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Glass fiber-reinforced polymer (GFRP) reinforcements are superior to traditional steel bars in concrete structures, particularly in vertical elements like columns, and offer significant advantages over conventional steel bars when subjected to axial and eccentric loadings. However, there is limited experimental and numerical research on the behavior of GFRP-reinforced concrete (RC) columns under eccentric loading having different spacing of stirrups. In this study, six specimens were cast under three different values of eccentricities (25 mm, 50 mm, and 75 mm) with two groups of stirrups spacing (50 mm and 100 mm). The experimental results showed that by increasing the eccentricity value, there was a reduction in the load-carrying capacity of the specimens. The finite element ABAQUS software was used for the numerical investigation of this study. The results from the finite element analysis (FEA) were close to the experimental results and within the acceptable range. The maximum difference between the experimental and FEA results was 3.61% for the axial load and 12.06% for the deformation.

Emerging nations have taken benefits from foreign capital inflows for increasing the amount of human capital, ultimately contributing to both economic development and growth. This research work evaluates the influence of foreign inflow of money and assistance from foreign countries on the lives of people in Pakistan. The study utilizes the post-facto design has and analyzed a sample of 32 years of secondary data ranging from 1990 to 2021, extracted from different data banks. The human capital is taken as a response variable. On the other hand, explanatory variables include official development assistance and remittance. Urban population and financial development are taken as control variables. The outcomes provide evidence that remittances, financial development and urban population provides a favorable and significant influence on the enhancement of human capital, while official development assistance provides no effect for the development of human capital in Pakistan. These findings establish a broader picture of this relationship and are in line with the related theories of economics. Furthermore, efforts should be made to provide policymakers with an understanding of critical factors which link foreign financial support, money transfers, growth in the economy, and improved human development.

In the past years, the food security issue has become clearer, and resources have been sought to improve the sustainable food chain with the aim of reducing the harm to the environment caused by traditional food systems. Through the arrival of edible insects, sustainability and nutritional prowess emerge as a solution in view. On the downside/on the other/however, public acceptance is one of the key factors that limit their (AV) placement. The scientists have frantically worked on the identification of the factors that make up one's decision to improve on the level of eating insects. This paper details the research conducted with a group of 125 students who come from numerous public institutions in Pakistan, with the focus to determine their readiness to incorporate consuming bugs which is a fresh protein source in their cultural sphere. The investigation was multifaceted one and covered the spectrum from disgust effects, safety risks, to cultural factors, knowledge gap, environmental issues, and religious considerations. Implementing a five-point Likert scale questionnaire administered through the use of Google Forms, the study brought to the fore critical aspects that stimulate the decisions and intentions of students regarding whether to eat insects to control the escalating locust population. The research pinpoints several factors as being the most important in shaping participants´ attitude towards the idea of either trying or avoiding the locust-based food – those would be something like disgust, deeply held religious views, and merely care toward the surrounding environment. On top of this, cultural influences and knowledge gaps became the essential determinants that worked to promote those decisions. This analysis applied a multipronged approach to investigate the multifarious parts that affect food choices, especially with regard to dietary innovation such as locusts. This research, not only shows a broader approach to the analysis of insect consumption in

The article explores the real-world connection involving population increase and economic development in Pakistan. This research examines the long-run and short-run implications of demographic parameters like population expansion, expectancy of life and fertility rate on the advancement of the Pakistani economy. The theoretical foundation of the study is built on demographic change, which takes into account life expectancy and the birth rate in the production function, as well as human capital development to establish research goals. The current study used Pakistan's time-series information from 1981 to 2021. The dataset was retrieved using the World Development Indicator engaging the Auto Regressive Distributed Lag model (ARDL) to figure out the short and long-run interactions between prosperity and population increase. The outcomes demonstrate an adverse and substantial association between the expansion of the population with the growth in the economy. On the contrary, life expectancy has a large and favourable impact on the development of the economy. According to the results, population growth has a detrimental impact on the development of the economy; nevertheless, appropriate usage of these assets must be made. Whenever a nation maintains an extensive population and fails to utilize its strength effectively, then it will become a strain on the economy. The authorities ought to implement comprehensive strategies for improving employment opportunities in the entire nation. Though Pakistan possesses an intelligent workforce, the challenge remains how to harness its potential profitably.

Purpose: This research concentrates on the technological factors (perceived ease of use, perceived usefulness, and subjective social norms) that are influenced by adolescent green purchasing intention (GBI), as well as the dimensions of green environmental awareness, environmental knowledge, and green product trust. Design/Methodology/Approach: a survey was administered to 405 students from various Punjabi universities PLS Sem approach is used to analysis the data. Findings: The green intentions of young consumers are substantially and positively influenced by technological factors (perceived utility, perceived ease of use, and subjective social norms), green environmental awareness, and green environmental knowledge, as indicated by the results. The green intentions of young consumers are significantly influenced by the trust they have in green products and their green purchasing behaviour. Implications/Originality/Value: This study contributes to the existing corpus of knowledge regarding the intentions and actual consumption behaviours of younger consumers by employing a novel methodology to evaluate the impact of technological advancements and relational factors. The study has garnered the attention of researchers, decision-makers, CEOs, and specialists in both theory and practice, resulting in a substantial advancement in the field.

Due to the rapid and ubiquitous development and acceptance of IoT, healthcare providers have changed their locational settings from solely based in clinics to extend more broadly into the reach of patients’ domestic homes. This IoMT focus extends to various medical devices and applications within the healthcare domain, such as any form of smartphones, surveillance cameras, wearable sensors, and actuators, that hold the capability to access IoT technologies. The aim of this scoping review has two important objectives. The first is to understand the best approaches towards acquisition and refinement of data in favour of an optimised cyber security posture for remote patient monitoring. The second is to understand how best to detect cyberattacks and vulnerabilities in Medical IoTs using automated reasoning. The review will be carried out according to the Joanna Briggs Institute (JBI) scoping review methodology. The key information sources are Springer Link, IEEE Xplore, Science Direct, SCOPUS, and ACM databases. The search is limited to studies written in English. The initial step in the review uses keywords and index terms to identify literature from the selected database information sources. The second step then takes the identified elements and searches each of the databases. The third step involves a search of the references to determine literature inclusion using a full-text screening process. Medical IoT devices, specifically designed for patient monitoring and diagnosis, excel in their ability to collect, transfer, and interact with real-time data. It focuses on intersections between IoMTs, cyberattacks and vulnerabilities, knowledge graph detection, and automated reasoning.

