bluuebunny/crossref_metadata_2025 huggingface dataset:
Divergent adaptive immune responses define two types of long COVID
Authors: Kervevan Jérôme , Staropoli Isabelle , Slama Dorsaf , Jeger-Madiot Raphaël , Donnadieu Françoise , Planas Delphine , Pietri Marie-Pierre , Loghmari-Bouchneb Wiem , Alaba Tanah Motolete , Robinot Rémy , Boufassa Faroudy , White Michael , Salmon-Ceron Dominique , Chakrabarti Lisa A.
Published: July 2023

Background

The role of adaptive immune responses in long COVID remains poorly understood, with contrasting hypotheses suggesting either an insufficient antiviral response or an excessive immune response associated with inflammatory damage. To address this issue, we set to characterize humoral and CD4+ T cell responses in long COVID patients prior to SARS-CoV-2 vaccination.

Methods

Long COVID patients who were seropositive (LC+, n=28) or seronegative (LC-, n=23) by spike ELISA assay were recruited based on (i) an initial SARS-CoV-2 infection documented by PCR or the conjunction of three major signs of COVID-19 and (ii) the persistence or resurgence of at least 3 symptoms for over 3 months. They were compared to COVID patients with resolved symptoms (RE, n=29) and uninfected control individuals (HD, n=29).

Results

The spectrum of persistent symptoms proved similar in both long COVID groups, with a trend for a higher number of symptoms in the seronegative group (median=6 vs 4.5; P=0.01). The use a highly sensitive S-flow assay enabled the detection of low levels of SARS-CoV-2 spike-specific IgG in 22.7% of ELISA-seronegative long COVID (LC-) patients. In contrast, spike-specific IgG levels were uniformly high in the LC+ and RE groups. Multiplexed antibody analyses to 30 different viral antigens showed that LC- patients had defective antibody responses to all SARS-CoV-2 proteins tested but had in most cases preserved responses to other viruses. A sensitive primary T cell line assay revealed low but detectable SARS-CoV-2-specific CD4 responses in 39.1% of LC- patients, while response frequencies were high in the LC+ and RE groups. Correlation analyses showed overall strong associations between humoral and cellular responses, with exceptions in the LC- group.

Conclusions

These findings provide evidence for two major types of antiviral immune responses in long COVID. Seropositive patients showed coordinated cellular and humoral responses at least as high as those of recovered patients. In contrast, ELISA-seronegative long COVID patients showed overall low antiviral responses, with detectable specific CD4+ T cells and/or antibodies in close to half of patients (52.2%). These divergent findings in patients sharing a comparable spectrum of persistent symptoms raise the possibility of multiple etiologies in long COVID.
Machine learning analysis reveals aberrant dynamic changes in amplitude of low-frequency fluctuations among patients with retinal detachment
Authors: Ji Yu , Wang Yuan-yuan , Cheng Qi , Fu Wen-wen , Huang Shui-qin , Zhong Pei-pei , Chen Xiao-lin , Shu Ben-liang , Wei Bin , Huang Qin-yi , Wu Xiao-rong
Published: July 2023

Background

There is increasing evidence that patients with retinal detachment (RD) have aberrant brain activity. However, neuroimaging investigations remain focused on static changes in brain activity among RD patients. There is limited knowledge regarding the characteristics of dynamic brain activity in RD patients.

Aim

This study evaluated changes in dynamic brain activity among RD patients, using a dynamic amplitude of low-frequency fluctuation (dALFF), k-means clustering method and support vector machine (SVM) classification approach.

Methods

We investigated inter-group disparities of dALFF indices under three different time window sizes using resting-state functional magnetic resonance imaging (rs-fMRI) data from 23 RD patients and 24 demographically matched healthy controls (HCs). The k-means clustering method was performed to analyze specific dALFF states and related temporal properties. Additionally, we selected altered dALFF values under three distinct conditions as classification features for distinguishing RD patients from HCs using an SVM classifier.

Results

RD patients exhibited dynamic changes in local intrinsic indicators of brain activity. Compared with HCs, RD patients displayed increased dALFF in the bilateral middle frontal gyrus, left putamen (Putamen_L), left superior occipital gyrus (Occipital_Sup_L), left middle occipital gyrus (Occipital_Mid_L), right calcarine (Calcarine_R), right middle temporal gyrus (Temporal_Mid_R), and right inferior frontal gyrus (Frontal_Inf_Tri_R). Additionally, RD patients showed significantly decreased dALFF values in the right superior parietal gyrus (Parietal_Sup_R) and right paracentral lobule (Paracentral_Lobule_R) [two-tailed, voxel-level p < 0.05, Gaussian random field (GRF) correction, cluster-level p < 0.05]. For dALFF, we derived 3 or 4 states of ALFF that occurred repeatedly. There were differences in state distribution and state properties between RD and HC groups. The number of transitions between the dALFF states was higher in the RD group than in the HC group. Based on dALFF values in various brain regions, the overall accuracies of SVM classification were 97.87, 100, and 93.62% under three different time windows; area under the curve values were 0.99, 1.00, and 0.95, respectively. No correlation was found between hamilton anxiety (HAMA) scores and regional dALFF.

