Structural Problems Research Articles

Public policies and attitudes towards gender equality in Peruvian university students

Gender inequality is a structural social problem, associated with history, culture, education, religion and politics, this difficulty occurs in all social institutions due to the heterogeneity of the structure in the sexual division of labor, socioeconomic inequality, inclusion and inequity in participation in the public space between men and women. Public policies and attitudes towards gender equality in Peruvian university students were analyzed according to socio-academic variables. A descriptive-comparative study, with a quantitative approach, and not experimental cross-sectional, involved 776 university students from a public and a private university in Peru, intentionally selected. Adaptive attitudes (57.9%) were found to tend to be sexist; Likewise, in the study dimensions, the same trend was found in the sociocultural and relational levels, while in the personal dimension students develop sexist attitudes (62.4%). It is concluded, attitudes towards gender equality are sexist reproduction that is influenced by the sociocultural environment of the family, this situation occurs to a greater extent in men, while female students present attitudes of equality in greater intensity to seek equity in the distribution of roles.

Health Monitoring Technology for Hydraulic Engineering Gates Based on Multi-Sensor Data Fusion

An essential part of the hydraulic engineering systems, hydraulic gates regulate water flow in dams and other structures. To avoid structural problems, they must ensured to be consistent. Multi-sensor data fusion has become a key tool for health monitoring with advances in machine learning (ML) and sensor technologies. This study aims to provide a novel multi-sensor data fusion health monitoring system for hydraulic engineering gates. To provide more effective and precise real-time monitoring of hydraulic engineering gates, a unique Dynamic Satin Bowerbird search-driven Bidirectional Long Short-Term Memory (DSB-BiLSTM) model were proposed. The hydraulic monitoring dataset was collected from Kaggle website, comprising information and some attributes obtained from hydraulic engineering gates. To improve data quality, noise reduction, and data normalization are used. Time-series analysis techniques extract important information such as vibration patterns and pressure variations. The DSB-BiLSTM model leverages bidirectional LSTM to progress time-dependent data, and the Satin Bowerbird search optimizes sensor selection. The method shows better accuracy and efficiency in identifying potential faults compared to traditional models. When compared to four attributes such as pump, cooling, valve and accumulator with the metrics outcomes of 0.9901, 0.9925, 0.9950, and 0.9890 in accuracy, 0.9930, 0.9960, 0.9902, and 0.9889 in precision, 0.9920, 0.9960, 0.9911, and 0.9890 in recall, and 0.9915, 0.9955, 0.9905, and 0.9889 in F1-score, the DSB-BiLSTM model performs better than all other approaches that were examined. This suggests better overall hydraulic engineering gate health monitoring performance. The proposed DSB-BiLSTM model is a show potential solution for real-time health monitoring of hydraulic gates, provided that it enhances accuracy and reduces computational complexity.

Efficacy of growth-led unemployment reduction hypothesis in India using Okun’s law

PurposeThe purpose of this paper is to examine whether increased labour productivity could reduce the impact of output growth on the unemployment rate in India over the period 1991–2019 through Okun’s law and its expanded form.Design/methodology/approachThe study uses Okun’s law and its expanded form, with the inclusion of labour productivity in the actual model. Further, the relationship between output growth, unemployment rate, and labour productivity is analysed by using the gap model, the difference model, the dynamic model, the error correction model (ECM), and the vector autoregressive (VAR) approach.FindingsThe empirical results from the applied models do not confirm an inverse relationship between output growth and the unemployment rate with an unexpected positive sign of Okun’s coefficient. The evidence of preference for more capital-intensive techniques in the Indian economy is also strongly supported by the results of the expanded form of Okun’s law with a statistically significant positive coefficient of GDP and labour productivity.Originality/valueThe study examined the proposed relationship using Okun’s law and its expanded form, which had not been employed in earlier studies in the context of India. The authors also show that a high economic growth rate is a necessary but not sufficient condition to solve the chronic and structural unemployment problem in India.

