bluuebunny/crossref_metadata_2025 huggingface dataset:
Late Deep Fusion of Color Spaces to Enhance Finger Photo Presentation Attack Detection in Smartphones
Authors: Marasco Emanuela , Vurity Anudeep
Published: November 2022

Finger photo recognition represents a promising touchless technology that offers portable and hygienic authentication solutions in smartphones, eliminating physical contact. Public spaces, such as banks and staff-less stores, benefit from contactless authentication considering the current public health sphere. The user captures the image of their own finger by using the camera integrated in a mobile device. Although recent research has pushed boundaries of finger photo matching, the security of this biometric methodology still represents a concern. Existing systems have been proven to be vulnerable to print attacks by presenting a color paper-printout in front of the camera and photo attacks that consist of displaying the original image in front of the capturing device. This paper aims to improve the performance of finger photo presentation attack detection (PAD) algorithms by investigating deep fusion strategies to combine deep representations obtained from different color spaces. In this work, spoofness is described by combining different color models. The proposed framework integrates multiple convolutional neural networks (CNNs), each trained using patches extracted from a specific color model and centered around minutiae points. Experiments were carried out on a publicly available database of spoofed finger photos obtained from the IIITD Smartphone Finger photo Database with spoof data, including printouts and various display attacks. The results show that deep fusion of the best color models improved the robustness of the PAD system and competed with the state-of-the-art.
Effects of Cadmium Exposure on Lactating Mice and Rats: A Systematic Review of Breastfeeding Experiments
Authors: Araujo-Padilla Xelha , Briseño-Bugarín Jorge , López-Luna Argelia , Flores de la Torre Juan Armando
Published: November 2022

Cadmium (Cd) is a widely spread pollutant in the environment and its identification in human breast milk has caused concern. Children are particularly vulnerable, since their detoxification mechanism is not fully developed and their organs still being formed. Human- and animal-based studies demonstrate health issues and adverse pregnancy outcomes related to prenatal and postnatal Cd exposure. However, investigations of the effects, mechanisms, and treatments are still required. Thus, this systematic review compiled studies of Cd effects on lactating mice and rats focusing on experimental features and reported effects. The search was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA), using PubMed, SciELO, and ScienceDirect databases. After identification, screening, and inclusion process 52 research articles were recovered and data, such as animal strain, metal compound, dose or concentration, administration route, exposure time, litter size normalization, lactation days, organs or samples examined, and effects reported were organized in tables. Three general schemes of Cd exposure on dams were identified: pregnancy, lactation, or pregnancy-lactation. The effects evaluated included neurotoxicity, sexual maturation, biochemical parameters, and Cd transfer and retention. Experimental features most common were Cd exposure during the pregnancy–lactation scheme and Cd administration by drinking water, while the most evaluated effect on offspring was Cd-neurotoxicity.
Effect of Residual Stress in Surface Layer on Plastic Yield Inception
Authors: Zhou Shujun , Huang Xiao , Wu Wei , Yang Yue
Published: November 2022

This study aimed to acquire a comprehensive explanation on how the residual stress in the surface layer affects the contact behavior of solids. Plastic yield inception of residual stressed surface layer/substrate system during contact is simulated using the finite element method with the software ANSYS Workbench. The critical loads and locations for yield inception were acquired for contact systems with different residual stress levels and different surface layer thicknesses. Results show that the residual stress in the surface layer has little influence on the stress field in the substrate during contact. The influence of the residual stress on the critical yield load is mainly due to variations in the stress field in surface layer. A moderate compressive residual stress is preferable for increasing the critical yield load. An optimal value of compressive residual stress of 60% of the yield strength of surface layer was found to increase the critical yield load. The surface layer thickness and residual stress determine the yield inception location and the critical load of the contact system jointly.
An Intelligent Approach for Fair Assessment of Online Laboratory Examinations in Laboratory Learning Systems Based on Student’s Mouse Interaction Behavior
Authors: Hassan Hosny Hadeer A. , Ibrahim Abdulrahman A. , Elmesalawy Mahmoud M. , Abd El-Haleem Ahmed M.
Published: November 2022

The COVID-19 pandemic has made the world focus on providing effective and fair online learning systems. As a consequence, this paper proposed a new intelligent, fair assessment of online examinations for virtual and remotely controlled laboratory experiments running through Laboratory Learning Systems. The main idea is to provide students with an environment similar to being physically present in a Laboratory while conducting practical experiments and exams and detecting cheating with high accuracy at a minimal cost. Therefore, an intelligent assessment module is designed to detect cheating students by analyzing their mouse dynamics using Artificial Intelligence. The mouse interaction behavior method was chosen because it does not require any additional resources, such as a camera and eye tribe tracker, to detect cheating. Various AI algorithms, such as KNN, SVC, Random Forest, Logistic Regression, XGBoost, and LightGBM have been used to classify student mouse behavior to detect cheating, and many metrics are used to evaluate their performance. Moreover, experiments have been conducted on students answering online laboratory experimentations while cheating and when answering the exams honestly. Experimental results indicate that the LightGBM AI algorithm achieves the best cheat detection results up to an accuracy of 90%, precision of 88%, and degree of separation of 95%.
A New Approach for Failure Modes, Effects, and Criticality Analysis Using ExJ-PSI Model—A Case Study on Boiler System
Authors: Patil Suyog S. , Bewoor Anand K. , Patil Rajkumar B. , Kumar Ravinder , Ongar Bulbul , Sarsenbayev Yerlan , PraveenKumar Seepana , Ibrahim Ahmed Mohamed Mahmoud , Alsoufi Mohammad S. , Elsheikh Ammar
Published: November 2022

