bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of a Low-Stakes Assessments Model with Retake in General Chemistry: Connecting to Student Attitudes and Self-Concept
Authors: Vyas Vijay S. , Nobile Llanie , Gardinier James R. , Reid Scott A.
Published: December 2023

Across a variety of fields, the use of low-stakes assessments has led to reductions in achievement gaps and improved student success. Here, we probe the use of a low-stakes assessment model with a retake option for failed quizzes in a two-semester general chemistry sequence. We find that the quiz-retake rate in general chemistry II was significantly higher for students who had completed a retake in a general chemistry I section, and the percentage of students who failed at least one quiz in general chemistry I but passed all quizzes in general chemistry II was significantly higher for students who had retaken at least one quiz in general chemistry I. However, across both semesters only 40% of students who failed a quiz and were offered a retake completed one. To examine this trend, we probed a connection to student attitudes and self-concept. As instruments, we used version 2 of the Attitudes towards Chemistry Subject Inventory (ASCIv2) and the Chemistry Subject Concept Inventory (CSCI), which were administered across all sections of our general chemistry I course in the fall 2021 semester, and the results subjected to confirmatory factor analysis. Two sections employed low-stakes assessments (quizzes), with one section offering a retake option, while the remaining two used a traditional assessment pattern of five exams. The instruments were applied again for the quiz-retake section of general chemistry II, affording a longitudinal comparison of students common to both sections. In a pairwise comparison, we find significant increases in factors corresponding to Intellectual Accessibility and Chemistry Self-Concept for students in the quiz-retake sections across semesters, with the former more pronounced for men and the latter for women. We take these results to provide additional data supporting the benefit of low-stakes assessments with a retake option, that may be particularly impactful for women in chemistry.
African American Students’ Academic and Web Programming Self-Efficacy, Learning Performance, and Perceptions towards Computer Programming in Web Design Courses
Authors: Kuo Yu-Tung , Kuo Yu-Chun
Published: December 2023

Computer programming has been included in computer literacy education in many countries in the last decade. This study examined the effects of gender and the prior programming experience of computer programming on academic and web programming self-efficacy and learning performance in the web design course among African American students, as well as their perceptions towards computer programming. This study’s 14-week web design course taught African American students multiple web programming languages, including HTML, CSS, and JavaScript, in order. A one-group pretest–posttest design was adopted in the experiment. The quantitative method was primarily used in data analysis. This study revealed that African American students’ academic and web programming self-efficacy significantly increased after the web design course. Most of the African American students’ perceptions of computer programming became positive after attending the web design course. This study also found that male African American students had a significantly higher level of web programming self-efficacy than female students before the web design course. Interestingly, this difference disappeared after the course. Additionally, both gender and prior experience in computer programming did not significantly affect students’ learning performance in the web design course. The findings of this study not only contribute to the understanding of the feasibility of teaching multiple programming languages in web programming courses for African American students, they also provide evidence of the positive influence of web programming on African American students’ perceptions of computer programming.
A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
Authors: Tera Sivarama Prasad , Alantattil Rajesh , Paily Roy
Published: December 2023

Iterative stochastic decoding is an alternative to standard fixed-point decoding of low-density parity check (LDPC) codes that can be used to minimize inter-node routing. A flexible field-programmable gate array (FPGA)-based stochastic decoding (SD) hardware architecture is presented in this paper. The architecture is designed to decode different code rates of LDPC codes that comply with the fifth-generation (5G) New Radio (NR) standard. This decoder’s runtime flexibility is desirable as it switches to a better-performing code rate automatically based on the channel conditions without the extra time needed to reprogram the FPGA. An offline design method is implemented to generate the hardware description language (HDL) code description of the decoder for the required code rate set, which is further synthesized and integrated into a Xilinx Kintex-7-series FPGA board to determine the hardware resource utilisation and processing throughput. Synopsys design tools were employed during both the simulation and synthesis stages in combination with TSMC 65 nm CMOS standard cell technology to facilitate comparative analysis. Compared with state-of-the-art designs, the proposed architecture reduces hardware utilization by up to 26% and increases energy efficiency by 52%.
GRU- and Transformer-Based Periodicity Fusion Network for Traffic Forecasting
Authors: Zhang Yazhe , Liu Shixuan , Zhang Ping , Li Bo
Published: December 2023

Accurate traffic prediction is vital for traffic management, control, and urbanization construction. Extensive research efforts have diligently focused on capturing the intricate spatio-temporal relationships that are inherent in traffic data. However, a limited number of studies have fully exploited the potential of periodicity, a distinctive and valuable characteristic of transportation systems. In this paper, we propose a novel GRU- and Transformer-Based Periodicity Fusion Network (GTPFN) to distinguish the effects of different types of periodic data and integrate them seamlessly and effectively. Initially, the proposed model captures dynamic spatio-temporal correlations and obtains the candidate prediction result by employing a GRU encoder–decoder with spatial attention, focusing on the hourly data. Subsequently, we design the Pattern Induction Block based on GRU layers to extract regular traffic patterns from daily and weekly data. Finally, the Pattern Fusion Transformer integrates these patterns, followed by a Feedforward layer, to yield the final prediction output. Experiments on the Caltrans Performance Measurement System (PEMS) datasets illustrate that the proposed model outperforms state-of-art baseline models on most predicted horizons.
Dwarf Mongoose Optimizer for Optimal Modeling of Solar PV Systems and Parameter Extraction
Authors: Moustafa Ghareeb , Smaili Idris H. , Almalawi Dhaifallah R. , Ginidi Ahmed R. , Shaheen Abdullah M. , Elshahed Mostafa , Mansour Hany S. E.
Published: December 2023

