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Traditionally, two different techniques have been used to enhance the spectral operating range of a photodetector, i.e., either combining materials of different bandgaps or combining semiconductor materials to form tertiary alloys. However, these methods involve multiple synthesis/processing steps and complex clean room-based fabrication. Addressing this, a simple and novel step in device architecture is demonstrated by surface engineering of Cu buffer layer in between a paper substrate and Cu2S to develop a highly responsive photodetector with tunable operating range. Cu2S nanoparticles were grown directly on the surface of Cu pre-deposited paper substrate using facile hydrothermal technique to obtain Ag/Cu2S/Ag - metal semiconductor metal (MSM) configuration on Cu/paper substrate. The morphological characterization revealed the uniform distribution of Cu2S nanoparticles on the surface of cellulose microfibers and the structural studies confirmed the formation of highly crystalline cubic - Cu2S with Cu-S stretching vibrations. In comparison to the pristine Ag/Cu2S/Ag on paper photodetector, the buffer-based Ag/Cu/Cu2S/Ag photodetector displayed excellent response from visible to NIR region. A superior responsivity and detectivity of 27 mA/W and 2.5 ×108 Jones respectively are observed for Cu buffer/Cu2S photodetector. The obtained response of the paper/Cu/Cu2S device can be attributed to the synergistic combination of Cu buffer layer induced reduction of defect sites and LSPR induced transfer of electrons from the Cu metal to Cu2S. The paper-based photodetector displayed a fast response time of 0.09 sec, and excellent mechanical stability and durability for 1000 bending cycles. The surface engineering of a buffer layer in the photodetector architecture certainly bridges a technological gap and presents a new strategy for development of high-performance optoelectronic devices. © 2022 Elsevier B.V.

Traditionally, two different techniques have been used to enhance the spectral operating range of a photodetector, i.e., either combining materials of different bandgaps or combining semiconductor materials to form tertiary alloys. However, these methods involve multiple synthesis/processing steps and complex clean room-based fabrication. Addressing this, a simple and novel step in device architecture is demonstrated by surface engineering of Cu buffer layer in between a paper substrate and Cu2S to develop a highly responsive photodetector with tunable operating range. Cu2S nanoparticles were grown directly on the surface of Cu pre-deposited paper substrate using facile hydrothermal technique to obtain Ag/Cu2S/Ag - metal semiconductor metal (MSM) configuration on Cu/paper substrate. The morphological characterization revealed the uniform distribution of Cu2S nanoparticles on the surface of cellulose microfibers and the structural studies confirmed the formation of highly crystalline cubic - Cu2S with Cu-S stretching vibrations. In comparison to the pristine Ag/Cu2S/Ag on paper photodetector, the buffer-based Ag/Cu/Cu2S/Ag photodetector displayed excellent response from visible to NIR region. A superior responsivity and detectivity of 27 mA/W and 2.5 ×108 Jones respectively are observed for Cu buffer/Cu2S photodetector. The obtained response of the paper/Cu/Cu2S device can be attributed to the synergistic combination of Cu buffer layer induced reduction of defect sites and LSPR induced transfer of electrons from the Cu metal to Cu2S. The paper-based photodetector displayed a fast response time of 0.09 sec, and excellent mechanical stability and durability for 1000 bending cycles. The surface engineering of a buffer layer in the photodetector architecture certainly bridges a technological gap and presents a new strategy for development of high-performance optoelectronic devices. © 2022 Elsevier B.V.

In the past few decades, we saw a proliferation of scientific articles available online. This data-rich environment offers several opportunities but also challenges, since it is problematic to explore these resources and identify all the relevant content. Hence, it is crucial that they are appropriately annotated with their relevant concepts so to increase their chance of being properly indexed and retrieved. In this paper, we present R-Classify, a web tool that assists users in identifying the most relevant concepts according to a large-scale ontology of research areas in the field of Computer Science.