Failure Modes, Effects, and Criticality Analysis are widely used to assess the failure modes of a system, along with their causes and effects. Several Multi-Criteria Decision-Making approaches have been developed to overcome the limitations of the traditional FMECA. In these approaches, several multiple criteria are considered to determine the criticality values and assign criticality ranks. In these developed approaches, only one expert can involve in criticality analysis. By incorporating several experts from design, manufacturing, and maintenance domains in the criticality analysis accuracy of the results can be improved. This paper proposes a new integrated Expert Judgment-based Preference Section Index (ExJ-PSI) model that combines MCDM approaches and integrates expert opinions. The proposed model is applied to a boiler system used in the textile industry. The results are compared with those obtained from the conventional FMECA and normalized median method. In the present study, the opinions of seven experts from various domains and organizations are discussed. To normalize the collected data, one scale is developed by considering four expert criteria, such as the experience of the expert, the number of boilers he is handling, the number of employees under his supervision, and the proficiency in the field. The proposed model is more effective and flexible in handling and analyzing data of complex configured systems consisting of many subsystems, components, and failure modes. The analysis reveals that the feed water pump, feed water pump motor, supply water temperature sensor, return water temperature sensor, header, and coal feed motor are some of the most critical components of the boiler system.
Kernel Search for the Capacitated Vehicle Routing Problem
Authors: Borčinová Zuzana
Published: November 2022

This paper addresses the Capacitated Vehicle Routing Problem (CVRP), which is a widely studied optimization problem due to its relevance to the field of transportation, distribution, and logistics. We present a matheuristic method for CVRP that adopts the main idea of the Kernel Search algorithm (KS) based on decomposing the original problem into sub-problems that are easier to solve. Unlike the original scheme of KS, our approach uses the Clarke–Wright savings algorithm to construct a sequence of smaller sub-problems, which are subsequently solved using mathematical programming strategies. The computational experiments performed on a set of benchmark instances showed that the proposed matheuristics achieves good results in acceptable computational time.
Valorization of Agro-Industrial Wastes and Residues through the Production of Bioactive Compounds by Macrofungi in Liquid State Cultures: Growing Circular Economy
Authors: Pilafidis Sotirios , Diamantopoulou Panagiota , Gkatzionis Konstantinos , Sarris Dimitris
Published: November 2022

Vast quantities of side streams produced worldwide by the agricultural and food industry present an environmental challenge and an opportunity for waste upcycling in the frame of the circular bioeconomy. Fungi are capable of transforming lignocellulosic residues and wastes into a variety of added-value compounds with applications in functional food products, pharmaceuticals, chemicals, enzymes, proteins, and the emerging sector of nutraceuticals. The liquid state culture of fungi is an efficient and potentially scalable and reproducible biotechnological tool that allows the optimized production of fungal metabolites. Particularly, the utilization of agro-industrial by-products, residues, and wastes as a substrate for the liquid culture of macrofungi is suggested as an attainable solution in the management of these streams, contributing to climate change mitigation. This review presents recently published literature in the field of liquid state cultures of macrofungi using agro-industrial side streams, the different substrates, methods, and factors affecting their growth and metabolite production, as well as their applications, focusing on the variety of natural valuable compounds produced.
Assessment of Coarse, Fine, and Ultrafine Particles in S-Bahn Trains and Underground Stations in Stuttgart
Authors: Samad Abdul , Arango Kathryn , Alvarez Florez Diego , Chourdakis Ioannis , Vogt Ulrich
Published: November 2022

