bluuebunny/crossref_metadata_2025 huggingface dataset:
Positive Impact, Creativity, and Innovative Behavior at Work: The Mediating Role of Basic Needs Satisfaction
Authors: Papachristopoulos Konstantinos , Gradito Dubord Marc-Antoine , Jauvin Florence , Forest Jacques , Coulombe Patrick
Published: November 2023

In recent research, a growing body of empirical evidence suggests that prosocial impact at work can play a significant role in enhancing creativity and innovativeness. Drawing from self-determination theory, we hypothesized that basic psychological needs and benevolence satisfaction could serve as a mediating factor in the relation between an employee’s perceived social impact and innovative work behavior and creativity, thus illuminating the manner in which the contentment of psychological needs fosters inventive proclivities within the organizational milieu. Results from a study in Greece and Canada (N = 528) showed that both perceived social impact and prosocial motivation are positively associated with innovative work behavior and creativity while autonomy and competence satisfaction mediate the relation between perceived social impact and the work outcomes examined within this study. Moreover, prosocial motivation was found to moderate the relation between benevolence satisfaction and innovativeness. Findings extend prior research on the role of prosociality on creative behavior at work and provide supporting evidence for the organizations that encourage and support employees’ initiatives to make a positive difference in the lives of others.
Rheological Properties of Carbon Nanotube Infused Cementitious Composites with Various Amounts of CNT
Authors: Goldmann Eryk , Cygan Grzegorz , Klemczak Barbara , Górski Marcin
Published: November 2023

The addition of carbon nanotubes (CNTs), which are hydrophobic materials, significantly influences the rheology of cementitious materials but requires important mix modifications in order to provide proper flowability for further use. This paper investigates the influence of various dosages of carbon nanotubes (0.05 wt.%, 0.1 wt.%, 0.2 wt.%, 0.5 wt.%, and 1 wt.%) on the flowability, rheological parameters, air content, and volume density of cement mortars. The results show an increase in the yield stress parameter with an increment in CNT dosage up to the threshold of 0.5 wt.% for mixes with an increased amount of cement. For standard proportions, it was on a stable level for all mixes except for 0.2 wt.%. The plastic viscosity parameter also increased with the CNT dosage; mixes with standard proportions of components were not higher than the reference, and mixes with an increased amount of cement were lower than the reference for dosages up to 0.5 wt.% of CNT. The addition of a superplasticizer and modifications of the ratio of the components were employed to achieve proper flowability and measure the rheological parameters. The presented results show that regardless of the negative influence of carbon nanotubes on the properties of fresh mortar, it is possible to achieve a stable flow and workability using simple modifications of the composition.
Review of Emerging Technologies for Reducing Ergonomic Hazards in Construction Workplaces
Authors: Rahman Md Hadisur , Ghasemi Alireza , Dai Fei , Ryu JuHyeong
Published: November 2023

In the era of Industry 4.0, marked by the integration of digitization, automation, and data synthesis, emerging technologies play a vital role in mitigating ergonomic hazards within construction work environments. This study investigates the research trends encompassing the adoption of three categories of emerging technologies—(1) wearable sensors; (2) extended reality, which combines virtual reality (VR), augmented reality (AR), and mixed reality (MR); and (3) exoskeletons and robotics—as the means to mitigate the risk of occupational nonfatal injuries in the construction industry. Employing bibliometric and scientometric analyses, a quantitative examination of the relationship in the literature is performed. From the Scopus database, 347 papers were selected from a pool of 1603 publications from 2018 to 2022. The conducted scientometric analyses encompass annual publication trends, keyword co-occurrence analysis, journal-source analysis, author analysis, and country analysis using VOSviewer (version 1.6.19) and bibliometrix software (version 4.1.3). The findings highlight the crucial role of advanced technologies in enhancing safety and health management in the construction industry. Wearable sensors, for example, offer promising capabilities for real-time monitoring, potentially reducing the risk of onsite injuries by alerting workers to hazards. Extended reality, especially VR, can enhance the effectiveness of safety-training education by simulating realistic scenarios while minimizing exposures to hazardous conditions that workers may face onsite challenges. Furthermore, the integration of exoskeletons and robotics has the potential to reduce physical strain and injury risks among workers, particularly in physically demanding tasks. The review paper identifies current research trends in applying emerging technologies to occupational safety and health within the construction industry, while also suggesting future research directions in this dynamic field.
Column Link Behavior in Eccentrically Braced Composite 3-Dimensional Frames
Authors: Reena G. Celine , Gurupatham Beulah Gnana Ananthi , Tsavdaridis Konstantinos Daniel
Published: November 2023