The Impact of Agricultural Investments on the Economic Efficiency of Production Factors: An Empirical Study of the Wielkopolska Voivodeship

The functioning and development of each economic entity requires the implementation of investments. In the case of farms in Poland, which are characterised by structural problems and a lack of capital, it is essential to undertake investment activities of a modern nature. The main objective of this research was to assess agricultural investments made in farms, considering the example of the Wielkopolska Voivodship in Poland, from the point of view of their impacts on changes in the economic efficiency of production factors. The data source was unpublished individual data from the agricultural accounting “Polish FADN” results for all farms in the Wielkopolska Voivodship who had continuously participated in the data collection system from 2009 to 2021. The project’s complexity was determined by measuring the scale of investments, relating the sum of investment expenditures to the average annual value of fixed assets (reduced by land value). Farmers endowed with a greater asset base had the ability to invest more comprehensively (i.e., in their most desirable scale regarding the value of possessed fixed assets) in a manner primarily concerning the value of fixed assets, especially the agricultural area, as a primary agricultural production factor. In terms of the effects of differentiating the scale of investment outlays (which was the aim of the study), a better initial state allowed farms to increase their area significantly, with an average annual rate of change of 3%. On the other hand, a lack of investment led to the decapitalisation of assets (−6.7% per year), which also concerned land sales (−1.5% per year). These research results indicate that not only does the undertaking of investments, in general, impact the development possibilities of farms—and, therefore, improvements in the resource situation of farms—but also the scale of investment (with respect to the value of possessed fixed assets).

A new model-based integration method with controllable numerical damping for structural dynamic problems

A new model-based integration method with controllable numerical damping for structural dynamic problems

DIAGNOSIS OF DAMP AND STRUCTURAL PATHOLOGIES IN A SAMPLE OF MUD HOUSES USING 3D SCANNING. CASE STUDY: A HOUSE IN THE KHANGUET SIDI NADJI KSAR, BISKRA, ALGERIA

Raw earth architecture is an important part of Algeria's cultural heritage, especially in arid and desert regions. These historic settlements, inhabited since the Middle Ages, have enabled people to survive in difficult conditions. However, these centres are now abandoned and neglected by their inhabitants, who prefer more modern lifestyles. The Ksar of Khanguet Sidi Nadji in Biskra, a fortified urban settlement, is renowned for its rich historical heritage and exceptional architectural beauty. Classified as a natural heritage site in 1968, the ksar is facing gradual deterioration and increasing abandonment, mainly due to the intrinsic fragility of the mud buildings, structural problems and various pathologies. These deteriorations are exacerbated by the passage of time and human impacts, leading to premature ageing of the buildings, a lack of maintenance and the abandonment of certain structures that have become obsolete. The aim of this article is to undertake an in-depth investigation of the pathologies affecting adobe architecture, using the 3D digital survey technique. The study will focus on a specific building (House 10) located in the ksar of Khanguet Sidi Nadji, selected from a representative sample. The in situ survey will be carried out in close collaboration with experts in the pathology of mud constructions, and will be supplemented by analyses carried out using specialist tools. This scientific approach will enable us to establish a precise diagnosis, which is a prerequisite for the rehabilitation of this endangered architectural heritage. It will also ensure rigorous traceability, while promoting the use of raw earth as a sustainable building material.

A regression based approach to phylogenetic reconstruction from multi-sample bulk DNA sequencing of tumors.

DNA sequencing of multiple bulk samples from a tumor provides the opportunity to investigate tumor heterogeneity and reconstruct a phylogeny of a patient's cancer. However, since bulk DNA sequencing of tumor tissue measures thousands of cells from a heterogeneous mixture of distinct sub-populations, accurate reconstruction of the tumor phylogeny requires simultaneous deconvolution of cancer clones and inference of ancestral relationships, leading to a challenging computational problem. Many existing methods for phylogenetic reconstruction from bulk sequencing data do not scale to large datasets, such as recent datasets containing upwards of ninety samples with dozens of distinct sub-populations. We develop an approach to reconstruct phylogenetic trees from multi-sample bulk DNA sequencing data by separating the reconstruction problem into two parts: a structured regression problem for a fixed tree [Formula: see text], and an optimization over tree space. We derive an algorithm for the regression sub-problem by exploiting the unique, combinatorial structure of the matrices appearing within the problem. This algorithm has both asymptotic and empirical improvements over linear programming (LP) approaches to the problem. Using our algorithm for this regression sub-problem, we develop fastBE, a simple method for phylogenetic inference from multi-sample bulk DNA sequencing data. We demonstrate on simulated data with hundreds of samples and upwards of a thousand distinct sub-populations that fastBE outperforms existing approaches in terms of reconstruction accuracy, sample efficiency, and runtime. Owing to its scalability, fastBE enables both phylogenetic reconstruction directly from indvidual mutations without requiring the clustering of mutations into clones, as well as a new phylogeny constrained mutation clustering algorithm. On real data from fourteen B-progenitor acute lymphoblastic leukemia patients, fastBE infers mutation phylogenies with fewer violations of a widely used evolutionary constraint and better agreement to the observed mutational frequencies. Using our phylogeny constrained mutation clustering algorithm, we also find mutation clusters with lower distortion compared to state-of-the-art approaches. Finally, we show that on two patient-derived colorectal cancer models, fastBE infers mutation phylogenies with less violation of a widely used evolutionary constraint compared to existing methods.