The Stuttgart S-Bahn network comprises six subway lines and is used by approximately 425,000 people on a daily basis. In previous studies in other cities, it was found that subways can be a source and collection point of particulate matter (PM), which is detrimental to human health. This study focused on making an initial assessment of the pollution situation inside the trains and on the underground platforms. Real-time measurements were performed with high time-resolution instruments inside the S-Bahn trains, two underground stations, and two outdoor stations in the Stuttgart subway network in November 2019. Firstly, the variation in concentration inside the train as it traveled through the tunnel was investigated, and it was recurrently observed that the pollutant concentration in the train increased while traveling through the tunnel and then decreased when nearing the tunnel exit. Secondly, the measurement location with the highest particulate matter concentrations was determined. The particulate matter concentrations on underground platforms were higher than those on the train and on the outdoor platforms. In addition, the dominant fraction of the particulate matter measured was in the range of ultrafine particles (UFP). Finally, the wind speed and wind direction data were analyzed in conjunction with specific locations along the platforms. From the wind measurement results, it was assumed that the combined airflows led to higher particle resuspension and particulate matter concentrations in these areas. In conclusion, it was determined that subway users were exposed to higher particle concentrations, particularly UFP (10–116 nm), while standing on underground platforms and when traveling through underground tunnels. It was found that the PNCs inside the train wagons as well as PM and BC mass concentrations increase when passing through the tunnel. Additionally, the average number concentration of UFPs on underground platforms was significantly higher than in other locations by factors of around 1.7 to 1.9 for UFPs and 1.6 to 2 for coarse and fine particles.
Impact of Assimilating FORMOSAT-7/COSMIC-2 Radio Occultation Data on Typhoon Prediction Using a Regional Model
Authors: Chen Ying-Jhen , Hong Jing-Shan , Chen Wen-Jou
Published: November 2022

As the successor of FORMOSAT-3/COSMIC (FS3/C1), FORMOSAT-7/COSMIC-2 (FS7/C2) was successfully launched on 25 June 2019. FS3 radio occultation (RO) data has contributed greatly to Taiwan’s meteorological progress, improving model representations of marine boundary layer heights, cyclogenesis, tropical cyclones/typhoons, and Mei-Yu front systems. The operational CWBWRF numerical weather prediction model with the 3DEnVar data assimilating system in the Taiwan Central Weather Bureau (CWB) was adopted to evaluate the impact of assimilating FS7 RO data. The following two experiments were conducted: one assimilated the in-situ observations as in the CWB operational task (nRO), and the other additionally assimilated FS7 RO refractivity profiles (wRO). Both experiments utilized 6-h assimilating window and full cycle data assimilation strategy and made 120-h forecasts after each assimilation. Within over 70 synoptic verification cases, the biases of geopotential height, temperature, and wind were reduced in the upper model levels in wRO results, and the typhoon track and intensity prediction error reductions were statistically significant. In addition, the wRO experiment improved the typhoon structure in the initial conditions and led to a better typhoon structure forecast. These results showed that the FS7 RO refractivity assimilation could improve model forecast performance, leading to its operational use in the CWB.
Quantifying the Effects of Drought Using the Crop Moisture Stress as an Indicator of Maize and Sunflower Yield Reduction in Serbia
Authors: Mimić Gordan , Živaljević Branislav , Blagojević Dragana , Pejak Branislav , Brdar Sanja
Published: November 2022

The drought in Serbia in the summer of 2017 heavily affected agricultural production, decreasing yields of maize, sunflower, soybean, and sugar beet. Monitoring moisture levels in crops can provide timely information about potential risk within a growing season, thus helping to create an early warning system for various stakeholders. The purpose of this study was to quantify the level of moisture stress in crops during summer and the consequences that it can have on yields. For that, maize and sunflower yield data provided by an agricultural company were used at specific parcels in the Backa region of Vojvodina province (Serbia) for 2017, 2018, 2019, and 2020. The crop moisture level was estimated at each parcel by calculating the normalized difference moisture index (NDMI) from Sentinel-2 data during the summer months (June–July–August). Based on the average NDMI value in July, the new crop moisture stress (CMS) index was introduced. The results showed that the CMS values at a specific parcel could be used for within-season estimation of maize and sunflower yield and the assessment of drought effects. The CMS index was tested for the current growing season of 2022 as an early warning system for yield reduction, demonstrating the potential to be included in a platform for digital agriculture, such as AgroSens, which is operational in Serbia.
Editorial for the Special Issue “Vegetation and Climate Relationships”
Authors: Shen Xiangjin , Liu Binhui
Published: November 2022

The study of the relationships between climate change and the vegetation of terrestrial ecosystemsis the core topicof biometeorology research [...]
Comparative Study of Mortality Rate Prediction Using Data-Driven Recurrent Neural Networks and the Lee–Carter Model
Authors: Chen Yuan , Khaliq Abdul Q. M.
Published: November 2022

The Lee–Carter model could be considered as one of the most important mortality prediction models among stochastic models in the field of mortality. With the recent developments of machine learning and deep learning, many studies have applied deep learning approaches to time series mortality rate predictions, but most of them only focus on a comparison between the Long Short-Term Memory and the traditional models. In this study, three different recurrent neural networks, Long Short-Term Memory, Bidirectional Long Short-Term Memory, and Gated Recurrent Unit, are proposed for the task of mortality rate prediction. Different from the standard country level mortality rate comparison, this study compares the three deep learning models and the classic Lee–Carter model on nine divisions’ yearly mortality data by gender from 1966 to 2015 in the United States. With the out-of-sample testing, we found that the Gated Recurrent Unit model showed better average MAE and RMSE values than the Lee–Carter model on 72.2% (13/18) and 67.7% (12/18) of the database, respectively, while the same measure for the Long Short-Term Memory model and Bidirectional Long Short-Term Memory model are 50%/38.9% (MAE/RMSE) and 61.1%/61.1% (MAE/RMSE), respectively. If we consider forecasting accuracy, computing expense, and interpretability, the Lee–Carter model with ARIMA exhibits the best overall performance, but the recurrent neural networks could also be good candidates for mortality forecasting for divisions in the United States.
Insect Models in Nutrition Research
Authors: Tonk-Rügen Miray , Vilcinskas Andreas , Wagner Anika E.
Published: November 2022