This article presents a modified intelligent metaheuristic form of the Dwarf Mongoose Optimizer (MDMO) for optimal modeling and parameter extraction of solar photovoltaic (SPV) systems. The foraging manner of the dwarf mongoose animals (DMAs) motivated the DMO’s primary design. It makes use of distinct DMA societal groups, including the alpha category, scouts, and babysitters. The alpha female initiates foraging and chooses the foraging path, bedding places, and distance travelled for the group. The newly presented MDMO has an extra alpha-directed knowledge-gaining strategy to increase searching expertise, and its modifying approach has been led to some extent by the amended alpha. For two diverse SPV modules, Kyocera KC200GT and R.T.C. France SPV modules, the proposed MDMO is used as opposed to the DMO to efficiently estimate SPV characteristics. By employing the MDMO technique, the simulation results improve the electrical characteristics of SPV systems. The minimization of the root mean square error value (RMSE) has been used to compare the efficiency of the proposed algorithm and other reported methods. Based on that, the proposed MDMO outperforms the standard DMO. In terms of average efficiency, the MDMO outperforms the standard DMO approach for the KC200GT module by 91.7%, 84.63%, and 75.7% for the single-, double-, and triple-diode versions, respectively. The employed MDMO technique for the R.T.C France SPV system has success rates of 100%, 96.67%, and 66.67%, while the DMO’s success rates are 6.67%, 10%, and 0% for the single-, double-, and triple-diode models, respectively.
Point Cloud Segmentation Network Based on Attention Mechanism and Dual Graph Convolution
Authors: Yang Xiaowen , Wen Yanghui , Jiao Shichao , Zhao Rong , Han Xie , He Ligang
Published: December 2023

To overcome the limitations of inadequate local feature representation and the underutilization of global information in dynamic graph convolutions, we propose a network that combines attention mechanisms with dual graph convolutions. Firstly, we construct a static graph based on the dynamic graph using the K-nearest neighbors algorithm and geometric distances of point clouds. This integration of dynamic and static graphs forms a dual graph structure, compensating for the underutilization of geometric positional relationships in the dynamic graph. Next, edge convolutions are applied to extract edge features from the dual graph structure. To further enhance the capturing ability of local features, we employ attention pooling, which combines max pooling and average pooling operations. Secondly, we introduce channel attention modules and spatial self-attention modules to improve the representation ability of global features and enhance semantic segmentation accuracy in our network. Experimental results on the S3DIS dataset demonstrate that compared to dynamic graph convolution alone, our proposed approach effectively utilizes both semantic and geometric relationships between point clouds using dual graph convolutions while addressing limitations related to insufficient local feature extraction. The introduction of attention mechanisms helps mitigate underutilization issues with global information, resulting in significant improvements in model performance.
Dynamic Malware Detection Using Parameter-Augmented Semantic Chain
Authors: Zhao Donghui , Wang Huadong , Kou Liang , Li Zhannan , Zhang Jilin
Published: December 2023

Due to the rapid development and widespread presence of malware, deep-learning-based malware detection methods have become a pivotal approach used by researchers to protect private data. Behavior-based malware detection is effective, but changes in the running environment and malware evolution can alter API calls used for detection. Most existing methods ignore API call parameters while analyzing them separately, which loses important semantic information. Therefore, considering API call parameters and their combinations can improve behavior-based malware detection. To improve the effectiveness of behavior-based malware detection systems, this paper proposes a novel API feature engineering method. The proposed method employs parameter-augmented semantic chains to improve the system’s resilience to unknown parameters and elevate the detection rate. The method entails semantically decomposing the API to derive a behavior semantic chain, which provides an initial representation of the behavior exhibited by samples. To further refine the accuracy of the behavior semantic chain in depicting the behavior, the proposed method integrates the parameters utilized by the API into the aforementioned semantic chain. Furthermore, an information compression technique is employed to minimize the loss of critical actions following truncation of API sequences. Finally, a deep learning model consisting of gated CNN, Bi-LSTM, and an attention mechanism is used to extract semantic features embedded within the API sequences and improve the overall detection accuracy. Additionally, we evaluate the proposed method on a competition dataset Datacon2019. Experiments indicate that the proposed method outperforms baselines employing vocabulary-based methods in both robustness to unknown parameters and detection rate.
Trajectory Tracking Control of Fast Parallel SCARA Robots with Fuzzy Adaptive Iterative Learning Control for Repetitive Pick-and-Place Operations
Authors: Wu Guanglei , Niu Bin , Li Qiancheng
Published: December 2023