Renin-angiotensin-aldosterone system (RAAS) inhibition is a mainstay of the pharmacological treatment of heart failure with reduced ejection fraction (HFrEF). In the last years RAAS blockade has been improved by the introduction of the Angiotensin Receptor-Neprilysin Inhibitor (ARNI) sacubitril/valsartan, that combines RAAS inhibition with the block of neprilysin, boosting the positive effects of natriuretic peptides. The PARADIGM-HF trial demonstrated a significant advantage of sacubitril/valsartan over enalapril on the reduction of cardiovascular (CV) mortality and heart failure hospitalizations rates. Then, several randomized clinical trials and observational studies investigated its role in different clinical settings and its efficacy has been fully recognized in the most recent HFrEF European and USA guidelines. The effects of sacubitril/valsartan on major CV outcomes are associated with reduction of NT-proBNP levels and reverse cardiac remodeling and mitral regurgitation, recognized as one of the mechanistic effects of the drug explaining the favorable prognostic effects. A careful evaluation of patients’ clinical profile is relevant to implement the use of ARNI in the clinical practice and to obtain the maximal treatment efficacy. The present Position Paper reports the opinion of the Italian Society of Cardiology on the optimal blockade of the RAAS system in HF patients with the aim of fostering widespread implementation of scientific evidence and practice guidelines in the medical community.

The CO2 concentration in the atmosphere is increasing and threatening the earth's climate. Selective CO2 capture at large point sources will help to reduce the CO2 emissions to the atmosphere. Biochar with microporous structure could be a potential material to capture CO2. The impact of feedstock type, pyrolysis temperature and steam activation of biochars were evaluated for CO2 adsorption capacity. Pine sawdust biochars were produced at 550 °C, and steam activated for 45 min at the same temperature after completing the pyrolysis (PS550 and PSS550). Paper mill sludge biochars were produced at 300 and 600 °C (PMS300 and PMS600). The CO2 adsorption capacity of biochars was tested at 25 °C using a volumetric sorption analyzer. Pine sawdust biochars showed significantly higher CO2 adsorption capacity than paper mill sludge biochars due to high surface area and microporosity. Pine sawdust biochars were then evaluated for dynamic adsorption under representative post-combustion flue gas concentration conditions (15% CO2, 85% N2) using a breakthrough rig. Both materials showed selective CO2 uptake over N2 which is the major component along with CO2 in flue gas. PSS550 had slightly higher CO2 adsorption capacity (0.73 mmol g^-1 vs 0.67 mmol g^-1) and CO2 over N2 selectivity (26 vs 18) than PS550 possibly due to increase of microporosity, surface area, and oxygen containing basic functional groups through steam activation. Pine sawdust biochar is an environmentally friendly and low-cost material to capture CO2.

QC 20201026

Scope Hepatitis B virus (HBV) infection reactivation is associated with high morbidity and mortality in patients with haematologic malignancy and/or haematopoietic stem cell transplantation (HSCT). However, information on this issue is limited. The scope of this position paper is to provide recommendations on HBV screening, monitoring, prophylaxis, treatment and vaccination in the patients described above. Methods These recommendations were developed from one meeting of experts attended by different Italian scientific societies as well as from a systematic literature review (of articles published through December 31, 2016) on HBV infection in haematologic patients and in patients who underwent haematopoietic stem cell transplantation published in the same issue of the journal. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to assess each recommendation's quality. Questions addressed These recommendations provide the answers to the following questions: (a) HBV screening and monitoring: Who should be screened before chemotherapy? Which screening tests should be used? Should HBV-DNA detection be used to monitor HBV reactivation before starting antivirals? What is the best timeline to monitor HBV reactivation? (b) Prophylaxis in HBsAg-positive patients: Which antiviral drugs should be used to treat HBsAg-positive patients? How long should antiviral prophylaxis be provided to HBsAg-positive patients? (c) Prophylaxis in patients with resolved HBV infection: Which patients with resolved HBV infection should receive antiviral prophylaxis? Which antiviral drug should be used? How long should antiviral prophylaxis be provided? (d) HBV infection management strategy in autologous (auto-HSCT) and allogeneic HSCT (allo-HSCT): Which HSCT recipients should receive antiviral prophylaxis? Which antiviral drug should be used? How long should antiviral prophylaxis be provided? (e) Choice of antiviral drugs in the treatment of HBV reac

This review summarizes our current understanding of nonalcoholic fatty liver disease (NAFLD), a multi-factorial systemic disease resulting from a complex interaction between a specific genetic background and multiple environmental/metabolic “hits”. The role of gut microbiota, lipotoxicity, inflammation and their molecular pathways is reviewed in-depth. We also discuss the epidemiology and natural history of NAFLD by pinpointing the remarkably high prevalence of NAFLD worldwide and its inherent systemic complications: hepatic (steatohepatitis, advanced fibrosis and cirrhosis), cardio-metabolic (cardiovascular disease, cardiomyopathy, arrhythmias and type 2 diabetes) and neoplastic (primary liver cancers and extra-hepatic cancers). Moreover, we critically report on the diagnostic role of non-invasive biomarkers, imaging techniques and liver biopsy, which remains the reference standard for diagnosing the disease, but cannot be proposed to all patients with suspected NAFLD. Finally, the management of NAFLD is also reviewed, by highlighting the lifestyle changes and the pharmacological options, with a focus on the innovative drugs. We conclude that the results of ongoing studies are eagerly expected to lead to introduce into the clinical arena new diagnostic and prognostic biomarkers, prevention and surveillance strategies as well as to new drugs for a tailored approach to the management of NAFLD in the individual patient.