Conclusion

Our findings offer important insights concerning the neuropathology that underlies RD and provide robust evidence that dALFF, a local indicator of brain activity, may be useful for clinical diagnosis.
A randomized 3-month, parallel-group, controlled trial of CALMA m-health app as an adjunct to therapy to reduce suicidal and non-suicidal self-injurious behaviors in adolescents: study protocol
Authors: Rodante Demián Emanuel , Chiapella Luciana Carla , Olivera Fedi Ramiro , Papávero Eliana Belén , Lavoie Kim L. , Daray Federico Manuel
Published: July 2023

Background

Suicidal and non-suicidal self-injurious behaviors are among the leading causes of death and injury in adolescents and youth worldwide. Mobile app development could help people at risk and provide resources to deliver evidence-based interventions. There is no specific application for adolescents and young people available in Spanish. Our group developed CALMA, the first interactive mobile application with the user in Spanish, which provides tools based on Dialectical Behavioral Therapy to manage a crisis of suicidal or non-suicidal self-directed violence with the aim of preventing suicide in adolescents and youth.

Methods

To test the effectiveness, safety and level of engagement of the CALMA app in people aged 10 to 19 who are treated in mental health services of two public hospitals, we will conduct a parallel-group, two-arm randomized controlled trial. Participants will be assessed face-to-face and via video call at four timepoints: day-0 (baseline), day-30, day-60, and day-90. A total of 29 participants per group will be included. Change in the frequency of suicidal and non-suicidal self-injurious behaviors will be compared between groups, as well as the level of emotional dysregulation, level of app engagement and time of psychiatric admission during the follow-up period.

Discussion

This study is particularly relevant to young people given their widespread use of mobile technology, while there are currently no available smartphone app-based self-guided psychological strategies in Spanish that attempt to reduce suicidal behavior in adolescents who are assisted in the public health sector from low and middle-income countries in Latin America.

Clinical trial registration

https://clinicaltrials.gov/, NCT05453370.
Eco-Innovation Drivers and Their Impact on Tourism and the Hospitality Business in Egypt
Authors: Ahmed Mohamed , Aliane Nadir , Khababa Nourredine , Abdou Maha Y. K. , Agina Mohamed Fathy
Published: July 2023

This research aims to examine the mediating effects of eco-innovation practices in the relationship between the drivers of eco-innovation and the performance of tourism and hospitality enterprises. A quantitative approach was adopted in this research using questionnaire surveys as an instrument for collecting primary data. Based on the convenience sampling technique, a total of 550 questionnaire forms were distributed to five-star hotels and travel agencies in Cairo. Out of the distributed forms, only 400 forms were filled out and returned to the researchers and thus valid for analysis, representing a response rate of 72.7%. Structural Equation Modeling (SEM) using partial least squares (PLS-SEM) was employed to test the research model. The results showed the significant effect of independent variables on dependent variables through mediating variables. Multiplied path coefficients were used to measure the confidence interval (CI) for measuring the mediating relationship. The results supported the effect of eco-innovation practices as a mediating factor in the relationship between the drivers of eco-innovation and the performance of tourism and hospitality enterprises. The findings also showed that the eco-innovation drivers, which include management awareness, organizational capabilities, and environmental strategy, have a significant effect on eco-innovation practices and tourism/hospitality enterprise performance when integrating the mediating variables of eco-innovation practices. There are a few studies that have also examined a conceptual framework to examine eco-innovation practices as mediating factors in the relationship between the drivers of eco-innovation and the performance of tourism and hospitality enterprises in terms of five-star hotels and travel agencies in Egypt. Consequently, this research contributes to the literature related to tourism and hospitality management through highlighting the mediating effect of eco-innovation practices on the relationship between the drivers of eco-innovation and the performance of tourism and hospitality enterprises.
Segmentation and Angle Calculation of Rice Lodging during Harvesting by a Combine Harvester
Authors: Zhuang Xiaobo , Li Yaoming
Published: July 2023

Rice lodging not only brings trouble to harvesting but also reduces yield. Therefore, the effective identification of rice lodging is of great significance. In this paper, we have designed a bilinear interpolation upsampling feature fusion module (BIFF) to decompose the quadruple upsampling of the connected part of encoder and decoder into two double upsampling processes and insert the intermediate feature layer in the backbone network for feature fusion in this process. The global attention mechanism(GAM) attention module is added to the feature extraction network, allowing the network to effectively focus on the lodging regions, thus effectively improving the segmentation effect. The average accuracy of the improved network is 93.55%, mrecall is 93.65%, and mIoU is 88.10%, and the feasibility of the improvement is demonstrated by ablation experiments and comparison with other algorithms. In addition, the angle calculation method is designed by combining the detection algorithm, adding a detection head branch to the output results for reading the distance information from the depth camera, and combining the distance information with mechanical analysis to determine the relationship between the angle of the stalk and the vertical direction when the rice is upright, tilted and fallen. A comparison of the calculated angle with the actual measured angle gives a final average error of approximately 5.364%, indicating that the harvest boundary extraction algorithm in this paper is highly accurate and has value for application in real-time harvesting scenarios.
Bacillus methylotrophicus Could Improve the Tolerance and Recovery Ability of the Tomato to Low-Temperature Stress and Improve Fruit Quality
Authors: Li Guobin , Peng Tieli , Qu Feng , Wang Junzheng , Long Yanghao , Hu Xiaohui
Published: July 2023

