bluuebunny/crossref_metadata_2025 huggingface dataset:
A Statistical Approach for Signal and Power Integrity Co-Design in High-Speed Interconnects Considering Non-Linear Power/Ground Noise and Bit-Patterns
Authors: Kim Youngwoo
Published: August 2023

In this article, a novel statistical approach is proposed and applied to co-design signal and power integrity (SI/PI) in high-speed interconnects considering the non-linear power/ground noise generated by parallel buffers and bit-patterns. With increased data rates and decreased operating voltages, the allowed noise margin in high-speed interconnects is continuously reduced, and this trend requires SI/PI co-design. Specifically, non-linear power/ground noise associated with simultaneous switching circuits sharing a power delivery network (PDN) and bit-patterns must be carefully considered during the interconnects’ design and analysis phase. In many cases, conventional electromagnetic (EM) and transient circuit simulators require heavy computational resources or even fail to deliver an accurate result. The proposed statistical method estimates the statistical eye-diagram in the high-speed interconnect considering power/ground noise and bit-patterns such as data bus inversion (DBI) coding. The accuracy and computational efficiency of the proposed method are validated by comparing the result with HSPICE transient simulation result. The proposed method is also compared with conventional statistical methods, such as peak distortion analysis (PDA) and statistical channel simulation in the transient simulator. Lastly, the proposed method is applied to the SI/PI co-design and co-analysis in the high bandwidth memory (HBM) interposer channel. Impacts of decoupling capacitors on hierarchical PDN impedance, statistical eye-diagram of the HBM channel, and bit error rate (BER) Bathtub curves are summarized. Finally, the BER eye-diagram is derived from the estimated statistical eye-diagram for timing and voltage analysis. The impacts of hierarchical PDN design and bit-patterns on SI/PI are discussed.
Computational Insights into the Adsorption Mechanism of Zn Ion on the Surface (110) of Sphalerite from the Perspective of Hydration
Authors: Zhang Chenyang , Zhang Feng , Liu Siyuan , Fan Dong , Rao Xin , Chen Jianhua , Wen Jing , Zhu Yangge , Sun Wei
Published: August 2023

Zn ions are widely reported to possess a depression effect on sphalerite in flotation. The effective adsorption of Zn ions on a sphalerite surface is critical to realize the inhibition effect. At the same time, zinc ions are easily hydrated in a slurry solution. Therefore, based on the first principle DFT calculation, the molecular mechanism of Zn ion adsorption on the surface of Sphalerite is further studied from the perspective of hydration. [Zn(H2O)5]2+, [Zn(OH)(H2O)3]+ and [Zn(OH)2(H2O)2] are found to be the effective components of Zn ions adsorbed on a sphalerite (110) surface in the neutral condition. Furthermore, the adsorption mechanism of [Zn(H2O)5]2+ on a sphalerite (110) surface is mainly via the hybridization of Zn 3d orbital in the structure of [Zn(H2O)5]2+ with surface S 3p orbitals. Additionally, the adsorption mechanism of [Zn(OH)(H2O)3]+ and [Zn(OH)2(H2O)2] on a sphalerite (110) surface is primarily ascribed to the hybridization of surface Zn 3d orbital with O atom of hydroxyl ligand 2p orbitals. In addition, the H 1s orbits in the water molecules have a weaker interaction with the sphalerite surface S 3p orbits. This work sheds new light on the adsorption and inhibition mechanism of zinc ions on a sphalerite surface in a neutral aqueous solution.
Forensic Analysis of Synthetic Cathinones on Nanomaterials-Based Platforms: Chemometric-Assisted Voltametric and UPLC-MS/MS Investigation
Authors: Dragan Ana-Maria , Feier Bogdan George , Tertiș Mihaela , Bodoki Ede , Truta Florina , Ștefan Maria-Georgia , Kiss Béla , Van Durme Filip , De Wael Karolien , Oprean Radu , Cristea Cecilia
Published: August 2023

Synthetic cathinones (SCs) are a group of new psychoactive substances often referred to as “legal highs” or “bath salts”, being characterized by a dynamic change, new compounds continuously emerging on the market. This creates a lack of fast screening tests, making SCs a constant concern for law enforcement agencies. Herein, we present a fast and simple method for the detection of four SCs (alpha-pyrrolidinovalerophenone, N-ethylhexedrone, 4-chloroethcathinone, and 3-chloromethcathinone) based on their electrochemical profiles in a decentralized manner. In this regard, the voltametric characterization of the SCs was performed by cyclic and square wave voltammetry. The elucidation of the SCs redox pathways was successfully achieved using liquid chromatography coupled to (tandem) mass spectrometry. For the rational identification of the ideal experimental conditions, chemometric data processing was employed, considering two critical qualitative and quantitative variables: the type of the electrochemical platform and the pH of the electrolyte. The analytical figures of merit were determined on standard working solutions using the optimized method, which exhibited wide linear ranges and LODs suitable for confiscated sample screening. Finally, the performance of the method was evaluated on real confiscated samples, the resulting validation parameters being similar to those obtained with another portable device (i.e., Raman spectrometer).
Design of Surfaces with Persistent Antimicrobial Properties on Stainless Steel Developed Using Femtosecond Laser Texturing for Application in “High Traffic” Objects
Authors: Daskalova Albena , Angelova Liliya
Published: August 2023