The relationship between liver disease and pregnancy is of great clinical impact. Severe liver disease in pregnancy is rare; however, pregnancy-related liver disease is the most frequent cause of liver dysfunction during pregnancy and represents a severe threat to foetal and maternal survival. A rapid differential diagnosis between liver disease related or unrelated to pregnancy is required in women who present with liver dysfunction during pregnancy. This report summarizes the recommendation of an expert panel established by the Italian Association for the Study of the Liver (AISF) on the management of liver disease during pregnancy. The article provides an overview of liver disease occurring in pregnancy, an update on the key mechanisms involved in its pathogenesis, and an assessment of the available treatment options. The report contains in three sections: (1) specific liver diseases of pregnancy; (2) liver disease occurring during pregnancy; and (3) pregnancy in patients with pre-existing chronic liver disease. Each topic is discussed considering the most relevant data available in literature; the final statements are formulated according to both scientific evidence and clinical expertise of the involved physicians, and the AISF expert panel recommendations are reported.

The relationship between liver disease and pregnancy is of great clinical impact. Severe liver disease in pregnancy is rare; however, pregnancy-related liver disease is the most frequent cause of liver dysfunction during pregnancy and represents a severe threat to foetal and maternal survival. A rapid differential diagnosis between liver disease related or unrelated to pregnancy is required in women who present with liver dysfunction during pregnancy. This report summarizes the recommendation of an expert panel established by the Italian Association for the Study of the Liver (AISF) on the management of liver disease during pregnancy. The article provides an overview of liver disease occurring in pregnancy, an update on the key mechanisms involved in its pathogenesis, and an assessment of the available treatment options. The report contains in three sections: (1) specific liver diseases of pregnancy; (2) liver disease occurring during pregnancy; and (3) pregnancy in patients with pre-existing chronic liver disease. Each topic is discussed considering the most relevant data available in literature; the final statements are formulated according to both scientific evidence and clinical expertise of the involved physicians, and the AISF expert panel recommendations are reported.

Superhydrophobic alkyl ketene dimer (AKD) layers were successfully produced on top of untreated paper surfaces by a rapid expansion of supercritical CO2 solution (RESS) process. The new method resulted in a degree of hydrophobicity, as measured by contact angles of water droplets on AKD surfaces, dramatically higher, up to 173 degrees, compared to a conventional method consisting in melting AKD granules directly on the paper substrate, giving contact angles of around 109 degrees. Experiments were conducted to investigate the effects of varying pre-expansion pressure (100-300 bar), pre-expansion temperature (40 and 60 degrees C) and spraying distance (10 and 50 mm) on the properties of the treated surfaces. The surfaces were analyzed regarding AKD particle size, surface morphology and hydrophobicity with the aid of scanning electron microscopy (SEM) and contact angle measurements. The average AKD particle size after RESS processing was between 1 and 2 mu m depending upon the experimental conditions used, being slightly smaller when using higher pre-expansion pressure and temperature as well as shorter spraying distance.

Industrial manual assembly, especially within the area of large-scale assembly, often lacks process monitoring since the feedback about performed assembly tasks is only generated by the worker. Optical sensor systems thereby offer the potential to monitor assembly states automatically and derive the required information without intervening the work in progress. With the growing trend of customization, not only the assembly process itself, but also the monitoring system must be adapt- able at short notice. However, most machine vision systems, as they are today, are commonly task-specific solutions and are therefore hard to be transferred to another inspection task or other work objects. To lower the barriers on applying machine vision into varying environments, this paper introduces an automated CAD-based sensor planning and implementation pipeline. An analysis and derivation of common constraints in assembly design is laid out, followed by a method of generating and optimizing inspection features and sensor poses. A strategy to implement the image processing pipeline based on the derived features is presented and applied on an assembly use case., Bundesministerium für Wirtschaft und Klimaschutz (BMWK)