Approaches to the conservation of archaeological sites in the Mediterranean in the current and changing climates, with a focus on the Prehistoric Temples of Malta

AbstractThis paper gives an overview of some of the current approaches to the preservation of UNESCO World Heritage Sites in the Mediterranean, represented by some of the most important archaeological remains, with a special focus on the Prehistoric Temples of Malta, and the key approaches to their conservation in the nineteenth, twentieth and twenty-first centuries. Preserving and conserving such sites, sometimes consisting of complex arrays of materials, is a challenge and approaches over time have varied. The lead example is the group of Temples of Malta because they (1) are made of porous limestones particularly susceptible to salt weathering; (2) have been exposed to adverse (exposed, marine) climatic conditions for millennia; (3) have suffered material deterioration over time leading also occasionally to structural problems; (4) have undergone various restoration attempts over the decades, not all particularly successful; (5) have resulted in complex decision-making processes leading to their sheltering; and (6) are facing (like all other sites) climate change, where new action/s may be required. This paper will therefore take us on a journey, led by the example of these Temples, to look also at various other iconic UNESCO sites in relation to past, present, and even future threats, including climate change, as well as approaches to conservation/restoration over the years, with indications for future decision makers on actions to take after holistically understanding the risks and threats. Graphical abstract

Ecovillage scale-up and its well-being challenges: a case study from Norway

Ecovillages are settlements that aim to combine human well-being and environmental sustainability. They serve as sites of social learning for sustainable consumption practices and may inspire lifestyle changes in mainstream society. However, most ecovillages fail in the first few years of establishment. Through an in-depth study of a Norwegian ecovillage’s efforts at expansion, this article explores the factors that adversely affected the well-being of ecovillage residents and threatened the community’s survival. The study applies mixed methods combining needs-based workshops, a well-being survey, and in-depth interviews. The transformation of the ecovillage into a larger settlement with modern houses was driven by a developer and with minimal involvement of residents. The resulting divergence in visions and values, coupled with structural problems of the homes, led to protracted conflicts, and resulted in the developer’s bankruptcy. The study identifies strong social institutions and affordable infrastructures as crucial elements for both well-being and the success of ecovillages.

Time-Domain Load Identification and Dynamic Displacement Inversion of Discharge Sluice in Large Hydropower Stations Based on the Conjugate Gradient Least Squares Iteration Algorithm

The identification of flow-induced vibration loads is the key to solving the vibration problem of hydraulic structures induced by high-speed water flow. In this study, a time-domain load identification method for discharge sluices in large hydropower stations based on the conjugate gradient least squares (CGLS) iteration algorithm is proposed. The main idea of this method is to first construct a mathematical model through the response signal and the structural modal parameters for solving the equivalent load acting on the sluice structure. Then, it uses the iterative regularization method to solve the ill-posed problem in the load identification process of the traditional time-domain method. Numerical simulations show that, in comparison to the traditional Tikhonov regularization algorithm, the CGLS algorithm is capable of reducing the relative error of the identified dynamic loads by approximately 100%, with higher accuracy and noise immunity. In this study, the CGLS algorithm is used to identify the external dynamic loads acting on the sluice structure during the flood discharge process of a large hydropower station in China. Three equivalent dynamic loads acting on the pier and the bottom plate are obtained. The positive analysis of the vibration response of the discharge structure is conducted using the equivalent load. The results show that the error between the measured vibration response signals and the vibration response signals obtained from the positive analysis is relatively minor, with the exception of a few individual measurement points. The displacement response mean square error is less than 10%, thereby substantiating the accuracy and validity of the CGLS load identification method proposed in this study in the actual engineering.