Aiming at enhanced suppression of external disturbances and high-precision trajectory tracking of parallel SCARA robot dedicating to fast pick-and-place operations, this work presents the integrated control design of iterative learning algorithm, adaptive control and fuzzy rules, namely, fuzzy adaptive iterative learning control, for such type of robots. A step-design approach is adopted to ensure the adaptability of the designed control law, which is reflected in two aspects: ① the feedback gain of the controller is adjusted by the fuzzy rules; ② the adaptive unknown parameters are obtained by means of iterative learning estimation to suppress the uncertainties and external disturbances. The stability of the designed controller is analyzed and proved by the Lyapunov theory, and the effectiveness is verified by observing the tracking errors in joint space along with the testing pick path, in comparison with different iterative learning based algorithms. After the first-iteration learning, the motion errors of the four actuated joints can be reduced by 56.5%, 45.8%, 46.4% and 39.8%, respectively, and after 15 iterations of learning control, the final angular errors by the designed control law converge to 0.7×10−4 degree maximally. The varying maximum, root-mean-squared and mean angular displacement errors of the actuation joints can converge to zero values with the increasing iterations rapidly, which shows the robustness, effectiveness and advantages of the designed control law. The designed control law can be generalized to high-speed parallel pick-and-place robot to ensure high-precision trajectory tracking for high-quality material handling tasks.
A Spiropyran-Doped Poly(methyl methacrylate) Matrix for Sensor Applications
Authors: Askirka Valiantsin , Miluski Piotr , Kochanowicz Marcin
Published: December 2023

In this paper, the relaxation isomerization properties of a spiropyran-doped poly(methyl methacrylate) (PMMA) UV light-responsive dynamic material are presented. The polar liquid (acetonitrile) and solid (PMMA matrix) medium may contain two merocyanine isoforms of the selected spiropyran. A complex equilibrium of the colored and colorless spiropyran isoforms resulted in specific transformations of the absorption spectra during the relaxation process. Bands at 522 nm and 580 nm characterized the absorption of the fabricated dynamic material in the ground (non-activated) state in the visible range. The presence of colored and colorless isoforms of spiropyran incorporated into the PMMA matrix using Raman spectroscopy was revealed. The photosensitive polymer samples were irradiated with a UV LED (365 nm and 390 nm) under various temperatures (0–30 °C). Spectral changes within the relaxation process demonstrated their complex dynamics. Surprisingly, the photodegradation of the dynamic material was estimated at about 25% (using 390 nm) and 7% (using 365 nm) after eight photoswitch cycles for one isoform, and no fatigue after an additional one (using 390 nm) was discovered. The presented light-responsive material is of interest for UV sensors and new hybrid material design.
Optimizing Real and Reactive Power Dispatch Using a Multi-Objective Approach Combining the ϵ-Constraint Method and Fuzzy Satisfaction
Authors: Villacrés Ricardo , Carrión Diego
Published: December 2023

Optimal power dispatch is essential to improve the power system’s safety, stability, and optimal operation. The present research proposes a multi-objective optimization methodology to solve the real and reactive power dispatch problem by minimizing the active power losses and generation costs based on mixed-integer nonlinear programming (MINLP) using the epsilon constraint method and fuzzy satisficing approach. The proposed methodology was tested on the IEEE 30-bus system, in which each objective function was modeled and simulated independently to verify the results with what is obtained via Digsilent Power Factory and then combined, which no longer allows for the simulation of Digsilent Power Factory. One of the main contributions was demonstrating that the proposed methodology is superior to the one available in Digsilent Power Factory, since this program only allows for the analysis of single-objective problems.
Risk Assessment Framework for Electric Vehicle Charging Station Development in the United States as an Ancillary Service
Authors: Haces-Fernandez Francisco
Published: December 2023

Promoting the accelerated adoption of electric vehicles (EVs) in the United States (US) is one of the main strategies for reducing risk related to climate change. However, the lack of public charging stations (EVCSs) in the US has been identified as a grave obstacle to EV market penetration. The US Federal Government is providing extensive financial incentives to promote EVCSs. This allows diverse businesses to offer EV charging as an ancillary service, without the risks associated with traditional fuel facilities. Locations offering these novel services will reduce their financial operational risks, increase customer traffic and receive additional revenue. However, selecting unsuitable equipment for particular business segments and locations creates a severe risk of underuse and disrepair, leading to the potential failure of these new projects. Furthermore, these unsuccessful EVCSs exacerbate consumer reluctance to EV adoption and foster social opposition to this new technology. This study provides stakeholders with a framework for the optimal placement of EVCSs to maximize their successful deployment and incentivize continuous growth in the EV market. It identifies risk factors related to the placement and operation of EVCSs, aiding in optimal equipment selection for each location. Results from this study highlight EVCS location trends based on location and type of business, with the potential for some retrofitting projects. This framework provides relevant geospatial results for business owners, policy makers, consumers and other stakeholders in the adequacy of new charging infrastructure.
Effect of Ash from Salix viminalis on the Biomass and Heating Value of Zea mays and on the Biochemical and Physicochemical Properties of Soils
Authors: Boros-Lajszner Edyta , Wyszkowska Jadwiga , Kucharski Jan
Published: December 2023