Graph-based clustering commonly provides promising clustering effectiveness as it can preserve samples’ local geometric information. Inspired by it, multi-view graph clustering was developed to integrate complementary information among graphs of diverse views and it has received intensive attention recently. Nevertheless, on the one hand, most existing methods require extra k-means after obtaining embedding representations to generate a discrete cluster indicator, which reduces effectiveness due to the two-stage mismatch. On the other hand, numerous complex noises in real-world multi-view data challenge the robustness of existing clustering methods. In this paper, we established a discrete correntropy-based multi-view anchor-graph clustering (DCMAC) model that not only emphasizes the aforementioned issues but also makes use of anchor graphs to improve the efficiency of the graph construction stage. To optimize this non-convex model, we propose a fast half-quadratic-based coordinate descent strategy to acquire the discrete cluster indicator directly without extra k-means. Furthermore, we extend the DCMAC model to a single-view form and provide optimization strategies for it. Extensive experiments illustrate that the proposed method is effective and robust compared to those advanced baselines. © 2023 Elsevier B.V.

This paper outlines a novel simple protocol for tuning the structure and properties of jute using sodium periodate (NaIO4) oxidation and coating with alginate. When compared to the raw jute, fabrics oxidized with a 0.2 or 0.4 % NaIO4 solution for 30–120 min exhibited an increased aldehyde group content (0.185 vs. 0.239–0.398 mmol/g), a significantly increased negative zeta potential (from − 8.57 down to − 20.12 mV), a slight disruption of fiber crystallinity, 15.1–37.5 % and 27.9–49.8 % lower fabric maximum force and stiffness, respectively. Owing to the removal of hydrophobic surface barrier, decreased crystallinity index and the presence of micropores on the fabrics' surfaces, oxidized fabrics have a 22.3–29.6 % improved ability for moisture sorption compared to raw fabric. Oxidized fabrics characterized by very long wetting times and excellent antioxidant activities (> 98 %), can find applications as hydrophobic packaging materials. To further extend the utilization of jute in biocarpet engineering such as water-binding geo-prebiotic supports, oxidized fabrics were coated with alginate resulting in 7.9–24.9 % higher moisture sorption and 352–660 times lower wetting times than their oxidized counterparts. This modification protocol has never been applied to lignocellulosic fibers and sheds new light on obtaining jute fabrics with tuned structure and properties intended for various applications.

This paper outlines a novel simple protocol for tuning the structure and properties of jute using sodium periodate (NaIO4) oxidation and coating with alginate. When compared to the raw jute, fabrics oxidized with a 0.2 or 0.4 % NaIO4 solution for 30–120 min exhibited an increased aldehyde group content (0.185 vs. 0.239–0.398 mmol/g), a significantly increased negative zeta potential (from − 8.57 down to − 20.12 mV), a slight disruption of fiber crystallinity, 15.1–37.5 % and 27.9–49.8 % lower fabric maximum force and stiffness, respectively. Owing to the removal of hydrophobic surface barrier, decreased crystallinity index and the presence of micropores on the fabrics' surfaces, oxidized fabrics have a 22.3–29.6 % improved ability for moisture sorption compared to raw fabric. Oxidized fabrics characterized by very long wetting times and excellent antioxidant activities (> 98 %), can find applications as hydrophobic packaging materials. To further extend the utilization of jute in biocarpet engineering such as water-binding geo-prebiotic supports, oxidized fabrics were coated with alginate resulting in 7.9–24.9 % higher moisture sorption and 352–660 times lower wetting times than their oxidized counterparts. This modification protocol has never been applied to lignocellulosic fibers and sheds new light on obtaining jute fabrics with tuned structure and properties intended for various applications.

Ontologies play a pivotal role in knowledge representation, particularly beneficial for the Architecture, Engineering, and Construction (AEC) sector due to its inherent data diversity and intricacy. Despite the growing interest in ontology and data integration research, especially with the advent of knowledge graphs and digital twins, a noticeable lack of consolidated academic synthesis still needs to be addressed. This review paper aims to bridge that gap, meticulously analysing 142 journal articles from 2000 to 2021 on the application of ontologies in the AEC sector. The research is segmented through systematic evaluation into ten application domains within the construction realm- process, cost, operation/maintenance, health/safety, sustainability, monitoring/control, intelligent cities, heritage building information modelling (HBIM), compliance, and miscellaneous. This categorisation aids in pinpointing ontologies suitable for various research objectives. Furthermore, the paper highlights prevalent limitations within current ontology studies in the AEC sector. It offers strategic recommendations, presenting a well-defined path for future research to address these gaps. © 2023 The Author(s)

