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Bamboo has being used as alternative raw material for construction, reinforcing fibers, paper production, among other applications. Although its recognized potentials as raw material, there are doubts about its environmental performance compared to traditional wood-based products, including paper production, which hinders bamboo-based paper plants in large scales. This study aims to assess the environmental performance of producing office paper from bamboo. Emergy synthesis (with 'm') and global warming potential indicators are calculated and compared with the traditional eucalyptus paper-based production. Results highlights the importance in including the renewability fractions of each input resources into emergy calculations for production systems with high human-labor intensity such as the bamboo agricultural production. Office paper produced from bamboo has similar renewability (28%), moderate environmental load (3.23 vs. 2.49), and emergy unsustainability (0.34 vs. 0.60) compared to paper produced from eucalyptus, but bamboo showed lower performance for global efficiency (568 vs. 442 sej/ton paper), emergy yield (1.09 vs. 1.49), and emergy investment (10.79 vs. 2.05). Focusing on global warming potential, office paper produced from bamboo releases 98 kgCO 2 eq. /ton paper compared to 56–267 kgCO 2 eq. /ton paper for eucalyptus. Notwithstanding, bamboo-based office paper demands four times more land area of agricultural production than eucalyptus, but it has a positive social aspect by requiring higher amount of direct human labor. This work shows the advantages of using eucalyptus rather than bamboo in producing office paper from an emergy and land demand perspectives, while global warming can still be considered inconclusive. Future efforts should consider a quantitative and qualitative analysis of human labor availability in both systems, as well an economic analysis to support discussions towards more sustainable office paper production from different raw materials. • Environmental load of bamboo and eucalyptus for office paper production are compared. • Bamboo has lower emergy performance than eucalyptus. • Including the renewability fraction of labor and services better reflects emergy results. • Bamboo paper emits 98kgCO2eq/ton, eucalyptus paper emits 56–267kgCO2eq/ton. • Bamboo paper needs 4x land compared to eucalyptus and requires more human labor. [ABSTRACT FROM AUTHOR]

The article discusses the positive pain reduction outcomes of a personalized care pathway for chronic knee pain post-total knee replacement.

The conservation of historic libraries can be referred towards both the ancient book collections and the buildings themselves. Heritage collections made of paper are threaten by climate-induced deterioration risks such as cellulose hydrolysis. Several studies have investigated the microclimate inside historic libraries but comparisons are difficult due to the lack of long-term microclimate observations and uniformity in the use of standards and risk assessment methods. For the first time, the long-term microclimate observations collected in three historic libraries in Italy were comparatively studied to outline differences and similarities of their microclimates in terms of paper preservation. A multidisciplinary approach was applied to assess the building performance (a) and the deterioration risks for the collections (b). As for a), a common feature of the libraries was the high thermal inertia and low indoor-outdoor air exchanges. As for b), the Time Weighted Expected Lifetime (TWEL) was defined to account for an average chemical risk on a seasonal and yearly basis. TWEL allowed to highlight the impact of the most adverse conditions on the overall chemical risk for acidic paper preservation (e.g., temperatures above 20 °C reached naturally in summer/artificially in winter). It resulted that the measured microclimate conditions in the libraries would lead to the loss of their acidic collections in less than 300 years. Demographic plots were finally used to inform about the risk resulting from the synergy between handling and microclimate as well as to explore the effectiveness of possible preservation measures such as the deacidification of 10% of the collections. [Display omitted] • The long-term microclimate data in three historic libraries in Italy are comparatively studied. • The investigation includes microclimate characterisation and conservation risk assessment. • A multidisciplinary approach is used to evaluate building performance and paper deterioration. • The current microclimate in the libraries reduces the expected lifetime of acidic collections. • Restoration interventions might not be effective if the current microclimate is maintained. [ABSTRACT FROM AUTHOR]

The article offers information on the task-oriented exercise which improved disability, pain and quality of life compared with general physiotherapy for surgically treated proximal humeral fractures.

Staphylococcus aureus is a frequent pathogenic bacterium that has a significant detrimental influence on the health of the human body. Therefore, developing a practical and portable detection platform is critical to ensuring food safety. Nanozymes are a kind of engineered nanomaterials with superior enzyme-like activities, providing infinite possibilities for the development of highly sensitive analytical assays. In this study, mesoporous core−shell palladium@platinum (Pd@Pt) nanozymes were synthesized and then applied as a signal amplifier in Staphylococcus aureus colorimetric immunoassay. At the same time, a careful comparative study of the catalytic performance with natural horseradish peroxidase (HRP), Pd@Pt nanozymes and their complexes Pd@Pt-HRP (HRP coupling with Pd@Pt nanozymes) were firstly performed, as well as clever using a colorimeter to achieve portable signal output. Pd@Pt-HRP bioprobes enable remarkable peroxidase-like catalytic activity, resulting in the highest sensitivity with a limit of detection (LOD) improved from 1 × 105 to 1 × 103 CFU/mL. In all, this work conducted a valuable comparative study and presented an improved strategy for the portable detection of pathogenic bacteria, which was expected to be used in a wide range of applications in food safety regulation and biomedicals arenas. [Display omitted] • A systematic comparative study of Pd@Pt nanozymes and HRP catalyzed detection of Staphylococcus aureus was performed. • Improved colorimetric detection of Staphylococcus aureus using a portable device with paper signal output. • The catalytic mechanism of nanozyme and HRP was thoroughly confirmed and discussed. [ABSTRACT FROM AUTHOR]

Nine paddy straw-degrading microbial consortia were isolated from 253 paddy straw samples from northern China and the Yangtze River Valley, which are two major rice production regions in China. The microbial consortia completely degraded filter paper within 15 d at 25 °C. The V4 region of the microbial consortia 16S rRNA gene was amplified, and the amplicons were sequencing using the Illumina Miseq high-throughput platform. Compilobacterota and Bacteroidota were the predominant phyla in the samples. At the genus level, the relative abundance of Macellibacteroides was the highest in Y2, and Arcobacter was predominant in the other samples. Analysis of the microbial community structure revealed that the diversity and richness in samples from the Yangtze River Valley were significantly higher compared with those of the samples from northern China, and the community structure of the four microbial consortia from the northern region had higher similarity than the five consortia from the Yangtze River Valley. • Campilobacterota and Bacteroidetes were the predominant phyla of microbial consortia. • The major genera in the two regions were significantly different. • Pseudomonas , Comamonas , Fibrobacter , Sphingobacterium , Ruminiclostridium , and Ruminococcus are the functional bacteria. • The northern bacterial community richness and diversity were lower than the Yangtze River Valley. • The northern microbial community structure was higher in similarity than the Yangtze River Valley. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a prevalent and debilitating neurological disorder affecting millions worldwide, necessitating early and precise diagnosis for effective management and therapy. Recent breakthroughs in artificial intelligence (AI) applications within the medical field have showcased promising potential in disease identification and diagnosis. This study presents a comprehensive analysis of cutting-edge AI-based methods for the identification of Parkinson's disease. This paper encompasses extensive testing on substantial datasets containing both PD patients and healthy individuals, aiming to assess the efficacy of AI models. Performance metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC-ROC) are employed to evaluate model performance rigorously. This paper is not only reviewing the state-of-the-art AI techniques for Parkinson's disease identification but also introduces novel contributions in the field. Our results demonstrate the potential of AI in achieving early and accurate PD diagnosis, offering hope for improved patient care and treatment outcomes. Furthermore, this study paves the way for future research and development in the domain of AI-driven healthcare, with the ultimate goal of enhancing the quality of life for individuals suffering from Parkinson's disease and similar neurodegenerative conditions. [ABSTRACT FROM AUTHOR]