Eccentrically braced frames are renowned for their capacity to absorb seismic forces while offering greater adaptability. These frames incorporate bracings that are joined to the beams with an intentional offset, forming a connection within the beams. Nevertheless, there are drawbacks associated with implementing these beam connections when renovating frames. This paper seeks to enhance the design approach by introducing an eccentric link within the column of a composite structure. Eccentric braced frames (EBFs) are hybrid systems that offer both ductility in moment resisting frames (MRFs) and lateral stiffening in the concentrically braced system. The study examines composite frames with 5, 10, and 15 stories using eccentric X- and V-type bracings with an eccentricity of 0.5 m and 1 m. Three different earthquake zones are considered, based on Indian seismic code provisions: zone 3, zone 4, and zone 5. The structures are analyzed computationally by nonlinear time history analyses. The lateral load-resisting behavior of the structure with the same eccentricity in beam links and column links is compared. Then, the structure is subjected to a pushover analysis to study the performance characteristics such as capacity curve, lateral displacement, inter-storey drift, and plastification of the structure. As anticipated, compared to conventional moment resisting frames (MRFs) and concentrically braced frames (CBFs), eccentrically braced frames have better energy dissipation. Furthermore, the behavior of X-braced column links is found to be similar to the performance of beam links, but V-braced frames showed better performance in column link frames than in beam link frames. Also, the increase of the link length played a major role in the ductility of the frames.
Development of a “Green” Emulsion with a Milk Protein Hydrolysate: An Evaluation of Rheology, Texture, In Vitro Bioactivity, and Safety
Authors: Vukašinović Mila , Pantelić Ivana , Savić Sanela , Cekić Nebojša , Vukašinović Sekulić Maja , Antić Stanković Jelena , Božić Dragana D. , Tošić Anđela , Tamburić Slobodanka , Savić Snežana D.
Published: November 2023

Bioactive peptides are promising cosmetic active ingredients that can improve skin health and appearance. They exhibit a broad spectrum of activity, including anti-aging, antioxidant, antimicrobial, and anti-inflammatory effects. The aim of this study was to develop a safe, stable, and efficacious environmentally friendly (“green”) emulsion using a milk protein hydrolysate as a model active ingredient. Potential emulsions were formulated with biodegradable emollients, stabilized with naturally derived mixed emulsifier, and prepared by cold process. They were evaluated for rheological behavior (continuous rotation and oscillation tests), physical stability (dynamic mechanical thermal analysis—DMTA test), and texture profiles, as well as cytotoxic, antioxidant, and antimicrobial effects. Rheological characterization revealed shear-thinning flow behavior with yield point from continuous rotation tests and predominantly elastic character from oscillation (amplitude and frequency sweep) tests, with small structural change detected in the DMTA test. These results implied satisfactory rheological properties and good stability. Texture analysis revealed acceptable spreadability and substantivity of the emulsions. The protein hydrolysate showed antioxidant activity. The developed emulsions showed low antibacterial activity against selected microorganisms, but this was due to the action of preservatives, not peptides. All potential emulsions showed a desirable safety profile. The results obtained provide the basis for the next stage of formulation development, i.e., in vivo efficacy tests.
AI-Enabled Fusion of Medical Imaging, Behavioral Analysis and Other Systems for Enhanced Autism Spectrum Disorder. Comment on Jönemo et al. Evaluation of Augmentation Methods in Classifying Autism Spectrum Disorders from fMRI Data with 3D Convolutional Neural Networks. Diagnostics 2023, 13, 2773
Authors: Giansanti Daniele
Published: November 2023

I am writing to you in regard to the research article “Johan Jönemo, David Abramian, and Anders Eklund—Evaluation of Augmentation Methods in Classifying Autism Spectrum Disorders from fMRI Data with 3D Convolutional Neural Networks” [...]
Usefulness of Saline Sealing in Preventing Pneumothorax after CT-Guided Biopsies of the Lung
Authors: Roman Andrei , Brozba Andreea , Necula Alexandru , Muntean Delia Doris , Kubelac Paul , Fekete Zsolt , Tomuleasa Ciprian , Csutak Csaba , Feier Diana , Pintican Roxana , Vlad Catalin
Published: November 2023

This study aimed to assess the effectiveness of saline sealing in reducing the incidence of pneumothorax after a CT-guided lung biopsy. This was a retrospective case-control study of patients who underwent CT-guided biopsies for lung tumors using 18 G semiautomatic core needles in conjunction with 17 G coaxial needles. The patients were divided into two consecutive groups: a historical Group A (n = 111), who did not receive saline sealing, and Group B (n = 87), who received saline sealing. In Group B, NaCl 0.9% was injected through the coaxial needle upon its removal. The incidence of pneumothorax and chest tube insertion was compared between the two groups. Multivariate logistic regression was performed to verify the contribution of other pneumothorax risk factors. The study included 198 patients, with 111 in Group A and 87 in Group B. There was a significantly (p = 0.02) higher pneumothorax rate in Group A (35.1%, n = 39) compared to Group B (20.7%, n = 18). The difference regarding chest tube insertion was not significant (p = 0.1), despite a tendency towards more insertions in Group A (5.4%, n = 6), compared to Group B (1.1%, n = 1). Among the risk factors for pneumothorax, only the presence of emphysema (OR = 3.5, p = 0.0007) and belonging to Group A (OR = 2.2, p = 0.02) were significant. Saline sealing of the needle tract after a CT-guided lung biopsy can significantly reduce the incidence of pneumothorax. This technique is safe, readily available, and inexpensive, and should be considered as a routine preventive measure during this procedure.
Risk Prediction Model for Chronic Kidney Disease in Thailand Using Artificial Intelligence and SHAP
Authors: Tsai Ming-Che , Lojanapiwat Bannakij , Chang Chi-Chang , Noppakun Kajohnsak , Khumrin Piyapong , Li Ssu-Hui , Lee Chih-Ying , Lee Hsi-Chieh , Khwanngern Krit
Published: November 2023

