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Microbial communities play a vital role in maintaining soil health. A multiphasic approach to assess the effect of pulp and paper mill effluent on both the structure and function of microbial soil communities is taken. Bacterial communities from agricultural soils irrigated with pulp and paper mill effluent were compared to communities form soils irrigated with well water. Samples were taken from fields in the state of Uttarakhand, India, where pulp and paper mill effluent has been used for irrigation for over 25 years. Comparisons of bacterial community structure were conducted using sequencing of the 16S rRNA gene from both isolates and clone libraries attained from the soil. Community-level physiological profiling was used to characterize the functional diversity and catabolic profile of the bacterial communities. The multiphasic approach using both physiological and molecular techniques proved to be a powerful tool in evaluating the soil bacterial community population and population differences therein. A significant and consistent difference in the population structure and function was found for the bacterial communities from soil irrigated with effluent in comparison to fields irrigated with well water. The diversity index parameters indicated that the microbial community in pulp and paper mill effluent irrigated fields were more diverse in both structure and function. This suggests that the pulp and paper mill effluent is not having a negative effect on the soil microbial community, but in fact may have a positive influence. In terms of soil health, this finding supports the continued use of pulp and paper mill effluent for irrigation. This is however only one aspect of soil health which was evaluated. Further studies on soil resistance and robustness could be undertaken to holistically evaluate soil health in this situation. [ABSTRACT FROM AUTHOR]

Background: The study tests the effects of data collection modes on patient responses associated with the multi-item measures such as Patient-Reported Outcomes Measurement System (PROMIS®), and single-item measures such as Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE), and Numerical Rating Scale (NRS) measures. Methods: Adult cancer patients were recruited from five cancer centers and administered measures of anxiety, depression, fatigue, sleep disturbance, pain intensity, pain interference, ability to participate in social roles and activities, global mental and physical health, and physical function. Patients were randomized to complete the measures on paper (595), interactive voice response (IVR, 596) system, or tablet computer (589). We evaluated differential item functioning (DIF) by method of data collection using the R software package, lordif. For constructs that showed no DIF, we concluded equivalence across modes if the equivalence margin, defined as ± 0.20 × pooled SD, completely surrounds 95% confidence intervals (CI's) for difference in mean score. If the 95% CI fell totally outside the equivalence margin, we concluded systematic score difference by modes. If the 95% CI partly overlaps the equivalence margin, we concluded neither equivalence nor difference. Results: For all constructs, no DIF of any kind was found for the three modes. The scores on paper and tablet were more comparable than between IVR and other modes but none of the 95% CI's were completely outside the equivalence margins, in which we established neither equivalence nor difference. Percentages of missing values were comparable for paper and tablet modes. Percentages of missing values were higher for IVR (2.3% to 6.5% depending on measures) compared to paper and tablet modes (0.7% to 3.3% depending on measures and modes), which was attributed to random technical difficulties experienced in some centers. Conclusion: Across all mode comparisons, there were some measures with CI's not completely contained within the margin of small effect. Two visual modes agreed more than visual-auditory pairs. IVR may induce differences in scores unrelated to constructs being measured in comparison with paper and tablet. The users of the surveys should consider using IVR only when paper and computer administration is not feasible. [ABSTRACT FROM AUTHOR]

Background: The essence of humanism in medicine and health care is relationships-caring relationships between clinicians and patients. While raising concerns regarding professional-patient boundaries has positively contributed to our understanding and prevention of potentially harmful boundary violations, there is controversy about which types of relationships, caring acts, and practices are acceptable versus cross boundary lines.Objective: To examine primary care physicians' practices and attitudes regarding acts that have been questioned as potentially "inappropriate" or "unethical" crossing of professional-patient boundaries.Design: Surveys conducted via in-person polling or electronic and mailed paper submissions from April 2016 to July 2017. We calculated descriptive statistics and examined associations with practices and attitudes using logistic regression.Participants: Random sample of all US primary care physicians who treat adult patients; convenience sample of attendees at medicine grand rounds presentations.Main Measures: Outcomes were self-reported practices and attitudes related to giving patients rides home, paying for patients' medication, helping patients find jobs, employing patients, going to dinner with patients, and providing care to personal friends.Key Results: Among 1563 total respondents, 34% had given a ride home, 34% had paid for medications, 15% helped patients find a job, 7% had employed a patient, 10% had dinner with patients, and 59% provided care to personal friends. A majority disapproved of dinner with a patient (75%) but approved of or were neutral on all other scenarios (61-90%).Conclusions: The medical profession is quite divided on questions related to drawing lines about appropriate boundaries. Contrary to official and widespread proscriptions against such practices (with exception of dinner dates), many have actually engaged in such practices and the majority found them acceptable. [ABSTRACT FROM AUTHOR]

Background: Patients with disabilities often require healthcare accommodations in order to access high-quality, equitable healthcare services. While attention has been paid to accommodation needs in specific disability populations, limited research to date has explored healthcare accommodations that cross-cut diverse disability populations.Objective: To identify a deeper understanding regarding accommodations in healthcare settings that could apply across disability populations and promote equitable healthcare.Design: We conducted qualitative focus groups with patients with disabilities and caregivers to understand their experiences and preferences for healthcare accommodations.Participants: We recruited patients and caregivers across all major disability categories to participate in focus groups. Participants were recruited through advocacy organizations and healthcare settings in Southeastern Minnesota.Approach: A total of eight focus groups were conducted with 56 participants. Participants described their healthcare experiences and desires for healthcare accommodations. The multidisciplinary research team recorded, transcribed verbatim, and coded all focus groups. The team thematically coded transcripts using content analysis within and across focus groups to identify major themes.Key Results: Patients identified four challenges and corresponding steps healthcare team could take to promote equitable care: (1) consistent documentation of disabilities and needed accommodations in the medical record; (2) allowance for accommodations to the environment, including adapting physical space, physical structures, and scheduling and rooming processes; (3) provide accommodations for administrative tasks, such as completing paper or electronic forms; and (4) adapt communication during interactions, such as speaking slower or using terms that patients can easily understand.Conclusion: These identified themes represent specific opportunities for healthcare teams to effectively provide accessible care to patients with disabilities. Many of the accommodations require minimal financial investment, but did require behavioral changes by the healthcare team to ensure equitable healthcare. [ABSTRACT FROM AUTHOR]