Low-temperature stress seriously affects the growth, development, yield, and quality of tomato production. Bacillus methylotrophicus is an important plant growth promoting rhizobacteria (PGPR). However, the role of B. methylotrophicus under low-temperature stress is poorly understood. Accordingly, the effects of B. methylotrophicus (‘VL-10′) on tomato cold stress (15 °C/8 °C, 12 h/12 h, and day/night) were studied. B. methyltrophicus ‘VL-10′ was added into the substrate at the time of sowing, and the plants were treated at a low temperature for 2 weeks after 40 days of growth. We found that the low temperature reduced the spatial distribution of the aboveground and underground sections of tomatoes and the leaf SPAD and photochemical efficiency of PS II (Fv/Fm). After low-temperature stress, the tomato flowering was delayed, the vitamin C and lycopene content in fruit decreased, and the nitrate content increased. However, inoculated with B. methyltrophicus ‘VL-10′ during sowing promoted the growth of tomato seedlings, enhanced the native defense ability of the tomatoes, and effectively reduced the cold shock response of the roots to cold damage and the adverse effects of low temperature on leaf SPAD and Fv/Fm. After the cultivation at normal temperature, the inoculat B. methyltrophicus ‘VL-10′ could rapidly regain plant growth levers, and eliminate the delay of low temperature on flowering. TOPSIS analysis showed that the nutritional quality of tomatoes could be effectively improved by inoculation with B. methyltrophicus ‘VL-10′ regardless of normal cultivation or low-temperature stress.
Intelligent Control Technology and System of on-Demand Irrigation Based on Multiobjective Optimization
Authors: Jia Weibing , Wei Zhengying , Tang Xiangyi , Zhang Yubin , Shen Ao
Published: July 2023

To solve the problem that the parameters of the multiple-input multiple-output (MIMO) irrigation system are difficult to control accurately, an on-demand irrigation control experimental device was developed. The main input parameters of the device are the opening degree of the main pipe valve, the opening frequency of and the pump station, the opening degree of the branch pipeline valve with the different combinations of different opening degrees. Based on these input parameters, four types of experimental methods were designed, and a total of 1695 groups of experiments were designed. The results show that the different opening degree combinations of the branch electric valve cannot significantly affect the flow of the branch pipeline but also significantly affect the pressure of the main pipeline. The prediction error of the operating frequency of the pump station and the opening degree of the branch valve were regarded as the objective function. Six intelligent prediction models were constructed, which are Back Propagation (BP), support vector regression (SVR) Linear, SVR-RBF, SVR-Poly, random forest (RF) and eXtreme Gradient Boosting (XGBoost), respectively. The results show that the XGBoost is the best model among the six models. For the opening degree of three branch valves, the mean absolute error (MAE) between the predicted value and actual value is less than 3.3%, the mean square deviation (RMSE) between the predicted values and actual values is less than 4.5%, and the R2 of between the predicted values and actual values is greater than 0.990. The control models and system can meet the needs of an on-demand irrigation system.
Optimization of Parameters Related to Grain Growth of Spring Wheat in Dryland Based on the Next-Generation APSIM
Authors: Cui Weinan , Nie Zhigang , Li Guang , Yuan Jianyu
Published: July 2023

To improve the applicability of crop models, this study compared two algorithms for optimizing the single objective parameters of the spring wheat in the dryland grain growth sub-model to identify the more efficient algorithm for application in future model parameter optimization. Based on field experiments from 2015 to 2021 in Gansu Province, this study combined weather data and yearbook yield data from 1984 to 2021 to optimize parameters related to grain growth of spring wheat in dryland based on the next-generation APSIM using two algorithms: the Nelder–Mead simplex algorithm and the DREAM-zs algorithm. The results were as follows: the optimization results of both algorithms were the same, but the DREAM-zs algorithm converged faster; the optimized parameters for the grain growth stage of Dingxi35 spring wheat were: a grain number per gram stem of 25 grains, an initial grain proportion of 0.05, and a maximum grain size of 0.049 g; after optimization, the root mean square error (RMSE) of observed and simulated yield values decreased from 186.84 kg/hm2 to 115.71 kg/hm2, and the normalized root mean square error (NRMSE) decreased from 10.33% to 6.40%. The optimized results were consistent with the growth and development process of wheat and had high applicability.
Stability of Bacillus and Enterococcus faecium 669 Probiotic Strains When Added to Different Feed Matrices Used in Dairy Production
Authors: Cappellozza Bruno I. , Segura Audrey , Milora Nina , Galschioet Christel , Schjelde Mette , Copani Giuseppe
Published: July 2023