Metal-based high-touch surfaces used for diverse applications in everyday use, like handrails, playground grab handles, doorknobs, ATM touch pads, and desks, are the most common targets for pollution with a variety of microbes; there is thus a need to improve their antimicrobial properties, an issue which has become a challenge in recent years, particularly after the COVID-19 pandemic. According to the World Health Organization (WHO), drug-resistant pathogens are one of the main concerns to global health today, as they lead to longer hospital stays and increased medical costs. Generally, the development of antimicrobial surfaces is related to the utilization of chemical methods via deposition on surfaces in the forms of various types of coatings. However, the addition of chemical substances onto a surface can induce unwanted effects, since it causes surface chemistry changes and, in some cases, cannot provide long-lasting results. A novel approach of utilising ultra-short laser radiation for the treatment of metallic surfaces by inducing a variety of micro- and nanostructuration is elaborated upon in the current research, estimating the optimum relation between the wettability and roughness characteristics for the creation of antimicrobial properties for such high-touch surfaces. In the current study, AISI 304–304L stainless steel metal was used as a benchmark material. Surface texturing via laser ablation with femtosecond laser pulses is an effective method, since it enables the formation of a variety of surface patterns, along with the creation of bimodal roughness, in one-step processing. In this investigation, a precise approach toward developing hydrophobic stainless steel surfaces with tunable adherence using femtosecond laser-induced modification is described. The impact of basic femtosecond laser processing parameters, like the scanning velocity, laser energy, and wettability properties of the laser-processed stainless steel samples, are examined. It is identified that the topography and morphology of laser-induced surface structures can be efficiently changed by adapting the laser processing parameters to create structures, which facilitate the transfer of surface properties from extremely low to high surface wettability.
Genetically Predicted Vegetable Intake and Cardiovascular Diseases and Risk Factors: An Investigation with Mendelian Randomization
Authors: Feng Qi , Grant Andrew J. , Yang Qian , Burgess Stephen , Bešević Jelena , Conroy Megan , Omiyale Wemimo , Sun Yangbo , Allen Naomi , Lacey Ben
Published: August 2023

Background: The associations between vegetable intake and cardiovascular diseases have been demonstrated in observational studies, but less sufficiently in randomized trials. Mendelian randomization has been considered a promising alternative in causal inference. The separate effects of cooked and raw vegetable intake remain unclear. This study aimed to investigate the associations between cooked and raw vegetable intake with cardiovascular outcomes using MR. Methods: We identified 15 and 28 genetic variants statistically and biologically associated with cooked and raw vegetable intake, respectively, from previous genome-wide association studies, which were used as instrumental variables to estimate associations with coronary heart disease (CHD), stroke, heart failure (HF), and atrial fibrillation (AF). The independent effects of genetically predicted cooked and raw vegetable intake were examined using multivariable MR analysis. We performed one-sample and two-sample MR analyses and combined their results using meta-analysis. Bonferroni correction was applied for multiple comparisons. We performed two-sample MR analysis for cardiometabolic risk factors (serum lipids, blood pressure, body mass index, and glycemic traits) to explore the potential mechanisms. Results: In the MR meta-analysis of 1.2 million participants, we found null evidence for associations between genetically predicted cooked and raw vegetable intake with CHD, HF, or AF. Raw vegetable intake was nominally associated with stroke (odds ratio [95% confidence interval] 0.82 [0.69–0.98] per 1 daily serving increase, p = 0.03), but this association did not pass the corrected significance level. We found consistently null evidence for associations with serum lipids, blood pressure, body mass index, or glycemic traits. Conclusions: We found null evidence for associations between genetically predicted vegetable intake with CHD, AF, HF, or cardiometabolic risk factors in this MR study. Raw vegetable intake may reduce risk of stroke, but this warrants more research. True associations between vegetable intake and CVDs cannot be completely ruled out, and future investigations are required for causal inference in nutritional research.
SLAB51 Multi-Strain Probiotic Formula Increases Oxygenation in Oxygen-Treated Preterm Infants
Authors: Baldassarre Maria Elisabetta , Marazzato Massimiliano , Pensa Marta , Loverro Maria Teresa , Quercia Michele , Lombardi Francesca , Schettini Federico , Laforgia Nicola
Published: August 2023

Preterm infants are at risk of hypoxia and hyperoxia because of the immaturity of their respiratory and antioxidant systems, linked to increased morbidity and mortality. This study aimed to evaluate the efficacy of a single administration of the SLAB51 probiotic formula in improving oxygenation in respiratory distress syndrome (RDS)-affected premature babies, thus reducing their need for oxygen administration. Additionally, the capability of SLAB51 in activating the factor-erythroid 2-related factor (Nrf2) responsible for antioxidant responses was evaluated in vitro. In two groups of oxygen-treated preterm infants with similar SaO2 values, SLAB51 or a placebo was given. After two hours, the SLAB51-treated group showed a significant increase in SaO2 levels and the SaO2/FiO2 ratio, while the control group showed no changes. Significantly increased Nrf2 activation was observed in intestinal epithelial cells (IECs) exposed to SLAB51 lysates. In preterm infants, we confirmed the previously observed SLAB51’s “oxygen-sparing effect”, permitting an improvement in SaO2 levels. We also provided evidence of SLAB51’s potential to enhance antioxidant responses, thus counteracting the detrimental effects of hyperoxia. Although further studies are needed to support our data, SLAB51 represents a promising approach to managing preterm infants requiring oxygen supplementation.
Probiotics and Prebiotics in Cardiovascular Diseases
Authors: Romero Miguel , Duarte Juan
Published: August 2023