There is now a growing interest in early-life influences on adult diseases in China. A number of birth cohorts have been established. This systematic review provided a better understanding of the development of mother–baby cohorts in China. Systematic review. We conducted a systematic review for research or profile papers in English/Chinese that reported data from mother–baby cohorts in mainland China, with ≥1y follow-up after birth. We identified 315 papers, corresponding to 31 cohorts from 19 provinces/megacities. All cohorts started in 1999–2017 (21 after 2010) and were set up with broad objectives or specific scientific focus. The baseline sample size varied, from <500 to >300,000 mothers. A majority of cohorts were initiated during pregnancy and followed children to <10y, only six to adolescence and none into adulthood. These cohorts mostly collected samples from mothers and babies, in addition to using interviews/questionnaires to collect information about pregnancy, birth and child health. Most cohorts were recruited from a single province/city. The large western region was understudied. Mother–baby cohorts have developed rapidly in China, but usually with a short follow-up duration. Extending the follow-up of children and developing cross-cohort collaboration will increase the diversity, size and coverage of the sample, allow studying early influences on life-course health and identify targets for early intervention in the Chinese population. [ABSTRACT FROM AUTHOR]

Travel Demand Management (TDM) initiatives are widely applied by transport planners to establish and enable appropriate use of critical transport infrastructure. Less attention has been given to the specific case of TDM in an education precinct (university) context. Travel Plans have been promoted as a means for an organisation to encourage sustainable travel choices by their employees, visitors and customers. This paper offers an empirical contribution to the literature through a comparative qualitative evaluation of selected University Sustainable Travel Plans (USTPs) in Australia to identify the most important questions that a USTP should address explicitly. The evaluation comprised identification of a set of evaluation questions, completion of a template for each USTP considered and application of a simple scoring exercise. We also identify TDM measures that have been introduced as part of a USTP in response to the typical travel patterns exhibited in university settings. A contribution of this paper is to create a means of comparison of USTPs and to establish the components of a comprehensive travel plan. • Conducts a comparative analysis of University Sustainable Travel Plans. • Explores international experience of Travel Demand Management (TDM) in an education precinct context. • Key components of University Sustainable Travel Plans are identified. • The important role of regular surveys of staff and student travel behaviour is highlighted. [ABSTRACT FROM AUTHOR]

Flooding is a threatening human/climate-induced hazard and the expected increase in the frequency and severity of floods is challenging cities worldwide. Modern flood risk management advocates the diversification of measures and strategies to face floods. This diversification is endorsed by the risk-based approach that – unlike the standards-based approach – emphasizes the need of addressing both the probability and consequences of floods and stresses the importance of collaboration between spatial planners and water managers in the field of flood risk management. In this paper, the politicized Institutional Analysis and Development (IAD) framework is used as an analytical ordering device to systematically examine the interplay between discursive, institutional, and contextual factors in enabling or hampering the shift toward a risk-based approach in flood management policies for the Metropolitan City of Naples and the Metropolitan City of Milan. Striking similarities across the cases and recurring patterns reveal three main entangled governance issues hampering the shift toward a risk-based approach, namely: poor coordination across levels of government, communities of actors, and policy domains. The paper concludes with a set of recommendations for policy-makers on how to address these three governance challenges for successful flood adaptation policy responses. • This research advances our understanding of the poor spatialization of flood risk management strategies. • By drawing on a multiple-case design, flood policies in Naples and Milan are comparatively analyzed. • Institutional, discursive, and contextual factors enable or constrain the shift toward a risk-based approach. • Due to poor multi-level, multi-actor, and multi-sector coordination, this shift has largely failed. • Recommendations on how to enhance vertical and horizontal coordination are provided. [ABSTRACT FROM AUTHOR]

Automatic identity recognition from ear images is an active research topic in the biometric community. The ability to secretly acquire images of the ear remotely and the stability of the ear shape over time make this technology a promising alternative for surveillance, authentication, and forensic applications. In recent years, significant research has been conducted in this area. Nevertheless, challenges remain that limit the commercial use of this technology. Several phases of the ear recognition system have been studied in the literature, from ear detection, normalization, and feature extraction to classification. This paper reviews the most recent methods used to describe and classify biometric features of the ear. We propose a first taxonomy to group existing approaches to ear recognition, including 2D, 3D, and combined 2D and 3D methods, as well as an overview of historical advances in this field. It is well known that data and algorithms are the essential components in biometrics, particularly in-ear recognition. However, early ear recognition datasets were very limited and collected in laboratory with controlled environments. With the wider use of deep neural networks, a considerable amount of training data has become necessary if acceptable ear recognition performance is to be achieved. As a consequence, current ear recognition datasets have increased significantly in size. This paper gives an overview of the chronological evolution of ear recognition datasets and compares the performance of conventional vs. deep learning methods on several datasets. We proposed a second taxonomy to classify the existing databases, including 2D, 3D, and video ear datasets. Finally, some open challenges and trends are debated for future research. [ABSTRACT FROM AUTHOR]

The mechanical vapor compression system (MVC) coupled with the evaporative-cooling condenser is an effective method to improve its high temperature adaptability. However, this hybrid systems typically use an evaporative cooler as a single-stage pre-cooling unit of condenser and is mainly designed for hot–dry climates. In this paper, based on conventional evaporative-cooling condenser configuration, three two-stage evaporative-cooling condenser systems (MVC-TSEC(A), MVC-TSEC(B) and MVC-TSEC(C)) were proposed. The MVC-TSEC(A) consists of one condenser and an indirect/direct evaporative-cooler. The MVC-TSEC(B) and MVC-TSEC(C) have two condensers (in series), which are coupled with a two-stage evaporative cooling system. The refrigerant in the MVC-TSEC(C) forms a counter-flow configuration with the outdoor air, while it is concurrent flow in MVC-TSEC(B). Subsequently, the effects of the ambient parameters and outdoor air flowrate on the three two-stage hybrid systems were analyzed comparatively based on detailed numerical models. Finally, the application potential of these hybrid systems was evaluated comprehensively in hot–humid climates. The results showed that the seasonal COP and energy-saving rate for the MVC-TSEC(C) are reached by 4.2–4.7 and 12.7–21.1 %, respectively. Moreover, the static equipment payback period of the three two-stage hybrid systems are 3.8, 3.3 and 3.0 years, respectively. [ABSTRACT FROM AUTHOR]