Few data are available evaluating the stability of direct-fed microbials (DFM) following their inclusion in different feed matrices. Therefore, six Exp. evaluated the recovery of bacilli spores (BOVACILLUSTM; Exp. 1 to 3) and an Enterococcus faecium DFM (LACTIFERM®; Exp. 4 to 6) when included in different feed preparations. The Bacillus-based DFM was included into pelleted feed prepared in different temperatures (75 to 95 °C), whereas both DFM were assessed in premix and milk replacer preparations. Bacillus spores and E. faecium recovery was evaluated through standard methodologies and data were reported as log10 colony forming units/gram of feed. The recovery of Bacillus spores was within the expected range and was not impacted by the temperature of pellet preparation (Exp. 1). Bacilli recovery was also stable up to 12 months in the premix and was not impacted by the temperature of milk replacer preparation. Regarding the Exp. with E. faecium (Exp. 4 to 6), its recoveries in the mineral premix and milk powder did not differ from T0 and were not impacted by the conditions of milk replacer preparation. These data are novel and demonstrate the stability of a Bacillus-based and an E. faecium-based DFM when included in different feed matrices often used in dairy production.
A Review of the Feather Mite Genus Lopharalichus Gaud & Atyeo, 1996 (Acariformes: Pterolichidae), with Descriptions of Three New Species from Brazilian Parrots (Psittaciformes: Psittacidae)
Authors: Hernandes Fabio Akashi
Published: July 2023

Feather mites of the genus Lopharalichus Gaud & Atyeo, 1996 (Pterolichidae: Pterolichinae), formerly containing three described species, are associated with New World parrots (Psittaciformes: Psittacidae) of the subfamily Arinae. Three new species of this genus are described: Lopharalichus tuimsp. nov. from Forpus xanthopterygius (Spix, 1824), L. spinosussp. nov. from Ara ararauna (Linnaeus, 1758), and L. chiririsp. nov. from Brotogeris chiriri (Vieillot, 1818). Type specimens of the previously described Lopharalichus species were examined, and a key to the known species is provided.
Biorefinery and Stepwise Strategies for Valorizing Coffee By-Products as Bioactive Food Ingredients and Nutraceuticals
Authors: Rebollo-Hernanz Miguel , Aguilera Yolanda , Gil-Ramírez Alicia , Benítez Vanesa , Cañas Silvia , Braojos Cheyenne , Martin-Cabrejas Maria A.
Published: July 2023

Coffee production generates significant amounts of by-products, posing challenges for waste management in the industry. Recent research has revealed that coffee by-products are rich in bioactive compounds suitable to produce functional food ingredients and nutraceuticals. In this review, we explore biorefinery strategies for extracting and utilizing bioactive compounds from coffee by-products, including the production of bio-based chemicals and materials, as well as the extraction of phenolic compounds, antioxidants, and dietary fiber for food applications. We propose a stepwise approach for the development of functional food ingredients and nutraceuticals from coffee by-products, covering the identification of needs, comprehensive characterization, in vitro and in vivo research, unraveling the mechanism of action, food and nutraceutical formulation, sensory analysis, shelf-life stability, scale-up, randomized control trials, and biostatistics and bioinformatic integration. Additionally, we discuss the market potential, regulatory issues, and technological innovation surrounding the commercialization of coffee by-product-based products. Emphasizing the importance of regulatory compliance and sustainability in the coffee industry, this review highlights the potential of coffee by-products to be transformed from waste into valuable functional food ingredients and nutraceuticals, offering a promising avenue for waste reduction and promoting sustainability in the coffee industry.
A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
Authors: Assaf Abbas Mohammed , Haron Habibollah , Abdull Hamed Haza Nuzly , Ghaleb Fuad A. , Qasem Sultan Noman , Albarrak Abdullah M.
Published: July 2023

The accuracy of solar energy forecasting is critical for power system planning, management, and operation in the global electric energy grid. Therefore, it is crucial to ensure a constant and sustainable power supply to consumers. However, existing statistical and machine learning algorithms are not reliable for forecasting due to the sporadic nature of solar energy data. Several factors influence the performance of solar irradiance, such as forecasting horizon, weather classification, and performance evaluation metrics. Therefore, we provide a review paper on deep learning-based solar irradiance forecasting models. These models include Long Short-Term Memory (LTSM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Generative Adversarial Networks (GAN), Attention Mechanism (AM), and other existing hybrid models. Based on our analysis, deep learning models perform better than conventional models in solar forecasting applications, especially in combination with some techniques that enhance the extraction of features. Furthermore, the use of data augmentation techniques to improve deep learning performance is useful, especially for deep networks. Thus, this paper is expected to provide a baseline analysis for future researchers to select the most appropriate approaches for photovoltaic power forecasting, wind power forecasting, and electricity consumption forecasting in the medium term and long term.
The Preparation of Active Support-Based Sealing Material and Sealing Effect Analysis
Authors: Zhang Jianguo , Zhou Yuejin , Zhou Xin , Wang Man , Liu Shuaitao , Yang Zhanbiao , Zheng Lihui
Published: July 2023