In recent years, there has been a significant increase in the design of neural network models for solving math word problems (MWPs). These neural solvers have been designed with various architectures and evaluated on diverse datasets, posing challenges in fair and effective performance evaluation. This paper presents a comparative study of representative neural solvers, aiming to elucidate their technical features and performance variations in solving different types of MWPs. Firstly, an in-depth technical analysis is conducted from the initial deep neural solver DNS to the state-of-the-art GPT-4. To enhance the technical analysis, a unified framework is introduced, which comprises highly reusable modules decoupled from existing MWP solvers. Subsequently, a testbed is established to conveniently reproduce existing solvers and develop new solvers by combing these reusable modules, and finely regrouped datasets are provided to facilitate the comparative evaluation of the designed solvers. Then, comprehensive testing is conducted and detailed results for eight representative MWP solvers on five finely regrouped datasets are reported. The comparative analysis yields several key findings: (1) Pre-trained language model-based solvers demonstrate significant accuracy advantages across nearly all datasets, although they suffer from limitations in math equation calculation. (2) Models integrated with tree decoders exhibit strong performance in generating complex math equations. (3) Identifying and appropriately representing implicit knowledge hidden in problem texts is crucial for improving the accuracy of math equation generation. Finally, the paper also discusses the major technical challenges and potential research directions in this field. The insights gained from this analysis offer valuable guidance for future research, model development, and performance optimization in the field of math word problem solving. [ABSTRACT FROM AUTHOR]

Low-value recyclable waste (LVRW) is an essential component of municipal domestic waste. Due to the lightweight and high quantity characteristics, the main disposal methods of LVRW are incineration and landfill, which are not conducive to the environmental protection requirements and the goal of carbon neutrality and emission peak. The paper focuses on the delicate separation of LVRW to realize a cost-effective solution for waste sorting. Firstly, a machine vision (MV) pneumatic sorting equipment was designed based on the MV detection system. Then, a large number of authentic waste images were captured and labeled to train a high accuracy LVRW prediction model through semi-automatic labeling and transfer learning. Subsequently, the LVRW waste sorting solution was designed and constructed, and a comparative experimental study about the MV sorting method and the whole solution was conducted. The experimental results show that the MV sorting method achieves 92% sorting accuracy, and the solution can dispose of 65 tons of LVRW per day with a waste recycling rate of 37.7%. The sorting equipment and solutions developed in the paper can effectively improve the resource utilization of LVRW at a lower cost. [ABSTRACT FROM AUTHOR]

While major advances have been made over the past years in comparing citizenship laws globally, most measures remain restricted to the law-on-the-books. Knowledge about the implementation of these laws remains limited. This poses the question to what extent these measures correspond with the law as experienced by targeted populations. In order to overcome the implementation gap when comparing and measuring citizenship law globally, this paper addresses the question of how to gain a coherent understanding of implementation. Developing a comprehensive typology, the paper distinguishes three crucial elements of implementation: (1) the entry into force of legal provisions (capturing the applicable state of citizenship law), (2) the interpretation of law (the specific interpretation of legal provisions used by the authorities responsible for their execution), and (3) the application of law (executing legal provisions in practice by the authorities). Subsequently, the paper illustrates how this typology can be applied to the analysis of citizenship law implementation by a case study of dual citizenship acceptance, focusing on the renunciation requirement for naturalisation and dual citizenship treaties. The paper concludes by outlining directions for a comparative research agenda that better corresponds with the lived experience of citizenship laws. [ABSTRACT FROM AUTHOR]

Due to an error during the production process, the incorrect handling editor was displayed in the original publication. The handling editor mentioned in the original paper works at the same department as some of the authors, and he concluded that this might constitute a conflict of interest. The paper was therefore handled by one of the associate editors in chief. The correct footnote is published here and should be treated as definitive: [ABSTRACT FROM AUTHOR]

Low-dose computed tomography (LDCT) is an effective way to reduce radiation exposure for patients. However, it will increase the noise of reconstructed CT images and affect the precision of clinical diagnosis. The majority of the current deep learning-based denoising methods are built on convolutional neural networks (CNNs), which concentrate on local information and have little capacity for multiple structures modeling. Transformer structures are capable of computing each pixel's response on a global scale, but their extensive computation requirements prevent them from being widely used in medical image processing. To reduce the impact of LDCT scans on patients, this paper aims to develop an image post-processing method by combining CNN and Transformer structures. This method can obtain a high-quality images from LDCT. A hybrid CNN-Transformer (HCformer) codec network model is proposed for LDCT image denoising. A neighborhood feature enhancement (NEF) module is designed to introduce the local information into the Transformer's operation, and the representation of adjacent pixel information in the LDCT image denoising task is increased. The shifting window method is utilized to lower the computational complexity of the network model and overcome the problems that come with computing the MSA (Multi-head self-attention) process in a fixed window. Meanwhile, W/SW-MSA (Windows/Shifted window Multi-head self-attention) is alternately used in two layers of the Transformer to gain the information interaction between various Transformer layers. This approach can successfully decrease the Transformer's overall computational cost. The AAPM 2016 LDCT grand challenge dataset is employed for ablation and comparison experiments to demonstrate the viability of the proposed LDCT denoising method. Per the experimental findings, HCformer can increase the image quality metrics SSIM, HuRMSE and FSIM from 0.8017, 34.1898, and 0.6885 to 0.8507, 17.7213, and 0.7247, respectively. Additionally, the proposed HCformer algorithm will preserves image details while it reduces noise. In this paper, an HCformer structure is proposed based on deep learning and evaluated by using the AAPM LDCT dataset. Both the qualitative and quantitative comparison results confirm that the proposed HCformer outperforms other methods. The contribution of each component of the HCformer is also confirmed by the ablation experiments. HCformer can combine the advantages of CNN and Transformer, and it has great potential for LDCT image denoising and other tasks. [ABSTRACT FROM AUTHOR]

There are limited comparative studies on modeling fluid transport in fractured porous media. Hence, this paper systematically compares the steady-state creeping flow Stokes–Brinkman and Darcy–Darcy models for computational efficiency and accuracy. Sensitivity analyses were also conducted on the effect of fracture orientations, fracture sizes, mesh resolution, and fractures with Local Grid Refinement (LGR) under the FEniCS computational framework. Both models were validated numerically, and the accuracy of their solution is compared using the R-squared metric and L2 norm estimates. Key results showed that both models have similar pressure and velocity field solutions for a given fracture orientation. The computational time required for solving the Stokes–Brinkman models for a single fracture case was unusually lower than that of the Darcy–Darcy model when the pressure and velocity terms in the Darcy–Darcy model were solved simultaneously using two equations, contrary to where only one equation solves for the pressure and the velocity is obtained by projecting the gradient of pressure onto a vector space. The Stokes–Brinkman model is more sensitive to mesh resolution, and as a result, the Darcy–Darcy model tends to be more accurate than the Stokes–Brinkman model at low resolutions. Local Grid Refinement (LGR) can improve the Stokes–Brinkman model's accuracy at low mesh resolution. Furthermore, both models showed similar results when compared for complex fracture systems such as multiple fracture cases: interconnecting and isolated fractured porous media systems under low-velocity and steady-state creeping flow conditions. The FEniCS code in this paper is shared for future researchers to reproduce results or extend the research work. [ABSTRACT FROM AUTHOR]