With the vigorous development of today’s wireless communication industry, the Internet of Vehicles (IoV), as one of its application scenarios, has received extensive attention from researchers. Ensuring the security of information transmission is one of the many problems to be solved in the IoV system. To fit the actual scene, this paper considers the impacts of both the channel characteristics of Nakagami-m fading and the distribution of the vehicles’ positions in the real road scenario of the IoV. According to the random distribution characteristics of vehicle terminal position, a system model with a transmitter base station, a legitimate vehicle terminal, and an eavesdropper is established. The approximate and asymptotic analytical expressions of the secrecy outage probability over the Nakagami-m fading channels are derived. Finally, the correctness of the proposed analysis models established in this paper is verified by Monte Carlo simulation and numerical analysis. The secrecy outage performance and the influencing factors of the considered model in the Nakagami-m fading environment are discussed and analyzed.

Rapidly progressive interstitial lung disease (RP-ILD) clearly harms the prognoses of dermatomyositis/polymyositis (DM/PM) patients, however there is a dearth of numerical prevalence and therapy comparison in this field. Therefore, the purpose of this study was to determine the prevalence of RP-ILD in DM/PM patients and compare prognoses, including remission rate and survival data, between treatments. Studies with reports of RP-ILD in DM/PM patients and studies with definite remission and/or survival data of DM/PM-RP-ILD were included in the study. Data sources were Pubmed, Embase, and Cochrane Library without language restrictions. Two authors (WHL and WWQ) extracted independently the data. Estimates of the pooled effects were calculated using the Mantel-Haenszel technique (random effects). The prevalence meta-analysis included 18 papers with 6058 DM/PM patients, and 31 papers were analyzed for treatment effects, including remission rate, 6-month survival rate, 1-year survival rate, and 5-year survival rate. Database search yielded 1816 articles. In the DM/PM population, the combined prevalence of RP-ILD was 8.9% (95% CI, 5.8% to 12.1%). Patients with RP-ILD have a remission rate of 58.4% (95% CI, 47.3% to 69.4%), with biologic treatment with the highest remission rate, followed by triple therapy (defined as adding a third intravenous medication, including cyclophosphamide and immunoglobulin). Biologics therapy had the highest overall survival rate at six months (95% CI, 49.8% to 73.9%), followed by cDMARDs, plasma exchange, and triple therapy. The 1-year survival rate was 77.4% (95% CI, 66.7% to 88.1%), and triple therapy and cDMARDs had the best survival rates. The 5-year survival rate was 40.0% (95% CI, 10.0% to 69.9%). The prevalence of RP-ILD in DM/PM was approximately 8.9%, with a poor long-term prognosis. The use of biological agents appears to provide the best therapeutic outcomes, providing RP-ILD management with a novel evidence-based therapy. The use of

The paper examines the determinants of FDI profit repatriation in the Czech Republic and OECD countries as a group from 2013 to 2019. By applying the Lintner model about repatriation decisions at the national level, the paper finds the following findings. Firstly, the repatriation rate in the Czech Republic is higher than in most OECD countries. Secondly, financial development and investment opportunities negatively affect the payout ratio of OECD countries as a group, while the appreciation of host countries' currency and higher effective rates positively affect the payout ratio of OECD countries as a group. However, in the case of the Czech Republic, two factors that cause a higher payout ratio are financial development and anti-corruption enforcement. It implies that the Czech Republic should improve its financial market development and reduce the level of corruption to keep the profit from FDI in the country. © 2023 Elsevier B.V.

Underwater object recognition is a prerequisite for the realization of automated seafood harvesting. To solve the problems of low recognition accuracy and poor generalization ability of existing underwater small object, this paper takes sea cucumber as research objects to research the small object recognition approach in complex underwater environment. In this paper, a sea cucumber dataset is established to solve the problem of lacking sea cucumber dataset in the real seabed environment. A deep learning model SO-YOLOv5 is proposed based on YOLOv5 for underwater small object recognition, which improves the recognition ability of the algorithm for different sizes of objects in complex environment. The proposed model embeds a large-scale feature extraction layer in structure to increase the detection ability of small objects by referring to the idea of the Bi-directional Feature Pyramid Network. SO-YOLOv5 fuses the features between deep and shallow layers and balances the feature information of different scales, and integrates the Coordinate Attention mechanism to enhance the sensitivity of the algorithm to the direction and position information. The experimental results illustrate that the mAP of the proposed approach achieves 95.47% for sea cucumber recognition in the complex seabed environment, which is 3.42%, 6.79%, and 5.46% higher than the traditional Faster R-CNN, SSD, and YOLOv5 algorithms, respectively. This research not only provides an effective approach for sea cucumber recognition in complex environment but also has certain reference significance for the recognition of small objects in other complex environments. In addition, the proposed approach has practical application value to improve intelligence level in aquaculture. © 2023 Elsevier B.V.