This research investigates the influence of kevlar and glass fiber reinforcements on the mechanical and thermal properties of epoxy/vinyl ester (hybrid resin) composite. The hybrid resin was synthesized by achieving an interpenetrating network between epoxy and vinyl ester. The composites were characterized using tensile, flexural, impact, and thermo‐gravimetric analysis (TGA). Scanning electron microscopy was employed to analyze surface morphology whereas Fourier‐Transformation Infrared Spectroscopy (FT‐IR) was used to investigate the possible interaction between the constituents of the composites. The findings have shown a notable improvement in the mechanical properties after the hybridization of the resin. For reference, the tensile strength of glass/hybrid resin and kevlar/hybrid resin composites were observed to increase by 8.33% and 23.65%, as compared to glass/epoxy and kevlar epoxy composites respectively, whereas, the bending strength of these composites was improved by 8.36% and 30.61%, respectively. TGA also showed an enhanced thermal stability of the hybrid resin‐based composites. Such improvements are noticed due to multi‐resin incorporation (the oxirane group of epoxy reacts with the hydroxyl group of vinyl ester), confirmed by the FTIR, TGA, and morphological analysis. This study signifies that the proposed hybrid composites are better in terms of strength and modulus relative to conventional metallic materials.

Prefabricated temporary buildings are a promising solution for post-disaster scenarios for their modularity, sustainability and transportation advantages. However, their low thermal mass building envelope shows a fast response to heat flux excitations. This leads to the risk of not meeting the occupant comfort and HVAC energy-saving requirements. The literature shows different measures implementable in opaque surfaces, like vacuum insulation panels (VIPs), phase change materials (PCMs) and switchable coatings, and in transparent surfaces (switchable glazing) to mitigate thermal issues, like overheating, while preserving the limited available internal space. This paper investigates the energy and overheating performance of the mentioned interventions by using building performance simulation tools to assess their effectiveness. The optimization also looks at the transportation flexibility of each intervention to better support the decision maker for manufacturing innovative temporary units. The most energy-efficient measures turn to be VIPs as a better energy solution for winter and PCMs as a better thermal comfort solution for summer.

Flowcharts and mind maps, collectively known as flowmind, are vital in daily activities, with hand-drawn versions facilitating real-time collaboration. However, there's a growing need to digitize them for efficient processing. Automated conversion methods are essential to overcome manual conversion challenges. Existing sketch recognition methods face limitations in practical situations, being field-specific and lacking digital conversion steps. Our paper introduces the Flowmind2digital method and hdFlowmind dataset to address these challenges. Flowmind2digital, utilizing neural networks and keypoint detection, achieves a record 87.3% accuracy on our dataset, surpassing previous methods by 11.9%. The hdFlowmind dataset, comprising 1,776 annotated flowminds across 22 scenarios, outperforms existing datasets. Additionally, our experiments emphasize the importance of simple graphics, enhancing accuracy by 9.3%.

While neural networks have excelled in video action recognition tasks, their black-box nature often obscures the understanding of their decision-making processes. Recent approaches used inherently interpretable models to analyze video actions in a manner akin to human reasoning. These models, however, usually fall short in performance compared to their black-box counterparts. In this work, we present a new framework named Language-guided Interpretable Action Recognition framework (LaIAR). LaIAR leverages knowledge from language models to enhance both the recognition capabilities and the interpretability of video models. In essence, we redefine the problem of understanding video model decisions as a task of aligning video and language models. Using the logical reasoning captured by the language model, we steer the training of the video model. This integrated approach not only improves the video model's adaptability to different domains but also boosts its overall performance. Extensive experiments on two complex video action datasets, Charades & CAD-120, validates the improved performance and interpretability of our LaIAR framework. The code of LaIAR is available at https://github.com/NingWang2049/LaIAR., Comment: Accepted by CVPR 2024

In this study, the tribological properties of a green lubricant synthesized from cottonseed oil through a two-step transesterification process are investigated, with a specific focus on the maximum throughput of the second step that involves the reaction of cottonseed methyl ester with 2-ethyl-1-hexanol alcohol and a titanium isopropoxide (TIS) catalyst. The research centers on evaluating the physiochemical characteristics of this biolubricant and comparing them with those of commercial oil (5W30) and the ISO VG40 standard. Furthermore, the influence of crucial process variables, such as temperature, pressure, reaction time, and TIS catalyst concentration, is examined by analyzing variance in experimental data. Fourier transform infrared (FTIR) analysis is employed to identify functional groups, particularly emphasizing the impact of temperature and reaction time. By optimizing the second transesterification process under specific conditions (pressure = 19.42 mmHg, temperature = 175°C, catalyst concentration = 0.63%, and reaction time = 4.0 h), a cottonseed oil-based biolubricant is successfully produced, exhibiting properties comparable to those of commercial mineral lubricants. Notably, the findings reveal significant enhancements in the coefficient of friction (CoF) with a 49% reduction and wear resistance with a maximum 19% reduction. This study contributes valuable insights into optimizing biolubricant production derived from cottonseed oil through two-step transesterification, emphasizing its novel potential in improving frictional and wear characteristics., Validerad;2023;Nivå 2;2023-10-25 (johcin);CC BY 4.0 License