Extensive research on the multi-bridge extension of cascaded rectifier stages is deemed important to meet the requirements of high-voltage and high-power applications. Two control structures for capacitor voltage balancing in cascaded rectifier stages are analyzed in this paper. On this basis, the balance strategy for the multi-bridge extension of single-phase cascaded rectifiers is explored. The balance method of ultra-fast balance modulation trajectories is introduced into single-phase multi-bridge cascaded rectification, and the optimal control strategy is selected through simulation verification and comparative analysis. During the actual experiments, some disturbance factors were introduced into the system, causing an instantaneous unbalanced state. However, through the control strategy proposed in this paper, the system can quickly regain balance. Through simulation validation and analysis of the experimental results, the reliability and innovation of this control strategy have been demonstrated. [ABSTRACT FROM AUTHOR]

Saturated information load is defined as the information received by a unmanned aerial vehicle (UAV) node in a swarm network reaches the overload limit of its processing capability. When a UAV swarm performs a mission in an uncertain and adversarial complex environment, overloading of UAVs will lead to information diversion, which may cause other UAVs to experience overloading and diversion as well, affecting the transmission efficiency and robustness of the entire swarm network, which in turn affects the information sensing ability, execution ability, and coordination ability of the swarm in performing the mission. Therefore, this paper proposes a saturated information load-based UAV swarm network topology modelling method, which sets the saturated information load of the nodes in the network model in order to reasonably allocate network resources and optimise the network topology. In addition, through robustness experiments of complex networks and comparative analysis of different saturated information loads and three typical modelling methods, the saturated information load-based network structure modelling method has outstanding advantages and performance in terms of network connectivity, network communication efficiency, and destruction resistance. [ABSTRACT FROM AUTHOR]

This paper presents comparative analysis of lattice-based cryptographic schemes, focusing on their runtime and memory performance to evaluate their suitability for secure IoT communications. Specifically, analyzing FrodoKEM, Kyber, and sntrup761, assessing their key generation, encapsulation, and decapsulation operations across various IoT processors. Kyber schemes, particularly Kyber512 and Kyber1024, demonstrate the highest efficiency in terms of battery utilization and computational speed, making them ideal for highly constrained IoT devices. FrodoKEM variants, while providing robust security, show moderate performance, suitable for devices with more processing capacity. sntrup761, however, is less efficient, highlighting the need for careful selection of cryptographic schemes based on specific IoT requirements. The findings underscore the importance of balancing security, speed, and resource usage in PQC implementations, with Kyber schemes emerging as the most promising candidates for secure, resource-constrained IoT environments. Article highlights: Kyber512 is the most battery-efficient choice for securing some IoT devices, ensuring long-lasting performance. FrodoKEM provides strong security but may require more power, making it less ideal for energy-constrained IoT. sntrup761 offers robust security, but its slower operations limit its use in real-time IoT applications. [ABSTRACT FROM AUTHOR]

This paper reports the results of a hybrid effectiveness-implementation randomized trial that systematically varied levels of human oversight required to support the implementation of a digital medicine intervention for persons with mild-to-moderate alcohol use disorder (AUD). Participants were randomly assigned to three groups representing possible digital health support models within a health system: self-monitored use (SM; n = 185), peer-supported use (PS; n = 186), or a clinically integrated model CI; (n = 187). Across all three groups, the percentage of self-reported heavy drinking days dropped from 38.4% at baseline (95% CI [35.8%, 41%]) to 22.5% (19.5%, 25.5%) at 12 months. The clinically integrated group showed significant improvements in mental health and quality of life compared to the self-monitoring group (p = 0.011). However, higher attrition rates in the clinically integrated group warrant consideration in interpreting this result. Results suggest that making a self-guided digital intervention available to patients may be a viable option for health systems looking to promote alcohol risk reduction. This study was prospectively registered at clinicaltrials.gov on 7/03/2019 (NCT04011644). [ABSTRACT FROM AUTHOR]

In recent years, the estimation of biometric parameters from facial visuals, including images and videos, has emerged as a prominent area of research. However, the robustness of deep learning-based models is challenged, particularly in the presence of changing illumination conditions. To overcome these limitations and unlock new opportunities, thermal imagery has arisen as a viable alternative. Nevertheless, the limited availability of datasets containing thermal data and the small amount of annotations on them limits the exploration of this spectrum. Motivated by this gap, this paper introduces the Label-EURECOM Visible and Thermal (LVT) Face Dataset for face biometrics. This pioneering dataset includes paired visible and thermal images and videos from 52 subjects along with metadata of 22 soft biometrics and health parameters. Due to the reduced number of existing datasets in this domain, the LVT Face Dataset aims to facilitate further research and advancements in the utilization of thermal imagery for diverse eHealth applications and soft biometric estimation. Moreover, we present the first comparative study between visible and thermal spectra as input images for soft biometric estimation, namely gender age and weight, from face images on our collected dataset. [ABSTRACT FROM AUTHOR]

AI (ethics) initiatives are essential in bringing about fairer, safer, and more trustworthy AI systems. Yet, they also come with various drawbacks, including a lack of effective governance mechanisms, window-dressing, and 'ethics shopping.' To address those concerns, hard laws are necessary, and more and more countries are moving in this direction. Two of the most notable recent legislations include the Biden Administration's Executive Order (EO) on AI and the E.U.'s AI Act (AIA). While several scholarly articles have evaluated the strengths and weaknesses of the AIA and proposed reform measures that could help strengthen the Act, only a couple of papers do the same for the EO or compare the two regulatory initiatives. The following sections try to close this research gap by providing an in-depth comparative analysis of the EO and AIA. They offer, in particular, a critical computer-ethical evaluation of the EO's and AIA's pros and cons and similarities and differences and discuss possible ways to improve both legislations. [ABSTRACT FROM AUTHOR]

The paper introduces a modified fractional least mean square (M-FLMS) filter-based control scheme designed for optimal control of dynamic voltage restorers. This scheme accurately estimates fundamental components from the supply voltage and performs exceptionally well, even under grid voltage anomalies. In this study, the original fractional least mean square (FLMS) control structure undergoes modifications incorporating an adjustable gain parameter, significantly enhancing its performance during voltage distortions. The resultant M-FLMS control scheme achieves swift convergence, reduced steady-state errors, and improved computational efficiency. Comparative analysis between the least mean square and FLMS control algorithms validates the superiority of the proposed M-FLMS approach, demonstrating faster settling times and decreased peak overshoot. Additionally, the grasshopper optimization algorithm is employed to efficiently tune load voltage and DC-link voltage proportional-integral controller gains, further augmenting the control scheme's effectiveness. The paper evaluates the suggested control scheme's effectiveness in both steady-state and dynamic behaviors through MATLAB-based simulations, offering a comprehensive assessment of its capabilities. [ABSTRACT FROM AUTHOR]