Recent research has focused on developing real-time sign language recognition systems (SLRSs) based on gesture recognition to classify hand motions into their equivalent meaning in spoken language, but no comprehensive system with all desirable features has been presented. The existence of different systems has hindered the process of selecting the most preferred system. Therefore, many researchers have compared and evaluated several recognition systems to identify the best one using multicriteria decision-making methods. These studies extended the fuzzy decision by opinion score method (FDOSM) using a single Likert scale under the Pythagorean fuzzy set (PFS) or one of its extensions. However, no comparative study has examined the influence of using multiple Likert scales with a single fuzzy set. Furthermore, the effect of employing multiple Likert scales on benchmarking results is a challenging task. Therefore, this paper examines the three Likert scales (five-, seven- and ten-point) under the same fuzzy environment. This paper extends FDOSM into PFSs based on the power Bonferroni mean (PBM) operator (named PFDOSM-PBM) to benchmark the real-time SLRS. The decision matrix is constructed based on 30 real-time SLRS-based wearable sensory devices and the 11 evaluation criteria. The results reveal that the five-point Likert scale is superior to other scales (i.e., seven- and ten-point) as it is flexible, easy to use and generates more accurate findings on the basis of uncertainty compared to other scales. Systematic ranking and comparative analysis are conducted to validate and evaluate the proposed method.

To provide direction and advice for future research on Industry 4.0 maintenance, we conducted a comprehensive analysis of 344 eligible journal papers published between 2013 and 2022. Our systematic literature review identifies key trends in advanced maintenance techniques and the consolidation of traditional maintenance concepts, which are driven by the increasing adoption of Industry 4.0 technologies and the need to optimize manufacturing systems' performance and reliability. In light of our findings, we highlight the importance of addressing sustainability factors, human aspects, and the implementation of environmental KPIs in future research. Building upon these insights, we introduce the Maintenance 5.0 framework, which emphasizes the integration of human-centered and AI-driven strategies for achieving efficient and sustainable maintenance in Zero-Defect Manufacturing (ZDM) systems. We propose a novel framework that links traditional and advanced maintenance policies for small and medium-sized enterprises (SMEs) to facilitate the adoption of Industry 4.0 technologies in the maintenance field. This work underscores the need for future research to bridge the gap between these policies, enabling a seamless transition for SMEs towards Industry 4.0 maintenance practices, while fostering sustainable and socially responsible operations.

Adaptation to climate change is impacted by a range of interrelated processes operating from local to global levels. There are often significant disconnects between different people's perceptions of responsibilities, capabilities and motivations, and divergent understandings of how the system works across actors, sectors and levels of governance. This results in misalignments of policies and practices, plus ineffective flows of resources and knowledge across the network of climate adaptation actors. As these disconnects are rooted in deep misunderstandings of the grounded realities of different actors, an experiential process of mutual discovery is required to build shared understanding and mutual respect. While it is common in the literature for people to talk about multi-level governance, most existing planning processes involve the production of separate plans at each individual level, based on the often-mistaken assumption that they will aggregate into an effective multi-level approach. This paper presents a new, multi-level integrated planning and implementation (MIPI) process, bringing together diverse actors from community, district, regional and national levels in the same workshop. The MIPI process creates a safe space that allows participants to interact directly in conducting systemic, cross-level analyses, as well as the multi-level integration of policies, plans and programs. The paper describes how the MIPI process was designed and facilitated in Ghana to address climate change, agricultural development and food security. This methodology has potential for much broader applicability to complex, multi-level planning and implementation processes.

The paper is devoted to expanding the composition of the studied elements of the human factor that affect the level of industrial injuries. The significant achievements of JSC "Russian Railways" are shown in reducing injuries and the tasks of implementing the "Vision Zero" concept, which can be solved if additional reserves are attracted. The purpose of the paper is to widely include motivational elements in the human factor, which allow approaching the implementation of the concept of zero injuries. An overview of the studied factors of injuries in the industry is given. Based on the conducted research, motivational elements that have an impact on the safety of production processes are identified. The use of the SHELL concept allowed approaching the systematic study of the problem of injury minimization based on the study of the relationship between a person and the organization's resources. Problematic behavioral characteristics of industry employees and ways to solve them are identified. Obtaining an objective picture of the real state of the motivational sphere is possible by using a comprehensive approach with the involvement of additional research results. Research methods: expert, sociological survey, statistical data analysis, comparison, system analysis. As a result, the following conclusions are obtained: the construction of an industry system of personnel motivation in relation to reducing the level of industrial injuries requires the use of research results on the motivational sphere of employees. These results will allow implementing a comprehensive approach, making additions to the mechanisms of management actions to reduce injuries. © 2021 Elsevier B.V.. All rights reserved.