Insects are the most diverse organisms on earth, accounting for ~80% of all animals. They are valuable as model organisms, particularly in the context of genetics, development, behavior, neurobiology and evolutionary biology. Compared to other laboratory animals, insects are advantageous because they are inexpensive to house and breed in large numbers, making them suitable for high-throughput testing. They also have a short life cycle, facilitating the analysis of generational effects, and they fulfil the 3R principle (replacement, reduction and refinement). Many insect genomes have now been sequenced, highlighting their genetic and physiological similarities with humans. These factors also make insects favorable as whole-animal high-throughput models in nutritional research. In this review, we discuss the impact of insect models in nutritional science, focusing on studies investigating the role of nutrition in metabolic diseases and aging/longevity. We also consider food toxicology and the use of insects to study the gut microbiome. The benefits of insects as models to study the relationship between nutrition and biological markers of fitness and longevity can be exploited to improve human health.
An Intelligent Detector for Sensing Pork Freshness In Situ Based on a Multispectral Technique
Authors: Zou Wenlong , Peng Yankun , Yang Deyong , Zuo Jiewen , Li Yang , Guo Qinghui
Published: November 2022

Fresh pork is prone to spoilage during storage, transportation, and sale, resulting in reduced freshness. The total viable count (TVC) and total volatile basic nitrogen (TVB-N) content are key indicators for evaluating the freshness of fresh pork, and when they reach unacceptable limits, this seriously threatens dietary safety. To realize the on-site, low-cost, rapid, and non-destructive testing and evaluation of fresh pork freshness, a miniaturized detector was developed based on a cost-effective multi-channel spectral sensor. The partial least squares discriminant analysis (PLS-DA) model was used to distinguish fresh meat from deteriorated meat. The detector consists of microcontroller, light source, multi-channel spectral sensor, heat-dissipation modules, display system, and battery. In this study, the multispectral data of pork samples with different freshness levels were collected by the developed detector, and its ability to distinguish pork freshness was based on different spectral shape features (SSF) (spectral ratio (SR), spectral difference (SD), and normalized spectral intensity difference (NSID)) were compared. The experimental results show that compared with the original multispectral modeling, the performance of the model based on spectral shape features is significantly improved. The model established by optimizing the spectral shape feature variables has the best performance, and the discrimination accuracy of its prediction set is 91.67%. In addition, the validation accuracy of the optimal model was 86.67%, and its sensitivity and variability were 87.50% and 85.71%, respectively. The results show that the detector developed in this study is cost-effective, compact in its structure, stable in its performance, and suitable for the on-site digital rapid non-destructive testing of freshness during the storage, transportation, and sale of fresh pork.
A Multipurpose and Multilayered Microneedle Sensor for Redox Potential Monitoring in Diverse Food Analysis
Authors: Mugo Samuel M. , Dhanjai  , Lu Weihao , Robertson Scott
Published: November 2022

This work presents a multipurpose and multilayered stainless steel microneedle sensor for the in situ redox potential monitoring in food and drink samples, termed MN redox sensor. The MN redox sensor was fabricated by layer-by-layer (LbL) approach. The in-tube multilayer coating comprised carbon nanotubes (CNTs)/cellulose nanocrystals (CNCs) as the first layer, polyaniline (PANI) as the second layer, and the ferrocyanide redox couple as the third layer. Using cyclic voltammetry (CV) as a transduction method, the MN redox sensor showed facile electron transfer for probing both electrical capacitance and redox potential, useful for both analyte specific and bulk quantification of redox species in various food and drink samples. The bulk redox species were quantified based on the anodic/cathodic redox peak shifts (Ea/Ec) on the voltammograms resulting from the presence of redox-active species. The MN redox sensor was applied to detect selected redox species including ascorbic acid, H2O2, and putrescine, with capacitive limits of detection (LOD) of 49.9, 17.8, and 263 ng/mL for each species, respectively. For the bulk determination of redox species, the MN redox sensor displayed LOD of 5.27 × 103, 55.4, and 25.8 ng/mL in ascorbic acid, H2O2, and putrescine equivalents, respectively. The sensor exhibited reproducibility of ~1.8% relative standard deviation (%RSD). The MN redox sensor was successfully employed for the detection of fish spoilage and antioxidant quantification in king mushroom and brewed coffee samples, thereby justifying its potential for food quality and food safety applications. Lastly, the portability, reusability, rapid sampling time, and capability of in situ analysis of food and drink samples makes it amenable for real-time sensing applications.
Detection of Aspartylglucosaminuria Patients from Magnetic Resonance Images by a Machine-Learning-Based Approach
Authors: Ruohola Arttu , Salli Eero , Roine Timo , Tokola Anna , Laine Minna , Tikkanen Ritva , Savolainen Sauli , Autti Taina
Published: November 2022