This Special Issue, titled “Probiotics and Prebiotics in Cardiovascular Diseases”, encompasses two comprehensive review articles examining the potential of gut-microbiota-targeted reprogramming interventions designed to prevent the onset and progression of cardiovascular diseases [...]
Feeding High-Fat Diet Accelerates Development of Peripheral and Central Insulin Resistance and Inflammation and Worsens AD-like Pathology in APP/PS1 Mice
Authors: Mengr Anna , Strnadová Veronika , Strnad Štěpán , Vrkoslav Vladimír , Pelantová Helena , Kuzma Marek , Comptdaer Thomas , Železná Blanka , Kuneš Jaroslav , Galas Marie-Christine , Pačesová Andrea , Maletínská Lenka
Published: August 2023

Alzheimer’s disease (AD) is a progressive brain disorder characterized by extracellular amyloid-β (Aβ) plaques, intracellular neurofibrillary tangles formed by hyperphosphorylated Tau protein and neuroinflammation. Previous research has shown that obesity and type 2 diabetes mellitus, underlined by insulin resistance (IR), are risk factors for neurodegenerative disorders. In this study, obesity-induced peripheral and central IR and inflammation were studied in relation to AD-like pathology in the brains and periphery of APP/PS1 mice, a model of Aβ pathology, fed a high-fat diet (HFD). APP/PS1 mice and their wild-type controls fed either a standard diet or HFD were characterized at the ages of 3, 6 and 10 months by metabolic parameters related to obesity via mass spectroscopy, nuclear magnetic resonance, immunoblotting and immunohistochemistry to quantify how obesity affected AD pathology. The HFD induced substantial peripheral IR leading to central IR. APP/PS1-fed HFD mice had more pronounced IR, glucose intolerance and liver steatosis than their WT controls. The HFD worsened Aβ pathology in the hippocampi of APP/PS1 mice and significantly supported both peripheral and central inflammation. This study reveals a deleterious effect of obesity-related mild peripheral inflammation and prediabetes on the development of Aβ and Tau pathology and neuroinflammation in APP/PS1 mice.
Comparison between the General Assessment of Hospitalised Patient Tool and the Barthel Index: A Retrospective Study
Authors: Danielis Matteo , Bortot Sara , Zanotti Renzo
Published: August 2023

Among hospitalised patients, functional decline and increased dependence on others are common health problems. Identifying critical needs is an important starting point to empower patients to improve their own health and behaviour. Once these needs are determined, the most relevant goals for addressing patients’ needs and health potential can be established. This study aimed to test a model for profiling patients using the General Assessment of Hospitalised Patients (ASGO) compared to the Barthel Index (BI) as the gold standard. A retrospective approach was conducted by reviewing administrative data recorded between 2017 and 2020 at the University of Padova, Italy. Data from patients (a) older than 18 years, (b) admitted to a local hospital, and (c) with a stay of at least three days were included in the study. The ASGO and the BI were both used on patients’ admission and discharge from the ward. Data were analysed using STATA software (v.16) (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC). The database used for the analysis consisted of 842 patient records, with more than 50% over 75 years of age and consisting mainly of men. The results of the ASGO and the BI were more correlated at discharge (rho = −0.79) than at admission (rho = −0.59). Furthermore, sensitivity and specificity, calculated with reference to the optimal cut-off point (Youden index), demonstrated the highest reliability of the test at discharge (sensitivity: 0.87; specificity: 0.78) compared to admission (sensitivity: 0.77; specificity: 0.72). This result was confirmed by the analysis of the ROC curve: The area under the curve was greater at discharge (89%) than at admission (82%). Analysis of the results obtained from assessments created with the ASGO demonstrates the applicability of this model in the context of hospital care and how well it can represent functional dependence. This study was not registered.
Recent Advances and Innovations in the Preparation and Purification of In Vitro-Transcribed-mRNA-Based Molecules
Authors: Zhang Jingjing , Liu Yuheng , Li Chao , Xiao Qin , Zhang Dandan , Chen Yang , Rosenecker Joseph , Ding Xiaoyan , Guan Shan
Published: August 2023

The coronavirus disease 2019 (COVID-19) pandemic poses a disruptive impact on public health and the global economy. Fortunately, the development of COVID-19 vaccines based on in vitro-transcribed messenger RNA (IVT mRNA) has been a breakthrough in medical history, benefiting billions of people with its high effectiveness, safety profile, and ease of large-scale production. This success is the result of decades of continuous RNA research, which has led to significant improvements in the stability and expression level of IVT mRNA through various approaches such as sequence optimization and improved preparation processes. IVT mRNA sequence optimization has been shown to have a positive effect on enhancing the mRNA expression level. The innovation of IVT mRNA purification technology is also indispensable, as the purity of IVT mRNA directly affects the success of downstream vaccine preparation processes and the potential for inducing unwanted side effects in therapeutic applications. Despite the progress made, challenges related to IVT mRNA sequence design and purification still require further attention to enhance the quality of IVT mRNA in the future. In this review, we discuss the latest innovative progress in IVT mRNA design and purification to further improve its clinical efficacy.
Hindu Nationalism, Gurus and Media
Authors: Copeman Jacob , Duggal Koonal , Longkumer Arkotong
Published: August 2023