This article examines the concept of the "middle technology trap" as a critical challenge for China in transitioning from a middle-income to a high-income economy. It argues that overcoming this trap is essential for China to achieve high-quality economic development and sustain its growth momentum. The study compares China's technological landscape with that of both developed economies and those stuck in the middle-income trap, identifying the necessity of transitioning from applied to original technology for economic leapfrogging. Through comprehensive analysis, it is highlighted that China's current technological capabilities, although leading in certain areas, predominantly remain at an intermediate level, characterized by a significant reliance on foreign core technologies and a lack of original technological innovation. The paper further explores historical contexts, policy implications, and strategic reforms required to navigate this trap, emphasizing the importance of an open policy, the cultivation of a robust innovation ecosystem, and the strategic role of government in facilitating technological progress and industrial upgrading. It draws on the experiences of developed economies and successful latecomers, advocating for a "new whole nation system" that embraces openness and global integration, rather than isolation. By analyzing the complex interplay between technology diffusion, talent migration, industrial policies, and the global geopolitical landscape, this study provides insights into the systemic reforms and strategic initiatives China must undertake. It stresses the need for a balanced approach between leveraging external technologies and fostering domestic innovation capabilities to escape the middle technology trap and secure a position as a global technology leader. The findings contribute to the broader discourse on technology-led development strategies and offer policy recommendations for economies facing similar technological and developmental challenges. [ABSTRACT FROM AUTHOR]

The synthetic control method (SCM) represents a notable innovation in estimating the causal effects of policy interventions and programs in a comparative case study setting. In this paper, we demonstrate that the data-driven approach to SCM requires solving a bilevel optimization problem. We show how the original SCM problem can be solved to the global optimum through the introduction of an iterative algorithm rooted in Tykhonov regularization or Karush–Kuhn–Tucker approximations. [ABSTRACT FROM AUTHOR]

There has been a substantially increased interest in biomedical research impact assessment over the past 5 years. This can be studied by a number of methods, but its influence on clinical guidelines must rank as one of the most important. In cancer, there are 43 UK guidelines (and associated Health Technology Assessments) published (up to October 2006) across three series, each of which has an evidence base in the form of references, many of which are papers in peer-reviewed journals. These have all been identified and analysed to determine their geographical provenance and type of research, in comparison with overall oncology research published in the peak years of guideline references (1999-2001). The UK papers were cited nearly three times as frequently as would have been expected from their presence in world oncology research (6.5%). Within the United Kingdom, Edinburgh and Glasgow stood out for their unexpectedly high contributions to the guidelines' scientific base. The cited papers from the United Kingdom acknowledged much more explicit funding from all sectors than did the UK cancer research papers at the same research level. [ABSTRACT FROM AUTHOR]

Effective learning depends on effective feedback, which in turn requires a set of skills, dispositions and practices on the part of both students and teachers which have been termed feedback literacy. A previously published teacher feedback literacy competency framework has identified what is needed by teachers to implement feedback well. While this framework refers in broad terms to the potential uses of educational technologies, it does not examine in detail the new possibilities of automated feedback (AF) tools, especially those that are open by offering varying degrees of transparency and control to teachers. Using analytics and artificial intelligence, open AF tools permit automated processing and feedback with a speed, precision and scale that exceeds that of humans. This raises important questions about how human and machine feedback can be combined optimally and what is now required of teachers to use such tools skillfully. The paper addresses two research questions: Which teacher feedback competencies are necessary for the skilled use of open AF tools? and What does the skilled use of open AF tools add to our conceptions of teacher feedback competencies? We conduct an analysis of published evidence concerning teachers' use of open AF tools through the lens of teacher feedback literacy, which produces summary matrices revealing relative strengths and weaknesses in the literature, and the relevance of the feedback literacy framework. We conclude firstly, that when used effectively, open AF tools exercise a range of teacher feedback competencies. The paper thus offers a detailed account of the nature of teachers' feedback literacy practices within this context. Secondly, this analysis reveals gaps in the literature, signalling opportunities for future work. Thirdly, we propose several examples of automated feedback literacy, that is, distinctive teacher competencies linked to the skilled use of open AF tools. [ABSTRACT FROM AUTHOR]

This paper studied the multilevel threshold image segmentation-based metaheuristics optimization methods and their applications. Image segmentation is a common problem in the image processing domain, and it is an essential process in image analysis, directly impacting image analysis results. Thresholding is one of the most manageable and extensively utilized methods for handling image segmentation problems. In this paper, four main parts are given; (1) We present the main procedures and definitions of the multilevel threshold image segmentation problem. The standard fitness function and the evaluation criteria are also given to facilitate the problem representation for the new researchers in this domain. (2) All the related works that have used optimization methods in solving the multilevel threshold image segmentation problems are presented in more detail, focusing on the image segmentation problem and its solutions. The given related works are outlined according to the used algorithms. (3) Comprehensive results and analysis of several well-known optimization algorithms are conducted to solve the multilevel threshold image segmentation problems. These comparative methods include Aquila Optimizer (AO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), Arithmetic Optimization Algorithm (AOA), Particle Swarm Optimizer (PSO), Marine Predators Algorithm (MPA), Krill Herd Algorithm (KHA), Multi-verse Optimizer (MVO), and Gray Wolf Optimizer (GWO). Eight standard benchmark images are used to test the comparative methods. The results are evaluated using three standard measures: fitness function, PeakSignal-to-Noise Ratio (PSNR), and the Structural Similarity Index (SSIM). (4) Discussion, open challenging, and new trends are given to help the scholars in future research get near the common problems and defect in that domain. The collected data in this review has been taken from google scholar using the stander search method. The main keywords that have been used in the search are multilevel, threshold, image, segmentation, optimization, and algorithm. We covered all the published papers in detail according to the given information, focusing on finding the common problems that still need further investigation. Furthermore, future research directions based on recently evolving designs are outlined, which should undoubtedly aid current researchers and practitioners and pave the way for new researchers interested in multilevel threshold image segmentation to seek their research in the field. [ABSTRACT FROM AUTHOR]

A study involving older New Zealanders (aged 65+) explored levels of life satisfaction reported by groups with and without impaired vision as well as factors contributing to and detracting from quality of life (QOL). Those with impaired vision ( n = 135) had a visual acuity of 6/24 (i.e., 20/80) or worse in the better eye with corrective lenses, or a field of vision not greater than 20° at its widest diameter and had a mean age of 82.3 (SD = 6.76). Those with no significant impairment of vision ( n = 425) were able read newsprint and legally drive and had a mean age of 74 (SD = 6.49). Overall, those with impaired vision reported a significantly lower level of life satisfaction in comparison to their sighted peers. There was a significant interaction effect of vision status and age, such that those with impaired vision aged 85+ reported life satisfaction scores higher than those reported by their sighted peers. Family and friends emerged as most important contributors to QOL for the vision-impaired group, where good health was the primary contributor to those with sight. Detractors to QOL reported by those with impaired vision included poor vision, inability to drive and poor health. Those with no significant impairment of vision reported poor health, physical impairment and poor finances detracted from QOL. Results could enable ageing and rehabilitation service providers to design rehabilitation programmes addressing areas reported to be most important to older adults with and without impaired vision. [ABSTRACT FROM AUTHOR]