A simple yet effective ALE-FE method for the nonlinear planar dynamics of variable-length flexible rods

With recent advances in variable-length structures for use in soft actuation, energy harvesting, energy dissipation and metamaterials, the mathematical modelling and numerical simulation of physical systems with time-varying domains is becoming increasingly important. The planar nonlinear dynamics of one-dimensional elastic structures with variable domain is formulated from a Lagrangian approach by using a non-material reference frame. An Arbitrary Lagrangian-Eulerian (ALE) scheme is proposed where the domain is reparametrized based on a priori unknown configuration parameters. Based on this formulation, a Finite Element (FE) method is developed for theoretically predicting the evolution of a rod constrained at its ends by one or two sliding-sleeves, whose position and inclination can be varied in time, and under external loadings. Finally, case studies and instability problems are investigated to assess the reliability of the proposed formulation against others available and to demonstrate its effectiveness. With respect to previously developed methods for this type of structural problems, the present ALE-FE approach shows a strong theoretical and implementation simplicity, maintaining an efficient and fast convergence according to the cases analyzed. An open source code realized for the present ALE-FE model is made available for solving the nonlinear dynamics of planar systems constrained by one or two independent sliding sleeves. The present research paves the way for further extensions to easily implement solvers for the three-dimensional dynamics of flexible one- and two-dimensional structural systems with moving boundary conditions.

Optimal design of spatial structures by a novel meta-heuristic algorithm: Sound energy optimizer

Sound signals are used by various species of living creaters to commonicate with each other. Scientific experiments have demonstrated that sound energy can travel through media and cause the receiver objects to vibrate or move. The present work simulates such a phenomenon in development of a new meta-heuristic algorithm called: Sound Energy Optimizer (SEO). Concerning physical rules, SEO leverages natural or ambient sounds for optimization. SEO utilizes a tunable virtual machine to control the decaying pattern of the sound energy in a medium representing the search space. Performance of the proposed algorithm is first evaluated on unconstrained optimization of some mathematical test functions. Then, a variety of pin-jointed structural problems are optimized including space tower, double-layer grid, barrel vault, helipad structure and power transmission towers. In this set, performance evaluation is done not only against the previous works but also by running SEO versus eight recent meta-heuristics under fair comparison conditions. New quantitative indices are introduced and traced as well as the common statistical measures and convergence curves. The results demonstrate competitive performance of SEO versus recent meta-heuristics in the discrete and continuous design of spatial structures.

A meshless Runge-Kutta-based Physics-Informed Neural Network framework for structural vibration analysis

In recent years, Physics-Informed Neural Networks (PINN) have emerged as powerful meshless numerical methods for solving partial differential equations (PDEs) in engineering and science, including the field of structural vibration. However, PINN struggles due to the spectral bias when the target PDEs exhibit high-frequency features. In this work, a meshless Runge-Kutta-based PINN (R-KPINN) framework for structural vibration modelling is proposed for the first time. In the framework, the meshless features of traditional PINN are retained while applying Runge-Kutta (R-K) time integration to discretise the temporal domain, thereby reducing computational demands and enhancing temporal flexibility. Besides, the proposed R-KPINN allows for segmental training of PINN, reducing the dimensionality of the structural vibration problems and offering accurate solutions to relatively high-frequency functions. Through numerical examples including free, damped, and forced vibration of Euler-Bernoulli beams, the proposed framework provides effective and precise solutions for predicting the responses of structures. In summary, the proposed R-KPINN framework provides a flexible, efficient, and interpretable meshless method for solving structural vibration problems with state-of-the-art PINN techniques.