To improve the effect of gas drainage, with the aim of overcoming the problem of air leakage of conventional sealing materials, this study proposed the concept of gas drainage borehole sealing with active support and developed a new type of sealing material with the properties of permeability, fluidity, expansibility, early strength and compressive resistance through orthogonal tests. Using a specially designed active support testing device, this study analyzed the development law of borehole fracture under the action of active support force, which was then verified via field tests at a mine in Henan Province, China. The results show that the higher the expansion rate, the smaller the internal friction angle and cohesion, which is reflected in the sharp decline in the peak strength of the sealing material. The active support force of the sealing material has a significant time effect. It increases rapidly at first, before its growth rate slows down and tends toward a certain value. The greater the active support force provided by the sealing material, the smaller the radius of the plastic zone and breakage zone; this observation is also true of the displacement around the borehole. The active support force of sealed boreholes can effectively inhibit the development of cracks around the hole and reduce the generation of gas leakage channels. Under the action of the same burial depth and active support force, the larger the mechanical parameters of rock, the smaller the radius of the plastic zone and the breakage zone, as well as the smaller the displacement around the borehole. As has been proven by engineering practice, under the same conditions, the gas drainage concentration of the new sealing material is 47.5%, which is 84.8% higher than that of the original sealing material.
Incorporation of Lignin in Bio-Based Resins for Potential Application in Fiber–Polymer Composites
Authors: Machado Marina , Hofmann Mateus , Garrido Mário , Correia João R. , Bordado João C. , Rosa Inês C.
Published: July 2023

Bio-based resins, obtained from renewable raw materials, are a more sustainable alternative to oil-based resins for fiber-reinforced polymer (FRP) composites. The incorporation of lignin in those resins has the potential to enhance their performance. This paper presents results of an experimental study about the effects of Lignoboost lignin incorporation on a partially bio-based vinyl ester (VE) resin. Two resins were prepared—without (reference) and with lignin addition (4% by weight) to its main chain—and their chemical, thermophysical, and mechanical properties were compared using Fourier transform infrared (FTIR) spectroscopy, gel permeation chromatography (GPC), dynamic mechanical analysis (DMA), thermogravimetric analysis (TGA), and tensile and shear tests. Results suggest that the addition of lignin to the base resin resulted in a copolymer of increased heterogeneity and higher molecular weight, incorporating stiff and complex aromatic structures in the polymer chain. While requiring high-temperature curing, the VE–lignin copolymer presented improvements of 27% in tensile strength, 4% in shear strength, and increased glass transition temperature by about 8 °C, thus confirming the potential of this natural biopolymer for FRP composite applications.
Development of a Flexible Thin Wearable Device for Tuning Temperature, Humidity, and Surface Friction on Its Surface
Authors: Nishimura Toshihiro , Kuramata Natsuko , Nojiri Seita , Horiuchi Yuki , Tadakuma Kenjiro , Watanabe Tetsuyou
Published: July 2023

This study proposes a wearable device that adapts its surface conditions to maximize user comfort using novel strategies to change its temperature, humidity, and friction. The device consists of three functional units, namely heating, liquid injection, and dry-air blowing units, composed of flexible materials and thin structures. Owing to its flexibility and thickness, it can be installed on garments. The surface conditions change according to the collaborative actions of the three functional units. The temperature is increased using the heating unit and decreased using both the liquid-injection and dry-air blowing units. Humidity is increased and decreased by the liquid-injection and dry-air blowing units, respectively. Finally, the friction of the contact surface area of the device with human skin is increased and decreased using the liquid-injection and dry-air blowing units, respectively. These methodologies are experimentally validated under different environmental conditions. The validation reveals that the injection of a liquid (deionized water) increases surface friction, whereas blowing air decreases friction; in particular, the presence of granular objects is effective at reducing friction. In addition, the environmental conditions of temperature and humidity influence the degree of increase or decrease, primarily because the amount of water is varied to change the humidity, lower the temperature, and increase friction: vaporization heating lowered temperature and adhesion force of water increased friction. The temperature, humidity, and kinetic friction of the wearable device range from −2.6 to +5.0 °C, −19% to +12%, and −73% to +45%, respectively.
Spin–Orbital Transformation in a Tight Focus of an Optical Vortex with Circular Polarization
Authors: Kotlyar Victor V. , Stafeev Sergey S. , Zaitsev Vladislav D. , Telegin Alexey M. , Kozlova Elena S.
Published: July 2023

In the framework of the Richards–Wolf formalism, the spin–orbit conversion upon tight focusing of an optical vortex with circular polarization is studied. We obtain exact formulas which show what part of the total (averaged over the beam cross-section) longitudinal spin angular momentum is transferred to the total longitudinal orbital angular momentum in the focus. It is shown that the maximum part of the total longitudinal angular momentum that can be transformed into the total longitudinal orbital angular momentum is equal to half the beam power, and this maximum is reached at the maximum numerical aperture equal to one. We prove that the part of the spin angular momentum that transforms into the orbital angular momentum does not depend on the optical vortex topological charge. It is also shown that by virtue of spin–orbital conversion upon focusing, the total longitudinal energy flux decreases and partially transforms into the whole transversal (azimuthal) energy flow in the focus. Moreover, the longitudinal energy flux decreases by exactly the same amount that the total longitudinal spin angular momentum decreases.
Fault-Prone Software Requirements Specification Detection Using Ensemble Learning for Edge/Cloud Applications
Authors: Muhamad Fatin Nur Jannah , Ab Hamid Siti Hafizah , Subramaniam Hema , Abdul Rashid Razailin , Fahmi Faisal
Published: July 2023