The paper provides a comprehensive and detailed description of design solutions of industrial rolling bearing vibration measurement systems used in quality control. The aim of the proposed article was to recognise what design solutions are used among such stands, to make a theoretical analysis of the advantages and disadvantages of each type of solution, as well as an attempt to identify such solutions, the application of which will allow high metrological properties to be achieved. The article is a starting point for the design of new equipment intended for the bearing industry. A theoretical analysis has been made of the most important components of the devices, such as the spindle assembly, drive assembly, clamp assembly, sensor assembly, and data transmission assembly. In the analysis, pictures showing the devices manufactured by various companies from all over the world were included. With the analysis carried out, further experimental work is possible. The analysis presented in the paper allows criteria to be established for the selection of test stands for comparative tests (tests of systems of different design, evaluation, and comparison of indications). It is also possible to relate the measurement result to the design of the device in use. The result of the analysis is the establishment of a design base, which will provide guidance for the construction of improved vibration measurement devices for the bearing industry. [ABSTRACT FROM AUTHOR]

In this paper, a comparative analysis of traditional and hybrid root finding algorithms is performed in estimating implied volatility for Ethereum Options using the Black–Scholes model. Results indicate the efficiency of Newton–Raphson method in terms of algorithmic convergence as well as computational time. Since Newton–Raphson method may not always lead to convergence, the best approximation technique is chosen from the convergent bracketed methods. The hybrid Bisection–Regula Falsi method serves as the best choice for root estimation among the bracketed methods under consideration. [ABSTRACT FROM AUTHOR]

Background: One of the potential dimensions on which exclusion and injustice may occur in energy transitions is age. Age-based patterns of exclusion—ageism—has recently been conceptualized in the context of decarbonization as energy ageism. This paper offers a comparative empirical analysis of the senior citizens' outlook towards an imminent energy transition as well as the impact of energy poverty in two European countries: Norway and Poland. Results: Drawing on interviews and focus groups with Polish and Norwegian seniors, we present the differences and similarities between the two countries, and the determinants of energy ageism, as well as the concept's overall applicability and empirical usefulness. We find that socioeconomic conditions outweigh ageism, that is, the resilience of senior citizens in dealing with energy poverty during a transition is conditioned by their material standing and welfare state robustness rather than age based. An important factor is household heating technology, combined with economic vulnerability can push some individuals into energy poverty, while others using alternative sources of heat can navigate through energy crises unscathed. Conclusions: We note the importance of mainstreaming social inclusion considerations in energy policy and of targeted digital competence building which can enhance senior citizen integration in the energy transition. Lower levels of digital competences among senior citizens certainly play a role and need to be addressed with education programs to increase participation. In both countries, household heating is a major issue and heating sources are strong predictors of energy poverty and regulatory measures and subsidies should be designed at national, regional, and municipal level to assist vulnerable groups in this area. [ABSTRACT FROM AUTHOR]

This paper analyzed the role of national economic factors, in addition to some key city-level variables, in the variation of the urban green space (UGS) in Mashhad City (Iran) during three decades (1998–2018). The correlation result revealed the effects of the increasing trend of land price, population rate, and construction of built-up areas in the decreasing trend of UGS in the study area (R from − 0.95 to -0.99 at p-value > 95%). Also, the country-level economic factors, i.e., GDP per capita, oil price, export total value, and FDI, represented the overall increasing trend from 1988 to 2018, correlating with the decrease of green space areas (R from − 0.76 to -0.92 at p-value > 75%). Some statistical analyses, such as the run-test, the skewness and kurtosis tests, the Kolmogorov-Smirnov test, and the ANOVA test were done to confirm the normality of the data distribution and reliability of the results. Ultimately, clustering the research variables based on the significance and confidence levels of the estimated correlation results revealed that the change in the oil price and national export values in the petroleum-dependent economy of Iran can be assumed as the lead key economic factors to fluctuate all city-level variables, particularly the UGS variations. [ABSTRACT FROM AUTHOR]

In this paper, a wideband polarization conversion metasurface is designed. Additionally, coding and chessboard metasurfaces are specifically tailored for radar cross-section reduction (RCS). Initially, a compact unit cell demonstrating exceptional polarization conversion performance is introduced, achieving a polarization conversion ratio (PCR) exceeding 90 % across frequencies ranging from 7.9 to 22.7 GHz. The PCR remains effective even when oblique incidence angles of up to 30 ∘ are utilized across this frequency band. Roger RT5880, with a thickness of 0.254 mm, serves as the substrate. An airgap is introduced between the substrate and the ground plane to enhance the polarization conversion bandwidth. This unit cell serves as the fundamental building block for subsequent metasurface configurations. To assess the scalability and effectiveness, a 36 × 36 unit array is assembled, confirming efficient polarization conversion capabilities extending to larger structures. Moreover, a 1-bit coding unit "0" and "1" are formed by the Pancharatnam-Berry phase based on the same-sized meta-atom with 90 ∘ orientations. The robustness and practicality of the design are demonstrated by creating 12 × 12 lattices and evaluating their RCS reduction potential under two distinct scenarios: a chessboard pattern and a coding-based scheme. Notably, its results indicate substantial RCS reduction across a broad frequency spectrum (7.9 to 22.7 GHz) for both configurations. This study demonstrates the wide-ranging applicability of metasurface design, making it a valuable contribution to the fields of microwave engineering, polarization control, and radar stealth technology. Owing to its simplicity, bandwidth, and versatility, this approach offers innovative solutions for diverse real-world applications. [ABSTRACT FROM AUTHOR]

Handwriting-based technologies have progressed significantly over the years. Scientists have worked beyond just recognizing pieces of plain text from paper. With archaeological advancements, discovery of ancient documents has not been as scarce as it was in the past. However, such documents are discovered in perfect condition once in a blue-moon. They are often subjected to degradation due to the perils of time and understanding them requires skilled manpower which is not easy to find. Here, an automated system is proposed for this task. The proposed system uses a new CNN-based framework to analyze the macro and micro features of an image to recognize the text. Experiments are performed with over 250K historical images of numerals from a publicly available dataset and a highest accuracy of 99.68% is obtained. The data is further subjected to different type of noises and distortions. A novel technique is used to introduce synthesized degradation in the documents. The system performs steadily in all these experimental scenarios. Comparative analysis indicates that our results are higher than the reported works and other standard techniques. [ABSTRACT FROM AUTHOR]