Magnetic resonance (MR) imaging data can be used to develop computer-assisted diagnostic tools for neurodegenerative diseases such as aspartylglucosaminuria (AGU) and other lysosomal storage disorders. MR images contain features that are suitable for the classification and differentiation of affected individuals from healthy persons. Here, comparisons were made between MRI features extracted from different types of magnetic resonance images. Random forest classifiers were trained to classify AGU patients (n = 22) and healthy controls (n = 24) using volumetric features extracted from T1-weighted MR images, the zone variance of gray level size zone matrix (GLSZM) calculated from magnitude susceptibility-weighted MR images, and the caudate–thalamus intensity ratio computed from T2-weighted MR images. The leave-one-out cross-validation and area under the receiver operating characteristic curve were used to compare different models. The left–right-averaged, normalized volumes of the 25 nuclei of the thalamus and the zone variance of the thalamus demonstrated equal and excellent performance as classifier features for binary organization between AGU patients and healthy controls. Our findings show that texture-based features of susceptibility-weighted images and thalamic volumes can differentiate AGU patients from healthy controls with a very low error rate.
A Fresh Look on Old Clothes: Laundry Smell Boosts Second-Hand Store Sales
Authors: de Groot Jasper H. B. , Walther Charly , Holland Rob W.
Published: November 2022

The clothing industry is one of the biggest polluters impacting the environment. Set in a sustainable environment, this study addresses whether certain ambient odors can influence the purchase of second-hand clothing. This study fulfilled three aims, increasing methodological, statistical, and theoretical rigor. First, replicating the finding that fresh laundry odor can boost purchasing behavior in a second-hand store—this time in a larger sample, using a fully counterbalanced design, in a pre-registered study. Second, assessing the effectiveness of another cleanliness priming control condition (citrus odor) unrelated to the products at hand, to test hypotheses from a hedonic vs. utilitarian model. Third, combining questionnaire data tapping into psychological processes with registered sales. The results (316 questionnaires, 6781 registered transactions) showed that fresh laundry odor significantly increased the amount of money spent by customers compared to the no smell condition, (replication) and compared to citrus odor (extension). Arguably, fresh laundry odor boosts the utilitarian value of the product at (second) hand by making it smell like non-used clothing, ultimately causing customers to purchase far greater amounts in this sustainable setting.
The Impact of Music Therapists’ Perspectives on Quality of Life in Building Relationships with Older Adults with Chronic Illness
Authors: Kim Kyung Min
Published: November 2022

Older adults with chronic illnesses have diminished qualities of life (QoL) due to physical and mental decline. To promote their QoL, music therapists create meaningful shared music experiences and positive therapeutic relationships to address their psychosocial needs. However, within this relationship-building process, healthcare professionals, staff, and even caregivers appear to project their own perceptions and expectations of what positive QoL of older adults should be. This misapprehension may challenge therapists to meet older adults’ actual QoL needs adequately. To date, no studies have explored music therapists’ perspectives on QoL in building relationships with older adults experiencing chronic illness. Eleven music therapists participated in online, semi-structured phenomenological interviews that were thematically analyzed. Three themes emerged: participants gained an increased awareness of unconscious biases, a deepened understanding of older adults’ QoL, and a purposeful alignment with older adults’ QoL. These highlighted the significance of therapists’ consistent self-reflection within the relationship-building process. Identifying their shared commonalities with older adults guided therapists to engage with clients more salutogenically and empathically. Additionally, recognizing their own biases being projected onto older adults empowered therapists to be more intentional to reconcile their perceptual discrepancies while prioritizing older adults’ authentic voices and capable selves.
Are Perfectionists Always Dissatisfied with Life? An Empirical Study from the Perspective of Self-Determination Theory and Perceived Control
Authors: Liu Qipeng , Zhao Xiaoyun , Liu Weidi
Published: November 2022

Compared to non-perfectionists, perfectionists may not be satisfied with the growing needs in their lives to the same extent. To test whether perfectionists are dissatisfied with their lives, we investigated whether trait perfectionism attenuates the relationship between basic psychological needs, perceived control, and life satisfaction. A total of 574 college students self-reported basic psychological needs, perceived control, life satisfaction, and perfectionistic strivings and concerns, with a mean age of 19.53 (SD = 1.61), including 299 women and 275 men. A correlation analysis showed that perfectionistic strivings were significantly positively related to life satisfaction, while perfectionistic concerns were significantly negatively related to life satisfaction. The moderation analysis showed that perfectionistic strivings not only moderated the relationship between basic psychological needs and life satisfaction but also moderated the relationship between perceived control and life satisfaction. Individuals with high perfectionistic strivings generally reported high levels of life satisfaction. Perfectionistic strivings, however, reduced the positive relationship between perceived control and life satisfaction. Perfectionistic concerns moderated the relationship between perceived control and life satisfaction—the higher the perfectionistic concerns, the weaker the positive relationship between perceived control and life satisfaction. The study found that individuals with high perfectionistic tendencies are not always dissatisfied with life, but that perfectionism weakens the relationship between basic psychological needs, perceived control, and life satisfaction. We argue that one way to improve happiness is by coaching individuals who are highly perfectionistic to become self-aware of their personality so both their perfectionistic strivings and concerns are more coherent with their values and goals or character.
Do I Really Want to Change? The Effectiveness of Goal Ambivalence Feedback on Dieters’ Motivation
Authors: Fadardi Javad S. , Borhani Samiyeh , Cox W. Miles , Stacy Alan W.
Published: November 2022