For over a decade, mobile applications are frequently used to make people's daily lives more accessible in all areas, such as commerce, tourism, education, medical care, and others. For implementing these applications, several frameworks exist that can accomplish this task, such as Google's Flutter, Microsoft's Xamarin, Facebook's React Native, Apple's Swift UI, Ionic, Django..etc. However, choosing the best framework to develop an application is not a simple enough decision for developers. This paper presents a detailed analysis of the four most widely used frameworks for implementing mobile applications: React Native, Swift UI, Flutter, and Xamarin. Furthermore, this study also aims to assist developers in choosing the most suitable framework by proposing an application that can recommend the best framework according to the needs entered by answering the questions, which are generally related to the relevant criteria resulting from the analysis of these frameworks. [ABSTRACT FROM AUTHOR]

This paper describes an application dedicated to collecting and mining reports of software safety vulnerabilities and exposures in physical systems. This work focuses on the clustering problem of such reports, which means grouping them through automated computing process. The clustering is carried out in two stages. In the first stage, potential similarities between the reports, together with the number of clusters are detected through automated text analysis. In the second stage, a hierarchical clustering is conducted to reduce the number of these clusters to provide potential number of appropriate clusters of the group of reports. The clustering of the second stage provides the user with greater flexibility in viewing individual reports. This paper focuses mainly on the first stage of described clustering method. Two selected clustering algorithms have been compared with the aim to show how to detect the most appropriate number of groups between scraped documents. The computational experiment results are presented and discussed in the experimental section of this work. [ABSTRACT FROM AUTHOR]

• A research article on detection and classification of various types of disfluencies such as Interjection, Sound Repetition, Word Repetition, Phrase Repetition, Revision and Prolongation is presented. • Various feature extraction techniques have been discussed for extracting meaningful information about the speech signals. • Various Machine learning algorithms have studied and analyzed to detect and classify the disfluencies in speech. • Various future research challenges in the present research are highlighted. Speech plays a vital role in communication, from expressing oneself, to utilizing speech-based platforms, speech is a necessity. Any disruption in speech is referred to as disfluency, and can impact one's quality of life. This paper presents an experimental study on various techniques for the detection and classification of speech disfluencies. Six different types of disfluencies are examined in this paper, namely Interjection, Sound Repetition, Word Repetition, Phrase Repetition, Revision and Prolongation (6 classes). However, this paper also goes a step further by including the clean speech signals as an added class alongside the six disfluencies, thereby making this work more robust with 7 classes. Various machine learning approaches have been investigated on the University College London Archive of Stuttered Speech (UCLASS) dataset; a standard disfluency dataset generated by University College London (UCL). Five different feature extraction techniques viz. Mel Frequency Cepstral Coefficients (MFCC), Linear Predictive Cepstral Coefficients (LPCC), Gammatone Frequency Cepstral Coefficients (GFCC), Mel-filterbank energy features, and Spectrograms have been used. Comparative analysis of various classifiers shows that MFCC, GFCC, and Spectrograms achieved greater than 90% accuracy on both 6 and 7 classes with the kNN classifier. As a future scope to this study, the authors aim to focus on tackling the challenges of detecting multiple disfluencies present simultaneously in a speech sample. [ABSTRACT FROM AUTHOR]

The immense growth of Android mobile malware threats has pushed cybersecurity researchers to develop efficient systems that can detect new Android malware. In spite of the academic and industrial attempts to establish a robust, reliable, and efficient solution for Android, malware classification is considered an open problem with many challenges. This paper sheds light on the performance of several machine learning algorithms and analyzes their efficiency in detecting android malware. Moreover, it applies Synthetic Minority Oversampling Technique (SMOTE), normalizes the numerical features and PCA to reach the maximum accuracy. Furthermore, the paper develops a Light Gradient Boosting Model to identify Android malware and classify their families into five classes: Adware, Banking Malware, SMS Malware, Mobile Riskware, and Benign. The paper uses a large and recent dataset, which consists of 11,598 APK collected from several sources and provided by the Canadian Institute of Cybersecurity. [ABSTRACT FROM AUTHOR]

Video Super-Resolution (VSR) is gaining massive attention in the digital age across various application domains such as video surveillance and ultra-high-definition displays. Deep learning (DL) has recently made significant progress in research and industry domains. Existing researchers used numerous VSR methods based on deep learning to improve the resolution. VSR is based on inter-frame information utilization. Video frames provide the leverage information of data that modalities record sequentially, i.e.; the object's temporal changes cause inconsistent measurements. Consequently, suitable models and algorithms must be developed to provide artifact-free images. This paper goes through two reconstruction methods incorporating various motion data sources. Initially, it describes an iterative approach that implicitly treats dynamic behavior as uncertainty in the forward model. Further direct reconstruction techniques can be derived for an explicit motion map. This paper compares both explicit and implicit motion compensation-based methods. Convolutional neural networks (CNN) and recurrent, residual network (RRN) based methods from a deep learning environment are analyzed to reconstruct the High-Resolution (HR) frames. This paper shows numerical findings for both methods using simulated data. Observations show that RRN can boost the super-resolution (SR) performance and achieve considerable visual quality after reconstruction; however, performance is limited to small datasets due to shallow network parameters. Extensive experiments show that RRN significantly impacts SR results due to its substantial computational efficiency. [ABSTRACT FROM AUTHOR]

Hydrogen stands out as a compelling alternative to fossil fuels for combustion engines. Predictive combustion models are instrumental in developing hydrogen-fuelled engines. A fundamental metric of these models is the laminar burning velocity (LBV), which can be precisely determined through laminar flame propagation simulations. In this context, the selection of an appropriate combustion mechanism is critical. This paper aims to propose the appropriate combustion mechanism for calculating LBV in predictive combustion models of hydrogen-fuelled engines. 15 state-of-the-art combustion mechanisms were applied to reproduce the LBV measurements in engine-like conditions, especially considering the application of lean combustion and water injection. The FFCM 1.0 mechanism was identified from them and further optimised to improve its prediction accuracy at elevated pressures for the lean mixture. The maximum deviation of LBV was reduced from 17.6 % to 8.7 % by this optimisation, in comparison to 10.5 % for its closest competitor mechanism, ELTE (Varga et al., 2015). • 15 combustion mechanisms were selected as the candidates for optimisation. • Test cases consider applications of lean combustion and water injection. • Many mechanisms predict laminar burning velocities at elevated pressure inadequately. • FFCM 1.0 mechanism was optimised to a new mechanism for modelling H2-fuelled engines. • Prediction errors of laminar burning velocity by the new mechanism are within 8.7 %. [ABSTRACT FROM AUTHOR]

• This works confirms the interest of differentiating car drivers and motorcyclists. • The results show an effect of both the type of vehicle and the health crisis on the rules respected. • This wok allows for theoretical reflection on the construction of the relationship to social and legal rule. The aim of this article is to examine the construction of the relationship with rules, their links in individuals throughout their lives and their possible interactions. More precisely, this paper looks at the impact of the health crisis, COVID 19, on compliance with road rules among three categories of road user: car drivers, motorcyclists with a cylinder capacity of no more or more than 125 cm3. A questionnaire was designed and distributed to 4382 car users, motorcyclists with a cylinder ca-pacity of more or no more than 125 cm3. representative of the French population. The results show an effect of both the type of vehicle and the health crisis on the frequency and number of safety rules respected. These results confirm the value of differentiating the road-user population by showing the differences between motorists and motorcyclists, which are not due solely to the mode of travel used. In terms of road safety policy, they show the importance of prevention campaigns targeting different modes of travel, and taking into account the specificities of their users in terms of perception of rules and the factors involved in their observance. [ABSTRACT FROM AUTHOR]