The aim of the paper is to carry out a comparative engineering-geological study of four different waste landfills using the evaluation criteria for the geological subsoil as a natural sealing barrier. The study evaluates 4 localities (Velké Pavlovice, Kvítkovice, Prakšice and Horní Suchá) using three variants (based on two standards) which approach the geological barrier requirements as a combination of impermeability requirements based on a filtration coefficient limit value. and the required geometry represented by investigation depths. The research was carried out in landfills in Moravia, in the east of the Czech Republic. The study's motivation is to point at the differences in engineering-geological investigations of waste landfills (as for the requirements for impermeable geological subsoil as a natural sealing barrier) when compared with other engineering structures (where the main goal is to evaluate load-bearing capacity and settlement). The purpose of the geological barrier is to prevent the spread of contamination, and the paper shows this can be approached differently, as shown in two different methodologies investigated herein. The first model (Model 1) assumes there is a 3-m-thick subsoil below the landfill's footing bottom, which manifests impermeability characterized by the filtration coefficient Kf ≤ 1.0 * 10–9 m/s, or a 30-m-thick subsoil of Kf ≤ 1.0 * 10–8 m/s. The second model (Model 2) assumes a 1-m thick, impermeable subsoil massif of Kf ≤ 1.0 * 10–9 m/s. We found that none of the landfills in the four selected localities had an impermeable layer in the required depth (a filtration coefficient Kf from 1.8 * 10–9 to 3.9 * 10–9 m/s), and thus did not comply with the limiting conditions. As a result, an anthropogenic technical barrier had to be installed. An important goal of the study from an environmental point of view was to assess the existence of a suitable geological barrier under the proposed landfills. The most important criterion from this point of view is permeability. An additional technical objective of the project was also the assessment of the possible creation of a technical anthropogenic isolation barrier. In the event that the natural sealing barrier would not be sufficient. This was shown in all solved case studies of engineering geological investigations of waste landfills. [ABSTRACT FROM AUTHOR]

The filtering characteristics of the output filter directly determine the grid-connected performance and cost and volume of the grid-connected converter. Based on a deep analysis of harmonic attenuation characteristics of LCL-type and LLCL-type filters, a unified circuit model of the output filter was proposed in this paper. According to this model, the possible structures of filters below the fifth order are deduced, summarized, and compared, and two new filters, LLCLC_A type and LLCLC_B type, are proposed. A comparative study of these filters in terms of filter characteristics, impedance characteristics, parameter design, and resonant spike damping was carried out. The results show that the LLCLC_A-type filter has advantages over the other three filters. The LLCLC_A-type filter is named the LLCLC-type filter. Finally, both simulation and experimental results verify the correctness and effectiveness of the proposed LLCLC-type filter. The research in this paper is a guide to the design of a grid-connected converter output filter. [ABSTRACT FROM AUTHOR]

The term 'innovation ecosystem' has become popular among stakeholders involved in innovation. The core idea is that innovation does not thrive through isolated actions of individual companies, but rather depends on a broad array of interrelated actors, institutions and policies. In this paper, we apply the concept of innovation ecosystems to ports by first providing a theoretical overview of its components and then comparing the efforts to build such an ecosystem in the port cities of Rotterdam and Valencia. Our main findings are as follows. First, the importance of innovation for the ability of ports to continue to create 'value for society' is widely acknowledged. Second, research and development (R&D) activities in both Rotterdam and Valencia are relatively limited and the dominant innovation challenge is the early application of new technologies developed outside the ports industry. Third, a 'systemic approach' is required to understand the innovation ecosystem in ports, given the strong interrelations among companies in the port and the need for broad coalitions to implement new technologies. Fourth and fifth, human capital formation and research cooperation, respectively, play a central role in improving the port innovation ecosystem. Finally, the ecosystem in Rotterdam is 'distributed and connected' while Valencia is more centralised. [ABSTRACT FROM AUTHOR]

The balancing of the disassembly line directly affects the productivity of the disassembly process. The disassembly line balancing (DLB) problem can be determined as assigning the tasks to serial workstations to optimize some performance measures like number of workstations, cycle time, removing hazardous parts earlier, etc. The aim of the paper is to develop an efficient heuristic algorithm to minimize the number of workstations under a pre-known cycle time. In this paper, a genetic algorithm (GA) and a constructive heuristic based on the Dijkstra algorithm is proposed to solve the DLB problem with stochastic task times that is caused by the nature of disassembly operation. The proposed algorithms are tested on benchmark problems and compared with the results of the piecewise-linear model (PLM) and simulated annealing (SA). The average relative percentage deviation is applied to transfer the obtained number of workstations. The results obtained by GA are clearly superior in all tests problem according to average relative percentage deviation. Moreover, the proposed constructive heuristic based on the Dijkstra algorithm is also superior to PLM and SA algorithm with respect to number of workstations and the computational times. The proposed approaches can be a very competitive and promising tool for further research in DLB literature and real cases in industries according to test results. Disassembly lines which need less time or number of workstations for balancing may be simply designed by the proposed techniques. [ABSTRACT FROM AUTHOR]

Background: Established health-related quality of life scores do not consider both subjective and objective indices of health. We propose the subjective and objective quality of life score (SOQOL) for the comprehensive assessment of health-related quality of life and aim to provide normative population data. The SOQOL is compatible with smartphone applications, allowing widespread use on a global scale. Methods: Normative SOQOL population data was sourced from pre-existing datasets on the EQ-5D-5L, daily step count, and walking speed. Normative values were calculated using weighted grand means. We trialled the SOQOL in a group of five patients presenting to a spinal neurosurgery clinic. Results: SOQOL scores decreased with age, and women had lower scores in every age group. In our case series, the spine patients with the biggest SOQOL deficit compared to age- and sex-matched population averages were found to be surgical while the rest were non-surgical. Conclusions: The SOQOL shows promise as a simple and effective scoring tool that is compatible with smartphones, potentially useful for screening in primary and specialized care settings, and for assessment following healthcare interventions. This study, however, is preliminary, and the findings are primarily suggestive. They underline the necessity for future, more comprehensive studies to validate and expand upon these initial observations. The conclusion of both this abstract and the full paper will clearly state these limitations and the preliminary nature of the study. [ABSTRACT FROM AUTHOR]

This study examines Einstein's field equations in the context of general relativity, comparing five distinct methodologies: (a) vanishing complexity, (b) embedding class one or the Karmarkar condition, (c) conformally flat spacetime, (d) conformal killing symmetry, and (e) the Karmarkar scalar condition. The first four methods reveal a significant connection between two metric potentials, while the fifth method provides a coordinate-independent condition expressed in terms of structural scalars. The paper provides a comprehensive comparative analysis of a new exact solution derived by assuming a common metric function and solving the remaining metric functions through corresponding bridge equations to evaluate their effectiveness and validity. Critical parameters such as thermodynamic factors, causality conditions, stability, and mass function analysis are investigated. [ABSTRACT FROM AUTHOR]

In this paper, the performance of different continuous-time and discrete-time models of the electrical subsystem of induction machines and permanent-magnet synchronous machines as well as methods based on them for decoupling the direct and quadrature axis components of the stator current are investigated and compared. The focus here is on inverter-fed, pulse width modulated drives when operated with a relatively large product of stator frequency and sampling time, where significant differences between the models and decoupling methods used come to light. Recommendations for a discrete-time model to be used uniformly in the future are made, as well as statements on whether feedforward or feedback decoupling structures are better suited and whether state controllers improve decoupling measures for very steep speed ramps. Simulation studies and measurement results support the statements made above. [ABSTRACT FROM AUTHOR]