Becoming committed to a new health-related goal and pursuing it is difficult for many people. The present study (a) developed and tested the psychometric properties of a brief Goal Ambivalence Scale (GAS) in a sample of dieters and (b) tested the effectiveness of providing dieters with feedback on their scores on the GAS. In Study 1, dieters (n = 334, 74% females) completed the GAS and a measure of Health-Related Concerns and Actions (HRCA). The standardization of the GAS was supported by CVR and CVI, the results of a PCA, and strong reliability and validity statistics. In Study 2, the experimental group of dieters (n = 107; 67.50% female) received feedback on their GAS scores, but the control group did not (n = 111; 62.30% female). Compared with the control group, the experimental group reported a greater need for information, greater readiness to change, and higher perceived situational confidence in resisting food that was inconsistent with their dieting goals. To conclude, the GAS could be used in health settings to provide clients and providers with an objective, fast measure of commitment to achieving health-related goals. Moreover, immediate feedback on health-related goals may improve change motivation.
A Systematic Review of Artificial Intelligence Applied to Facility Management in the Building Information Modeling Context and Future Research Directions
Authors: Pedral Sampaio Rodrigo , Aguiar Costa António , Flores-Colen Inês
Published: November 2022

Throughout the operation and maintenance (O&M) stage, facility management (FM) teams collect and process data from different sources, often needing to be adequately considered when making future decisions. This data could feed statistical models based on artificial intelligence (AI), thus improving decision-making in FM. Building information modeling (BIM) appears in this context, leveraging how data and information are systematized, enabling structured information and its use. This article addresses the state-of-the-art of using AI techniques applied to FM in the BIM context, analyzing articles between 2012 and 2021 related to this area. It is interesting to note that only from 2018 onwards, there is a substantial increase in these publications, from about 8 publications (2012 to 2017) to 24 publications (2018 to 2021) on average. This growth shows the progressive application of the optimization methods mentioned above, which opens new opportunities for the FM profession. This study contributes to the body of knowledge by highlighting the investigated tendency and gaps in critical areas and their relationship with the research topic. Noteworthy future directions are suggested, directing on (i) data and system integration; (ii) predictive models; (iii) automatic as-built/classification; (iv) internet of things; (v) energy management; and (vi) augmented/virtual reality.
Mechanical Strength and Energy Absorption Optimization of Biomimetic Honeycomb Anti-Collision Pier
Authors: Wei Jianjun , Wang Yufei , Wang Jiaqing , Yao Xupei , Wang Di , Wang Xiangyu
Published: November 2022

The anti-collision pier plays an irreplaceable role in road traffic protection due to its significance. In this research, the biomimetic honeycomb structure was applied to internal anti-collision pier interior structures. The enhancement of mechanical strength and energy absorption characteristics was explored and optimized by five anti-collision pier honeycomb structures. Finite elements of the piers are designated as 650 mm in diameter and 850 mm in height. Polypropylene Acetate (PLA) material is utilized in this research due to its environment-friendly characteristics. Displacement loading in finite element simulation is 50 mm to the middle region of the model at YOZ direction. The energy-absorbing properties of five optimized honeycomb anti-collision piers at the same force position will be carefully compared. Moreover, the influence of internal hexagon direction-quantity configuration upon loading resistance under displacement loading is outlined. The results determined the best biomimetic structure to be three honeycomb shapes with a central triangle area, with maximum stress of 503.8 MPa and fracture displacement of 58.02 mm. Furthermore, the numerical simulation shows that the number of nest increases has a negative relationship with the effect upon force and deformation of the model. Moreover, the triangular central area is superior to the Y-shape central area in both mechanical strength and energy absorption performance.
Experimental Investigation on Uniaxial Compressive Strength of Thin Building Sandstone
Authors: Huang Baofeng , Xu Yixian , Zhang Guojun
Published: November 2022