Ambiguous software requirements are a significant contributor to software project failure. Ambiguity in software requirements is characterized by the presence of multiple possible interpretations. As requirements documents often rely on natural language, ambiguity is a frequent challenge in industrial software construction, with the potential to result in software that fails to meet customer needs and generates issues for developers. Ambiguities arise from grammatical errors, inappropriate language use, multiple meanings, or a lack of detail. Previous studies have suggested the use of supervised machine learning for ambiguity detection, but limitations in addressing all ambiguity types and a lack of accuracy remain. In this paper, we introduce the fault-prone software requirements specification detection model (FPDM), which involves the ambiguity classification model (ACM). The ACM model identifies and selects the optimal algorithm to classify ambiguity in software requirements by employing the deep learning technique, while the FPDM model utilizes Boosting ensemble learning algorithms to detect fault-prone software requirements specifications. The ACM model achieved an accuracy of 0.9907, while the FPDM model achieved an accuracy of 0.9750. To validate the results, a case study was conducted to detect fault-prone software requirements specifications for 30 edge/cloud applications, as edge/cloud-based applications are becoming crucial and significant in the current digital world.
A Review of Magnetoelectric Composites Based on ZnO Nanostructures
Authors: Bardakas Achilleas , Kaidatzis Andreas , Tsamis Christos
Published: July 2023

The recent advancements in magnetoelectric (ME) materials have enabled the development of functional magnetoelectric composites for sensor applications in the medical and engineering sectors, as well as in energy harvesting and material exploration. Magnetoelectric composites rely on the interaction between piezoelectric and magnetoelastic materials by coupling the magnetization-induced strain to the strain-generated potential of the piezoelectric phase. This creates an increased interest around the development of novel piezoelectric materials that not only possess favorable piezoelectric properties but also fulfill specific material criteria such as biocompatibility, bioactivity, ease of fabrication and low cost. ZnO, and its nanostructures, is one such material that has been employed in the magnetoelectric research due to its remarkable piezoelectric, semiconducting and optical properties. Thus, this article provides a comprehensive review of the available literature on magnetoelectric composites based on ZnO micro- and nanostructures, aiming to present a concise reference on the methods, applications and future prospects of ZnO-based ME composites. Specifically, a brief introduction is provided, presenting the current research interests around magnetoelectric composites, followed by a concise mention of the magnetoelectric effect and its key aspects. This is followed by separate sections describing the relevant research on ZnO magnetoelectric composites based on ZnO thin-films, either pure or doped, and nano- and microrods composites, as well as nano composites comprised of ZnO nanoparticles mixed with ferromagnetic nanoparticles. Finally, the future prospects and the extension of ME ZnO research into nanowire and nanorod composites are discussed.
Numerical and Experimental Analysis of Pressure Pulsation Attenuator Based on Helmholtz Resonator
Authors: Zhou Shenghao , Jiao Hangyu , Liu Dongxu , Liu Weizhen , Lin Junzhe , Han Qingkai , Luo Zhong
Published: July 2023

Due to the development of aviation hydraulic systems towards high pressure and high flow, the frequency range of pressure pulsation becomes wider, and the amplitude of pulsation increases. This puts higher requirements on the attenuation characteristics of the pressure pulsation attenuator. To reduce the damage caused by pressure pulsation to the pipeline, a Helmholtz-type pulsation attenuator (HTPA) is designed, which works through the Helmholtz resonant chamber. The theoretical model of HTPA is established by the method of lumped parameter method and distribution parametric method. The insertion loss is adopted to evaluate its attenuation characteristics. The internal pressure dynamic characteristics and attenuation effect of the HTPA are analyzed by simulation and experimentation. The results show that the pulsation attenuation rate δ was 40% after the installation of the attenuator. In the frequency range of 0–1000 Hz, the maximum insertion loss is 19 dB, which verifies the validity and correctness of the theoretical model.
Recent Progress in Hybrid Additive Manufacturing of Metallic Materials
Authors: Nyamuchiwa Kudakwashe , Palad Robert , Panlican Joan , Tian Yuan , Aranas Clodualdo
Published: July 2023

Additive Manufacturing (AM) is an advanced technology that has been primarily driven by the demand for production efficiency, minimized energy consumption, and reduced carbon footprints. This process involves layer-by-layer material deposition based on a Computer-Aided Design (CAD) model. Compared to traditional manufacturing methods, AM has enabled the development of complex and topologically functional geometries for various service parts in record time. However, there are limitations to mass production, the building rate, the build size, and the surface quality when using metal additive manufacturing. To overcome these limitations, the combination of additive manufacturing with traditional techniques such as milling and casting holds the potential to provide novel manufacturing solutions, enabling mass production, improved geometrical features, enhanced accuracy, and damage repair through net-shape construction. This amalgamation is commonly referred to as hybrid manufacturing or multi-material additive manufacturing. This review paper aimed to explore the processes and complexities in hybrid materials, joining techniques, with a focus on maraging steels. The discussion is based on existing literature and focuses on three distinct joining methods: direct joining, gradient path joining, and intermediate section joining. Additionally, current challenges for the development of the ideal heat treatment for hybrid metals are discussed, and future prospects of hybrid additive manufacturing are also covered.
Rethinking the Public Space Design Process Using Extended Reality as a Game Changer for 3D Co-Design
Authors: Matthys Mario , De Cock Laure , Mertens Lieze , Boussauw Kobe , De Maeyer Philippe , Van de Weghe Nico
Published: July 2023