Diversity inventories are critical to creating accurate species range maps and estimating population sizes, which in turn lead to better informed landscape and wildlife management decisions. Metabarcoding has facilitated large-scale environmental diversity surveys. However, the use of a metabarcoding approach with bird feces to survey arthropod diversity is still rela- tively undeveloped. The aim of this study was to see if and how a metabarcoding approach with bird feces could contribute to a saproxylic Coleoptera survey of traditional insect traps. We compared two methods of surveying saproxylic Coleoptera diversity (metabarcoding birds feces and deploying traditional traps) over two elevations in a mountain system. The two methods caught different species and different levels of functional guild richness. The metabarcoding method successfully recorded both distinct and overlapping portions of diversity from traditional collections, and the approach was also effec- tive in signaling the presence of both rare species and nine country records. Our results show that metabarcoding Passerine bird feces can be successful when used alongside traditional collection methods to capture a broad diversity of saproxylic Coleoptera. This method, however, has quantitative and qualitative limitations, including the inability to produce species abundance data as well as the generation of false positives and negatives due to biases within the metabarcoding pipeline. Implications for insect conservation As many terrestrial ecosystems lose insect diversity, insect diversity surveys are essential to understand the scope of the loss. Despite metabarcoding approach shortcomings, the declining costs and shorter survey and processing time required for this approach compared to traditional survey methods indicate that it can be a valuable addition to the toolkit for saproxylic Coleoptera diversity survey, Diversity inventories are critical to creating accurate species range maps and estimating population sizes, which in turn lead to better informed landscape and wildlife management decisions. Metabarcoding has facilitated large-scale environmental diversity surveys. However, the use of a metabarcoding approach with bird feces to survey arthropod diversity is still rela- tively undeveloped. The aim of this study was to see if and how a metabarcoding approach with bird feces could contribute to a saproxylic Coleoptera survey of traditional insect traps. We compared two methods of surveying saproxylic Coleoptera diversity (metabarcoding birds feces and deploying traditional traps) over two elevations in a mountain system. The two methods caught different species and different levels of functional guild richness. The metabarcoding method successfully recorded both distinct and overlapping portions of diversity from traditional collections, and the approach was also effec- tive in signaling the presence of both rare species and nine country records. Our results show that metabarcoding Passerine bird feces can be successful when used alongside traditional collection methods to capture a broad diversity of saproxylic Coleoptera. This method, however, has quantitative and qualitative limitations, including the inability to produce species abundance data as well as the generation of false positives and negatives due to biases within the metabarcoding pipeline. Implications for insect conservation As many terrestrial ecosystems lose insect diversity, insect diversity surveys are essential to understand the scope of the loss. Despite metabarcoding approach shortcomings, the declining costs and shorter survey and processing time required for this approach compared to traditional survey methods indicate that it can be a valuable addition to the toolkit for saproxylic Coleoptera diversity survey, Diversity inventories are critical to creating accurate species range maps and estimating population sizes, which in turn lead to better informed landscape and wildlife management decisions. Metabarcoding has facilitated large-scale environmental diversity surveys. However, the use of a metabarcoding approach with bird feces to survey arthropod diversity is still rela- tively undeveloped. The aim of this study was to see if and how a metabarcoding approach with bird feces could contribute to a saproxylic Coleoptera survey of traditional insect traps. We compared two methods of surveying saproxylic Coleoptera diversity (metabarcoding birds feces and deploying traditional traps) over two elevations in a mountain system. The two methods caught different species and different levels of functional guild richness. The metabarcoding method successfully recorded both distinct and overlapping portions of diversity from traditional collections, and the approach was also effec- tive in signaling the presence of both rare species and nine country records. Our results show that metabarcoding Passerine bird feces can be successful when used alongside traditional collection methods to capture a broad diversity of saproxylic Coleoptera. This method, however, has quantitative and qualitative limitations, including the inability to produce species abundance data as well as the generation of false positives and negatives due to biases within the metabarcoding pipeline. Implications for insect conservation As many terrestrial ecosystems lose insect diversity, insect diversity surveys are essential to understand the scope of the loss. Despite metabarcoding approach shortcomings, the declining costs and shorter survey and processing time required for this approach compared to traditional survey methods indicate that it can be a valuable addition to the toolkit for saproxylic Coleoptera diversity survey

In the USA alone, osteoarthritis affects > 3 billion people, making it the most predominant type of joint disorder. It costs the US more than $185 billion a year and is the primary cause of persistent disability in older persons. The results of our cases will help encourage local health researchers to continue this procedure and review the available resources thereof. Objective: To determine efficacy of arthroscopic debridement with proximal fibular osteotomy in early medial joint osteoarthritis of the knee in patients presented to tertiary care hospital. Methods: This descriptive Case Series was held in the Orthopedic Surgery department, MTI-Hayatabad Medical Complex, Peshawar from 30 Dec, 2020 to 30 Jun, 2021. Patients with medial knee osteoarthritis were treated by proximal fibular osteotomy combined with arthroscopic debridement. The efficacy of the knee function was evaluated by VAS score, AKSS, and ML ratio at 1 week and 3 months after operation. Results: As per frequencies and percentages for efficacy, 114 (69.1%) patients showed effective results of arthroscopic debridement. Conclusion: This study demonstrated that arthroscopic debridement with proximal fibular osteotomy is an effective procedure in the management of early medial joint osteoarthritis of the knee in terms of improved knee function.

Failure due to cracks is a major structural safety issue for engineering constructions. Human examination is the most common method for detecting crack failure, although it is subjective and time-consuming. Inspection of civil engineering structures must include crack detection and categorization as a key component of the process. Images can automatically be classified using convolutional neural networks (CNNs), a subtype of deep learning (DL). For image categorization, a variety of pre-trained CNN architectures are available. This study assesses seven pre-trained neural networks, including GoogLeNet, MobileNet-V2, Inception-V3, ResNet18, ResNet50, ResNet101, and ShuffleNet, for crack detection and categorization. Images are classified as diagonal crack (DC), horizontal crack (HC), uncracked (UC), and vertical crack (VC). Each architecture is trained with 32,000 images equally divided among each class. A total of 100 images from each category are used to test the trained models, and the results are compared. Inception-V3 outperforms all the other models with accuracies of 96%, 94%, 92%, and 96% for DC, HC, UC, and VC classifications, respectively. ResNet101 has the longest training time at 171 min, while ResNet18 has the lowest at 32 min. This research allows the best CNN architecture for automatic detection and orientation of cracks to be selected, based on the accuracy and time taken for the training of the model.

Structural health monitoring is crucial for ensuring the safety and reliability of civil infrastructures. Traditional monitoring methods involve installing sensors across large regions, which can be costly and ineffective due to the sensors damage and poor compliance with structural members. This study involves systematically varying the graphene nanoplatelets (GNPs) concentration and analyzing the strength performance and piezoresistive behavior of the resulting composites. Two different composites having natural and recycled sands with varying percentages of GNPs as 2%, 4%, 6%, and 8% were prepared. Dispersion of GNPs was performed in superplasticizer and then ultrasonication was employed by using an ultrasonicator. The four-probe method was utilized to establish the piezoresistive behavior. The results revealed that the compressive strength of mortar cubes with natural sand was increased up to a GNP content of 6%, beyond which it started to decline. In contrast, specimens with recycled sand showed a continuous decrease in the compressive strength. Furthermore, the electrical resistance stability was observed at 4% for both natural and recycled sands specimens, exhibiting linearity between the frictional change in the resistivity and compressive strain values. It can be concluded from this study that the use of self-sensing sustainable cementitious composites could pave their way in civil infrastructures.

Reinforced concrete corbels were examined in this study for the cracking behavior and strength evaluation, focusing on defects typically found in these structures. A total of 11 corbel specimens were tested, including healthy specimens (HS), specimens with lower concrete strength (LC), specimens with less reinforcement ratio (LR), and specimens with more concrete cover than specifications (MC). The HS specimens were designed using the ACI conventional method. The specimens were tested under static loading conditions, and the actual strengths along with the crack patterns were determined. In the experimental tests, the shear capacity of the HS specimens was 28.18% and 57.95% higher than the LR and LC specimens, respectively. Similarly, the moment capacity of the HS specimens was 25% and 57.52% greater than the LR and LC specimens, respectively. However, in the case of the built-up sections, the shear capacity of the HS specimens was 9.91% and 37.51% higher than the LR and LC specimens, respectively. Likewise, the moment capacity of the HS specimens was 39.91% and 14.30% higher than the LR and LC specimens, respectively. Moreover, a detailed nonlinear finite element model (FEM) was developed using ABAQUS, and a more user-friendly strut and tie model (STM) was investigated toward its suitability to assess the strengths of the corbels with construction defects. The results from FEM and STM were compared. It was found that the FEM results were in close agreement with their experimental counterparts.