‘Although it's my home, it's not my house’ – Exploring impacts of retrofits with social housing residents

The UK is currently experiencing a cost-of-living crisis that is impacting access to affordable energy, which is worsening energy poverty issues. While the crisis continues, policymakers are attempting to meet climate change goals. Our paper discusses the ways in which energy poverty and energy justice are relevant to the shift to decarbonise the social housing sector. We use a case study of 22 interviews of social housing residents on an estate in the North West of England to problematise how retrofitting social housing to decrease energy consumption impacts residents' access to energy justice. Our work contributes to the emerging concept of retrofit justice, and we assess how the forms of tripartite energy justice occur through the retrofit project, furthering debates on retrofit justice. We find that while the housing provider made attempts to involve residents in retrofit planning, many residents were disengaged from the process due to disenfranchisement and misrecognition issues, contributing to limited procedural and recognition justice. We also found that while retrofits increased the energy efficiency of the buildings, this could not solve structural problems causing energy poverty, reducing the retrofit's capacity to deliver distributive energy justice. Retrofit is a hugely important aspect of meeting climate change goals, but energy poverty cannot be adequately challenged without structural changes to the electricity market. Our paper is based on a UK case study, but the findings are relevant to international conceptual debates around retrofit and energy justice and can be useful for any national context retrofitting their social housing stock.

The gift of freedom? Exploring the realities of rescue from child trafficking in Ghana

ABSTRACT This paper is a critical reflection on the rescue and reintegration regime associated with the child trafficking intervention in Ghana. The paper acknowledges efforts by anti-trafficking NGOs (Non-Governmental Organisations) to address conditions of poverty, child/youth economic exploitation, and human rights abuses. However, it also observes that these interventions have had very little impact for some of the beneficiaries. Drawing on ethnographic research with young adults considered as trafficked victims by a child rights NGO in Ghana, the paper demonstrates how reintegrated young adults are still confronted with the socio-structural factors (residential instability, economic exploitation, physical abuse, and attendance difficulties at school) that preceded their transportation to riverine communities for work in fishing. The paper attributes these post-rescue challenges to the inability of the existing remedial regime to address the structural problems that underline children and young adults’ economic mobility. The paper argues for a more nuanced approach to child trafficking intervention in Ghana.

Correlation Study of Shrinkage Expansion Potential of Clay Soil In PLTMH Cileunca, Pangalengan, Bandung, Indonesia

Mini Hydro Power Plants (MHPs) utilize the potential of water to generate electrical energy. However, construction on clay soils that have the potential to expand and contract can affect the stability of the structure. This study aims to evaluate the potential for soil development in the Cileunca MHP. The methods used to determine soil development potential include Chen (1988), Skempton (1953), and Seeds (1962) methods. Mineral identification was conducted through X-Ray Diffraction (XRD) testing of soil samples taken by the handboring method. The results of the analysis showed that the Skempton method showed 67% of the samples had activity values > 0.75 (“high” category). The Chen method indicated 60% of the samples had a plasticity index (PI) > 35% (“very high” category). The Seeds method indicated 73% of the samples had a development potential > 25% (very high category). XRD testing indicated the dominant mineral was dehydrated Halloysite (Al2Si5(OH)4). The soil at the Cileunca MHP site has a high development potential that can damage building foundations. Foundation stability is strongly influenced by expansive soil properties, so soil stabilization treatment is needed to prevent structural damage. This study concludes that the soil at the Cileunca MHP site has a high development potential, which can cause structural problems. Therefore, soil stabilization treatment is highly recommended to ensure the stability of the building foundation

AKS-YOLOv8: a lightweight and accurate network model for infrared ship detection

Abstract Combining infrared technology and artificial intelligence technology to achieve efficient ship detection is practically important for monitoring and scheduling of ships in harbours. Due to the infrared image texture features are fewer and the background is complex, these lead to the algorithm model having the problems of complex structure, high amount of memory consumption and not being easy to deploy. To address these problems, we propose a lightweight and accurate infrared ship detection model named AKS-YOLOv8, which is based on YOLOv8 design. Firstly, the Split Asymptotic Feature Pyramid Network (SA-FPN) is used in the neck network. It improves model to detect multi-scale infrared ship in large scenes while keeping the model lightweight. Secondly, we introduced the dynamic convolution module KernelWarehouse Dynamic Convolution (KConv) in the backbone network for enhancing the recognition of small targets in infrared images. And we also used FasterBlock instead of Bottleneck to reduce feature map redundancy and improve inference speed. Finally, we adopt Simple, Parameter-free Attention Module (SimAM) to improve the feature extraction ability for key information of infrared ships. To verify the effectiveness of AKS-YOLOv8, a comprehensive test is performed on the infrared ship target recognition database issued by Yantai iraytek Co., Ltd in comparison with some widely-used detection algorithms. The model precision of AKS-YOLOv8 is improved by 1%, and the parameters and model size are reduced by 37.2% and 32.1%, respectively. The results of the experiment indicate that AKS-YOLOv8 has successfully struck a favorable equilibrium between being lightweight and maintaining accuracy. It can provide a valuable technical reference for infrared ship detection technology.