This commentary offers a reflection on the triangular interactive relationship between Hindutva, gurus and media. It suggests that Hindu nationalists understand gurus to be a specific form of valued Hindu cultural good, which helps to explain mediatised activist attempts to defend gurus from legal and media scrutiny, and historicises the theme of guru domination, caste politics and Hindutva through the optics of matter and media, exploring both the mass remediation of Brahmanical guruship models that attended Hindutva’s rise in the 1990s and the oppositional response it provoked, which we term ‘the subaltern counter-publicity of the guru’. It discloses how Hindutva is itself structurally composed of guru logics at different scales; it embodies a kind of ‘fractal guruship’. However, if Hindutva mediates principles of guruship, we also see how a multitude of public gurus mediates principles of Hindutva. This ‘bi-instrumentalism’ of Hindu nationalism and some gurus is witnessed in the instances we describe of gurus—and the idea of India as a guru—being used as a means of branding in order to convey and normalise the ‘Hindutva idea of India’. We suggest, in light of the frequent mutual mediation of gurus and Hindutva, that continued investment by devotees and commentators in gurus as figures embodying hope and the promise of post-communal amity can aptly be described using Berlant’s evocative phrase ‘cruel optimism’.
HyFormer: Hybrid Grouping-Aggregation Transformer and Wide-Spanning CNN for Hyperspectral Image Super-Resolution
Authors: Ji Yantao , Shi Jingang , Zhang Yaping , Yang Haokun , Zong Yuan , Xu Ling
Published: August 2023

Hyperspectral image (HSI) super-resolution is a practical and challenging task as it requires the reconstruction of a large number of spectral bands. Achieving excellent reconstruction results can greatly benefit subsequent downstream tasks. The current mainstream hyperspectral super-resolution methods mainly utilize 3D convolutional neural networks (3D CNN) for design. However, the commonly used small kernel size in 3D CNN limits the model’s receptive field, preventing it from considering a wider range of contextual information. Though the receptive field could be expanded by enlarging the kernel size, it results in a dramatic increase in model parameters. Furthermore, the popular vision transformers designed for natural images are not suitable for processing HSI. This is because HSI exhibits sparsity in the spatial domain, which can lead to significant computational resource waste when using self-attention. In this paper, we design a hybrid architecture called HyFormer, which combines the strengths of CNN and transformer for hyperspectral super-resolution. The transformer branch enables intra-spectra interaction to capture fine-grained contextual details at each specific wavelength. Meanwhile, the CNN branch facilitates efficient inter-spectra feature extraction among different wavelengths while maintaining a large receptive field. Specifically, in the transformer branch, we propose a novel Grouping-Aggregation transformer (GAT), comprising grouping self-attention (GSA) and aggregation self-attention (ASA). The GSA is employed to extract diverse fine-grained features of targets, while the ASA facilitates interaction among heterogeneous textures allocated to different channels. In the CNN branch, we propose a Wide-Spanning Separable 3D Attention (WSSA) to enlarge the receptive field while keeping a low parameter number. Building upon WSSA, we construct a wide-spanning CNN module to efficiently extract inter-spectra features. Extensive experiments demonstrate the superior performance of our HyFormer.
Monitoring Waterlogging Damage of Winter Wheat Based on HYDRUS-1D and WOFOST Coupled Model and Assimilated Soil Moisture Data of Remote Sensing
Authors: Zhang Jian , Pan Bin , Shi Wenxuan , Zhang Yu
Published: August 2023

Waterlogging harms winter wheat growth. To enable accurate monitoring of agricultural waterlogging, this paper conducts a winter wheat waterlogging monitoring study using multi-source data in Guzhen County, Anhui Province, China. The hydrological model HYDRUS-1D is coupled with the crop growth model WOFOST, and the Ensemble Kalman Filter is used to assimilate Sentinel-1 inversion soil moisture data. According to the precision and continuity of soil moisture, the damage of winter wheat waterlogging were obtained. The experimental results show that the accuracy of the soil moisture is improved after data assimilation compared with that before data assimilation, and the Nash–Sutcliffe efficiency (NSE) of the simulated soil moisture values at three monitoring sites increased from 0.528, 0.541 and 0.575 to 0.752, 0.692 and 0.731, respectively. A new waterlogging identification criterion has been proposed based on the growth periods and probability distribution of soil moisture. The proportion, calculated from this identification criterion, of the waterlogging wheat farmland in total farmland shows a high correlation with the yield reduction rate. The correlation coefficient of the waterlogging farmland proportion and the yield reduction rate in 11 towns of Guzhen County reaches 0.78. Through the synchronization of geography, agriculture and meteorology, the framework shows great potential in waterlogging monitoring.
The Partial Reconstruction Symplectic Geometry Mode Decomposition and Its Application in Rolling Bearing Fault Diagnosis
Authors: Liu Yanfei , Cheng Junsheng , Yang Yu , Bin Guangfu , Shen Yiping , Peng Yanfeng
Published: August 2023