Chronic kidney disease (CKD) is a multifactorial, complex condition that requires proper management to slow its progression. In Thailand, 11.6 million people (17.5%) have CKD, with 5.7 million (8.6%) in the advanced stages and >100,000 requiring hemodialysis (2020 report). This study aimed to develop a risk prediction model for CKD in Thailand. Data from 17,100 patients were collected to screen for 14 independent variables selected as risk factors, using the IBK, Random Tree, Decision Table, J48, and Random Forest models to train the predictive models. In addition, we address the unbalanced category issue using the synthetic minority oversampling technique (SMOTE). The indicators of performance include classification accuracy, sensitivity, specificity, and precision. This study achieved an accuracy rate of 92.1% with the top-performing Random Forest model. Moreover, our empirical findings substantiate previous research through highlighting the significance of serum albumin, blood urea nitrogen, age, direct bilirubin, and glucose. Furthermore, this study used the SHapley Additive exPlanations approach to analyze the attributes of the top six critical factors and then extended the comparison to include dual-attribute factors. Finally, our proposed machine learning technique can be used to evaluate the effectiveness of these risk factors and assist in the development of future personalized treatment.
An Umbrella Review of the Fusion of fMRI and AI in Autism
Authors: Giansanti Daniele
Published: November 2023

The role of functional magnetic resonance imaging (fMRI) is assuming an increasingly central role in autism diagnosis. The integration of Artificial Intelligence (AI) into the realm of applications further contributes to its development. This study’s objective is to analyze emerging themes in this domain through an umbrella review, encompassing systematic reviews. The research methodology was based on a structured process for conducting a literature narrative review, using an umbrella review in PubMed and Scopus. Rigorous criteria, a standard checklist, and a qualification process were meticulously applied. The findings include 20 systematic reviews that underscore key themes in autism research, particularly emphasizing the significance of technological integration, including the pivotal roles of fMRI and AI. This study also highlights the enigmatic role of oxytocin. While acknowledging the immense potential in this field, the outcome does not evade acknowledging the significant challenges and limitations. Intriguingly, there is a growing emphasis on research and innovation in AI, whereas aspects related to the integration of healthcare processes, such as regulation, acceptance, informed consent, and data security, receive comparatively less attention. Additionally, the integration of these findings into Personalized Medicine (PM) represents a promising yet relatively unexplored area within autism research. This study concludes by encouraging scholars to focus on the critical themes of health domain integration, vital for the routine implementation of these applications.
Coexistence of Ehlers–Danlos Syndrome with Coronary–Pulmonary Arterial Fistula and Other Multiple Coronary Artery Anomalies
Authors: Gać Paweł , Jaworski Arkadiusz , Karwacki Jakub , Jarocki Michał , Ams Artur , Poręba Rafał
Published: November 2023

This case report presents a 34-year-old male with Ehlers–Danlos syndrome, type 2 diabetes mellitus, aortic valve regurgitation, and aortic bulb aneurysm. Following spine surgery for thoracic–lumbar stabilization, the patient underwent assessment for aortic bulb aneurysm and aortic valve replacement surgeries. Five months post spinal surgery, a coronary computed tomography angiography was performed. The coronary computed tomography angiography revealed unique findings, including the absence of the left main coronary artery, right coronary artery dominance, ectopic origin of the left circumflex artery from the right sinus of the valsalva, a coronary–pulmonary arterial fistula originating from the right sinus of the valsalva, and an additional right pulmonary vein. The patient was qualified for surgical treatment for an aortic bulb aneurysm, was informed about the high surgical risk, and is awaiting surgery. This case underscores the rarity of Ehlers–Danlos syndrome coexisting with multiple coronary artery anomalies. The presence of a coronary–pulmonary arterial fistula further emphasizes the need for specialized patient monitoring when Ehlers–Danlos syndrome and coronary anomalies converge.
Analysis of Online Learning Issues within the Higher Education Quality Assurance Frame: ‘Pandemic Lessons’ to Address the Hard Time Challenges
Authors: Lysenko Iryna , Verbytska Anna , Novomlynets Oleh , Stepenko Serhii , Dyvnych Hanna
Published: November 2023

Amid the challenges posed by the COVID-19 pandemic, this study conducts a rigorous analysis of the online learning landscape within higher education. It scrutinizes the manifold issues that emerged during the era of quarantine restrictions, investigating the perspectives and experiences of students and academic staff in this transformative educational paradigm. Employing a comprehensive suite of research methodologies, including content analysis, observation, comparative analysis, questionnaires, correlation studies, and statistical and graphical methods, this research unearths the substantial challenges faced by participants in online learning. It meticulously evaluates the advantages and limitations of this pedagogical shift during the pandemic, probing into satisfaction levels regarding the quality of online instruction and the psychological aspects of adapting to new learning environments. Moreover, this study offers practical recommendations to address the identified challenges and proposes solutions. The findings serve as invaluable insights for higher education management, particularly within the framework of quality assurance, equipping administrators with the requisite tools and strategies to confront the extraordinary challenges that have arisen in contemporary higher education. These lessons gleaned from the crucible of the pandemic’s trials also hold a unique promise. The results of this research are not confined to a singular crisis but carry a profound implication: the effective application of online learning, even under the most arduous conditions. These ‘pandemic lessons’ become the guiding light for resilient education in the face of any adversity.
Research on a High-Performance Rock Image Classification Method
Authors: Ma Mingshuo , Gui Zhiming , Gao Zhenji
Published: November 2023