Wood ash is sometimes used as an alternative to mineral fertilizers; however, there is still a paucity of reliable data concerning its effect on plants—and on biological properties of soil. The present study aimed to determine the possible extent of soil pollution with ash from Salix viminalis that does not disturb the growth of Zea mays L., intended for energetic purposes, in order to identify how the increasing ash doses affect biochemical and physicochemical properties of soil and to finally to establish the neutralizing effects of soil additives, i.e., compost and HumiAgra preparation, on this soil pollutant. The study demonstrated that the heating value of Zea mays L. was stable and not modified by the excess content of ash from Salix viminalis in the soil. This finding points to the feasibility of Zea mays L. cultivation on soils contaminated with ash from Salix viminalis and its use in bio-power engineering. The biomass of the aboveground parts of Zea mays L. was significantly reduced after soil contamination with Salix viminalis ash dose of 20 g kg−1 d.m. soil, whereas the smaller ash doses tested (5–10 g kg−1 d.m. soil) did not impair either the growth or the development of Zea mays L. The ash inhibited activities of all analyzed soil enzymes but increased soil pH and sorption capacity. Fertilization with compost proved more effective in neutralizing the adverse effect of ash on enzymatic activity of the soil.
Polymer-Coated Nanoparticles and Pickering Emulsions as Agents for Enhanced Oil Recovery: Basic Studies Using a Porous Medium Model
Authors: Ntente Christina , Strekla Anastasia , Iatridi Zacharoula , Theodoropoulou Maria , Bokias Georgios , Tsakiroglou Christos D.
Published: December 2023

Globally the overall oil recovery factors for primary and secondary recovery range from 35% to 45%, and a tertiary recovery method that can enhance the recovery factor by 10–30% could contribute to the energy supply. The use of nanoparticles in enhanced oil recovery (EOR) processes comprises an emerging and well-promising approach. Polymer-coated nanoparticles (PNPs) were synthesized through the free radical polymerization (FRP) of the monomers 2-acrylamido-2-methyl-1-propanesulfonic acid (AMPSA) and dodecyl methacrylate (DMA) on the surface of acrylic-modified spherical silica nanoparticles. The obtained PNPs were characterized using Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) and thermogravimetric analysis (TGA). Dispersions of PNPs were prepared in salt (NaCl, CaCl2) aqueous solutions, the static oil/water interfacial tension were measured using the Du Nouy ring method, and changes caused based on the oil/water contact angle were recorded optically. The PNP dispersions were used to stabilize and characterize shear-thinning oil-in-water Pickering emulsions. The capacity of the PNP dispersions and Pickering emulsions to mobilize the trapped ganglia of viscous paraffin oil, which remained after successive tests of drainage and primary imbibition, was tested with visualization experiments of the secondary imbibition in a transparent glass-etched pore network. The synthesized SiO2-P(AMPSA-co-DMA) nanoparticles were stable even at high temperatures (~200–250 °C) and displayed excellent stability in aqueous dispersions at high ionic strengths with the presence of divalent cations, and their dispersions generated stable oil-in-water Pickering emulsions with a shear-thinning viscosity. The oil-recovery efficiency is maximized when the most viscous Pickering emulsion is selected, but if energy cost factors are also taken into account, then the less viscous Pickering emulsion is preferable.
Retesting Schemes That Improve Test Quality and Yield Using a Test Guardband
Authors: Yeh Chung-Huang , Chen Jwu-E
Published: December 2023

The digital integrated circuit (IC) testing model module is applied in this study to simulate the fabrication and testing of integrated circuits. The yield and quality of ICs are analyzed by assuming that the wafer devices under test conditions are normal probability distributions. The difficulties of testing and verification become increasingly great as the design function of the chip becomes remarkably complex. Conversely, the automotive industry chip supply chain has been substantially affected since the COVID-19 outbreak. The shortage of chips in the auto-market has always existed; therefore, increasing available chips under a limited production capacity has become a top priority. Therefore, this study applies the digital integrated circuit testing model (DITM) and proposes a retest plan. This method does not require considerable time to collect large wafer data, nor does it require additional hardware equipment. Furthermore, the required test quality parameters are set, and the test is repeated on the device by adjusting the test guardband (TGB). Moreover, three retesting schemes are proposed to improve the IC test quality (Yq) and test yield (Yt) to meet the requirements of consumers for product quality. A set of 2021 IEEE International Roadmap for Devices and Systems (IRDS) parameters is used to demonstrate the three proposed retesting schemes. The simulation results from the 2021 IRDS data prove that the retest method can effectively improve the test yield (Yt). A comparison of the estimated results of the three retest methods shows that using the repeat test method can maximize the test yield without sacrificing the test quality (Yq). By contrast, repeat testing can indeed improve the test yield (Yt) by 14% or more. Moreover, the increase in sellable ICs not only increases additional earnings for corporations, but also alleviates the current global shortage of automotive ICs.
A Comprehensive Review of Risk Assessments of Organic Effluents in Car Workshops
Authors: Bouchiat Rémi , Veignie Etienne , Kaczmarek Fabien , Dorchy Julien , Fortunato Anne-Danièle , Rafin Catherine
Published: December 2023