Public space design processes are complex. Numerous preconditions and the involvement of stakeholders impede rapid decision making. Two-dimensional drawings remain the norm, although these are difficult for citizen stakeholders to understand. Public space designers rarely use 3D city models, infrastructure building information modeling, digital twins, or extended reality. Usually, 3D images (without animation) are only rendered after decision making for communication purposes. This study consists of an online questionnaire of 102 Flemish region (Belgium) stakeholders to show the appeal of and resistance to the use of 3D and extended reality in public space design processes. In a follow-up experiment, 37 participants evaluated various graphic techniques by their designs and observations. The questionnaire showed that all stakeholders lack experience with the use of virtual reality in design processes. We found that non-designer stakeholders and designers indicated that using virtual reality and interactive online 3D tools using game engines provided a better insight into communication and design. Reusing 3D designs in cycling simulators during the design process results in cost-effective quality optimization, and integration into digital twins or animated spatial time machines paves the way for hybrid, 4D cities. Extended reality supports 3D co-design that has simplicity and clarity from the outset of the design process, a trait that makes it a game changer.
Application and Improvement of the Particle Swarm Optimization Algorithm in Source-Term Estimations for Hazardous Release
Authors: Lu Jinshu , Huang Mengqing , Wu Wenfeng , Wei Yonghui , Liu Chong
Published: July 2023

Hazardous gas release can pose severe hazards to the ecological environment and public safety. The source-term estimation of hazardous gas leakage serves a crucial role in emergency response and safety management practices. Nevertheless, the precision of a forward diffusion model and atmospheric diffusion conditions have a significant impact on the performance of the method for estimating source terms. This work proposes the particle swarm optimization (PSO) algorithm coupled with the Gaussian dispersion model for estimating leakage source parameters. The method is validated using experimental cases of the prairie grass field dispersion experiment with various atmospheric stability classes. The results prove the effectiveness of this method. The effects of atmospheric diffusion conditions on estimation outcomes are also investigated. The estimated effect in extreme atmospheric diffusion conditions is not as good as in other diffusion conditions. Accordingly, the Gaussian dispersion model is improved by adding linear and polynomial correction coefficients to it for its inapplicability under extreme diffusion conditions. Finally, the PSO method coupled with improved models is adapted for the source-term parameter estimation. The findings demonstrate that the estimation performance of the PSO method coupled with improved models is significantly improved. It was also found that estimated performances of source parameters of two correction models were significantly distinct under various atmospheric stability classes. There is no single optimal model; however, the model can be selected according to practical diffusion conditions to enhance the estimated precision of source-term parameters.
Downscaling of Regional Air Quality Model Using Gaussian Plume Model and Random Forest Regression
Authors: Kawka Marcin , Struzewska Joanna , Kaminski Jacek W.
Published: July 2023

High PM10 concentrations are still a significant problem in many parts of the world. In many countries, including Poland, 50 μg/m3 is the permissible threshold for a daily average PM10 concentration. The number of people affected by this threshold’s exceedance is challenging to estimate and requires high-resolution concentration maps. This paper presents an application of random forests for downscaling regional model air quality results. As policymakers and other end users are eager to receive detailed-resolution PM10 concentration maps, we propose a technique that utilizes the results of a regional CTM (GEM-AQ, with 2.5 km resolution) and a local Gaussian plume model. As a result, we receive a detailed, 250 m resolution PM10 distribution, which represents the complex emission pattern in a foothill area in southern Poland. The random forest results are highly consistent with the GEM-AQ and observed concentrations. We also discuss different strategies of training random forest on data using additional features and selecting target variables.
Management of Migraine-Associated Vestibulocochlear Disorders
Authors: Umemoto Kayla K. , Tawk Karen , Mazhari Najva , Abouzari Mehdi , Djalilian Hamid R.
Published: July 2023

Migraine is a chronic neurological disorder that frequently coexists with different vestibular and cochlear symptoms (sudden hearing loss, tinnitus, otalgia, aural fullness, hyperacusis, dizziness, imbalance, and vertigo) and disorders (recurrent benign positional vertigo, persistent postural perceptual dizziness, mal de debarquement, and Menière’s disease). Despite evidence of an epidemiological association and similar pathophysiology between migraine and these vestibulocochlear disorders, patients suffering from migraine-related symptoms are usually underdiagnosed and undertreated. Current migraine treatment options have shown success in treating vestibulocochlear symptoms. Lifestyle and dietary modifications (reducing stress, restful sleep, avoiding migraine dietary triggers, and avoiding starvation and dehydration) and supplements (vitamin B2 and magnesium) offer effective first-line treatments. Treatment with migraine prophylactic medications such as tricyclic antidepressants (e.g., nortriptyline), anticonvulsants (e.g., topiramate), and calcium channel blockers (e.g., verapamil) is implemented when lifestyle and dietary modifications are not sufficient in improving a patient’s symptoms. We have included an algorithm that outlines a suggested approach for addressing these symptoms, taking into account our clinical observations. Greater recognition and understanding of migraine and its related vestibular and cochlear symptoms are needed to ensure the appropriate diagnosis and treatment of affected patients.
The Effect of Implementation Intentions on Prospective Memory Commission Errors under Different Cognitive Loads
Authors: Guo Yunfei , Gan Jiaqun , Ding Yi , Li Yongxin
Published: July 2023