Efficient and convenient rock image classification methods are important for geological research. They help in identifying and categorizing rocks based on their physical and chemical properties, which can provide insights into their geological history, origin, and potential uses in various applications. The classification and identification of rocks often rely on experienced and knowledgeable professionals and are less efficient. Fine-grained rock image classification is a challenging task because of the inherent subtle differences between highly confusing categories, which require a large number of data samples and computational resources, resulting in low recognition accuracy, and are difficult to apply in mobile scenarios, requiring the design of a high-performance image processing classification architecture. In this paper we design a knowledge distillation and high-accuracy feature localization comparison network (FPCN)-based learning architecture for generating small high-performance rock image classification models. Specifically, for a pair of images, we interact with the feature vectors generated from the localized feature maps to capture common and unique features, let the network focus on more complementary information according to the different scales of the objects, and then the important features of the images learned in this way are made available for the micro-model to learn the critical information for discrimination via model distillation. The proposed method improves the accuracy of the micro-model by 3%.
AIBPSF-IoMT: Artificial Intelligence and Blockchain-Based Predictive Security Framework for IoMT Technologies
Authors: Alshammari Bandar M.
Published: November 2023

The latest advancements in artificial intelligence (AI) technologies, including machine and deep learning models, in prediction, recommending, and automating processes have greatly impacted IoT devices in general, and protect them from cyberattacks in particular. Blockchain also has features that assist in creating more secure IoT devices due to its abilities of traceability, acceptability, and trust. This paper studies the current advancements in the IoT and blockchain, their architectures, and their effect on security. The paper proposes a novel framework that takes into consideration the advantages and benefits of machine/deep learning models and blockchain in order to provide a solution that makes IoT devices more secure. This framework is based on the IoT four-layer architecture, and it aims to enhance the way IoT devices detect and recognise cyberattacks using blockchain and machine/deep learning algorithms. Machine and deep learning algorithms are responsible for detecting security attacks in the IoT, based on their patterns. The blockchain platform is responsible for verifying whether a specific request is secure, and it also uses cryptography to sign all new requests in order to recognise them in future requests. The MQTTset dataset, which is contains data associated with intrusion detection cases, has been used to implement a case study that aims to prove the validity of this framework. Various machine and deep learning algorithms have been used in this case study which have all achieved high results with regard to precision, recall, accuracy, and F1 performance measurements. Such results have proven the validity and reliability of the proposed framework to detect and predict new attacks before their requests are processed within a particular IoT system.
Semantic Segmentation Algorithm Fusing Infrared and Natural Light Images for Automatic Navigation in Transmission Line Inspection
Authors: Yuan Jie , Wang Ting , Huo Guanying , Jin Ran , Wang Lidong
Published: November 2023

Unmanned aerial vehicles (UAVs) are widely used in power transmission line inspection nowadays and they need to navigate automatically by recognizing the category and accurate position of transmission pylon equipment in line inspection. Semantic segmentation is an effective method for recognizing transmission pylon equipment. In this paper, a semantic segmentation algorithm that fuses infrared and natural light images is proposed. A cross-modal attention interaction activation mechanism is adopted to fully exploit the complementation between natural light and infrared images. Firstly, a global information block with a feature pyramid structure is used to deeply mine and fuse multi-scale global contextual information of fused features, and then the block is used to conduct feature aggregation in the decoding processing, and enough aggregation with multi-scale features of infrared and natural light images is used to enhance the expression ability of the model and improve the accuracy of semantic segmentation of transmission pylon equipment in complex scenes. Our method guides the process of low-level up-sampling and restoration by denser global and high-level features. Experimental results on a dataset of transmission pylon equipment collected by us show that the proposed method achieved better semantic segmentation results than the state-of-the-art methods.
Design, Implementation and Evaluation of Deusto XRL, a Reference Architecture for Extended Remote Laboratories (XRLs)
Authors: Silva Isabela Nardi da , García-Zubía Javier , Hernández-Jayo Unai , Alves João Bosco da Mota , Dandolini Gertrudes Aparecida
Published: November 2023

Extended Remote Laboratories (XRLs) have emerged as a potential solution to address the lack of science laboratories in educational institutions, a deficit that hinders the attainment of a Quality Education outlined in the 2030 Agenda for Sustainable Development. By integrating physical assets with Extended Reality (XR) technologies, XRLs provide an immersive remote laboratory experience. This study presents the development of Deusto XRL, a reference architecture for XRLs, and investigates User eXperience (UX) via a survey involving 150 high-school students. The participants were divided into groups and exposed to different online laboratory experiences: traditional Remote Laboratories (RLs) and XRLs. Feedback scores from the UX survey indicated that the XRLs received the highest ratings, reflecting a positive UX. The results highlight the potential of XRLs to enhance the accessibility and quality of science education, enabling students to engage in immersive learning experiences despite physical laboratory limitations. Future research endeavors will focus on exploring the educational implications and learning outcomes associated with XRL technology. By further investigating the pedagogical aspects of XRLs, this study aims to uncover their potential benefits and educational value. Implementing XRLs in educational settings can revolutionize science education, providing students with interactive and engaging opportunities to deepen their understanding of scientific concepts.
Detection of Vulnerabilities in Smart Buildings Using the Shodan Tool
Authors: Mulero-Palencia Sofía , Monzon Baeza Victor
Published: November 2023