Extracting the fault characteristic information of rolling bearings from intense noise disturbance has been a heated research issue. Symplectic geometry mode decomposition (SGMD) has already been adopted for bearing fault diagnosis due to its advantages of no subjective customization of parameters and the ability to reconstruct existing modes. However, SGMD suffers from rapidly decreasing calculation efficiency as the amount of data increases, in addition to invalid symplectic geometry components affecting decomposition accuracy. The regularized composite multiscale fuzzy entropy (RCMFE) operator is constructed to evaluate the complexity of each initial single component and minimize the residual energy. Combined with the partial reconstruction threshold indicator to filter out specific significant initial single components, the raw signal can be decomposed into multiple physically meaningful symplectic geometric mode components. Therefore, the decomposition efficiency and accuracy can be enhanced. Thus, a rolling bearing fault diagnosis method is proposed based on partial reconstruction symplectic geometry mode decomposition (PRSGMD). Both simulated and experimental analysis results show that PRSGMD can improve the speed of SGMD analysis while increasing the decomposition accuracy, thereby augmenting the robustness and effectiveness of the algorithm.
How the Material Characteristics of Optical Fibers and Soil Influence the Measurement Results of Distributed Acoustic Sensing
Authors: Jiang Ke , Liang Lei , Tong Xiaoling , Zeng Feiyu , Hu Xiaolong
Published: August 2023

Fiber optic distributed acoustic sensing (DAS) technology is widely used in security surveillance and geophysical survey applications. The response of the DAS system to external vibrations varies with different types of fiber optic cable connections. The mechanism of mutual influence between the cable’s characteristics and DAS measurement results remains unclear. This study proposed a dynamic model of the interaction between the optical cable and the soil, analyzed the impact of the dynamic parameters of the optical cable and soil on the sensitivity of the DAS system, and validated the theoretical analysis through experiments. The findings suggest that augmenting the cable’s bending stiffness 5.5-fold and increasing its unit mass 4.2-fold result in a discernible reduction of the system’s response to roughly 0.15 times of its initial magnitude. Cables with lower unit mass and bending stiffness are more sensitive to vibration signals. This research provides a foundation for optimizing vibration-enhanced fiber optic cables and broadening the potential usage scenarios for DAS systems.
Sleep CLIP: A Multimodal Sleep Staging Model Based on Sleep Signals and Sleep Staging Labels
Authors: Yang Weijia , Wang Yuxian , Hu Jiancheng , Yuan Tuming
Published: August 2023

Since the release of the contrastive language-image pre-training (CLIP) model designed by the OpenAI team, it has been applied in several fields owing to its high accuracy. Sleep staging is an important method of diagnosing sleep disorders, and the completion of sleep staging tasks with high accuracy has always remained the main goal of sleep staging algorithm designers. This study is aimed at designing a multimodal model based on the CLIP model that is more suitable for sleep staging tasks using sleep signals and labels. The pre-training efforts of the model involve five different training sets. Finally, the proposed method is tested on two training sets (EDF-39 and EDF-153), with accuracies of 87.3 and 85.4%, respectively.
A Smartphone-Enabled Imaging Device for Chromotropic Acid-Based Measurement of Nitrate in Soil Samples
Authors: Lavanya Veerabhadrappa , Nayak Anshuman , Deb Roy Partha , Dasgupta Shubhadip , Dey Subhadip , Li Bin , Weindorf David C. , Chakraborty Somsubhra
Published: August 2023

In this study, a novel chromotropic acid-based color development method was proposed for quick estimation of soil nitrate (NO3−). The method utilized a 3D printed device integrated with the rear-end camera of a smartphone and a stand-alone application called SMART NP. By analyzing the mean Value (V) component of the sample’s image, the SMART NP provides instant predictions of soil NO3− levels. The limit of detection was calculated as 0.1 mg L−1 with a sensitivity of 0.26 mg L−1. The device showed a % bias of 0.9% and a precision of 1.95%, indicating its reliability. Additionally, the device-predicted soil NO3− data, combined with kriging interpolation, showcased spatial variability in soil NO3− levels at the regional level. The study employed a Gaussian model of variogram for kriging, and the high Nugget/Sill ratio indicated low spatial autocorrelation, emphasizing the impact of management factors on the spatial distribution of soil NO3− content in the study area. Overall, the imaging device, along with geostatistical interpolation, provided a comprehensive solution for the rapid assessment of spatial variability in soil NO3−content.
Drivers’ Mental Engagement Analysis Using Multi-Sensor Fusion Approaches Based on Deep Convolutional Neural Networks
Authors: Aminosharieh Najafi Taraneh , Affanni Antonio , Rinaldo Roberto , Zontone Pamela
Published: August 2023