Water is an essential resource for the functioning of society, where it is involved in key areas such as domestic use, agriculture, energy production, industry, and transport. Climate change exacerbates water scarcity. In the context of preserving water resources, effluents from the automobile sector need to be deeply considered due to their environmental impacts. This review focuses especially on the water effluents generated by car workshops. In car workshops, daily floor washing waters containing organic pollutants, either from vehicle components or from products used for maintenance and repair, can represent large volumes of water (between 15 L and 50 L per day) that are discharged into the wastewater system. These particular industrial organic effluents are not well characterized and can represent severe environmental risks. The aim of this review is to help automotive maintenance and repair professionals better manage these industrial organic effluents. We first describe the potential sources of organic pollution emissions in car workshops with the purpose of identifying the organic compounds to be monitored as a priority in the wash water. Then, we apply a risk prevention management tool, based on the criticality matrix, to identify products with a high risk of leaking onto the ground in order to limit organic pollutants at the source.
Kinematic Analysis of the Forestry Workers’ Upper Body during Chainsaw Starting Activity
Authors: Landekić Matija , Bačić Marin , Pandur Zdravko , Bakarić Matija , Šporčić Mario , Nakić Josipa
Published: December 2023

The application of various graphic analytical software solutions in the research aimed at designing and determining the ergonomic and biomechanical harmony between forestry workers’ anthropometric measures and their working positions and movements is still scientifically incomplete and largely unexplored in forestry, especially regarding motor manual work with chainsaws. Accordingly, the aim of this research is to numerically represent the general kinematic model of forestry workers’ upper body while starting a chainsaw. The same is determined by using a state-of-the-art motion capture suit with inertial measurement units (IMUs). An additional research aim is to investigate numerical values of the forestry worker kinematic model against the three potentially applicable methods of starting a chainsaw and work experience with a chainsaw. Field data collection was carried out during the spring and summer of 2022 and 2023, and a total of 51 chainsaw workers were sampled in different relief areas. The results of the subject research show that none of the observed methods of starting a chainsaw are entirely safe for the worker, concerning the aspect of MSD development, if the body movements within the working element are performed in an incorrect way. From safety and kinematic aspects, regarding forestry workers’ upper body values, the most neutral work posture (N-pose) is when a worker is using the “between the knees” method. The method “from the ground” can be used as secondary option if the workers are educated in taking the correct position of the upper body, i.e., to practice the rule of hip hinge. With the aim of extending the working ability and working life of forestry workers, interdisciplinary cooperation between occupational medicine, kinesiologists, and forestry experts is needed to create a guide of statodynamic efforts and correct postural movement habits during motor manual work in logging operations.
Investigation of Mineral Oil and CuO Mixed Synthetic Oil in Compression Ignition Engines: A Comparison of Physicochemical Attributes
Authors: Nasir Aamir Sajjad , Usman Muhammad , Malik Muhammad Ali Ijaz , Shah Asad Naeem , Jafry Ali Turab , Saleem Muhammad Wajid , Abbas Naseem , Sajjad Uzair , Karim Mohammad Rezaul , Kalam Md Abul
Published: December 2023

Mineral oil resources are depleting rapidly, and the slower conventional oil biodegradation process results in environmental pollution. To resolve this issue, cupric oxide (CuO) nanoparticles (1% wt) were introduced into a base oil to improve the lubricating capability of castor oil. In addition, 1% wt. sodium dodecyl sulfate was also blended with the base oil in order to attain the maximum dispersion stability of CuO nanoparticles in the castor oil. Afterward, thermophysical property, atomic absorption spectroscopy, and Fourier transform infrared radiation (FTIR) testing of the lubricant oil sample were performed before and after 100 h of engine operations at 75% throttle and 2200 rpm for each lubricant sample in order to check the capability of the novel oil with mineral oil. Compared with the natural mineral oil, the behavior of the CuO-based lubricant has essentially the same physical features, as measured according to ASTM standard methods. The physicochemical properties like (KV)40 °C, (KV)100 °C, FP, ash, and TBN decrease more in the case of the synthetic oil by 1.15, 1.11, 0.46, 1.1, and 1.2% than in the conventional oil, respectively. FTIR testing shows that the maximum peaks lie in the region of 500 to 1750 cm−1, which shows the presence of C=O, C-N, and C-Br to a maximum extent in the lubricant oil sample. AAS testing shows that the synthetic oil has 21.64, 3.23, 21.44, and 1.23% higher chromium, iron, aluminum, and zinc content. However, the copper and calcium content in the synthetic oil is 14.72 and 17.68%, respectively. It can be concluded that novel bio-lubricants can be utilized as an alternative to those applications that are powered by naturally produced mineral oil after adding suitable additives that further enhance their performance.
Experimental Study on the Isolation Effect of an Active Flame-Proof Device on a Gas Explosion in an Underground Coal Mine
Authors: Huang Zichao , Si Rongjun , Wen Guangcai , Jin Songling , Xue Shaoqian
Published: December 2023