The degradation of infrastructures such as bridges, highways, buildings, and dams has been accelerated due to environmental and loading consequences. The most popular method for inspecting existing concrete structures has been visual inspection. Inspectors assess defects visually based on their engineering expertise, competence, and experience. This method, however, is subjective, tiresome, inefficient, and constrained by the requirement for access to multiple components of complex structures. The angle, width, and length of the crack allow us to figure out the cause of the propagation and extent of the damage, and rehabilitation can be suggested based on them. This research proposes an algorithm based on a pre-trained convolutional neural network (CNN) and image processing (IP) to obtain the crack angle, width, endpoint length, and actual path length in a concrete structure. The results show low relative errors of 2.19%, 14.88%, and 1.11%, respectively for the crack angle, width, and endpoint length from the CNN and IP methods developed in this research. The actual path length is found to be 14.69% greater than the crack endpoint length. When calculating the crack length, it is crucial to consider its irregular shape and the likelihood that its actual path length will be greater than the direct distance between the endpoints. This study suggests measurement methods that precisely consider the crack shape to estimate its actual path length.

Glass fiber-reinforced polymer (GFRP) reinforcements are useful alternatives to traditional steel bars in concrete structures, particularly in vertical structural elements such as columns, as they are less prone to corrosion, and impart increasing strength and endurance of buildings. There is limited research on the finite element analysis (FEA) of the structural behavior of hollow glass fiber-reinforced polymer reinforced concrete (GFRPRC) columns. The hollow portion can be used for the service duct and for reducing the self-weight of the members. Numerical analysis of the compressive response of circular hollow concrete columns reinforced with GFRP bars and spirals is performed in this study. This article aims to investigate the axial behavior of hollow GFRP concrete columns and compare it with that of solid steel reinforced concrete (RC) columns as well as hollow steel RC columns. The Abaqus software is used to construct finite element models. After calibration of modeling using an experimental test result as a control model, a parametric study is conducted. The columns with the same geometry, loading, and boundary conditions are analyzed in the parametric study. It is resulted that the hollow GFRP concrete columns provide a greater confinement effect than the solid steel RC columns. The average variation in the ultimate axial load-carrying capacities of the experimental results, from that of the FEA values, is noted to be only 3.87%, while the average difference in the corresponding deformations is 7.08%. Moreover, the hollow GFRP concrete columns possess greater axial load and deformation capacities compared with the solid steel RC columns.

The prolonged hypoxic conditions hinder chronic wounds from healing and lead to severe conditions such as delayed re-epithelialization and enhanced risk of infection. Multifunctional wound dressings are highly required to address the challenges of chronic wounds. Herein, we report polyurethane-coated sodium per carbonate-loaded chitosan hydrogel (CSPUO2) as a multifunctional dressing. The hydrogels (Control, CSPU, and CSPUO2) were prepared by freeze gelation method and the developed hydrogels showed high porosity, good absorption capacity, and adequate biodegradability. The release of oxygen from the CSPUO2 hydrogel was confirmed by the increase in pH and a sustained oxygen release was observed over the period of 21 days, due to polyurethane (CSPU) coating. The CSPUO2 hydrogel exhibited around 2-fold increased angiogenic potential in CAM assay when compared with Control and CSPU dressing. CSPUO2 also showed good level of antibacterial efficacy against E. coli and S. aureus. In a full-thickness rat wound model, CSPUO2 hydrogel considerably accelerated wound healing with exceptional re-epithelialization granulation tissue formation less inflammatory cells and improved skin architecture highlighting the tremendous therapeutic potential of this hydrogel when compared with control and CSPU to treat chronic diabetic and burn wounds.

Smoking, which accounts for a large portion of preventable deaths worldwide, has a huge negative impact on public health expenditures, reduces lifespans, and reduces daily productivity. This study aimed to determine the prevalence of smoking and its associated factors among students enrolled in public sector universities in Karachi in order to address this urgent issue. Data were gathered using questionnaires that students completed, and they were then rigorously statistically analyzed using the chi-square method and IBM SPSS Statistics 20. The results of this study provide insight into important factors affecting students' smoking habits. Notably, gender was crucial, with significant differences between male and female pupils. Another important component that emerged was age, with smoking prevalence rising sharply as people aged. The survey also found that there were significant correlations between students' smoking habits and those of their siblings and close friends. Additionally, it was discovered that people's religious beliefs have a big impact on their smoking habits. These findings highlight the critical importance of including these aspects in the creation of successful anti-smoking initiatives geared toward college students. The prevalence of smoking among this group can be lowered by using personalized treatments that consider factors including gender, age, peer pressure, and religious attitudes. This will ultimately enhance public health and lower healthcare costs.

High-elevation insectivorous birds are currently confronted with the reality of a changing climate, land use shifts, and the decline of many prey groups. The diet dynamics among many imperiled animals in this group are still unresolved. Exam- ining the diets of tree-line Passerine birds to the species level of the prey allows for stronger population predictions. This study uses DNA metabarcoding to identify prey arthropods from adult Passerine bird feces at and slightly below tree-line in a Pyrenean forest. Our objective was to quantify the intra-and inter-species richness and overlap of Passerine bird diet over time and space. The results showed that adult Passerine diets have high inter- and intra-species dietary variability and low inter- and intra-species dietary overlap. The lack of association between dietary richness and open space, season, and eleva- tion and lack of differences between dietary overlap and open space and elevation suggest high-elevation Passerine birds have very high dietary flexibility. The results also showed that aphids known to be pests to conifers, and other conifer pests, were prevalent in the birds’ diets. The Passerine diets and high rate of rare dietary items are mainly in line with other recent DNA metabarcoding studies. Implications for the long-term projections relative to tree-line Passerine populations are discussed., High-elevation insectivorous birds are currently confronted with the reality of a changing climate, land use shifts, and the decline of many prey groups. The diet dynamics among many imperiled animals in this group are still unresolved. Exam- ining the diets of tree-line Passerine birds to the species level of the prey allows for stronger population predictions. This study uses DNA metabarcoding to identify prey arthropods from adult Passerine bird feces at and slightly below tree-line in a Pyrenean forest. Our objective was to quantify the intra-and inter-species richness and overlap of Passerine bird diet over time and space. The results showed that adult Passerine diets have high inter- and intra-species dietary variability and low inter- and intra-species dietary overlap. The lack of association between dietary richness and open space, season, and eleva- tion and lack of differences between dietary overlap and open space and elevation suggest high-elevation Passerine birds have very high dietary flexibility. The results also showed that aphids known to be pests to conifers, and other conifer pests, were prevalent in the birds’ diets. The Passerine diets and high rate of rare dietary items are mainly in line with other recent DNA metabarcoding studies. Implications for the long-term projections relative to tree-line Passerine populations are discussed., High-elevation insectivorous birds are currently confronted with the reality of a changing climate, land use shifts, and the decline of many prey groups. The diet dynamics among many imperiled animals in this group are still unresolved. Exam- ining the diets of tree-line Passerine birds to the species level of the prey allows for stronger population predictions. This study uses DNA metabarcoding to identify prey arthropods from adult Passerine bird feces at and slightly below tree-line in a Pyrenean forest. Our objective was to quantify the intra-and inter-species richness and overlap of Passerine bird diet over time and space. The results showed that adult Passerine diets have high inter- and intra-species dietary variability and low inter- and intra-species dietary overlap. The lack of association between dietary richness and open space, season, and eleva- tion and lack of differences between dietary overlap and open space and elevation suggest high-elevation Passerine birds have very high dietary flexibility. The results also showed that aphids known to be pests to conifers, and other conifer pests, were prevalent in the birds’ diets. The Passerine diets and high rate of rare dietary items are mainly in line with other recent DNA metabarcoding studies. Implications for the long-term projections relative to tree-line Passerine populations are discussed.

Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learning system for the prediction of cohesive networks using face images from photo albums. The proposed deep learning model consists of hierarchical CNN architecture to learn descriptive features related to each cohesive network. The paper also proposes a novel Face Co-occurrence Frequency algorithm to quantify existence of people in images, and a novel photo ranking method to analyze the strength of relationship between different individuals in a predicted social network. We extensively evaluate the proposed technique on PIPA dataset and compare with state-of-the-art methods. Our experimental results demonstrate the superior performance of the proposed technique for the prediction of relationship between different individuals and the cohesiveness of communities.

In this paper, we compare the scalar field dynamics in axion-like and power law potentials for both positive and negative values of the exponents. We find that, for positive exponents, both the potentials exhibit similar scalar field dynamics and it can be difficult to distinguish them at least at the background level. Even though the potentials are oscillatory in nature for positive exponents scaling solutions can be achieved for larger values of the exponent for which the dynamics can be different during early times. Because of the presence of this scaling nature there is a turnaround in the values of the scalar field equation of state as we increase the values of the exponent in both the potentials. This indicates the deviation from the oscillatory behaviour for the larger values of the exponent. For negative values of the exponent, the dynamics of the scalar field is distinguishable and axion-like potential can give rise to cosmologically viable tracker solutions unlike the power law potentials. So, while for positive exponents we may not distinguish the two potentials for negative exponents the dynamics of the scalar field is distinguishable., Comment: 12 pages, one table and 11 figures. Comments are welcome

This article deals with the study of predicting the confinement effect of carbon fiber reinforced polymers (CFRPs) on concrete cylinder strength using metaheuristics-based artificial neural networks. A detailed database of 708 CFRP confined concrete cylinders is developed from previously published research with information on 8 parameters including geometrical parameters like the diameter (d) and height (h) of a cylinder, unconfined compressive strength of concrete (fco'), thickness (nt), the elastic modulus of CFRP (Ef), unconfined concrete strain confined concrete strain and the ultimate compressive strength of confined concrete fcc'. Three metaheuristic models are implemented including particle swarm optimization (PSO), grey wolf optimizer (GWO), and bat algorithm (BA). These algorithms are trained on the data using an objective function of mean square error and their predicted results are validated against the experimental studies and finite element analysis. The study shows that the hybrid model of PSO predicted the strength of CFRP-confined concrete cylinders with maximum accuracy of 99.13% and GWO predicted the results with an accuracy of 98.17%. The high accuracy of axial compressive strength predictions demonstrated that these prediction models are a reliable solution to the empirical methods. The prediction models are especially suitable for avoiding full-scale time-consuming experimental tests that make the process quick and economical., Comment: 28 Pages, 19 Figures

This research aims at comparative analysis of shear strength prediction at slab-column connection, unifying machine learning, design codes and Finite Element Analysis. Current design codes (CDCs) of ACI 318-19 (ACI), Eurocode 2 (EC2), Compressive Force Path (CFP) method, Feed Forward Neural Network (FNN) based Artificial Neural Network (ANN), PSO-based FNN (PSOFNN), and BAT algorithm-based BATFNN are used. The study is complemented with FEA of slab for validating the experimental results and machine learning predictions.In the case of hybrid models of PSOFNN and BATFNN, mean square error is used as an objective function to obtain the optimized values of the weights, that are used by Feed Forward Neural Network to perform predictions on the slab data. Seven different models of PSOFNN, BATFNN, and FNN are trained on this data and the results exhibited that PSOFNN is the best model overall. PSOFNN has the best results for SCS=1 with highest value of R as 99.37% and lowest of MSE, and MAE values of 0.0275%, and 1.214% respectively which are better than the best FNN model for SCS=4 having the values of R, MSE, and MAE as 97.464%, 0.0492%, and 1.43%, respectively., Comment: 34 Pages,25 Figures

Neural Radiance Fields (NeRF) is a novel implicit 3D reconstruction method that shows immense potential and has been gaining increasing attention. It enables the reconstruction of 3D scenes solely from a set of photographs. However, its real-time rendering capability, especially for interactive real-time rendering of large-scale scenes, still has significant limitations. To address these challenges, in this paper, we propose a novel neural rendering system called UE4-NeRF, specifically designed for real-time rendering of large-scale scenes. We partitioned each large scene into different sub-NeRFs. In order to represent the partitioned independent scene, we initialize polygonal meshes by constructing multiple regular octahedra within the scene and the vertices of the polygonal faces are continuously optimized during the training process. Drawing inspiration from Level of Detail (LOD) techniques, we trained meshes of varying levels of detail for different observation levels. Our approach combines with the rasterization pipeline in Unreal Engine 4 (UE4), achieving real-time rendering of large-scale scenes at 4K resolution with a frame rate of up to 43 FPS. Rendering within UE4 also facilitates scene editing in subsequent stages. Furthermore, through experiments, we have demonstrated that our method achieves rendering quality comparable to state-of-the-art approaches. Project page: https://jamchaos.github.io/UE4-NeRF/., Comment: Accepted by NeurIPS2023

From the last decade, researchers in the field of machine learning (ML) and assistive developmental robotics (ADR) have taken an interest in artificial empathy (AE) as a possible future paradigm for human-robot interaction (HRI). Humans learn empathy since birth, therefore, it is challenging to instill this sense in robots and intelligent machines. Nevertheless, by training over a vast amount of data and time, imitating empathy, to a certain extent, can be possible for robots. Training techniques for AE, along with findings from the field of empathetic AI research, are ever-evolving. The standard workflow for artificial empathy consists of three stages: 1) Emotion Recognition (ER) using the retrieved features from video or textual data, 2) analyzing the perceived emotion or degree of empathy to choose the best course of action, and 3) carrying out a response action. Recent studies that show AE being used with virtual agents or robots often include Deep Learning (DL) techniques. For instance, models like VGGFace are used to conduct ER. Semi-supervised models like Autoencoders generate the corresponding emotional states and behavioral responses. However, there has not been any study that presents an independent approach for evaluating AE, or the degree to which a reaction was empathetic. This paper aims to investigate and evaluate existing works for measuring and evaluating empathy, as well as the datasets that have been collected and used so far. Our goal is to highlight and facilitate the use of state-of-the-art methods in the area of AE by comparing their performance. This will aid researchers in the area of AE in selecting their approaches with precision.