In this paper, we present a comprehensive assessment of individuals’ mental engagement states during manual and autonomous driving scenarios using a driving simulator. Our study employed two sensor fusion approaches, combining the data and features of multimodal signals. Participants in our experiment were equipped with Electroencephalogram (EEG), Skin Potential Response (SPR), and Electrocardiogram (ECG) sensors, allowing us to collect their corresponding physiological signals. To facilitate the real-time recording and synchronization of these signals, we developed a custom-designed Graphical User Interface (GUI). The recorded signals were pre-processed to eliminate noise and artifacts. Subsequently, the cleaned data were segmented into 3 s windows and labeled according to the drivers’ high or low mental engagement states during manual and autonomous driving. To implement sensor fusion approaches, we utilized two different architectures based on deep Convolutional Neural Networks (ConvNets), specifically utilizing the Braindecode Deep4 ConvNet model. The first architecture consisted of four convolutional layers followed by a dense layer. This model processed the synchronized experimental data as a 2D array input. We also proposed a novel second architecture comprising three branches of the same ConvNet model, each with four convolutional layers, followed by a concatenation layer for integrating the ConvNet branches, and finally, two dense layers. This model received the experimental data from each sensor as a separate 2D array input for each ConvNet branch. Both architectures were evaluated using a Leave-One-Subject-Out (LOSO) cross-validation approach. For both cases, we compared the results obtained when using only EEG signals with the results obtained by adding SPR and ECG signals. In particular, the second fusion approach, using all sensor signals, achieved the highest accuracy score, reaching 82.0%. This outcome demonstrates that our proposed architecture, particularly when integrating EEG, SPR, and ECG signals at the feature level, can effectively discern the mental engagement of drivers.
Factors Influencing Voting Decision: A Comprehensive Literature Review
Authors: Kulachai Waiphot , Lerdtomornsakul Unisa , Homyamyen Patipol
Published: August 2023

The voting decisions of a population are vital in forming the political structure of a country. Recognizing what influences voters’ selections is key for politicians, candidates, and those crafting policy. This article offers an examination of different factors that shape voting choices within the American populace. Through a comprehensive synthesis and analysis of various studies, this review seeks to give an understanding of the principal elements that drive voter conduct. Additionally, it looks at what these factors mean for democracy and proposes possible directions for continued research.
Benefits of Implementing Occupational Health and Safety Management Systems for the Sustainable Construction Industry: A Systematic Literature Review
Authors: Kineber Ahmed Farouk , Antwi-Afari Maxwell Fordjour , Elghaish Faris , Zamil Ahmad M. A. , Alhusban Mohammad , Qaralleh Thikryat Jibril Obied
Published: August 2023

Accidents are more prevalent in the construction industry compared to other economic sectors. Therefore, understanding the benefits of occupational health and safety management systems (OHSMSs) in terms of their sustainable implementation, management and performance, as well as the awareness of OHMSs and barriers to their implementation, are important for improving OHSMSs in the sustainability of the construction industry. Although there is considerable research on OHSMSs, further assessments are needed concerning other aspects of OHSMSs, particularly the benefits of OHSMSs. Thus, this review paper summarises the empirical state of the art of OHSMS activities. Scopus, Web of Science and other databases were searched using predefined standards. The query was limited to articles published from 1999 to 2023. Consequently, one hundred and four articles were selected and analysed. These articles present analyses of OHSMSs and their potential benefits concerning the implementation of OHSMSs and management, performance, awareness, and barriers in relation to OHSMSs. The results reveal that 12.50% of the reviewed studies assessed the implementation of OHSMSs in the construction industry, and 25.96% studied the management of OHSMSs. Analyses of the performance of OHSMSs in the construction industry accounted for 8.65%, analyses of the awareness of OHSMSs accounted for 4.81%, model-related analyses accounted for 13.46%, studies on the significance/benefits of OHSMSs accounted for 3.85%, studies on the barriers/challenges associated with OHSMSs accounted for 5.77%, analyses on the safety indicators of OHSMSs accounted for 2.88% and other types of studies accounted for 20.19%. This study further reveals that the implementation of OHSMSs is characterised by a dearth of proper communication, the non-utilisation of personal protective equipment (PPE), wrong postures and work activities, a dearth of training, physiological factors including burnout and stress, and a dearth of safety culture and orientation; in addition, matters relating to compliance with effective laws are significant safety challenges in the construction industry. However, the rationality for evaluating the benefits of OHSMSs, comprising their implementation, management and performance, as well as awareness of and barriers to OHSMSs, is challenging to authenticate because appropriate field, survey, organisational and clinical data concerning incident occurrences in the construction industry are lacking for comprehensive evaluations. Thus, this novel study presents our effort to narrow this gap by establishing a framework for increasing our understanding of the benefits of implementing OHSMSs and accident reduction.
Evaluation of Electroosmotic Permeability Using Different Models and Investigation of Its Effect on Chromium Removal
Authors: Liu Xi-Yin , Zhuang Yan-Feng
Published: August 2023