Passive explosion-isolation facilities in underground coal mines, such as explosion-proof water troughs and bags, face challenges aligned with current trends in intelligent and unmanned technologies, due to restricted applicability and structural features. Grounded in the propagation laws and disaster mechanisms of gas explosions, the device in this paper enables accurate identification of explosion flames and pressure information. Utilizing a high-speed processor for rapid logical processing enables judgments within 1 ms. Graded activation of the operating mechanism is enabled by the device. The tunnel flame-proof device’s flame-extinguishing agent has a continuous action time of 6075 ms. Experiments on the active flame-proof effect of a 100 m3 gas explosion were conducted using a cross-sectional 7.2 m2 large-tunnel test system. With a dosage of 5.6 kg/m2, the powder flame-extinguishing agent completely extinguished the explosion flame within a 20 m range behind the explosion isolator. Numerical calculations unveiled the gas-phase chemical suppression mechanism of the powder flame-extinguishing agent NH4H2PO4 in suppressing methane explosions. Building upon these findings, application technology for active flame-proofing was developed, offering technical support for intelligent prevention and control of gas explosions in underground coal mines.
The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products
Authors: Tarlak Fatih
Published: December 2023

Microbial shelf life refers to the duration of time during which a food product remains safe for consumption in terms of its microbiological quality. Predictive microbiology is a field of science that focuses on using mathematical models and computational techniques to predict the growth, survival, and behaviour of microorganisms in food and other environments. This approach allows researchers, food producers, and regulatory bodies to assess the potential risks associated with microbial contamination and spoilage, enabling informed decisions to be made regarding food safety, quality, and shelf life. Two-step and one-step modelling approaches are modelling techniques with primary and secondary models being used, while the machine learning approach does not require using primary and secondary models for describing the quantitative behaviour of microorganisms, leading to the spoilage of food products. This comprehensive review delves into the various modelling techniques that have found applications in predictive food microbiology for estimating the shelf life of food products. By examining the strengths, limitations, and implications of the different approaches, this review provides an invaluable resource for researchers and practitioners seeking to enhance the accuracy and reliability of microbial shelf life predictions. Ultimately, a deeper understanding of these techniques promises to advance the domain of predictive food microbiology, fostering improved food safety practices, reduced waste, and heightened consumer confidence.
Protection of Whey Polypeptide on the Lipid Oxidation, Color, and Textural Stability of Frozen–Thawed Spanish Mackerel Surimi
Authors: Li Yunying , Kong Lingru , Zhang Xiaotong , Wen Rongxin , Peng Xinyan
Published: December 2023

Repeated freeze–thaw (FT) cycles can have an impact on surimi quality. In this study, we used 0.02% BHA as a positive control group. We examined the effects of different concentrations (0%, 5%, 10%, and 15%) of whey protein hydrolysate (WPH) on surimi, focusing on alterations in color metrics (L* for brightness, a* for red–green, b* for yellow–blue, and overall whiteness), textural characteristics, and antioxidant capacity during various freeze–thaw (FT) cycles. The results showed that the lipid oxidant values of surimi, as well as its a* and b* values, rose as the number of FT cycles increased; whereas the adhesiveness, resilience, gumminess, and shear force dropped, as did L* and the whiteness values, leading to an overall darkening of color and gloss. By contrast, the study found that the addition of WPH could effectively slow down the decrease of surimi textural stability after repeated freeze–thawing, with the textural stability of the group with 15% WPH being significantly superior to those of the other groups (p < 0.05). Under the same number of cycles, adding 15% WPH to the experimental group could successfully lower total volatile basic nitrogen (TVB-N) and effectively increase the antioxidant activity of surimi. This finding suggested that 15% WPH had the greatest effect on increasing surimi FT stability. To conclude, it was proved that WPH can be added to frozen surimi and improve its quality.
Understanding Hyperuricemia: Pathogenesis, Potential Therapeutic Role of Bioactive Peptides, and Assessing Bioactive Peptide Advantages and Challenges
Authors: Chen Yanchao , Yang Jing , Rao Qinchun , Wang Chen , Chen Xiaoyong , Zhang Yu , Suo Huayi , Song Jiajia
Published: December 2023