The study focuses on the critical concern of designing secure and resilient bridge piers, especially regarding scour phenomena. Traditional equations for estimating scour depth are limited, often leading to inaccuracies. To address these shortcomings, modern data-driven models (DDMs) have emerged. This research conducts a comprehensive comparison involving DDMs, including support vector machine (SVM), gene expression programming (GEP), multilayer perceptron (MLP), gradient boosting trees (GBT) and multivariate adaptive regression spline (MARS) models, against two regression equations for predicting scour depth around cylindrical bridge piers. Evaluation employs statistical indices, such as root-mean-square error (RMSE), coefficient of determination (R2), mean average error (MAE) and normalized discrepancy ratio (S(DDRmax)), to assess their predictive performance. A total of 455 datasets from previous research papers are employed for assessment. Dimensionless parameters Froude number F r = U gy , Pier Froude number F r P = U g ′ D , and the ratio of scour depth to pier diameter (y D) are carefully selected as influential model inputs through dimensional analysis and the gamma test. The results highlight the superior performance of the SVM model. In the training phase, it exhibits an RMSE of 0.1009, MAE of 0.0726, R2 of 0.9401, and SDDR of 2.9237. During testing, the SVM model shows an RMSE of 0.023, MAE of 0.017, R2 of 0.984, and SDDR of 5.301. Additionally, it has an average error of − 0.065 and a total error of − 20.642 in the training set and an average error of − 0.005 and a total error of − 0.707 in the testing set. Conversely, the M5 model exhibits the lowest accuracy. The statistical metrics unequivocally establish the SVM model as significantly outperforming the experimental models, placing it in a higher echelon of predictive accuracy. [ABSTRACT FROM AUTHOR]

As the first study in the field regarding CB, this paper investigates the implications of interchanging between RDB and CB through manufacturing bends with various angles and investigates the difference between the two methods in form of springback, cross section compression and widening, cross section shape and wall thinning/thickening. The experiments conducted consists of aluminum alloy 6060-T4 round tubes of 60 mm diameter and 3 mm wall thickness, bent around 222 mm radius tooling with various bend angles. In this case, we have found that CB is capable of manufacturing bends with quality near those manufactured by RDB with less springback, however with a dimensional penalty regarding increased deformation of the cross section. [ABSTRACT FROM AUTHOR]

Speech recognition in noisy environments is one of the long-standing research themes but remains a very important challenge nowadays. Therefore, there is much research into all techniques and approaches to improve the performance of speech recognition systems, even in poor conditions. This paper presents a comparative study under various conditions based on two architectures (GMM-HMM and DNN-HMM), the Hybrid GMM-HMM models using the CMU Sphinx tools and the Hybrid DNN-HMM using the KALDI toolkit in noise environment. In this study, we compare the Hybrid GMM-HMM models and the Hybrid DNN-HMM models to evaluate the performance of the proposed system. The novelty of this paper is to test if the presented tools could be, with good accuracy, recognize the Arabic speech principally in noisy environment. In addition, we adopted the noisy training theory in this paper based on GMM-HMM and DNN-HMM model. We use the public Arabic Speech Corpus for Isolated Words (20 words), three noise levels, and three noise types. The implementation of our system consists of two phases: Features extraction using Mel-frequency Cepstral Coefficient (MFCC) and the classification phase will use separately the previous two models. In order to test the performance of these methods a simulation will presented for different SNR and for different district type of noise. [ABSTRACT FROM AUTHOR]

Presents an overview of the papers contained in the 2004 issue of "Journal of Exposure Analysis and Environmental Epidemiology" that address indoor air exposure . Intervention study of students' health symptoms over time at a moisture-damaged school; Comparative study that delves into personal samples among family members with indoor and outdoor VOC concentrations in Mexico City homes; Examination of differences in VOC source emission rates using a data set of indoor and outdoor VOC measurements plus air-exchange rates in New York City and Los Angeles homes; Others.

Coprime arrays (CPA) are extensively applied to estimate the direction of arrival (DOA). Yet, the effectiveness of existing DOA estimation algorithms for coprime arrays is predicated on the precise calibration of the array. Gain-phase errors in the sensors, when present, are magnified by the co-array, greatly impacting the performance of DOA estimation algorithms. To address the issue, this paper proposes a joint DOA estimation algorithm combining auxiliary arrays with atomic norm minimization. Exploiting the sub-array decomposition feature of coprime array, gain-phase error estimation is accomplished with the addition of precisely calibrated auxiliary arrays. Besides, the estimated gain-phase errors are used to correct the covariance of the signals received by the array. By combining array interpolation, all virtual sensors are fully utilized, and the interpolated virtual received signals are reconstructed into a Toeplitz matrix. The covariance matrix of these signals is then refined using atomic norm minimization, preparing the data for the final DOA estimation performed by subspace algorithms. In addition, for comparative analysis, the Cramér–Rao bound has also been derived under the condition of gain-phase errors, providing a benchmark for the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction is an essential task in ensuring reliability in intelligent manufacturing. Recent advances in deep learning-based data-driven methods have shown promising results. However, one non-ignorable challenge is that distribution shift across various machine individuals often results in a performance decline. Domain adaptation approaches appear to be effective in tackling this issue, whereas they often require sufficient unlabeled target data, which is causally infeasible in prognostic tasks in practical scenarios. In this paper, we discuss the significance of prognostic generalization for RUL prediction, and a domain generalization-based scheme is proposed. A domain conditional invariance and specificity disentanglement network (DCISD) is proposed to learn domain conditional-invariant and domain-specific information simultaneously in a unified network. Domain conditional-invariant features are extracted through conditional domain adversarial learning and samples are conditioned by multiple RUL fuzzy sets. Domain-specific features correlated to individual degradation patterns are disentangled to promote sufficiency of degradation information. Moreover, a degradation dynamics-based augmentation method is proposed to mitigate domain imbalance following the degradation dynamics in the latent space. Two bearing run-to-failure datasets are utilized to evaluate the proposed method. Comparative and ablation studies validate the method effectiveness and superiority. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel protection scheme for N-qubit Greenberger–Horne–Zeilinger (GHZ) states against amplitude damping noise, using unilateral operations. The key innovation lies in the implementation of local operations on a single qubit, irrespective of the total number of qubits, thereby significantly reducing the operational complexity and resource requirements compared to existing methods. The scheme involves a unilateral rotation to transform the GHZ state into a less vulnerable configuration before exposing it to noise, followed by a weak measurement and another unilateral rotation to recover the initial state. We provide a comprehensive mathematical analysis of the proposed unilateral protection scheme, deriving expressions for both fidelity and success probability. The practical feasibility of our scheme is demonstrated through simulations using the Qiskit SDK, in which we provide the corresponding quantum circuit for performing the partial weak measurement used in our scheme. Comparative analysis with pioneer protection schemes reveals a substantial enhancement in success probability and fidelity for both GHZ and generalized GHZ states, emphasizing the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Introduction: This research paper investigates whether robotic total knee replacement (TKR) reduces bone resection compared to conventional TKR. While TKR is a successful procedure, revision surgery remains a challenge with up to 8.3% of all knee replacement procedures requiring revisions. Materials and methods: The study retrospectively analyzed the tibial polyethylene inserts used and bone cuts made in 157 primary TKRs performed by a single surgeon. The results show that 93.3% of robotic TKRs used the base size tibial polyethylene of 9 mm, and the average distal femoral cut was 7.4 mm and the average tibial cut was 6.4 mm, with the minimum being 3 mm. Conclusion: The study suggests that robotic TKR reduces bone resection compared to conventional TKR. [ABSTRACT FROM AUTHOR]