Smart buildings, integral components of modern urban landscapes, are confronted with diverse vulnerabilities that jeopardize system robustness, cybersecurity, data confidentiality, and the well-being of the occupants. This work aimed to identify and evaluate vulnerabilities specific to smart buildings, introducing an innovative assessment approach leveraging the Shodan tool. The analysis comprised three stages: information collection, result extraction using Shodan, and vulnerability identification, culminating in a comprehensive evaluation. This study pioneers the use of Shodan for smart building vulnerability detection, together with databases and associated nomenclature, to serve as a robust foundational tutorial for future research. The findings yielded a meticulous analysis of primary security risks inherent in building systems, advocating for implementing targeted measures to mitigate potential impacts. Additionally, this study proposes an evaluation methodology encompassing metrics to gauge the effect of vulnerabilities on integrity, availability, and scope. By addressing insecure configurations, deployment inadequacies, and suboptimal cybersecurity practices, this framework fortifies smart buildings against potential threats. This study’s originality lies in its Shodan-centric framework, revolutionizing the approach to smart building applications and vulnerability detection. This research contributes to the field by identifying critical vulnerabilities and proposing effective mitigation strategies, thereby elevating the overall security and safety of interconnected smart spaces.
Android Malware Detection Based on Behavioral-Level Features with Graph Convolutional Networks
Authors: Xu Qingling , Zhao Dawei , Yang Shumian , Xu Lijuan , Li Xin
Published: November 2023

Android malware detection is a critical research field due to the increasing prevalence of mobile devices and apps. Improved methods are necessary to address Android apps’ complexity and malware’s elusive nature. We propose an approach for Android malware detection based on Graph Convolutional Networks (GCNs). Our method focuses on learning the behavioral-level features of Android applications using the call graph extracted from the application’s Dex file. Combining the call graph with sensitive permissions and opcodes creates a new subgraph representing the application’s runtime behavior. Subsequently, we propose an enhanced detection model utilizing graph convolutional networks (GCNs) for Android malware detection. The experimental results demonstrate our proposed method’s high precision and accuracy in detecting malicious code. With a precision of 98.89% and an F1-score of 98.22%, our approach effectively identifies and classifies Android malicious code.
Wireless Power Transfer System with Current-Doubler Rectifier on the Secondary Side—Analysis, Modeling, and Verification
Authors: Kindl Vladimir , Zavrel Martin , Tyrpekl Miroslav , Frivaldsky Michal , Skorvaga Jakub
Published: November 2023

In this paper, the proposal for the performance optimization of the wireless power transfer (WPT) system is given. The solution is based on the alternative configuration of the secondary-side rectifier. It is represented by a diode rectifier with a current doubler. Compared to the bridge rectifier, two diodes are replaced by the inductors. Initially, a system analysis was performed to investigate the electrical behavior and find the most optimal conditions referred to as terms of efficiency performance at nominal power. Due to this requirement, the rectifier inductors must be designed accordingly to meet this condition. The experimental verification was realized as well, while the proposed solution was compared to other common alternatives of the secondary-side rectification. The load sensitivity analysis in terms of efficiency performance was realized as well to observe the system behavior for a wide operation range. From the results, it is seen that the proposed alternative of the secondary-side rectification of the WPT system gives promising results in terms of high operating efficiency.
Prediction of Matching Prices in Electricity Markets through Curve Representation
Authors: Foronda-Pascual Daniel , Alonso Andrés M.
Published: November 2023

In the Spanish electricity market, after the daily market is held in which prices are set for the next day, the secondary and tertiary markets take place, which allow companies more accurate adjustment of the electricity they are able to offer. The objective of this paper is to predict the final price reached in these markets by predicting the supply curve in advance, which is the aggregate of what companies offer. First, we study a procedure to represent the supply curves, and then we consider different machine learning approaches to obtain the day-ahead supply curves for the secondary market. Finally, the predictions of the supply curves are crossed with the system requirements to obtain the expected price predictions. Histogram-Based Gradient Boosting is the best performing algorithm for predicting supply curves. The most relevant variables for the prediction are the lagged values, the daily market price, the price of gas and values of the wind recorded in the Spanish provinces.
Day-Ahead Hourly Solar Photovoltaic Output Forecasting Using SARIMAX, Long Short-Term Memory, and Extreme Gradient Boosting: Case of the Philippines
Authors: Benitez Ian B. , Ibañez Jessa A. , Lumabad Cenon III D. , Cañete Jayson M. , Principe Jeark A.
Published: November 2023