Human desire to explore Mars has persisted throughout history, and especially, it is required to achieve precise and safe landings on the Martian surface. The atmospheric entry phase plays a crucial role in ensuring mission success, and active research on entry guidance methods continues. This paper presents a comparative study between the two primary types of entry guidance methods: (1) path planning-tracking and (2) predictor–corrector methods by applying them to solve two distinct Mars atmospheric entry guidance problems: (a) robotic entry and (b) human entry missions. Optimization approaches are adopted for the path planning-tracking method. After generating an optimal reference trajectory based on a mission concept-defined cost function, the receding horizon control method is employed to minimize tracking errors. In the predictor–corrector method, two parameterized entry guidance approaches are explored and analyzed. A well-known numerical predictor–corrector method that utilizes a parameterized bank angle profile for entry trajectory generation is tested to demonstrate its robustness against random dispersions. On the other hand, another predictor–corrector type guidance method that assumes a polynomial shape to shape an altitude profile over range-to-go is adopted to show its range control capability. Numerical simulations reveal the pros and cons of each type of entry guidance method, and a suitable guidance method for each Mars entry mission is identified depending on the mission concept and requirements. The analysis results provide future work. • A comparative study between path planning-tracking and predictor-corrector guidance methods. • Mars atmospheric entry problem for robotic and human landing missions. • Optimization approaches for reference trajectory generation and path tracking. • Numerical predictor-corrector based on parameterized bank angle and altitude profiles. [ABSTRACT FROM AUTHOR]

Fetal ventriculomegaly is one of the major risks in prenatal diagnosis, which is an enlargement of the ventricles of the developing fetus's brain. Timely prediction of these brain disorders helps patients and healthcare providers make informed decisions and treatment plans. This paper investigates the classification of ventriculomegaly from MRI images using a deep learning approach. Segmentation of ventricles is performed using DeeplabV3+ and U-Net architectures and the accuracy, dice coefficient, and loss of these two approaches are compared. Additionally, a novel approach for size estimation was developed, utilizing contour detection to delineate ventricle boundaries and compute their areas by enclosing them within rectangles. A threshold-based classification method was then applied to differentiate between abnormal ventriculomegaly and normal cases. This research contributes to the assessment and comparison of the segmentation algorithms for aiding in the identification and classification of ventriculomegaly cases, offering potential implications for clinical diagnosis and treatment planning. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has emerged as a transformative technology with applications spanning diverse domains, from healthcare to smart cities. Efficient utilization of IoT data is essential for extracting meaningful insights and supporting decision-making processes. This research paper focuses on the critical task of feature extraction from IoT data, with a particular emphasis on the IoT 2023 dataset—a comprehensive and widely-used benchmark dataset in the IoT domain. In this study, exploration and comparison of various feature extraction methods are undertaken to enhance understanding of IoT 2023 dataset characteristics and its potential applications. Traditional statistical techniques, as well as machine learning-based approaches for feature extraction, are investigated. Evaluation of these methods is based on their ability to capture relevant information, reduce dimensionality, and enhance the performance of downstream IoT analytics tasks. Extensive experiments are conducted, and a comprehensive performance analysis is presented to guide the selection of the most suitable feature extraction approach for specific IoT applications. The outcomes of this research contribute to ongoing efforts to harness the potential of IoT data by offering a comprehensive understanding of the impact of feature extraction methods on data quality and predictive accuracy. This aids in paving the way for more informed and effective IoT solutions in 2023 and beyond. [ABSTRACT FROM AUTHOR]

Steel demand forecasting is crucial for steel industries, enabling effective production planning, inventory management, resource optimization, and strategic decision-making. This also holds true for steel plant manufacturers, who require insights into future demand trends to assess market potential, evaluate project feasibility, and identify growth regions. This paper addresses the predictability of steel demand across countries and regions worldwide, employing a comparative analysis of linear and non-linear modeling approaches to forecast steel demand per capita. A global model is trained in order to integrate information from diverse countries at varying stages of development, allowing the model to learn from comparable conditions and historical patterns. For each modeling approach hyperparameter tuning and selection of lagged input variables was performed. The performance of different model configurations was evaluated and compared for individual countries. This evaluation reveals that the predictability varies across countries and regions and shows for which countries a simple regression model based on past data is sufficient and for which countries more sophisticated models are needed. The developed models are useful in providing insights into consumption patterns, comparative risk assessment, and development of steel consumption. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of the performance of machine learning techniques for enhancing predictive quality in thermal spray coating technology, with a focus on the high velocity oxygen fuel (HVOF) process. The study aims for accurately predicting coating properties, which are pivotal in determining coating quality in HVOF applications. By investigating five influential factors, including the fuel-to-oxygen ratio and coating velocity, this research was designed to improve the understanding and control of these properties. Through advanced statistical design of experiments, correlations between these factors and key coating properties are established. Six machine learning models are trained and evaluated to assess their predictive capabilities for coating properties. The obtained results provide valuable insights into the HVOF coating process and highlight the potential of predictive model-based techniques for accurately predicting such properties. Furthermore, this research offers guidance for selecting an effective machine learning technique in the context of thermal spray technology. [ABSTRACT FROM AUTHOR]

• Methodology for a complete geometrical analysis of the vaults of Notre-Dame de Paris after the fire. • 2D image-based method for comparison before and after the fire. • 3D point cloud-based method for comparison before and after the fire. • New method for accurate alignment of very large point clouds. The work presented in this article was carried out following the fire (April 15, 2019) that ravaged Notre-Dame de Paris. The goal was to establish in a short and limited time-span the structural diagnosis of the remaining vaults of the cathedral. The analysis relied on 2D and 3D data acquired before and after the fire, and available at that time. Two complementary methods have been developed to estimate potential displacements from multi-temporal surveys. The first method is mainly based on the processing of image collections obtained with photogrammetric routines right after the fire. The second method, based on the processing of raw 3D data acquired with Terrestrial Laser Scanning, was developed afterward to refine and complete the analysis. Both methods have benefited from essential functionalities offered by two open-source solutions proven to enable "real-based" modeling in Cultural Heritage contexts, namely Micmac and CloudCompare. This paper presents the complementary use of these two software packages whilst detailing some procedural computing approaches to document and support structural diagnosis in an emergency situation. The limits and capabilities of each method are exposed and analyzed, toward the objective to develop 2.5D and 3D based geometrical analysis, respectively from photogrammetric and lasergrammetric sources. The results are presented and discussed regarding their potential usage for a quantitative and accurate diagnosis of vaults. [ABSTRACT FROM AUTHOR]