Potholes are fatal and can cause severe damage to vehicles as well as can cause deadly accidents. In South Asian countries, pavement distresses are the primary cause due to poor subgrade conditions, lack of subsurface drainage, and excessive rainfalls. The present research compares the performance of three pre-trained Convolutional Neural Network (CNN) models, i.e., ResNet 50, ResNet 18, and MobileNet. At first, pavement images are classified to find whether images contain potholes, i.e., Potholes or Normal. Secondly, pavements images are classi-fied into three categories, i.e., Small Pothole, Large Pothole, and Normal. Pavement images are taken from 3.5 feet (waist height) and 2 feet. MobileNet v2 has an accuracy of 98% for detecting a pothole. The classification of images taken at the height of 2 feet has an accuracy value of 87.33%, 88.67%, and 92% for classifying the large, small, and normal pavement, respectively. Similarly, the classification of the images taken from full of waist (FFW) height has an accuracy value of 98.67%, 98.67%, and 100%., Comment: 24 Pages, 26 Figures

Australia has a reputation for producing a reliable supply of high-quality barley in a contaminant-free climate. As a result, Australian barley is highly sought after by malting, brewing, distilling, and feed industries worldwide. Barley is traded as a variety-specific commodity on the international market for food, brewing and distilling end-use, as the intrinsic quality of the variety determines its market value. Manual identification of barley varieties by the naked eye is challenging and time-consuming for all stakeholders, including growers, grain handlers and traders. Current industrial methods for identifying barley varieties include molecular protein weights or DNA based technology, which are not only time-consuming and costly but need specific laboratory equipment. On grain receival, there is a need for efficient and low-cost solutions for barley classification to ensure accurate and effective variety segregation. This paper proposes an efficient deep learning-based technique that can classify barley varieties from RGB images. Our proposed technique takes only four milliseconds to classify an RGB image. The proposed technique outperforms the baseline method and achieves a barley classification accuracy of 94% across 14 commercial barley varieties (some highly genetically related).

SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally, producing new variants and has become a pandemic. People have lost their lives not only due to the virus but also because of the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop robust artificial intelligence techniques to predict the spread of COVID-19. In this paper, we propose a deep learning technique, called Deep Sequential Prediction Model (DSPM) and machine learning based Non-parametric Regression Model (NRM) to predict the spread of COVID-19. Our proposed models are trained and tested on publicly available novel coronavirus dataset. The proposed models are evaluated by using Mean Absolute Error and compared with the existing methods for the prediction of the spread of COVID-19. Our experimental results demonstrate the superior prediction performance of the proposed models. The proposed DSPM and NRM achieve MAEs of 388.43 (error rate 1.6%) and 142.23 (0.6%), respectively compared to 6508.22 (27%) achieved by baseline SVM, 891.13 (9.2%) by Time-Series Model (TSM), 615.25 (7.4%) by LSTM-based Data-Driven Estimation Method (DDEM) and 929.72 (8.1%) by Maximum-Hasting Estimation Method (MHEM).

Purpose: Online retailing has grown tremendously over the last decade, particularly in the COVID 19 period but little attention has been given to ethical problems arising from it. The purpose of this study is to investigate the impact of e-retailing ethics on consumer repurchase intention aiming to highlight the significance of ethical consideration in e-retailing business..   Design/Methodology/Approach: This is a quantitative study and the sample size is 212 participants. Male participants were 129 and  Female participants were  83. Respondents mainly included consumers with varying frequencies of online purchasing. The conceptual framework comprised of reliability, non-deception, security, trust and e-service quality as independent variables moderated by shopping habits. The dependent variable was repurchase intention. PlS-SEM was used to analyze the data. The participants mostly were residents of Karachi. Findings: The repurchase intention of consumers is directly impacted by three independent variables of e-retailing ethics out of five tested in the study i.e. E-service quality, reliability and non-deception while remaining variables trust and security proved insignificant. The study results further revealed that reliability, non-deception, and e-service quality are also positively moderated by shoping habits while security and trust proved insignificant. Implications/Originality/Value: The findings can be used by online retailing outlets to design marketing strategies for successfully running their store.

Purpose: Literature is scarce on the possible relationship between investors’ biases, risk tolerance attitude, and stock return volatility. The researcher investigated what are the investor biases, and how they contribute to the risk-averse and risk-seeking attitudes and developed a taxonomy model of investors’ biases in the form of a causal framework that impacts stock return volatility. Methodology: The study employs a systematic literature review approach. The analysis of literature includes 65 articles from impact factor journals including three seminal papers in the fields of traditional and behavioral finance. The time frame ranges from 2008 to 2022. Findings: The findings suggest that investors encounter certain biases such as cognitive, emotional, cultural, religious, financial, macroeconomic, demographic, etc. Literature has identified positive, negative, and mixed impacts of investors' biases on stock return volatility. The systematic analysis of literature helps in identifying recently evolving biases such as individualism, uncertainty avoidance, a religious adherence, investor mood, weather bias, fear sentiments, sports sentiments, power distance, masculinity, social media sentiments so on and so forth. Conclusion: The study has proposed an integrated taxonomy model comprised of possible investors’ biases as independent variables along with mediating, controlling, and moderating variables that impact stock return volatility. Moreover, investors’ risk tolerance profile is also constructed which indicates the role of behavioral biases in shaping investors’ attitudes as risk seekers and risk-averse.

The rise of the intelligent, local charging facilitation and environmentally friendly aspects of electric vehicles (EVs) has grabbed the attention of many end-users. However, there are still numerous challenges faced by researchers trying to put EVs into competition with internal combustion engine vehicles (ICEVs). The major challenge in EVs is quick recharging and the selection of an optimal charging station. In this paper, we present the most recent research on EV charging management systems and their role in smart cities. EV charging can be done either in parking mode or on-the-move mode. This review work is novel due to many factors, such as that it focuses on discussing centralized and distributed charging management techniques supported by a communication framework for the selection of an appropriate charging station (CS). Similarly, the selection of CS is evaluated on the basis of battery charging as well as battery swapping services. This review also covered plug-in charging technologies including residential, public and ultra-fast charging technologies and also discusses the major components and architecture of EVs involved in charging. In a comprehensive and detailed manner, the applications and challenges in different charging modes, CS selection, and future work have been discussed. This is the first attempt of its kind, we did not find a survey on the charging hierarchy of EVs, their architecture, or their applications in smart cities.