Electroosmotic permeability is an important parameter in the electrokinetic remediation (EKR) of heavy-metal-contaminated soil. This study focuses on the applicability of electroosmotic permeability models and the relationship between electroosmotic permeability and heavy metal removal efficiency. The electroosmotic permeability models of ion hydration and the ion hydration–friction model were developed and investigated. Moreover, 11 EKR experiments were conducted in the laboratory to remediate Cr6+-contaminated soil. The results showed that the electroosmotic permeability calculated by the H-S model was 3–7 times larger than the measured value, and it was 65–90% of the measured value as calculated by the ion hydration model. However, the ion hydration–friction model, which combined the ion hydration and Spiegler friction theories, predicted the electroosmotic permeability more accurately compared to the H-S and ion hydration models. In addition, the parameters in the ion hydration–friction model were determined easily, meaning that the ion hydration–friction model is of good applicability. The experimental results showed that the soil properties changed and the electroosmotic flow rate decreased when acid was added to the soil, and the Cr6+ removal efficiency improved.
Research on Durability and Long-Term Moisture Stability Improvement of Asphalt Mixture Based on Buton Rock Asphalt
Authors: Zhang Yinglong , Zhou Yutong , Hu Xiaodi , Wan Jiuming , Gan Wenxia , Jing Yafei , Liu Jiakun , Chen Zongwu
Published: August 2023

Buton rock asphalt (BRA) has been used in asphalt pavement for its contribution to high-temperature stability. However, how BRA affects the durability of a corresponding asphalt mixture requires systemic discussion. This study investigated how BRA affected durability in terms of the fatigue resistance, thermo-oxidative aging resistance, and long-term moisture stability of asphalt mixture. Furthermore, improvement of the long-term moisture stability of asphalt mixture containing BRA modified asphalt (BRAM) was also included. An AC-20C asphalt mixture based on BRA asphalt, neat asphalt, and SBS-modified asphalt were prepared and their high-temperature, low-temperature, and moisture performance were examined. A semi-circular bending cyclic loading test was used to characterize fatigue performance. Thermo-oxidative aging tests in both the short-term and long-term were used to indicate the aging performance. Freeze–thaw cyclic splitting tests were carried out to investigate BRAM’s long-term moisture stability. Finally, the optimization and enhancement of BRAM’s long-term moisture stability was discussed in terms of ceramic, basalt, and polyester fiber, as well as hydrated lime. Results showed that BRA can enhance the high-temperature, low-temperature, and moisture performance of BRAM. The cracking fatigue resistance and thermo-oxidative aging resistance of BRAM were also improved by BRA. The long-term moisture stability of BRAM was lower than that of the asphalt mixture based on SBS and neat asphalt. It was found that the long-term moisture durability of BRAM can be optimally enhanced by replacing mineral filler with 50% hydrated lime by equal volume and using 0.2wt% ceramic fiber as an additive in BRAM.
A Study of Initial Water Rights Allocation Coupled with Physical and Virtual Water Resources
Authors: Xu Xia , Yuan Jing , Yu Qianwen , Sun Zehao
Published: August 2023

Virtual water exerts an indispensable influence on water resources, yet the existing studies on the water rights allocation of transboundary rivers hardly consider virtual water transfer (VWT). Therefore, in this paper, we used Taihu Lake as an example with data collected in 2017 that described both physical and virtual water use. We used these data to evaluate water rights allocation schemes by coupling virtual and physical water use. In order to achieve this goal, we first determined the physical water rights allocated for the four regions connected to the Basin. Next, we employed the multi-regional input–output (MRIO) approach to calculate the VWT among the four regions; then, we converted the VWT to the riparian level via the water efficiency coefficient. Finally, with virtual water included in the physical water rights allocation, we formulated a final water rights allocation for Taihu Lake. The results showed the following findings: (1) The ranking of the amount of physical water rights allocation is: Jiangsu > Zhejiang > Shanghai > Anhui. (2) Anhui and Jiangsu produce a net export of virtual water (2.259 billion m3 and 1.78 billion m3, respectively), while Zhejiang and Shanghai have a net import of virtual water (2.344 billion m3 and 1.695 billion m3, respectively), indicating that Anhui houses more water-consuming industries and is in greater need of economic restructuring. (3) The integration of virtual water makes a difference: Jiangsu achieved 16.208 billion m3 in terms of the amount of water rights allocated, Zhejiang achieved 6.606 billion m3, Shanghai achieved 3.040 billion m3, and Anhui achieved 4.319 billion m3, with a ranking of Jiangsu > Zhejiang > Anhui > Shanghai. The results detailed above prove that virtual water exerts an indispensable influence, and integrating virtual water can make the physical water rights allocation of transboundary rivers more equal and reasonable.
Diurnal Temperature Range and Its Response to Heat Waves in 16 European Cities—Current and Future Trends
Authors: Katavoutas George , Founda Dimitra , Varotsos Konstantinos V. , Giannakopoulos Christos
Published: August 2023