This paper concerns the tracking control problem of a class of uncertain nonlinear systems subject to deferred time-varying state constraints and external disturbances. The states of the system are free in the initial phase and are restricted by some time-varying constraints after a particular time. A class of novel shifting functions are defined, which make any initial states that beyond the constraint region move to the desired position (such as zero). Thereafter, a new state transformation is implemented for the shifted state, which transforms the state constraint problem into the boundedness of a new variable. Compared with the existing BLF method, this approach avoids feasibility test for virtual control variables. Adaptive backstepping control and dynamic surface control are used in system controller design and stability analysis, and the ideal tracking performance is achieved. Finally, simulation example and comparative studies are carried out to illustrate the effectiveness and outstanding characteristics of the proposed approach. Simulation results show that the proposed control scheme broadens the scope of application, shortens running time and improves control efficiency compared with the existing control strategies. • A class of lower-degree shifting function are defined. • A novel state transformation is constructed. • The proposed control scheme broadens the application scope and improves the control efficiency. [ABSTRACT FROM AUTHOR]

This paper details RDF/OWL storage and management in two popular Relational Database Management Systems (RDBMSs): Oracle and Virtuoso. Popularity, sustainability, and conformance with the SPARQL language are the main reasons for choosing these systems. This work combines empirical and analytical studies guided by a comparative framework developed and motivated in the paper. Several dimensions are considered, including RDF data type preservation, SPARQL query and update processing, reasoning capabilities, custom inferences, blank node management, and other functional and non-functional features. Furthermore, a review of the performance assessments reported in the literature has been conducted. This study's results identify the advantages and shortcomings of these RDBMSs for storing and managing RDF/OWL. They can help improve these systems or serve as a guide in choosing an appropriate system to use in a project context. Opportunities for further research efforts are also suggested. [ABSTRACT FROM AUTHOR]

As a hydrogen fuel for real-time production without storage, HHO has great research prospect and significance. In this paper, we conducted experiments on a spark ignition (SI) engine which has two independent fuel supply systems to compare two combination modes of gasoline port injection plus HHO (GPI + HHO) and gasoline direct injection plus HHO (GDI + HHO) at different HHO flow rate, λ, engine speed and load. The results show that, in both modes, HHO addition increases the maximum cylinder pressure and torque. With the increase of HHO flow rate, the flame development period and flame propagation period shorten, the crank angle corresponding to the maximum cylinder pressure is closer to top dead center. In addition, GDI + HHO mode has better engine performance. HHO has a significant effect on improving combustion stability. Especially at λ = 1.4, as HHO flow rate increases from 0 to 16 L/min, the coefficient of indicated mean effective pressure variation of GPI + HHO and GDI + HHO mode decreases by 69.17% and 58.29%, respectively. Moreover, HHO addition improves HC and CO emissions but increases NOx emissions. CO and HC emissions of GDI + HHO mode are the lowest under all conditions, and reaching the lowest value when HHO flow rate = 16 L/min. Besides, GDI + HHO mode not only has lower NO emissions under normal working conditions (λ = 1) but also can maintain a better combustion environment under lean-burn conditions (λ = 1.2, 1.4). In general, the application of HHO as fuel in engine can improve combustion and emission characteristics and GDI + HHO mode is the best combination of gasoline and HHO. • Two combustion systems of GDI + HHO and GPI + HHO were built in this paper. • The addition of HHO optimizes the combustion of gasoline and improves torque. • HHO can effectively improve combustion stability, especially under lean-burn conditions. • The CO and HC emissions decrease with HHO flow rate increasing. • GDI + HHO mode has better combustion and emission characteristics than GPI + HHO mode. [ABSTRACT FROM AUTHOR]

The permeability rebound and recovery of coal reservoirs is one of the key factors affecting the efficient recovery of coalbed methane (CBM). Most studies focus on the permeability rebound and recovery of intact coal reservoirs. Conversely, the permeability rebound characteristics of tectonic coal have only been rarely investigated. In this paper, an improved fully coupled mathematical model for methane transport of tectonic coal and intact coal seam was established. Then, the phenomenon of permeability rebound and recovery was analyzed theoretically, and the permeability rebound time, rebound value and recovery time were proposed to compare the difference in permeability evolution. In addition, the permeability rebound characteristics of tectonic and intact coal were evaluated for different geological parameters of coal reservoirs. The results indicate that the rebound time and recovery time of both intact and tectonic coal increase with the increase of initial gas pressure. As the initial permeability increases, the permeability rebound time of intact and tectonic coal show a decreasing trend. The permeability rebound time of intact coal decreases with increasing initial diffusion coefficient, while that of tectonic coal shows the opposite trend. Furthermore, the change in permeability recovery time for tectonic coal is 6.59 times larger than for intact coal, indicating that the effect of permeability rebound phenomenon is more significant during gas extraction in tectonic coal reservoirs. Finally, a conceptual model was proposed to explain the differential mechanism of permeability rebound between tectonic and intact coal, and its implications for CBM recovery in tectonic coal reservoirs was discussed. Therefore, the results presented in this paper can provide a theoretical basis for the efficient development of CBM in tectonic coal reservoirs. • Methane transport mathematical models of tectonic and intact coal are developed. • The permeability rebound in tectonic and intact coal is quantitatively analyzed. • The evolutionary mechanism of permeability rebound of tectonic coal is revealed. [ABSTRACT FROM AUTHOR]

Selecting weighting factors for H-infinity controller requires tones of knowledge and experience without guaranteeing obtaining the best results. This paper proposes novel idea for H-infinity controller synthesis using metaheuristics (MHs). The optimisation problem for the H-infinity controller synthesis is posed to find the weighting factors to optimise the control performance subject to stability and control handling constraints. A number of established MHs are used to solve the problem while their performance are investigated. The results indicate that the Artificial Hummingbird Algorithm and Grey Wolf Optimizer is the most efficient algorithm for the optimisation problem of H-infinity flight control design. Based on this study, the performance of several MHs on solving the H-infinity flight control design are examined while the optimum weighting factors for H-infinity flight controller are obtained. This paper is said to be a based line of studying in easy and efficient way for H-infinity controller synthesis using MHs. [ABSTRACT FROM AUTHOR]

Interactive teaching is very popular in the field of education, especially in college preschool education teaching. In this study, the experimental performance of the evaluation system based on scientific emotion construction is analyzed by the machine learning algorithm. The experimental results show that the classification model realized by the machine learning algorithm is feasible and effective for the construction of an interactive design teaching evaluation system of preschool education in colleges and universities. The main research content of this experiment is for the teaching evaluation text, this paper intends to use NB, KNN, LR, and SVM algorithms as the meta-learning algorithm in the framework, the classification effect of the meta-classifier is ranked as SVM > LR > KNN > NB. From the above data comparison, the SVM meta-classifier in the algorithm in this paper has achieved the best classification effect. The SVM meta-classifier based on the algorithm in this paper performs well, and the classification accuracy rate reaches 92.1%. This shows that this model is very suitable for studying the. And the classification accuracy rate reaches 92.1%. This shows that this model is very suitable for studying the construction of interactive teaching evaluation systems under emotional education. • The experimental results show that the classification model realized by machine learning algorithm. • This shows that this model is very suitable for studying the construction of interactive teaching. • The main research content of this experiment is for the teaching evaluation text. • This paper intends to use NB, KNN, LR and SVM algorithms as the meta-learning algorithm. [ABSTRACT FROM AUTHOR]