Hyperuricemia is a medical condition characterized by an elevated level of serum uric acid, closely associated with other metabolic disorders, and its global incidence rate is increasing. Increased synthesis or decreased excretion of uric acid can lead to hyperuricemia. Protein peptides from various food sources have demonstrated potential in treating hyperuricemia, including marine organisms, ovalbumin, milk, nuts, rice, legumes, mushrooms, and protein-rich processing by-products. Through in vitro experiments and the establishment of cell or animal models, it has been proven that these peptides exhibit anti-hyperuricemia biological activities by inhibiting xanthine oxidase activity, downregulating key enzymes in purine metabolism, regulating the expression level of uric acid transporters, and restoring the composition of the intestinal flora. Protein peptides derived from food offer advantages such as a wide range of sources, significant therapeutic benefits, and minimal adverse effects. However, they also face challenges in terms of commercialization. The findings of this review contribute to a better understanding of hyperuricemia and peptides with hyperuricemia-alleviating activity. Furthermore, they provide a theoretical reference for developing new functional foods suitable for individuals with hyperuricemia.
Tea Harvesting and Processing Techniques and Its Effect on Phytochemical Profile and Final Quality of Black Tea: A Review
Authors: Aaqil Muhammad , Peng Chunxiu , Kamal Ayesha , Nawaz Taufiq , Zhang Fei , Gong Jiashun
Published: December 2023

Tea (Camellia sinensis) has grown for over 300 years and is recognized worldwide as among other well-renowned crops. The quality of black tea depends on plucking (method, standard, season, and intervals), withering and rolling (time and temperature), fermentation (time, temperature, and RH), drying (temperature and method), and storage conditions, which have a high influence on the final quality of black tea. At the rolling stage, the oxidation process is initiated and ends at the early drying stage until the enzymes that transform tea polyphenols into thearubigins (TRs) and theaflavins (TFs) are denatured by heat. By increasing fermentation time, TRs increased, and TF decreased. Each is liable for black tea’s brightness, taste, and color. The amino acids and essential oils also grant a distinctive taste and aroma to black tea. Throughout withering, rolling, and fermentation, increases were found in essential oil content, but during drying, a decrease was observed. However, the Maillard reaction, which occurs when amino acids react with sugar during drying, reimburses for this decrease and enhances the flavor and color of black tea. As compared to normal conditions, accelerated storage showed a slight decrease in the total color, TF, and TRs. It is concluded that including plucking, each processing step (adopted technique) and storage system has a remarkable impact on black tea’s final quality. To maintain the quality, an advanced mechanism is needed to optimize such factors to produce high-quality black tea, and an objective setting technique should be devised to attain the desirable quality characteristics.
Application of White-Wine-Pomace-Derived Ingredients in Extending Storage Stability of Fresh Pork Burgers
Authors: Martín-Mateos María Jesús , Delgado-Adámez Jonathan , Moreno-Cardona Daniel , Valdés-Sánchez M. Esperanza , Ramírez-Bernabé M. Rosario
Published: December 2023

White wine pomace, a by-product from winemaking, was stabilized after the application of thermal blanching (with the aim of deactivating the polyphenoloxidase enzyme), milling, and processing by hydrostatic high-pressure treatment (with the aim of reducing initial microbial loads while preserving phenolic compounds content). The valorized pomace (VP) ingredient was added at different proportions to pork burgers (0.5%, 1%, and 3% w/w) to improve their preservation, and the effect was compared to those produced by sulfites and with a control (without sulfites or VP). Burgers were vacuum-packed and refrigerated for 7 days. Microbiological, color, oxidation, and sensory parameters were analyzed. Neither sulfites nor VP reduced the microbial development of most microorganism groups evaluated (p > 0.05); however, both prevented coliform growth during storage (p < 0.01). The use of sulfites prevented the discoloration of burgers during storage, while VP had no effect (p < 0.001). On the contrary, VP limited lipid and protein oxidation development during storage (p > 0.05), while sulfites had no effect. Therefore, the use of VP from white wine production could have an antioxidant effect but a limited antimicrobial or color-protective effect for the preservation of pork burgers.
Radial Basis Functions Approximation Method for Time-Fractional FitzHugh–Nagumo Equation
Authors: Alam Mehboob , Haq Sirajul , Ali Ihteram , Ebadi M. J. , Salahshour Soheil
Published: December 2023

In this paper, a numerical approach employing radial basis functions has been applied to solve time-fractional FitzHugh–Nagumo equation. Spatial approximation is achieved by combining radial basis functions with the collocation method, while temporal discretization is accomplished using a finite difference scheme. To evaluate the effectiveness of this method, we first conduct an eigenvalue stability analysis and then validate the results with numerical examples, varying the shape parameter c of the radial basis functions. Notably, this method offers the advantage of being mesh-free, which reduces computational overhead and eliminates the need for complex mesh generation processes. To assess the method’s performance, we subject it to examples. The simulated results demonstrate a high level of agreement with exact solutions and previous research. The accuracy and efficiency of this method are evaluated using discrete error norms, including L2, L∞, and Lrms.
Fueling Processes on (Sub-)kpc Scales
Authors: Combes Francoise
Published: December 2023