This study explores the forecasting accuracy of SARIMAX, LSTM, and XGBoost models in predicting solar PV output using one-year data from three solar PV installations in the Philippines. The research aims to compare the performance of these models with their hybrid counterparts and investigate their performance. The study utilizes the adjusted shortwave radiation (SWR) product in the Advanced Himawari Imager 8 (AHI-8), as a proxy for in situ solar irradiance, and weather parameters, to improve the accuracy of the forecasting models. The results show that SARIMAX outperforms LSTM, XGBoost, and their combinations for Plants 1 and 2, while XGBoost performs best for Plant 3. Contrary to previous studies, the hybrid models did not provide more accurate forecasts than the individual methods. The performance of the models varied depending on the forecasted month and installation site. Using adjusted SWR and other weather parameters, as inputs in forecasting solar PV output, adds novelty to this research. Future research should consider comparing the accuracy of using adjusted SWR alone and combined with other weather parameters. This study contributes to solar PV output forecasting by utilizing adjusted satellite-derived solar radiation, and combining SARIMAX, LSTM, and XGBoost models, including their hybrid counterparts, in a single and comprehensive analysis.
Comparing the Utility of Coupled Aero-Hydrodynamic Analysis Using a CFD Solver versus a Potential Flow Solver for Floating Offshore Wind Turbines
Authors: Siddiqui Mohd Atif , Hanssen Finn-Christian Wickmann , Greco Marilena , Anda Eirik
Published: November 2023

There has been a great effort towards development of renewable energy systems to combat global warming with significant interest towards research and development of floating offshore wind turbines (FOWTs). With commercial projects such as Hywind Scotland, Hywind Tampen and others, there is a shift of industry attention from bottom-fixed offshore turbines to FOWTs. In this work, we focus on comparing industry standard Potential Flow (PF) methods versus Computational Fluid Dynamics (CFD) solvers for a scaled version of the IEA 15 MW turbine and associated FOWT system. The results from the two solvers are compared/validated using experimental thrust values for the fixed turbine. The motions and the thrust for the FOWT system are then compared for the two solvers along with hydrodynamic properties of the floater hull. The wake features downstream of the turbine are analyzed for the fixed and floating turbine using the CFD solver. The wake from the CFD solver is also compared with a simplified PF model. Finally, a simplified cost-benefit analysis is presented for the two solvers to compare the usefulness and utility of a CFD solver as compared to presently used industry-standard PF methods.
Succinate Enhances Lipolysis and Decreases Adipocytes Size in Both Subcutaneous and Visceral Adipose Tissue from High-Fat-Diet-Fed Obese Mice
Authors: Ji Tengteng , Fang Bing , Zhang Ming , Liu Yaqiong
Published: November 2023

Obesity is a risk factor for many chronic diseases related to the overexpansion of adipose tissue during obesity, leading to metabolic dysfunction and ectopic lipids. Previous studies reported a close relationship between succinate and obesity and its co-morbidities, and studies have also reported on its anti-obesity potential. To confirm its efficacy in obesity interventions, we supplemented mice with obesity induced by a high-fat diet with succinate (1.5% m/v in drinking water) for 11 weeks without changing the diet. After succinate supplementation, the changes in body weight, adipose tissue deposition, glucose tolerance, energy expenditure and lipid metabolism were evaluated. It was found that succinate supplementation significantly decreased subcutaneous adipose tissue (HFD: 4239.3 ± 211.2 mg; HFD-SA: 3268.9 ± 265.7 mg. p < 0.05), triglyceride contents (decreased by 1.53 mmol/g and 0.39 mmol/g in eWAT and ingWAT, respectively, p < 0.05) and NEFA (decreased by 1.41 μmol/g and 1.31 μmol/g in eWAT and ingWAT, respectively, p < 0.05). The adipocytes’ sizes all significantly decreased in both subcutaneous and visceral adipose tissue (the proportion of adipocytes with diameters larger than 100 μm in eWAT and ingWAT decreased by 16.83% and 11.96%, respectively. p < 0.05). Succinate significantly enhanced lipolysis in adipose tissue (eWAT: Adrb3, Hsl and Plin1; ingWAT: Hsl and CPT1a; p < 0.05), whereas the expression of lipogenesis-related genes remained unchanged (p > 0.05). Succinate supplementation also enhanced the activity of BAT by stimulating the expression of Ucp1 and Cidea (p < 0.05). Our results reported that succinate has a potential beneficial effect on obesity pathogenesis but cannot efficiently decrease bodyweight.
Use of Human Intestinal Enteroids for Recovery of Infectious Human Norovirus from Berries and Lettuce
Authors: Wales Samantha , Kulka Michael , Keinard Brianna , Ngo Diana , Papafragkou Efstathia
Published: November 2023