The escalating demand and complexity of monitoring services handled by Network Operations Centers (NOCs) have led Mobile Network Operators (MNOs) to prioritize automated solutions for network fault detection and diagnosis. Consequently, various Machine Learning (ML)-based Root Cause Analysis (RCA) systems have been developed, however their lack of explainability poses a challenge due to the predominantly black-box nature of ML models. This paper addresses this issue by presenting a supervised clustering methodology capable of integrating both glass-box and black-box models, the latter complemented by post-hoc explainability techniques. While black-box models excel in predictive capabilities, necessitating post-hoc techniques for explainability, glass-box models prioritize transparent decision-making, fostering a clearer understanding of the model's behavior. This work delineates a methodology for performing RCA of faults in the User Downlink (DL) Average Throughput Key Performance Indicator (KPI), simultaneously comparing the performance of black-box models (Light Gradient-Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost)) with glass-box models (Logistic Regression (LR) and Explainable Boosting Machine (EBM)). Results revealed that the LightGBM black-box algorithm coupled with the SHapley Additive exPlanations (SHAP) method demonstrated superior performance in fault detection and diagnosis, without compromising the overall explainability. Consequently, it was possible to identify faults related to radio conditions, low network usage in specific user groups, low network capacity, and mobility issues. The paper concludes with practical mitigation strategies for each identified fault cluster. • Supervised clustering detects and diagnoses critical mobile network KPI failures. • LightGBM + SHAP outperformed other models in detecting User DL THP faults. • The methodology diagnosed User DL faults as radio, usage, capacity, and mobility. [ABSTRACT FROM AUTHOR]

As many services convert to digital based on human-machine communication technology, older adults face considerable challenges. With the goal to identify ways to design digital services that are easier for older adults to use, this paper uses the strategy of making self-ordering kiosks more similar to the original version (i.e., paper menu) focusing on three familiarity design approaches: representation, manipulation, and organization. A 2 (skeuomorphic vs. flat design) x 2 (hierarchical vs. linear navigation structure) between-subject design experiment was conducted with individuals aged over 65 years (N = 60). Participants perceived that the skeuomorphic design was easier to use compared to the flat design, and the linear navigation structure increased perceived self-efficacy, usefulness, and intention to use compared to the hierarchical navigation structure. Participants also completed the tasks more quickly in the skeuomorphic design (vs. flat design) and linear navigation structure (vs. hierarchical structure) conditions. Interaction effects suggest that the task completion time was further reduced when skeuomorphic design was used with a linear navigation structure. Furthermore, fewer people requested assistance when they used a linear navigation structure. Serial multiple mediation results showed that the influence of linear navigation structure on intention to use was mediated by perceived self-efficacy and usefulness. • Skeuomorphic design offers better user experience for seniors than the flat one. • Linear navigation structure offers better user experience for seniors than the hierarchical one. • Combining skeuomorphism and linear structure decreases the task completion time. • The effect of linear navigation structure on intention to use is mediated by self-efficacy and perceived usefulness. [ABSTRACT FROM AUTHOR]

At present, ubiquitous learning has been becoming an important learning style for college students. However, the current studies on ubiquitous learning mostly focus on aspects such as communication and data interaction methods, while ignoring students' academic emotions. In this paper, we propose an individual adaptive ubiquitous learning paradigm that focuses on college students' academic emotions. The paradigm collects and analyses students' academic emotions to grasp student's individual characteristics. Then, the adaptive planning and adjusting learning contents is beneficial for improving students' academic emotions and enhancing the ubiquitous learning effects, initiative and concentration. In this study, the 2-year-long comparative experiments and tracking analyses were conducted from four aspects: academic grades, average study hours in after-school spare time, academic emotions status and classroom participation. The experimental results show that the individual adaptive ubiquitous learning paradigm proposed in this paper has significant positive effects and impacts on college students' learning outcomes, initiative, engagement and academic emotions. • An individual adaptive ubiquitous learning paradigm considering the impact of academic emotions is proposed. • The paradigm focuses on academic emotions to adaptively adjust individual learning content. • Academic emotions analysis and adjustment algorithms are designed to improve learning outcomes. [ABSTRACT FROM AUTHOR]

Social gradients in health and aging have been reported in studies across many human populations, and - as the papers included in this special collection highlight – also occur across species. This paper serves as a general introduction to the special collection of Neuroscience and Biobehavioral Reviews entitled "Social dimensions of health and aging: population studies, preclinical research, and comparative research using animal models". Authors of the fourteen reviews are primarily members of a National Institute of Aging-supported High Priority Research Network on "Animal Models for the Social Dimensions of Health and Aging". The collection is introduced by a foreword, commentaries, and opinion pieces by leading experts in related fields. The fourteen reviews are divided into four sections: Section 1: Biodemography and life course studies; Section 2: Social behavior and healthy aging in nonhuman primates; Section 3: Social factors, stress, and hallmarks of aging; Section 4: Neuroscience and social behavior. [ABSTRACT FROM AUTHOR]

• Childcare policy responses towards Covid-19 differed in European welfare states. • Policy responses cause informalization of childcare to different degrees. • Informalization of childcare is mainly connected with social risks for women. • Cultural and institutional differences help to understand varying policy responses. The closure of extra-familial childcare facilities by European governments in 2020 was an important part of interventions against the spread of the COVID-19 pandemic. One consequence was that childcare was provided by parents at home, mainly by women. As a result, women mainly experienced financial and employment risks related to this "informalization" of childcare. The childcare policies of European welfare states differ in the extent to which they include measures to reduce the social risks related to informalization. Against this backdrop, this paper asks: How should one understand cross-national differences in childcare policies during the pandemic? We are also particularly interested in the effects of childcare policies on the social risks connected with the informalization of childcare and what these mean for the gendered division of paid work and care. Differences in childcare policies during the pandemic are commonly explained in terms of the path dependence of such policies. Using the theoretical approach of "care arrangement," this article introduces a broader theoretical framework that considers the role of cultural and institutional factors for understanding the cross-national differences in childcare policies during the pandemic. We introduce the findings of a comparative empirical study of childcare policies in three European welfare states—Denmark, Germany and England—that represent different types of care arrangements. This paper uses policy and media documents, quantitative data on childcare and women's employment, cultural ideas and secondary analysis of empirical studies. We find that governments did not per se respond to the pandemic based on institutional path dependence regarding childcare policies, while the integration of culture into the theoretical framework allows for a more comprehensive understanding. [ABSTRACT FROM AUTHOR]