Online Task Switching and Scheduling Method for Attitude–Orbit–Shape-Distributed Control of Large Space Ring Structures

The attitude, orbit, and shape control actuators of large space structures on orbits are functional overlapping and task coupled, resulting in actuator redundancy and high-energy consumption. This paper explores the integrated attitude–orbit–shape control problem of large space ring structures and proposes an online task switching and scheduling method for multiple actuators of space structures (OTSS-MASS). An adaptive allocation framework is proposed for the task switching of distributed actuators. The objectives of attitude–orbit–shape control are decomposed into attitude adjustment, orbit change, and shape maintenance tasks through a task decomposition algorithm. Task value estimation models are designed to guide a greedy task allocation of actuators. The adaptive allocation framework achieves online task reconfiguration for multiple distributed actuators, thereby enhancing the maneuverability of large space structures, improving actuator work efficiency, and reducing energy consumption in orbit. Simulation results demonstrate that OTSS-MASS outperforms the conventional actuator distribution methods. The overall effectiveness is improved by over 24% in terms of the comprehensive index, time index, trajectory index, and shape index.

Psychometric Properties of Turkish Adaptation of the Environmental Risk Coping Scale

Objective: This study aims to adapt Environmental Risk Coping Scale into Turkish and examine its psychometric properties. Method: For this aim, the psychometric properties of the scale adapted into Turkish were tested in a sample of 230 participants living in 6 cities with high earthquake risk in Türkiye. Participants responded to the Environmental Risk Coping Scale, Environmental Risk Perception Scale, questions on Present Fatalistic and Future Time Orientation, and a demographic information form (i.e., age, gender, education level, city of residence, and homeowner/renter status, past earthquake experience, extent of earthquake damage). Results: The findings of the analyses (i.e., confirmatory factor analysis, reliability analysis) showed that this 12-item scale is reliable and valid in the Turkish sample. Specifically, desirable fit indices (χ2 / sd = 2.06, p < .001, CFI = .96, TLI = .95, RMSEA = .07, 90 % CI [.05, .09], SRMR = .04) demonstrated that the confirmatory factor analysis confirmed the two-factor structure (problem focused and emotion focused coping strategies) and Cronbach α values (.89 for 9-item problem focused coping strategies factor and .72 for 3-item emotion focused coping strategies factor) indicate that the internal consistency of the scale is high. In addition, the sub-dimensions of the Turkish version of the scale were correlated with variables such as risk perception, present-fatalistic time orientation, future time orientation, and demographic factors such as age, earthquake experience, and the extent of damage in earthquake(s) in line with the literature. Conclusion: The Turkish adaptation of the Coping with Environmental Risk Scale was found to be a reliable and valid scale in the Turkish sample.

Revolutionizing cardiovascular disease classification through machine learning and statistical methods.

Cardiovascular diseases (CVDs) include abnormal conditions of the heart, diseased blood vessels, structural problems of the heart, and blood clots. Traditionally, CVD has been diagnosed by clinical experts, physicians, and medical specialists, which is expensive, time-consuming, and requires expert intervention. On the other hand, cost-effective digital diagnosis of CVD is now possible because of the emergence of machine learning (ML) and statistical techniques. In this research, extensive studies were carried out to classify CVD via 19 promising ML models. To evaluate the performance and rank the ML models for CVD classification, two benchmark CVD datasets are considered from well-known sources, such as Kaggle and the UCI repository. The results are analysed considering individual datasets and their combination to assess the efficiency and reliability of ML models on the basis of various performance measures, such as precision, kappa, accuracy, recall, and the F1 score. Since some of the ML models are stochastic, we repeated the simulation 50 times for each dataset using each model and applied nonparametric statistical tests to draw decisive conclusions. The nonparametric Friedman - Nemenyi hypothesis test suggests that the Extra Tree Classifier provides statistically superior accuracy and precision compared with all other models. However, the Extreme Gradient Boost (XGBoost) classifier provides statistically superior recall, kappa, and F1 scores compared with those of all the other models. Additionally, the XGBRF classifier achieves a statistically second-best rank in terms of the recall measures.