This research linguistically explores the naturalistic elements in Thomas Hardy's book "The Mayor of Casterbridge" i.e., Immanent will, fate, the Law of Nature, and Naturalism as a whole are the main naturalist tenets that are considered. This research investigates how various forces, such as immanent will, fate, and natural laws, interact to determine and frame a person's actions and life. This study looks into the impact of coincidence and chance in our lives. A man is not solely accountable for his actions which lead to his miserable destiny, and it is also explored that free will is not entirely free. According to this research work, nature significantly impacts how our lives are affected. Regardless of our decisions and desires, our lives are directed and governed by an absolute force known as Immanent Will. According to the research, because natural law controls every aspect of human life, all people are subject to it.

Deep learning, a branch of artificial intelligence, has achieved unprecedented performance in several domains including medicine to assist with efficient diagnosis of diseases, prediction of disease progression and pre-screening step for physicians. Due to its significant breakthroughs, deep learning is now being used for the diagnosis of arthritis, which is a chronic disease affecting young to aged population. This paper provides a survey of recent and the most representative deep learning techniques (published between 2018 to 2020) for the diagnosis of osteoarthritis and rheumatoid arthritis. The paper also reviews traditional machine learning methods (published 2015 onward) and their application for the diagnosis of these diseases. The paper identifies open problems and research gaps. We believe that deep learning can assist general practitioners and consultants to predict the course of the disease, make treatment propositions and appraise their potential benefits.

The physical and textural attributes of objects have been widely studied for recognition, detection and segmentation tasks in computer vision.~A number of datasets, such as large scale ImageNet, have been proposed for feature learning using data hungry deep neural networks and for hand-crafted feature extraction. To intelligently interact with objects, robots and intelligent machines need the ability to infer beyond the traditional physical/textural attributes, and understand/learn visual cues, called visual affordances, for affordance recognition, detection and segmentation. To date there is no publicly available large dataset for visual affordance understanding and learning. In this paper, we introduce a large scale multi-view RGBD visual affordance learning dataset, a benchmark of 47210 RGBD images from 37 object categories, annotated with 15 visual affordance categories. To the best of our knowledge, this is the first ever and the largest multi-view RGBD visual affordance learning dataset. We benchmark the proposed dataset for affordance segmentation and recognition tasks using popular Vision Transformer and Convolutional Neural Networks. Several state-of-the-art deep learning networks are evaluated each for affordance recognition and segmentation tasks. Our experimental results showcase the challenging nature of the dataset and present definite prospects for new and robust affordance learning algorithms. The dataset is publicly available at https://sites.google.com/view/afaqshah/dataset.

Federated Learning (FL) provides privacy preservation by allowing the model training at edge devices without the need of sending the data from edge to a centralized server. FL has distributed the implementation of ML. Another variant of FL which is well suited for the Internet of Things (IoT) is known as Collaborated Federated Learning (CFL), which does not require an edge device to have a direct link to the model aggregator. Instead, the devices can connect to the central model aggregator via other devices using them as relays. Although, FL and CFL protect the privacy of edge devices but raises security challenges for a centralized server that performs model aggregation. The centralized server is prone to malfunction, backdoor attacks, model corruption, adversarial attacks and external attacks. Moreover, edge device to centralized server data exchange is not required in FL and CFL, but model parameters are sent from the model aggregator (global model) to edge devices (local model), which is still prone to cyber-attacks. These security and privacy concerns can be potentially addressed by Blockchain technology. The blockchain is a decentralized and consensus-based chain where devices can share consensus ledgers with increased reliability and security, thus significantly reducing the cyberattacks on an exchange of information. In this work, we will investigate the efficacy of blockchain-based decentralized exchange of model parameters and relevant information among edge devices and from a centralized server to edge devices. Moreover, we will be conducting the feasibility analysis for blockchain-based CFL models for different application scenarios like the internet of vehicles, and the internet of things. The proposed study aims to improve the security, reliability and privacy preservation by the use of blockchain-powered CFL., Comment: Preliminary work

Deep learning techniques have led to remarkable breakthroughs in the field of generic object detection and have spawned a lot of scene-understanding tasks in recent years. Scene graph has been the focus of research because of its powerful semantic representation and applications to scene understanding. Scene Graph Generation (SGG) refers to the task of automatically mapping an image into a semantic structural scene graph, which requires the correct labeling of detected objects and their relationships. Although this is a challenging task, the community has proposed a lot of SGG approaches and achieved good results. In this paper, we provide a comprehensive survey of recent achievements in this field brought about by deep learning techniques. We review 138 representative works that cover different input modalities, and systematically summarize existing methods of image-based SGG from the perspective of feature extraction and fusion. We attempt to connect and systematize the existing visual relationship detection methods, to summarize, and interpret the mechanisms and the strategies of SGG in a comprehensive way. Finally, we finish this survey with deep discussions about current existing problems and future research directions. This survey will help readers to develop a better understanding of the current research status and ideas., Comment: Submitted to TPAMI

Gulf Cooperation Countries (GCC) governments are encouraging advances in renewable energy and its deployment to reform domestic energy market policy. GCC countries including United Arab Emirates (UAE) all have begun to explore renewable energy technologies. This research intended to develop a framework model to use Grid Tied photovoltaic renewable energy (GT-PV) in the housing design of the GCC region particularly in UAE. In this regard, research has analyzed the various types of housing plans and worked out the possibilities of retrofit design to adapt renewable energy model to implement (GT-PV) in existing housing for their energy needs.  This study has determined the total energy need of each type of house, data of energy need was statically analyzed, and renewable energy output was calculated. The end result of research has surfaced a comprehensive model to design a grid-tied photovoltaic renewable energy system to cater the need for private housing in UAE.

Thermochromic (TC) materials can switch solar absorptance (α) based on temperature stimuli. When coatings with TC properties are applied on building envelope surfaces, the amount of solar heat gains can be controlled to reduce the heating and cooling demand of buildings. To date, limited research has been conducted in investigating optimal TC coating properties for application on opaque building envelopes in various scenarios. In this research, a method to model TC coatings using building performance simulation (BPS) tools has been developed and coupled with python to optimize solar absorption states (α) and switching temperatures and reduce the annual heating and cooling demand. The simulation-based approach has been employed to perform early-stage exploration studies on multiple building types and climates to support material R&D in developing optimized coatings for target applications and assess the potential energy savings. The results indicate that the optimum TC properties are unique to climate and building types. TC coatings with high switching temperatures result in larger energy savings for scenarios with high heating demands, while TC coatings with low switching temperatures produce larger energy savings in scenarios with high cooling demands. Similarly, increasing the high solar absorption (αhigh) to 1 increases the heating savings, while reducing the low solar absorption (αlow) to 0 results in higher cooling savings. Furthermore, it was found that solar irradiance causes temperature spikes triggering the TC coatings to unnecessarily switch from high to low absorptance state in winters leading to heating penalties. Replacing optimal static with TC coatings on terraced houses in the Spanish climate with a 2:3 heating to cooling demand ratio results in 2 to 13% energy savings.