In decision tree building, the choice of the splitting criteria highly affects the quality of model that is developed. In this paper, decision tree models are developed on dispersed data using entropy measure and twoing criterion as the splitting criteria. Dispersed data in this sense has multiple independent local tables on which decision tree models are built. Prediction vectors are generated based on the local models and a final prediction is made from aggregation using majority voting. In effort to improve model quality ensemble method technique (bagging) is applied to build multiple models for each local table. The main purpose of this paper is to make a comparative study on the classification quality of decision tree models built on dispersed data using entropy and twoing splitting measure. The main observation is that when knowledge is highly dispersed in a lot of local tables, using twoing criterion in building decision tree models is better than using entropy measure. [ABSTRACT FROM AUTHOR]

The focus of this paper is to determine how the mechanisms of each algorithm contribute to search performance. swarm-based algorithms are composed of several mechanisms. This paper regards these independent mechanisms as modules. The modules constituting each algorithm are classified into three categories: (1) the common module: a collection of mechanisms that all swarm-based algorithms have in common; (2) the main module: the unique mechanisms of each algorithm that updates the solution by interacting with other solutions; and (3) the sub module: the unique mechanisms of each algorithm that do not directly improve the quality of the solution. Testing the algorithms combined into these modules reveals the contribution of each module to performance. The experiments are conducted for the three algorithms PSO, ABC, and CS to test the usefulness of this concept. The results of these experiments revealed the following implications: (a) the main module of ABC alone can have good performance; (b) the main module of CS does not perform well as a stand-alone; (c) the combination algorithm with the main module of PSO and the sub module of ABC can improve applicability. [ABSTRACT FROM AUTHOR]

Coherence and coordination among interdependent policy sectors are considered key for the implementation of the 2030 Agenda for Sustainable Development. Literature on policy coherence argues that a lack of coordination may lead to policy incoherence; however, literature on coordination also sometimes points to the reversed causality that incoherencies in policies or in governance functions (functional incoherence) may hinder coordinated policy outcomes; in fact, these assumptions have rarely been further theorized or tested empirically. In this paper, we hypothesize the higher functional or policy coherence, the higher coordination at process level and the higher the likelihood that coordination at process level is translated into coordination at outcome level. We test this hypothesis for cross-sectoral coordination challenges among different water using sectors in six different basins located in Germany, Iran, Mongolia, Spain, and South Africa. At first glance, four cases seem to confirm the first part of the hypothesis for functional coherence and three for policy coherence. It remains difficult to establish causality. Whether functional and policy coherence translate into coordination at process level seems to depend on a functioning coordination body. We further find that functional and policy incoherencies may either lead to coordination problems (in view of conflicts of interest) or even go along with a high level of coordination at the process level, possibly to compensate for incoherencies. Neither functional nor policy coherence change the relationship of coordination at process and outcome level. To explain coordination at the outcome level, other factors need to be considered. • The paper explores how (in)coherence in policies and responsibilities affects coordination at process and outcome level. • It presents a rigorous comparative study of cross-sectoral coordination in six river basins worldwide. • Coherence may be conducive towards coordination at process level, but it remains difficult to establish causality. • Incoherencies may both hinder or stimulate coordination at process level. • Coherence in policies and responsibilities does not change the relationship of coordination at process and outcome level. [ABSTRACT FROM AUTHOR]

The existing decentralized wind farm control incorporating the wind turbine wake interaction has almost all concentrated on the condition of fixed wind speed/direction without considering the variability of wind in nature. Meanwhile, most of the current wake models cannot meet the requirement for wind turbine control optimization research with the high demand of wake calculation speed and accuracy. In this paper, a novel decentralized control strategy, named the wind-interval-based (WIB) control which adopts the uniform operation among the same interval of variable wind speeds/directions, is proposed for the control optimization study of in-line two scaling wind turbines for demonstration. To prove the effectiveness of the new control mechanism, the ideal instantaneous control is introduced for the comparative study. At the same time, a wake model incorporating the two directly controllable parameters (i.e., the tip speed ratio λ and pitch angle γ) is established based on artificial neural network (ANN). The comparative results show that the total power output applying the instantaneous control and the new WIB control mechanisms are increased by 0.1%–4.1% and 0.45%–3.9% depending on the tested wind scenarios, respectively. By comparing the optimized power output with the two controls, it is found that the error between them is generally lower than 3%, while the proposed WIB control reduces the operational difficulty to a large extent facilitating its application to the real wind turbine operation in reality. In summary, this paper shows that the new proposed WIB control not only reduces the difficulties of the wind turbine control mechanism, but maintains the control effectiveness by achieving a comparable total power output with respect to the traditional instantaneous control, which is of great significance to the wind farm developer. [ABSTRACT FROM AUTHOR]

During shading conditions, performance of physical relocation techniques for PV formations is more effective than total cross-tied (TCT) type of PV formation. The main purpose of physical relocation technique is to rearrange the physical position of PV modules in TCT formation without changing the electrical connections. Due to relocation of PV modules, the lengths of required wires are increased. So, the appropriate selection of PV formation is also important. In this paper, three different variants of physical relocation techniques are considered for 6×6 size TCT formation. These three variants are named as SuDoKu (SDK), ShapeDoKu (SPDK) and Futoshiki (FS). Moreover, these variants are analyzed on realistic assumptions of shading situations and these shading situations are categorized into eight different cases. Also, the comparative analysis of PV formations is reported in terms of P-V profiles, effects of shadings on individual rows and generation of energy and incomes. Further, from the obtained results of this paper, it is concluded that these variants of physical relocation techniques enhance the output power, energy and income generation in comparison of TCT type of PV formation. The SPDK variant of physical relocation technique is performing better than the other variants. [ABSTRACT FROM AUTHOR]

This paper offers an exhaustive comparison of quantitative PCR (qPCR) and digital PCR (dPCR) technologies, focusing on their critical roles in molecular diagnostics, ranging from pathogen detection, and cancer screening to environment monitoring. The manuscript outlines the foundational theories behind both technologies and scrutinizes their capabilities, advances, and limits. It deeply investigates the primary factors based on the sample's nature, sample preparation strategies, and employing key parameters like reliability, accuracy, and specificity, thus critically evaluating the merits and demerits of each technology. It also proposes the avenues of research and development that impact both technologies. The paper concludes that the choice between qPCR and dPCR depends on factors such as the nature of the sample, the purpose of the study, and practical considerations like cost and operational ease. Our overarching goal is to furnish a guide that aids researchers in selecting the optimal PCR-based technique for their assay development. • Present an exhaustive comparison of quantitative PCR (qPCR) and digital PCR (dPCR) for biosensing applications. • Discusse the foundational theories, recent advances, and limitations of both qPCR and dPCR technologies. • Evaluate qPCR and dPCR based on reliability, accuracy, limit of detection, and specificity. • Provide a guide for researchers to select the optimal PCR technique based on study purpose, and practical considerations. • Forecast the development trajectories of qPCR and dPCR, including integration with AI and POC applications. [ABSTRACT FROM AUTHOR]