Thin sandstone is a widely used building material; however, its compressive behavior is not well understood. Four groups of cylinders were manufactured in a factory to investigate the uniaxial compressive behavior of red sandstone. Uniaxial compression tests were performed to determine the compressive behavior and failure mode of the specimens. The geometry of the stress–strain diagram varied among the four groups. The critical strain generally increased with a decrease in the height of the cylinder, whereas the compressive strength exhibited an inverse trend. The experimental diagrams were normalized with the peak stress and corresponding critical strain to represent the stress–strain diagram of each group of cylinders. A formula consisting of two parabolas was employed for regression to obtain a representative mathematical expression of the diagram. The correlations between porosity, compressive strength, and elastic modulus were evaluated based on empirical expressions. Normalized strength was employed to evaluate the size effect on the diameter and length–diameter ratio (L/D) of the cylinder; the latter provided a better prediction of the experimental results than the former. A new expression in terms of L/D was proposed based on the regression analysis of the experimental results. This study is beneficial for the engineering application of sandstone as a construction material.
Predicting the Cost Outcome of Construction Quality Problems Using Case-Based Reasoning (CBR)
Authors: Doğan Neşet Berkay , Ayhan Bilal Umut , Kazar Gokhan , Saygili Murathan , Ayözen Yunus Emre , Tokdemir Onur Behzat
Published: November 2022

Quality problems are crucial in construction projects since poor quality might lead to delays, low productivity, and cost overruns. In case preventive actions are absent, a lack of quality results in a chain of problems. As a solution, this study deals with non-conformities proactively by adopting an AI-based predictive model approach. The main objective of this study is to provide an automated solution structured on the data recording system for the adverse impacts of construction quality failures. For this purpose, we collected 2527 non-conformance reports from 59 diverse construction projects to develop a predictive model regarding the cost impact of the quality problems. The first of three stages forming the backbone of the study determines crucial attributes linked to quality problems through a literature survey and the Delphi method. Secondly, the Analytical Hierarchy Process (AHP) and a Genetic Algorithm (GA) were used to determine the attribute weights. In the final stage, we developed models to predict the cost impacts of non-conformities, using Case-based Reasoning (CBR). We made a comparison between the developed models to select the most precise one. The results show that the performance of CBR-GA using an automated weighting model is slightly better than CBR-AHP based on a subjective weighting system, whereas the case is the opposite in standard deviation in forecasting the cost outcome of the quality failures. Using both automated and expert systems, the study forecasts the cost impact of failures and reveals the factors linked to poor record-keeping. Ultimately, we concluded that the outcome of non-conformities can be predicted and prevented using past events via the developed AI-based predictive model.
Sound-Based Construction Activity Monitoring with Deep Learning
Authors: Xiong Wuyue , Xu Xuenan , Chen Long , Yang Jian
Published: November 2022

Automated construction monitoring assists site managers in managing safety, schedule, and productivity effectively. Existing research focuses on identifying construction sounds to determine the type of construction activity. However, there are two major limitations: the inability to handle a mixed sound environment in which multiple construction activity sounds occur simultaneously, and the inability to precisely locate the start and end times of each individual construction activity. This research aims to fill this gap through developing an innovative deep learning-based method. The proposed model combines the benefits of Convolutional Neural Network (CNN) for extracting features and Recurrent Neural Network (RNN) for leveraging contextual information to handle construction environments with polyphony and noise. In addition, the dual threshold output permits exact identification of the start and finish timings of individual construction activities. Before training and testing with construction sounds collected from a modular construction factory, the model has been pre-trained with publicly available general sound event data. All of the innovative designs have been confirmed by an ablation study, and two extended experiments were also performed to verify the versatility of the present model in additional construction environments or activities. This model has great potential to be used for autonomous monitoring of construction activities.
Experimental Investigation on the Cyclic In-Plane Behavior of GFRP-Reinforced Concrete Shear Walls
Authors: El-Kady Hany , Amer Osama , Ali Ahmed H. , Haggag Hesham
Published: November 2022

The current study presents an experimental investigation performed on slender reinforced concrete shear walls, representing a common lateral-load resisting system of mid-rise buildings. The walls were reinforced with steel and glass fiber-reinforced polymer (GFRP) bars and tested up to failure under reversed quasi-static cyclic loading to investigate the capability of GFRP bars in reinforcing RC shear walls under seismic loads. Moreover, the effect of the GFRP reinforcement ratio on the structural response, deformation performance, and failure mode resulting in RC walls, compared with its behavior when reinforced with steel bars, is also investigated. Six full-scale shear walls with an aspect ratio of 3.25 were constructed. The reference wall was entirely reinforced with steel bars. Two specimens were reinforced by hybrid scheme of GFRP–steel bars. The remaining three shear walls were entirely reinforced with GFRP bars. The overall performance of each wall was characterized by investigating the lateral load capacity, hysteretic response, cracks propagation, ductility, and the behavior of energy dissipation. The experimental results showed that GFRP-reinforced concrete walls had an elastic behavior characterized by a stable hysteretic response with recoverable deformation of more than 80% of the ultimate load. However, sudden and brittle failure was attained for the wall with a high GFRP reinforcement ratio. GFRP decreases the displacement ductility of the shear walls by an average of 32.9%, depending on the reinforcement ratio, compared to that reinforced by steel bars. Moreover, lower energy dissipation through inelastic deformation was obtained for the walls reinforced with GFRP bars. Nonetheless, when GFRP bars are combined with steel bars, acceptable levels of dissipated energy are attained compared to the steel-reinforced wall.
Combined Shear Wave Elastography and EU TIRADS in Differentiating Malignant and Benign Thyroid Nodules
Authors: Chambara Nonhlanhla , Lo Xina , Chow Tom Chi Man , Lai Carol Man Sze , Liu Shirley Yuk Wah , Ying Michael
Published: November 2022