This paper is devoted to the ductile–brittle transition behavior of high strength low alloyed structural steel NIOMOL 490K. This steel grade is designed for welded pressure vessels exposed to dynamic loads, operating at temperatures below zero, and that is why it must have acceptable toughness. Due to its importance for the safety assessment of pressure vessels, a characterization of this steel with Charpy V-notch test in range of temperature between-60 ⁰C and +60 ⁰C was undertaken. Ductile-to-brittle transition temperature curves were generated in the present study by using instrumented Charpy pendulum impact test on V-notch specimens. Notches were located in the parent material, heat-affected zone and weld metal. The nil-ductility temperature and tensile properties in temperature range from -60 ⁰C to +60 ⁰C are presented in this paper. We used different test temperatures to better understand ductile-to brittle transition response than just use either Charpy energy or other related ductility criteria.

This paper is devoted to the ductile-brittle transition behavior of high strength low alloyed structural steel NIOMOL 490K. This steel grade is designed for welded pressure vessels exposed to dynamic loads, operating at temperatures below zero, and that is why it must have acceptable toughness. Due to its importance for the safety assessment of pressure vessels, a characterization of this steel with Charpy V-notch test in range of temperature between-60 0C and +60 0C was undertaken. Ductile-to-brittle transition temperature curves were generated in the present study by using instrumented Charpy pendulum impact test on V-notch specimens. Notches were located in the parent material, heat-affected zone and weld metal. The nil-ductility temperature and tensile properties in temperature range from -60 0C to +60 0C are presented in this paper. We used different test temperatures to better understand ductile-to brittle transition response than just use either Charpy energy or other related ductility criteria.

This paper is devoted to the ductile-brittle transition behavior of high strength low alloyed structural steel NIOMOL 490K. This steel grade is designed for welded pressure vessels exposed to dynamic loads, operating at temperatures below zero, and that is why it must have acceptable toughness. Due to its importance for the safety assessment of pressure vessels, a characterization of this steel with Charpy V-notch test in range of temperature between-60 0C and +60 0C was undertaken. Ductile-to-brittle transition temperature curves were generated in the present study by using instrumented Charpy pendulum impact test on V-notch specimens. Notches were located in the parent material, heat-affected zone and weld metal. The nil-ductility temperature and tensile properties in temperature range from -60 0C to +60 0C are presented in this paper. We used different test temperatures to better understand ductile-to brittle transition response than just use either Charpy energy or other related ductility criteria.

Using machine learning algorithms in social environments and systems requires stricter and more detailed control. More specifically, the cost of error in such systems is much higher. Therefore, one should ensure that important decisions, such as whether to convict a person or not based on the previous criminal record, are by the legal requirements and not biased toward a group of people. One can find many many papers in the literature aimed at mitigating or eliminating unwanted bias in machine learning models. A significant part of these efforts add fairness constraint to the mathematical model or adds a regularization term to the loss function. In this paper, we show that optimizing the loss function given the fairness constraint or regularization for unfairness can surprisingly yield unfair solutions. This is due to the linear relaxation of the fairness function. By analyzing the gap between the true value of fairness and the one obtained using linear relaxation, we found that the gap can be as high as around 21% for the COMPAS dataset, and around 35% for the Adult dataset. In addition, we show that the fairness gap is consistent regardless of the strength of the fairness constraint or regularization.

Science communication has a number of bottlenecks that include the rising number of published research papers and its non-machine-accessible and document-based paradigm, which makes the exploration, reading, and reuse of research outcomes rather inefficient. Recently, Knowledge Graphs (KG), i.e., semantic interlinked networks of entities, have been proposed as a new core technology to describe and curate scholarly information with the goal to make it machine readable and understandable. However, the main drawback of the use of such a technology is that researchers are asked to manually annotate their research papers and add their contributions within the KGs. To address this problem, in this paper we propose SCICERO, a novel KG generation approach that takes in input text from research articles and generates a KG of research entities. SCICERO uses Natural Language Processing techniques to parse the content of scientific papers to discover entities and relationships, exploits state-of-the-art Deep Learning Transformer models to make sense and validate extracted information, and uses Semantic Web best practices to formally represent the extracted entities and relationships, making the written content of research papers machine-actionable. SCICERO has been tested on a dataset of 6.7M papers about Computer Science generating a KG of about 10M entities. It has been evaluated on a manually generated gold standard of 3,600 triples that cover three Computer Science subdomains (Information Retrieval, Natural Language Processing, and Machine Learning) obtaining remarkable results.

