory power by up to 9% compared to the existing model, and some hourly models had an explanatory power of 0.30. These results indicated that the model needs to be subdivided by time period to more accurately predict indoor PM2.5 concentrations.