Although multimodal ultrasound approaches have been suggested to potentially improve the diagnosis of thyroid cancer; the diagnostic utility of the combination of SWE and malignancy-risk stratification systems remains vague due to the lack of standardized criteria. The purpose of the study was to assess the diagnostic value of the combination of grey scale ultrasound assessment using EU TIRADS and shear wave elastography. 121 patients (126 nodules–81 benign; 45 malignant) underwent grey scale ultrasound and SWE imaging of nodules between 0.5 cm and 5 cm prior to biopsy and/or surgery. Nodules were analyzed based on size stratifications: <1 cm (n = 43); 1–2 cm (n = 52) and >2 cm (n = 31) and equivocal cytology status (n = 52), and diagnostic performance assessments were conducted. The combination of EU TIRADS with SWE using the SD parameter; maintained a high sensitivity and significantly improved the specificity of sole EU TIRADS for nodules 1–2 cm (SEN: 72.2% vs. 88.9%, p > 0.05; SPEC: 76.5% vs. 55.9%, p < 0.01) and >2 cm (SEN: 71.4% vs. 85.7%, p > 0.05; SPEC: 95.8% vs. 62.5%, p < 0.01). For cytologically-equivocal nodules; the combination with the SWE minimum parameter resulted in a significant reduction in sensitivity with increased specificity (SEN: 60% vs. 80%; SPEC: 83.4% vs. 37.8%; all p < 0.05). SWE in combination with EU TIRADS is diagnostically efficient in discriminating nodules > 1 cm but is not ideal for discriminating cytologically-equivocal nodules.
LRRC8A Is a Promising Prognostic Biomarker and Therapeutic Target for Pancreatic Adenocarcinoma
Authors: Xu Rong , Hu Yaohua , Xie Qinghua , Zhang Caiqin , Zhao Yong , Zhang He , Shi Hailong , Wang Xiaoming , Shi Changhong
Published: November 2022

Pancreatic adenocarcinoma (PAAD) is a highly malignant tumor of the digestive system with increasing morbidity and mortality. The lack of sensitive and reliable biomarkers is one of the main reasons for the poor prognosis. Volume-regulated anion channels (VRAC), which are ubiquitously expressed in the vertebrate cell membrane, are composed of leucine-rich repeat-containing 8A (LRRC8A) and four other homologous family members (LRRC8B–E). VRAC heterogeneous complex is implicated in each of the six “hallmarks of cancer” and represents a novel therapeutic target for cancer. In this study, LRRC8A was speculated to be a promising prognostic biomarker and therapeutic target for PAAD based on a series of bioinformatics analyses. Additional cell experiments and immunohistochemical assays demonstrated that LRRC8A can affect the prognosis of PAAD and is correlated to cell proliferation, cell migration, drug resistance, and immune infiltration. Functional analysis indicated that LRRC8A influences the progression and prognosis of patients with PAAD by the regulation of CD8+ T cells immune infiltration. Taken together, these results can help in the design of new therapeutic drugs for patients with PAAD.
A Novel m7G-Related Gene Signature Predicts the Prognosis of Colon Cancer
Authors: Chen Jing , Song Yi-Wen , Liang Guan-Zhan , Zhang Zong-Jin , Wen Xiao-Feng , Li Rui-Bing , Chen Yong-Le , Pan Wei-Dong , He Xiao-Wen , Hu Tuo , Xian Zhen-Yu
Published: November 2022

Colon cancer (CC), one of the most common malignancies worldwide, lacks an effective prognostic prediction biomarker. N7-methylguanosine (m7G) methylation is a common RNA modification type and has been proven to influence tumorigenesis. However, the correlation between m7G-related genes and CC remains unclear. The gene expression levels and clinical information of CC patients were downloaded from public databases. Twenty-nine m7G-related genes were obtained from the published literature. Via unsupervised clustering based on the expression levels of m7G-related genes, CC patients were divided into three m7G clusters. Based on differentially expressed genes (DEGs) from the above three groups, CC patients were further divided into three gene clusters. The m7G score, a prognostic model, was established using principal component analysis (PCA) based on 15 prognosis-associated m7G genes. KM curve analysis demonstrated that the overall survival rate was remarkably higher in the high-m7G score group, which was much more significant in advanced CC patients as confirmed by subgroup analysis. Correlation analysis indicated that the m7G score was associated with tumor mutational burden (TMB), PD-L1 expression, immune infiltration, and drug sensitivity. The expression level of prognosis-related m7G genes was further confirmed in human CC cell lines and samples. This study established an m7G gene-based prognostic model (m7G score), which demonstrated the important roles of m7G-related genes during CC initiation and progression. The m7G score could be a practical biomarker to predict immunotherapy response and prognosis in CC patients.