Norovirus (NoV) is the leading cause of viral foodborne gastroenteritis globally. Currently, the gold standard for detecting NoV in clinical, food, and environmental samples is via molecular-based methods, primarily RT-PCR. Nevertheless, there is a great need for confirmatory assays that can determine the infectivity of viral particles recovered from contaminated matrices. The use of the human intestinal enteroids system (HIEs) has allowed for the expansion of norovirus replication, although it still suffers from limitations of strain preferences and the requirement of high titer stocks for infection. In this study, we wanted to explore the feasibility of using the HIEs to support the replication of NoV that had been recovered from representative food matrices that have been associated with foodborne illness. We first confirmed that HIEs can support the replication of several strains of NoV as measured by RT-qPCR. We subsequently chose two of those strains that reproducibly replicated, GII.4 and GII.6, to evaluate in a TCID50 assay and for future experiments. Infectious NoV could be recovered and quantified in the HIEs from lettuce, frozen raspberries, or frozen strawberries seeded with high titers of either of these strains. While many experimental challenges still remain to be overcome, the results of this study represent an important step toward the detection of infectious norovirus from representative produce items.
Evaluation of the Quality and Lipid Content of Artisan Sausages Produced in Tungurahua, Ecuador
Authors: Pérez Lander , Pincay Rosa , Salazar Diego , Flores Nelly , Escolastico Consuelo
Published: November 2023

The consumption of sausage worldwide increases every year; because of this increase, artisanal products have appeared and are intended to be perceived as natural and healthy. Obesity and cardiovascular diseases associated with consuming meat and meat derivatives have been estimated to be the leading cause of death in several countries. This study aimed to evaluate the nutritional quality, lipid content, and presence of saturated and unsaturated fatty acids, contributing to demonstrating the real nutritional value of artisanal sausages produced in Ecuador. Sausages from 10 factories in Ambato, Pelileo, and Píllaro, located in Tungurahua, Ecuador, were evaluated. The pH and acidity, color, proximal, sensory, microbiological, and lipid content were assessed. The pH and acidity showed a slight variation in all of the samples. Proximal analysis (moisture, protein, fat, and ash) established that the artisan sausages did not differ from the type of sausages reported in the literature. Microbiological analyses showed a good microbial quality, and there was no presence of Staphylococcus aureus, Enterobacteria, molds, or yeasts. The sensory attributes were similar for all of the sausages; the panelists did not notice any strange taste or odor. The lipid content showed that the artisanal sausages contained the highest percentage of palmitic, stearic, elaidic, and linolelaidic fatty acids. Unsaturated fatty acids were the most prevalent in all of the sausages collected from different locations. The results showed that the nutritional, microbiological, and sensory quality of the artisanal sausages did not show any parameter that would allow them to be classified as different or as having a better nutritional value.
U.S. Consumers’ Tree Nut Food Safety Knowledge, Perceptions, and Handling Practices across Demographic Groups
Authors: Swinehart Maeve Riley , Feng Yaohua
Published: November 2023

Tree nuts are often perceived as presenting a low risk for foodborne illness, despite their association with several foodborne outbreaks and recalls in recent years. An online survey was designed to assess how consumers’ food safety knowledge, perception of risks and benefits, and preferred sources for food safety information influence their tree nut handling behavior. Participants (n = 981) who soaked tree nuts or prepared nut-based dairy analogs (NBDAs) at home completed the survey. Their responses indicated insufficient knowledge about potential contaminations of tree nuts. Only 25% of participants had heard of a tree nut-related outbreak or recall. Few (30%) participants perceived a risk of contracting a foodborne illness from tree nuts. The participants were more concerned with the health benefits than potential microbial risks of raw tree nuts and preferred government agencies for tree nut food safety information. Based on a cluster analysis, demographics with lower food safety knowledge and risk perceptions (ages 18–24 or 45 and above, female, suburban and rural communities, have less than a bachelor’s degree, and earned less than USD 100,000 annually) tended to engage in risky tree nut handling practices (p < 0.05). The findings of this study support the development of audience-targeted food safety extension materials for tree nuts.
Essential Trace and Toxic Element Content in Lacaune Sheep Milk during Lactation
Authors: Antunović Zvonko , Mioč Boro , Novoselec Josip , Širić Ivan , Držaić Valentino , Klir Šalavardić Željka
Published: November 2023

The aim of this study was to investigate the concentrations of essential trace and toxic elements in the milk of Lacaune sheep during lactation in intensive rearing systems. This research was conducted with 30 Lacaune sheep that were monitored in the early (60 days of lactation), medium (120 days of lactation), and late (180 days of lactation) stages of lactation. The sheep were fed a pelleted feed mixture (1 kg/day), a cereal mixture (600 g/day), and alfalfa hay (ad libitum). The essential (Fe, Zn, Cu, Co, Mn, Mo, Se, Cr, and Ni) and toxic element (heavy metals: Cd, Pb, As, and Hg) concentrations in the feed and milk were determined using an inductively coupled plasma mass spectrometer. Significant variations in the main essential trace and toxic elements, except for the Mo, Se, Ni, As, and Hg concentrations, were found in the milk of Lacaune sheep during lactation. As lactation progressed, in the late stage of lactation, significantly higher concentrations of Co, Mn, Mo, Cr, and Pb were found, while Zn and Cu in the milk of Lacaune sheep decreased significantly (4.15 and 0.21 mg/kg) compared to their concentrations in the early stage of lactation (5.66 and 0.43 mg/kg). Significantly lower concentrations of Fe and higher concentrations of Cd were found in the medium stage (0.23 mg/kg and 1.08 µg/kg) of lactation compared to both the early and late stages of lactation. An analysis of the correlation coefficients between the essential trace and toxic elements in Lacaune sheep milk during lactation determined a significantly positive correlation between Fe:Cr, Fe:Mn, Fe:Co, Fe:Se, Zn:Ni, Zn:Se, Cr:Mn, Cr:Co, Cr:Se, Cr:Mo, Mn:Co, Mn:Pb, Co:Ni, Co:Se, Ni:Se, Se:Mo, Se:Pb, and Cd:Pb. A significantly negative correlation was also found between Cu:Mn, Zn:Mo, Cg:Hg, and Hg:Pb. Based on the obtained results, it is recommended that the influence of the stage of lactation, as well as the breed of sheep, should be included when designing experiments. In general, sheep milk is rich in essential trace elements, but it also contains a very low content of toxic elements, which provides justification for increasing the breeding of Lacaune sheep and indicates the convenience of consuming their milk without risking the consumer’s health.
Comparison of Biological Activities and Protective Effects on PAH-Induced Oxidative Damage of Different Coffee Cherry Pulp Extracts
Authors: Preedalikit Weeraya , Chittasupho Chuda , Leelapornpisid Pimporn , Potprommanee Siriporn , Kiattisin Kanokwan
Published: November 2023