• Many mental health disorders first emerge during early childhood or adolescence. • Wait times for children's mental health services have been an international and widespread problem. • Long wait times prolong the emotional distress of children and/or adolescents and their caregivers which can exacerbate mental health difficulties. • The majority of research in this area has been conducted in the United Kingdom and Canada and focus on one waitlist strategy. • Common strategies include alternative service delivery models, increasing system capacity, and improving intake and assessment processes. Background: Although many mental health disorders first emerge during early childhood or adolescence, there is a significant gap between demand and availability of mental health resources, leading to long waitlists for services. Objective: The objective of this scoping review was to identify and characterize the research literature related to the range of waitlist management strategies that have been implemented in outpatient child and adolescent mental health care. Methods: Electronic databases reviewed included: Medline (Ovid), Embase (Ovid), PubMed, PsychINFO, SCOPUS, CINAHL, and ISI Web of Science. Grey literature databases included: OpenGrey, Conference Papers Index, and Proquest Digital Dissertations. Articles were screened by two reviewers in two steps: first by title and abstract, then full text level. Data were extracted using an a-priori developed data extraction framework, which was piloted and modified iteratively. Results: A total of 119 papers related to waitlist interventions in child and adolescent mental health were reviewed. Of these 119 papers, 11% were reviews, summary, or theoretical papers; 8% used a randomized control trial design and 2.5% were trial protocols. Most studies used less rigorous designs, such as uncontrolled before-and- after designs. The large majority focused on just one waitlist strategy each. The most commonly used approaches included: prioritization/triage and initial assessment; brief consultation and advice or brief therapy approaches; group-based models; interim services; increasing capacity; and strategies to decrease non-attendance. Most studies were conducted in the United Kingdom or Canada. Discussion: While mental health systems are complex, most studies examining waitlist initiatives explored the implementation of single initiatives. It is unlikely that a single waitlist strategy can be effective in managing wait times for children's mental health. Rather, consistent and systemic approaches to address wait times that consider the impact of the reduction approach on the patient, the program, and the community are needed. [ABSTRACT FROM AUTHOR]

The paper measures environmental efficiency and productivity change to then carry out a decomposition analysis of an environmental efficiency index and a productivity change index. The paper includes a novel comparative assessment to determine the drivers of environmental efficiency and the sources of productivity change of the manufacturing sector in Europe, Asia and the whole world. We explore the capability to convert in an environmentally efficient way, selected inputs into desirable and undesirable outputs. Our estimation results of environmental efficiency confirm that Europe is ahead the world's average. Our analysis shows, however, a slowdown of Europe compared with the productivity growth of Asia. The latter shows a better performance in management capabilities, optimal resource allocation, and economies of scale. The analysis carried out in a second stage using the dynamic effects of the generalized method of moments also shows that the annual growth rate of manufacturing value added and manufacturing exports positively influence the level of environmental efficiency in both Asia and Europe. Therefore, the internationalization and extroversion of manufacturing can be crucial for sustainable development in Europe and Asia. • Benchmarking the strategy for sustainable development in Asia and Europe. • Decomposition of environmental efficiency indices and productivity change. • Effect of manufacturing value added and exports on environmental efficiency. • Benchmarking the manufacturing sector in Asia and Europe. • Showing Asia's best practices and European pillar of industrial restructuring. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate the zone-by-zone changes in Lochkovian conodont biodiversity in the Pyrenees region and compare the results with previous studies. Additionally, the paper seeks to identify evolutionary phases within the Lochkovian. We analyse the stratigraphic range data from eight classical Pyrenean Lochkovian sections. A total of 48 taxa at the species level, primarily confined to the Lochkovian, are included in the analysis. We employ both qualitative and quantitative approaches, including Clark's method for the conodont evolution index and Foote's parameters for measuring taxonomic diversity and rates. The zonal scheme employed in this study follows the global three-fold subdivision of the Lochkovian stage, with additional subdivisions based on local records. The zone-by-zone analysis of stratigraphically well-controlled successions contributes to our understanding of the conodont diversity dynamics in this region. The results are consistent with previous studies, but also highlight the unique aspects of the Pyrenean conodont record. Their highest diversity occurs in the middle Lochkovian within the kutscheri - pandora beta Zone, succeeded by an extinction event towards the end of this interval. Following a brief proliferation of species at the onset of the upper Lochkovian, another extinction event concludes this Stage. Results from all different rate measurements applied to Pyrenean Lochkovian conodonts are in disagreement with the prevailing perspective suggesting an escalation in the proportion of singletons and a concomitant decline in per-taxon rates with an increase in interval length. Comparative analysis of conodont records across various regions (Carnic Alps, Prague Synform, Central Nevada, and the Pyrenees) during the Lochkovian reveals significant differences in biodiversity, taxonomic composition, and zonal distribution. • This study delves into the dynamics of conodont diversity within the Pyrenees region. • The peak of conodont diversity is observed towards the end of middle Lochkovian. • An extinction event affecting conodont is identified at the end of Lochkovian. • Lochkovian conodont rates challenge previous interval length assumptions. • Total diversity and estimated mean diversity produce measurements discrepancy. [ABSTRACT FROM AUTHOR]

This paper investigates a novel CNN-based architecture for image resizing forensics in the presence of Double-JPEG compression. Two sub-problems are addressed as part of this paper: first, the detection of resizing in DJPEG images, and second, determining the factor used to resize the image before the second JPEG compression. The image resizing technique used in this paper is image scaling. The proposed network is based on utilizing specially chosen high pass filters that constitute the pre-processing layer, followed by four convolutional layers, two fully connected layers, and a final softmax layer. The high-pass filters preserve the resizing clues in the form of residuals applied at the pre-processing layer, followed by utilizing the residuals as inputs to a CNN to detect the presence of resizing and also for estimating the resizing factor. The proposed network is fully end-to-end and does not rely on any hand-crafting. Experimental validation is carried out utilizing images from the RAISE dataset. Comparative analysis with four state-of-the-art methods demonstrates the proposed CNN-based architecture's superiority for resizing detection and resize factor determination sub-problems. [ABSTRACT FROM AUTHOR]

As a hot topic that attracts more and more attention, credit risk has become the social focus that ordinary people pay attention to from the professional term of financial world. No matter how the form and system of the economy change, credit risk is an important factor in assessing the social stability and determining the profit of social and economic participants. Among them, society, as our living environment, and the credit between individuals in each network, will inevitably lead to the risk problems of relevance, infection and relevance. This paper uses WOS and CNKI databases as data sample sources, CiteSpaceV as tool, and uses scientific measurement and knowledge map analysis methods to make a visual comparative analysis of relevant research on credit risk contagion at domestic and abroad from 2000 to 2020. The research shows that, compared with the related research at domestic and abroad, China started late and developed weakly, but it has been trying to catch up with and surpass, the key words in the related literature show that, compared with the relative mature system of related fields abroad, China has begun to discuss the universality and particularity of each credit risk network individual; The domestic research is rather basic, At present, it is still groping for the overall layout of credit risk network, network connection characteristics, network individual definition and other aspects, At present, the focus is on related network and network individual response. The research results show that credit risk contagion and related topics are in a research upsurge in the whole international environment. In addition, this paper also analyzes the research framework at domestic and abroad from the perspectives of basic knowledge, core topics and research hotspots, in order to show the existing research results and future research directions. [ABSTRACT FROM AUTHOR]