Machine Learning is a relatively new and emergent field in video game development domain. Despite considerable relevance to the video game industry, there has yet to be a significant commercial product utilising machine learning in its design or function. Although previous research has shown significant potential in the use of video games in developing and testing Artificial Intelligence, the reverse i.e., using Artificial Intelligence to develop and test video games is far less common. This paper provides a survey of existing techniques and reviews current and future applications of machine learning in the field of video game development, both as a tool to streamline development and management processes and as an integrated part of video game end products themselves. This paper also explores a number of machine learning technologies not yet applied to use in the video game field and discusses their potential in future research and product development. Despite the relative newness and lack of development in this field, this paper finds that there is potential for machine learning to significantly improve and expedite production in the video game industry. Machine Learning can potentially be harnessed to develop new or improved products or automate development processes in conventional video game products.

Temporomandibular joint (TMJ) replacement with an implant is only used when all other conservative treatments fail. Despite the promising short-term results, the long-term implications of TMJ replacement in masticatory function are not fully understood. Previous human and animal studies have shown that perturbations to the normal masticatory function can lead to morphological and functional changes in the craniomaxillofacial system. A clearer understanding of the biomechanical implications of TMJ replacement in masticatory function may help identify design shortcomings that hinder their long-term success. In this study, patient-specific finite element models of the intact and implanted mandible were developed and simulated under four different biting tasks. In addition, the impact of re-attaching of the lateral pterygoid was also evaluated. The biomechanics of both models was compared regarding both mandibular displacements and principal strain patterns. The results show an excessive mediolateral and anteroposterior displacement of the TMJ implant compared to the intact joint in three biting tasks, namely incisor (INC), left moral (LML), and right molar (RML) biting. The main differences in principal strain distributions were found across the entire mandible, most notably from the symphysis to the ramus of the implanted side. Furthermore, the re-attachment of the lateral pterygoid seems to increase joint anteroposterior displacement in both INC, LML and RML biting while reducing it during LGF. Accordingly, any new TMJ implant design must consider stabilising both mediolateral and anteroposterior movement of the condyle during biting activities and promoting a more natural load transmission along the entire mandible., Materials Innovation Institute

With the growing use of robots and intelligent machines in our day-to-day as well as in-dustrial practices, it has become immensely important for machines to understand human feelings and emotions. This work addresses one of the contemporary issues in computer vi-sion i.e, Facial Emotion Recognition (FER). Many researchers have used machine learning methods to perform FER from video, image, text and audio data. Image set classification, which is more versatile than single images, has gained popularity in recent years. Although several works have incorporated image set classification with geometrical representation methods, deep learning methods have not been yet used for facial emotion recognition. We propose a novel methodology that pioneers the implementation of deep neural net-works for FER using image set classification. The proposed network is pretrained with VGGFace weights, and is built on VGG16 network with added layers as per the task’s requirement. Predictions are based on a combination of two different voting strategies including, Majority Voting and Weighted Voting. Besides reaching superior accuracy for one of the most challenging datasets for facial analysis i.e., SFEW (Static Facial Emotion in the Wild), the proposed approach achieves state-of-the-art accuracy on FER2013 (Fa-cial Emotion Recognition 2013) and FERG (Facial Emotion Recognition Group) datasets, without data augmentation, feature extraction and any additional training data. Our ex-perimental results demonstrate that the proposed image set classification provides more accurate results than the existing state-of-the-art FER approaches. In addition, an au-tonomous robot is set up and configured that can perform tasks, such as remote view, tele-operation, and 3D-mapping.

Cyclohexanone monooxygenase (CHMO), a member of the Baeyer‐Villiger monooxygenase family, is a versatile biocatalyst that efficiently catalyzes the conversion of cyclic ketones to lactones. In this study, an Acidovorax‐derived CHMO gene was expressed in Pseudomonas taiwanensis VLB120. Upon purification, the enzyme was characterized in vitro and shown to feature a broad substrate spectrum and up to 100% conversion in 6 h. Further, we determined and compared the cyclohexanone conversion kinetics for different CHMO‐biocatalyst formats, i.e., isolated enzyme, suspended whole cells, and biofilms, the latter two based on recombinant CHMO‐containing P. taiwanensis VLB120. Biofilms showed less favorable values for KS (9.3‐fold higher) and kcat (4.8‐fold lower) compared to corresponding KM and kcat values of isolated CHMO, but a favorable KI for cyclohexanone (5.3‐fold higher). The unfavorable KS and kcat values are related to mass transfer‐ and possibly heterogeneity issues and deserve further investigation and engineering, in order to exploit the high potential of biofilms regarding process stability. Suspended cells showed an only 1.8‐fold higher KS, but 1.3‐ and 4.2‐fold higher kcat and KI values than isolated CHMO. This together with the efficient NADPH regeneration via glucose metabolism makes this format highly promising from a kinetics perspective.

Deep learning has become an integral part of various computer vision systems in recent years due to its outstanding achievements for object recognition, facial recognition, and scene understanding. However, deep neural networks (DNNs) are susceptible to be fooled with nearly high confidence by an adversary. In practice, the vulnerability of deep learning systems against carefully perturbed images, known as adversarial examples, poses a dire security threat in the physical world applications. To address this phenomenon, we present, what to our knowledge, is the first ever image set based adversarial defence approach. Image set classification has shown an exceptional performance for object and face recognition, owing to its intrinsic property of handling appearance variability. We propose a robust deep Bayesian image set classification as a defence framework against a broad range of adversarial attacks. We extensively experiment the performance of the proposed technique with several voting strategies. We further analyse the effects of image size, perturbation magnitude, along with the ratio of perturbed images in each image set. We also evaluate our technique with the recent state-of-the-art defence methods, and single-shot recognition task. The empirical results demonstrate superior performance on CIFAR-10, MNIST, ETH-80, and Tiny ImageNet datasets.

The astounding success made by artificial intelligence (AI) in healthcare and other fields proves that AI can achieve human-like performance. However, success always comes with challenges. Deep learning algorithms are data-dependent and require large datasets for training. The lack of data in the medical imaging field creates a bottleneck for the application of deep learning to medical image analysis. Medical image acquisition, annotation, and analysis are costly, and their usage is constrained by ethical restrictions. They also require many resources, such as human expertise and funding. That makes it difficult for non-medical researchers to have access to useful and large medical data. Thus, as comprehensive as possible, this paper provides a collection of medical image datasets with their associated challenges for deep learning research. We have collected information of around three hundred datasets and challenges mainly reported between 2013 and 2020 and categorized them into four categories: head & neck, chest & abdomen, pathology & blood, and ``others''. Our paper has three purposes: 1) to provide a most up to date and complete list that can be used as a universal reference to easily find the datasets for clinical image analysis, 2) to guide researchers on the methodology to test and evaluate their methods' performance and robustness on relevant datasets, 3) to provide a ``route'' to relevant algorithms for the relevant medical topics, and challenge leaderboards., Comment: This paper has been submitted to one journal