Polycyclic aromatic hydrocarbons (PAHs) are the main toxic components of ambient air particulate matter (PM), causing oxidative damage to the skin and ultimately resulting in skin aging. This study was conducted to determine the anti-oxidant, anti-aging properties and protective effects of the extracts of coffee cherry pulp (Coffea arabica L.), which is a by-product of the coffee industry, against the oxidative damage induced by PAH exposure in human epidermal keratinocytes (HaCaT). Three different techniques were used to extract the coffee cherry pulp: maceration, Soxhlet and ultrasonication to obtain CCM, CCS and CCU extract, respectively, which were then compared to investigate the total phenolic content (TPC) and total flavonoid content (TFC). The chemical compositions were identified and quantified using high-performance liquid chromatography (HPLC). The results demonstrated that Soxhlet could extract the highest content of chlorogenic acid, caffeine and theophylline. CCS showed the significantly highest TPC (324.6 ± 1.2 mg GAE/g extract), TFC (296.8 ± 1.2 mg QE/g extract), anti-radical activity against DPPH free radicals (98.2 ± 0.8 µM Trolox/g extract) and lipid peroxidation inhibition (136.6 ± 6.2 µM Trolox/g extract). CCS also showed the strongest anti-aging effects based on collagenase, elastase, hyaluronidase and tyrosinase inhibitory enzymes. In addition, CCS can protect human keratinocyte cells from PAH toxicity by increasing the cellular anti-oxidant capacity. This study suggests that CCS has the potential to be used as a cosmetic material that helps alleviate skin damage caused by air pollution.
The Influence of the Vacuum Impregnation, Beetroot Juice, and Various Drying Methods on Selected Properties of Courgette and Broccoli Snacks
Authors: Kręcisz Magdalena , Stępień Bogdan , Łyczko Jacek , Kamiński Piotr
Published: November 2023

The drying process is used in the food industry to extend the shelf life of fruits and vegetables without the use of preservatives. As quality, visual, and aroma characteristics are important determinants of consumer interest, they play a key role in the development of new foods. In the present study, vacuum impregnation (VI) was used prior to vacuum drying (VD) and freeze drying (FD) of courgette and broccoli. Organic beet juice was used to produce the novel snacks. The study showed that the use of vacuum impregnation significantly affected the VOCs profile (volatile organic compounds profile), in which the following compounds were found: viz: 2-(E)-hexen-1-ol, 2-(Z)-hexen-1-ol and aceto-phenone. VI caused a decrease in volumetric gel index (VGI), drying shrinkage (S), water activity (AW), decreased color saturation (∆C), and increased dry matter content (DM). All these properties testify to the positive effect of the pretreatment used. The drying methods used had a significant effect on the properties of the dried vegetables. The dries obtained by the FD method showed higher density and water activity, as well as better preserved color (lower ∆E) and higher VOCs, so it is considered that freeze drying is a suitable method for obtaining novel courgette and broccoli snacks.
Correlation Analysis between Volatile Compounds and Quality Attributes in Pork Tenderloin in Response to Different Stir-Frying Processes
Authors: Wang Ziqiang , Nie Tianjie , Zhang Huiying , Wang Wenqian , Chen Haitao , Wang Shuqi , Sun Baoguo
Published: November 2023

Volatile compounds and physicochemical properties of meat are significantly changed by cooking processes. This study explored the influence of different stir-frying temperatures and times on the dynamic changes of the physicochemical characteristics and volatiles of pork tenderloin and determined the correlation between them. Results showed that time played more of a role than temperature. At the same temperature, the water content decreased (p < 0.05) and the cooking loss increased (p < 0.05) with stir-frying time extending. The L* value and the b* value showed first an increasing and then decreasing trend (p < 0.05), while the a* value significantly increased (p < 0.05). The higher the cooking temperature of sample, the faster the indexes changed. In stir-fried samples, 50 volatiles were identified. Correlation analysis showed that among the quality attributes, b* value and water content had the strongest impact on volatiles. The water content was negatively correlated with most of the compounds attributed to the desired aroma of stir-fried samples, while the correlation between the b* value and these volatiles was positive. Hence, changes in the types and contents of volatiles in stir-fried pork tenderloin could be predicted by detection of b* value and water content.