Prospective memory (PM) refers to the ability to remember to perform a planned event or activity at a specific time or situation in the future. Implementation intentions can promote a connection between PM cues and intended actions, thus improving an individual’s PM performance. However, this simple and effective encoding strategy may also have negative effects. For example, an implementation intention may result in PM commission errors that occur when an individual makes a false PM response to repeated PM cues that are no longer relevant as the PM task has been completed. Existing studies have explored the effect of implementation intentions on PM commission errors under low cognitive load. However, the role of implementation intentions in promoting linkages between PM cues and actions tends to disintegrate under high cognitive loads. Therefore, this study aimed to explore the effect of implementation intentions on PM commission errors under different cognitive load conditions. In this study, 58 college students participated in a mixed experimental design of 2 (encoding methods: implementation intention, standard) × 2 (cognitive load conditions: low, high). The results showed that implementation intentions promoted PM commission errors under the low-cognitive-load condition only, and there was no difference in the performance of ongoing tasks between the implementation intention encoding and the standard encoding conditions. However, individuals in the implementation intention condition reacted more slowly when encountering previous PM cues. The results suggest that the effect of implementation intentions on PM commission errors relies upon automated processing as a whole. However, individuals in the implementation intention condition required more attentional resources to suppress the no-longer-relevant intended actions when previous PM cues appeared, supporting the dual-mechanism theory.
KV Channel-Interacting Proteins in the Neurological and Cardiovascular Systems: An Updated Review
Authors: Wu Le-Yi , Song Yu-Juan , Zhang Cheng-Lin , Liu Jie
Published: July 2023

KV channel-interacting proteins (KChIP1-4) belong to a family of Ca2+-binding EF-hand proteins that are able to bind to the N-terminus of the KV4 channel α-subunits. KChIPs are predominantly expressed in the brain and heart, where they contribute to the maintenance of the excitability of neurons and cardiomyocytes by modulating the fast inactivating-KV4 currents. As the auxiliary subunit, KChIPs are critically involved in regulating the surface protein expression and gating properties of KV4 channels. Mechanistically, KChIP1, KChIP2, and KChIP3 promote the translocation of KV4 channels to the cell membrane, accelerate voltage-dependent activation, and slow the recovery rate of inactivation, which increases KV4 currents. By contrast, KChIP4 suppresses KV4 trafficking and eliminates the fast inactivation of KV4 currents. In the heart, IKs, ICa,L, and INa can also be regulated by KChIPs. ICa,L and INa are positively regulated by KChIP2, whereas IKs is negatively regulated by KChIP2. Interestingly, KChIP3 is also known as downstream regulatory element antagonist modulator (DREAM) because it can bind directly to the downstream regulatory element (DRE) on the promoters of target genes that are implicated in the regulation of pain, memory, endocrine, immune, and inflammatory reactions. In addition, all the KChIPs can act as transcription factors to repress the expression of genes involved in circadian regulation. Altered expression of KChIPs has been implicated in the pathogenesis of several neurological and cardiovascular diseases. For example, KChIP2 is decreased in failing hearts, while loss of KChIP2 leads to increased susceptibility to arrhythmias. KChIP3 is increased in Alzheimer’s disease and amyotrophic lateral sclerosis, but decreased in epilepsy and Huntington’s disease. In the present review, we summarize the progress of recent studies regarding the structural properties, physiological functions, and pathological roles of KChIPs in both health and disease. We also summarize the small-molecule compounds that regulate the function of KChIPs. This review will provide an overview and update of the regulatory mechanism of the KChIP family and the progress of targeted drug research as a reference for researchers in related fields.
Non-Invasive Characterization of Maiolica Layer Structure by Terahertz Time-Domain Imaging
Authors: Manca Rosarosa , Chiarantini Laura , Tartaglia Edoardo , Soldovieri Francesco , Miliani Costanza , Catapano Ilaria
Published: July 2023

The characterization of the layered structure is of paramount importance for the study of maiolica wares (tin-glazed pottery). This paper presents the potentialities of Terahertz Time-Domain Imaging (THz-TDI) as a tool to perform non-invasive stratigraphic analysis of the maiolica objects under test. Samples with different types of stratigraphy, as testified by preliminary SEM-EDS analysis, were investigated by THz-TDI in laboratory conditions. The collected THz data were processed by means of noise filtering procedures and a time-of-flight-based imaging approach and the achieved results corroborate the ability to identify glaze layers, whose electromagnetic properties, i.e., the refraction index and the dielectric permittivity value, were estimated by taking into account both THz-TDI and SEM-EDS data. However, layers applied over the white glaze (namely, a transparent overglaze and a luster decoration) were not detected, probably since their thickness is below the range resolution of the adopted THz-TDI system. Morphological features hidden under the surface were also identified and they provided information about the manufacturing technique.