An important indicator of climate change is the diurnal temperature range (DTR), defined as the difference between the daily maximum and daily minimum air temperature. This study aims to investigate the DTR distribution in European cities of different background climates in relation to the season of the year, climate class and latitude, as well as its response to exceptionally hot weather. The analysis is based on long-term observational records (1961–2019) coupled with Regional Climate Model (RCM) data in order to detect any projected DTR trends by the end of the 21st century under intermediate and high emission greenhouse gases (GHGs) scenarios. The analysis reveals marked variations in the magnitude of DTR values between the cities, on the one hand, and distinct patterns of the DTR distribution according to the climate class of each city, on the other. The results also indicate strong seasonal variability in most of the cities, except for the Mediterranean coastal ones. DTR is found to increase during hot days and heat wave (HW) days compared to summer normal days. High latitude cities experience higher increases (3.7 °C to 5.7 °C for hot days, 3.1 °C to 5.7 °C for HW days) compared to low latitude cities (1.3 °C to 3.6 °C for hot days, 0.5 °C to 3.4 °C for HW days). The DTR is projected to significantly decrease in northernmost cities (Helsinki, Stockholm, Oslo), while it is expected to significantly increase in Madrid by the end of the 21st century under both the intermediate- and high-emission scenarios, due to the asymmetric temperature change. The asymmetrical response of global warming is more pronounced under the high-emission scenario where more cities at higher latitudes (Warsaw, Berlin, Rotterdam) are added to those with a statistically significant decrease in DTR, while others (Bucharest, Nicosia, Zurich) are added to those with an increase in DTR.
Machine Learning Techniques in Predicting Bottom Hole Temperature and Remote Sensing for Assessment of Geothermal Potential in the Kingdom of Saudi Arabia
Authors: Alqahtani Faisal , Ehsan Muhsan , Abdulfarraj Murad , Aboud Essam , Naseer Zohaib , El-Masry Nabil N. , Abdelwahed Mohamed F.
Published: August 2023

The global demand for energy is increasing rapidly due to population growth, urbanization, and industrialization, as well as to meet the desire for a higher standard of living. However, environmental concerns, such as air pollution from fossil fuels, are becoming limiting factors for energy sources. Therefore, the appropriate and sustainable solution is to transition towards renewable energy sources to meet global energy demands by using environmentally friendly sources, such as geothermal. The Harrat Rahat volcanic field, located in the western region of the Kingdom of Saudi Arabia (KSA), gets more attention due to its geothermal potential as a viable site for geothermal energy exploration due to its high enthalpy. The prime objective of this study is to present up-to-date and comprehensive information on the utilization of borehole temperature and remote sensing data to identify the most prospective zones with significant geothermal activity favorable for exploration and drilling. A brief description of the selected wells and the methodology used to determine the petrophysical parameters relevant to the geothermal potential assessment are presented. Special emphasis is given to gamma-ray ray and temperature logs for calculating heat production and the geothermal gradient. The effectiveness of various machine learning techniques are assessed throughout this study for predicting the temperature-at-depth to evaluate the suitability of employing machine learning models for temperature prediction, and it is found that XG Boost provided excellent results. It can be observed that some linear anomalies can be traced in the NW, trending on the west side of the Harrat volcanic field based on magnetic data interpretation. The land surface temperature in 2021 exhibited higher temperatures compared to 2000, suggesting potential volcanic activity in the subsurface. It is concluded that the integration of remote sensing data with subsurface data provides the most reliable results.
Short Food Supply Chain and Resilience: An Analysis during COVID-19 Pandemic in Inner Areas of Campania Region
Authors: Uliano Anna , Stanco Marcello , Nazzaro Concetta
Published: August 2023

COVID-19 pandemic highlighted food systems’ fragility, especially concerning global trade. On the other hand, the pandemic underlined the resilient character of Short Food Supply Chains (SFSCs), as citizen-consumers continued to buy through such sales channels, in order to limit virus spread, and farms developed innovations and rapid strategic responses. In a fragile context, such as that of inner areas, SFSC may represent, for farms, a significant competitive tool to increase their resilience. This study aims to investigate the degree of resilience of farms in inner areas of Campania Region (South Italy) and the role of SFSC during the pandemic. A questionnaire was administered to a sample of wineries in inner areas of Campania in order to detect their resilience score, which was measured, according to literature, through three variables: effectiveness, flexibility, and responsiveness. A linear regression was run to identify variables affecting farms’ resilience. Results show that both farmers’ and farms’ characteristics turned out to affect farms’ resilience score, such as the turnover, the use of SFSC, and farmers’ age and education. This study’s findings could be helpful for decision-making in the development of inner areas, and for providing targeted policies to manage unexpected events in different scenarios.
Green Eco-Innovation and Supply of Critical Metals: Evidence from China
Authors: Ruan Shengzhe , Song Yi , Cheng Jinhua , Zhan Cheng
Published: August 2023

Ensuring a green supply of critical metals is essential to achieve high-quality economic development and ecological security. Based on data from 2000 to 2019 on five critical metals in China (copper, aluminum, nickel, antimony, and titanium), this study employs a series of econometric methods, such as fixed-effects regression and moderating effects, to examine the influence of green eco-innovation on primary and recycling supply of critical metals, as well as the underlying mechanisms. The findings indicate: (1) Green eco-innovation has an inverted U-shaped impact on the primary supply of bulk critical metals, and it is currently in the declining phase of the curve. (2) Green eco-innovation has a U-shaped impact on the recycling supply of critical metals and the primary supply of minor critical metals. The former is currently in the rising phase of the curve, while the latter is in the declining phase. (3) The impact of green eco-innovation on the supply of critical metals is stronger in industrially underdeveloped regions. (4) The improvement of energy efficiency, optimization of energy structure, and strengthening of environmental regulations enhance the impact of green eco-innovation on the supply of critical metals. Finally, the optimization of the energy mix is conducive to securing the supply of key metals. This study provides a theoretical basis for utilizing green eco-innovation to achieve a green supply of critical metals and enriches the theoretical research on green eco-innovation.