Since the 1970s, astronomers have struggled with the issue of how matter can be accreted to promote black-hole growth. While low-angular-momentum stars may be devoured by a black hole, they are not a sustainable source of fuel. Gas, which could potentially provide an abundant fuel source, presents another challenge due to its enormous angular momentum. While viscous torques are not significant, gas is subject to gravity torques from non-axisymmetric potentials such as bars and spirals. Primary bars can exchange angular momentum with the gas within corotation, causing it to spiral inwards until reaching the inner Lindblad resonance. An embedded nuclear bar can then take over. As the gas reaches the black hole’s sphere of influence, the torque becomes negative, fueling the center. Dynamical friction also accelerates the infall of gas clouds closer to the nucleus. However, because of the Eddington limit, growing a black hole from a stellar-mass seed is a slow process. The existence of very massive black holes in the early universe remains a puzzle that could potentially be solved through direct collapse of massive clouds into black holes or super-Eddington accretion.
Enhancement of Curcumin’s Anti-Psoriatic Efficacy via Formulation into Tea Tree Oil-Based Emulgel
Authors: Reena Km , Mittal Saurabh , Faizan Mohammad , Jahan Iram , Rahman Yasir , Khan Rahmuddin , Singh Lalit , Alhalmi Abdulsalam , Noman Omar M. , Alahdab Ahmad
Published: December 2023

Psoriasis is a chronic inflammatory skin disease characterized by the hyperproliferation and aberrant differentiation of epidermal keratinocytes. It is a debilitating condition that can cause significant physical and emotional distress. Natural anti-psoriatic agents have been investigated as alternatives to conventional allopathic medications, as they have notable limitations and drawbacks. Curcumin and tea tree oil are cost-efficient and effective anti-inflammatory medicines with less adverse effects compared to synthetic psoriasis medications. Our research endeavors to harness the therapeutic potential of these natural compounds by developing an herbal anti-psoriatic topical drug delivery system. This novel method uses curcumin and tea tree oil to create a bi-phasic emulgel drug delivery system. Formulations F1 (gel) and F2 (emulgel) have high drug content percentages of 84.2% and 96.7%, respectively. The emulgel showed better spreadability for cutaneous applications, with a viscosity of 92,200 ± 943 cp compared to the gel’s 56,200 ± 1725 cp. The emulgel released 94.48% of the drugs, compared to 87.58% for the gel. These formulations conform to the zero-order and Higuchi models, and their stability over a three-month period is crucial. In vivo, the emulgel healed psoriasis symptoms faster than the usual gel. The gathered results confirmed the emulgel’s potential as a drug delivery method, emphasizing the complementary benefits of tea tree oil and curcumin as an effective new therapy for psoriasis.
Enhancing Photocatalytic Properties of TiO2 Photocatalyst and Heterojunctions: A Comprehensive Review of the Impact of Biphasic Systems in Aerogels and Xerogels Synthesis, Methods, and Mechanisms for Environmental Applications
Authors: Tinoco Navarro Lizeth Katherine , Jaroslav Cihlar
Published: December 2023

This review provides a detailed exploration of titanium dioxide (TiO2) photocatalysts, emphasizing structural phases, heterophase junctions, and their impact on efficiency. Key points include diverse synthesis methods, with a focus on the sol-gel route and variants like low-temperature hydrothermal synthesis (LTHT). The review delves into the influence of acid-base donors on gelation, dissects crucial drying techniques for TiO2 aerogel or xerogel catalysts, and meticulously examines mechanisms underlying photocatalytic activity. It highlights the role of physicochemical properties in charge diffusion, carrier recombination, and the impact of scavengers in photo-oxidation/reduction. Additionally, TiO2 doping techniques and heterostructures and their potential for enhancing efficiency are briefly discussed, all within the context of environmental applications.
Eco-Friendly Method for Wood Aerogel Preparation with Efficient Catalytic Reduction of 4-Nitrophenol
Authors: Yu Qianqian , Sun Xiaohan , Liu Feng , Yang Zhaolin , Wei Shulei , Wang Chengyu , Li Xin , He Zechen , Li Xiaodong , Li Yudong
Published: December 2023

The advancement of science and technology and the growth of industry have led to an escalating discharge of domestic sewage and industrial wastewater containing dyes. This surge in volume not only incurs higher costs but also exacerbates environmental burdens. However, the benefits of green and reusable catalytic reduction materials within dye processes are still uncertain. Herein, this study utilized the eco-friendly deep eutectic solvent method (DESM) and the chlorite-alkali method (CAM) to prepare a cellulose-composed wood aerogel derived from natural wood for 4-nitrophenol (4-NP) reduction. The life cycle assessment of wood aerogel preparative process showed that the wood aerogel prepared by the one-step DESM method had fewer environmental impacts. The CAM method was used innovatively to make uniform the chemical functional groups of different wood species and various wood maturities. Subsequently, palladium nanoparticles (Pd NPs) were anchored in the skeleton structure of the wood aerogel with the native chemical groups used as a reducing agent to replace external reducing agents, which reduced secondary pollution and prevented the agglomeration of nanoparticles. Results showed that the catalytic reduction efficiency of 4-NP can reach 99.8%, which shows promises for applications in wastewater treatment containing dyes. Moreover, investigation of the advantages of preparation methods of wood aerogel has important implications for helping researchers and producers choose suitable preparation strategies according to demand.