Contingency analysis has been vastly explored within the context of power systems operation and security assessment. However, the impact of power quality indices into the contingency ranking and selection has not been well investigated in the literature. In order to fulfil this gap, a novel approach is proposed in this paper considering the effects of transmission lines outages in the voltage magnitudes and harmonic proliferation in power systems. The compensation theorem is applied to obtain the approximate effect of small changes in the elements of an electric network based on sensitivity analysis with the purpose of determining estimated harmonic voltages and currents in a lower computational time when compared with the traditional application of the iterative Newton–Raphson load flow methodology. Computational simulations are carried out using the IEEE 30 bus test system modified by the inclusion of non linear loads modelled as harmonic current injections. A comparative analysis is also presented considering the contingency selection using the proposed method, load flow algorithms and different ranking factors considering the impact of single contingencies into the power system voltages and total harmonic distortion variations. [ABSTRACT FROM AUTHOR]

In recent years, the widespread applications of open-source software (OSS) have brought great convenience for software developers. However, it is always facing unavoidable security risks, such as open-source code defects and security vulnerabilities. To find out the OSS risks in time, we carry out an empirical study to identify the indicators for evaluating the OSS. To achieve a comprehensive understanding of the OSS assessment, we collect 56 papers from prestigious academic venues (such as IEEE Xplore, ACM Digital Library, DBLP, and Google Scholar) in the past 21 years. During the process of the investigation, we first identify the main concerns for selecting OSS and distill five types of commonly used indicators to assess OSS. We then conduct a comparative analysis to discuss how these indicators are used in each surveyed study and their differences. Moreover, we further undertake a correlation analysis between these indicators and uncover 13 confirmed conclusions and four cases with controversy occurring in these studies. Finally, we discuss several possible applications of these conclusions, which are insightful for the research on OSS and software supply chain. [ABSTRACT FROM AUTHOR]

A computer views all kinds of visual media as an array of numerical values. As a consequence of this approach, they require image processing algorithms to inspect contents of images. This project compares 3 major image processing algorithms: Single Shot Detection (SSD), Faster Region based Convolutional Neural Networks (Faster R-CNN), and You Only Look Once (YOLO) to find the fastest and most efficient of three. In this comparative analysis, using the Microsoft COCO (Common Object in Context) dataset, the performance of these three algorithms is evaluated and their strengths and limitations are analysed based on parameters such as accuracy, precision and F1 score. From the results of the analysis, it can be concluded that the suitability of any of the algorithms over the other two is dictated to a great extent by the use cases they are applied in. In an identical testing environment, YOLO-v3 outperforms SSD and Faster R-CNN, making it the best of the three algorithms. [ABSTRACT FROM AUTHOR]

Our world consists not only of objects and scenes but also of materials of various kinds. Being able to recognize the materials that surround us (e.g., plastic, glass, concrete) is important for humans as well as for computer vision systems. Unfortunately, materials have received little attention in the visual recognition literature, and very few computer vision systems have been designed specifically to recognize materials. In this paper, we present a system for recognizing material categories from single images. We propose a set of low and mid-level image features that are based on studies of human material recognition, and we combine these features using an SVM classifier. Our system outperforms a state-of-the-art system (Varma and Zisserman, TPAMI 31(11):2032-2047, ) on a challenging database of real-world material categories (Sharan et al., J Vis 9(8):784-784a, ). When the performance of our system is compared directly to that of human observers, humans outperform our system quite easily. However, when we account for the local nature of our image features and the surface properties they measure (e.g., color, texture, local shape), our system rivals human performance. We suggest that future progress in material recognition will come from: (1) a deeper understanding of the role of non-local surface properties (e.g., extended highlights, object identity); and (2) efforts to model such non-local surface properties in images. [ABSTRACT FROM AUTHOR]

The bcl-2 protein plays an essential role in preventing cell death. Its activity is regulated through association with bcl-2 homologous and nonhomologous proteins and also by serine phosphorylation. We now report that bcl-2 can be proteolytically cleaved towards its N-terminus by a cysteine proteinase present in RL-7 lymphoma cell lysates, yielding a major product of apparent MW 20 kDa, different from the products of bcl-2 cleavage by HIV protease. Moreover, bcl-2 proteins mutated for Asp residues at positions 31 and 34 were efficiently cleaved by RL-7 cell lysates, indicating that this proteolytic activity is distinct from the caspase-3 that cleaves bcl-2 at Asp 34. This bcl-2 cleaving activity is inhibited by E-64 and is therefore distinct from the proteinases of the ICE/Ced-3 family (caspases), whereas reciprocally, ICE (caspase-1) is unable to cleave bcl-2. It is optimally active at pH 5, a feature distinguishing it from calpain, another non-ICE cysteine proteinase which has been associated with apoptosis. This novel bcl-2 cleaving protease, although constitutively present in RL-7 cells and resting peripheral blood lymphocytes (PBL) was upregulated following induction of apoptosis in RL-7 cells or mitogen activation in PBL. The N-terminus of bcl-2 which contains the BH4 domain that binds the kinase Raf-1 and the phosphatase calcineurin is essential for anti-apoptotic activity. Its cleavage might provide a novel post-translational mechanism for regulating bcl-2 function and could amplify ongoing programmed cell death. [ABSTRACT FROM AUTHOR]

The paper examines how U.S.-American movies stage and convey intercultural encounters. Drawing from the case study of cinematic New York City, it tackles intercultural encounters and spaces emerging from interactions of protagonists that are staged as being 'culturally different'. Theoretical ideas on urban encounters, interculturality, and boundaries are intertwined. A comparative analysis 17 movies reveals three key dimensions of intercultural spaces: boundary drawing, boundary crossing, and boundary commuting. As polysemous staging strategies these provide insight into how movies display everyday intercultural encounters in an urban context. The paper concludes that New York City is imagined as a place in which intercultural encounters are consciously reflected as cultural coexistence—the cinematic city serves as canvas for a culturally separated society. This finding disenchants the medially widespread urban myth of New York City being a harmonious intercultural metropolis. [ABSTRACT FROM AUTHOR]