In this paper, we develop a multi-Agent model for opinion dynamics that offers rich behaviors of opinion formations. The states of the model are the opinions of the agents which are designed to evolve on a unit-sphere. The model is novel in that the input to the system is designed based on a correlation matrix of the opinions of neighboring agents. Interestingly, the model demonstrates both stable consensus as well as stable bipartite dissensus behaviors using an unsigned communication graph. This is different than existing results where stable dissensus can only be achieved via a signed graph. We present various stability results about the different equilibrium configurations. Additionally, we provide simulation results for a 2-dimensional system demonstrating the different resulting behaviors of the system. © 2022 Elsevier B.V.. All rights reserved.

Context: GUI-based testing is a sub-field of software testing research that has emerged in the last three decades. GUI-based testing techniques focus on verifying the functional conformance of the system under test (SUT) through its graphical user interface. However, despite the research domains growth, studies in the field have low reproducibility and comparability. One observed cause of these phenomena is identified as a lack of research rigor and commonly used metrics, including coverage metrics. Objective: We aim to identify the most commonly used metrics in the field and formulate a taxonomy of coverage metrics for GUI-based testing research. Method: We adopt an evidence-based approach to build the taxonomy through a systematic literature review of studies in the GUI-based testing domain. Identified papers are then analyzed with Open and Axial Coding techniques to identify hierarchical and mutually exclusive categories of metrics with common characteristics, usages, and applications. Results: Through the analysis of 169 papers and 315 metric definitions, we obtained a taxonomy with 55 codes (common names for metrics), 17 metric categories, and 4 higher level categories: Functional Level, GUI Level, Model Level and Code Level. We measure a higher number of mentions of Model and Code level metrics over Functional and GUI level metrics. Conclusions: We propose a taxonomy for use in future GUI-based testing research to improve the general quality of studies in the domain. In addition, the taxonomy is perceived to help enable more replication studies as well as macro-analysis of the current body of research. © 2022 Elsevier B.V.

The increasing use of electronic converters is making possible the development of more sustainable, efficient, and reliable electric power systems, but it is also introducing new challenges that need to be addressed. Power system stability is one of them since the converter multiscale coupling between control loops and the mutual effects of multiple converters make the traditional approaches and specialized software available not fully adequate. In this context, this paper presents a software tool called CSTEP capable of carrying out small- and large-signal stability analysis for electric power systems strongly dominated by electronic converters. By utilizing the tool description, the paper provides the reader with a systematic methodology to obtain the system dynamic representation, and with some software tools for the analysis of the large and small-signal stability. Finally, the validity and functionality of CSTEP have been tested with the implementation of three use cases: a simple ac use case to illustrate the application of the system algorithm step by step, an IEEE 5-bus use case to validate the tool with already established commercial software and a more realistic system based on a medium voltage CIGRÉ benchmark to highlight the potential applications of the tool. The results show that, unlike existing tools that simplify or neglect the electric part of the grid (filters, line impedances, etc.), CSTEP can predict power system instabilities caused by the interactions of fast control loops of electronic converters with the electric part.

Technological Forecasting and Social Change (TFSC) is a leading peer-reviewed journal that addresses issues at the intersection of technology and society. A major strategy of the journal is to actively solicit and publish Special Issues (SIs). These SIs were first launched in 1979 to highlight and solicit manuscripts from the “hot” emerging issues of the discipline. This paper aims to analyze SIs and to highlight it's impact on TFSC as compared to Regular Issues (RIs). Using bibliometric analysis, this study first establishes that SIs have a higher impact on the field than RIs when evaluated based on average citations per manuscript, percentage of ‘hot’ papers, and the rate of citations per annum. The study then identifies leading actors (authors, affiliated institutions, and countries) and journals (knowledge inflow/outflow) that have contributed to the success of TFSC-SIs. Finally, using bibliographic coupling, seven thematic clusters of TFSC's SIs were identified. These clusters were compared with the knowledge clusters developed by Singh et al. (2020) for the entirety of TFSC journals, and four clusters unique to SIs were identified i.e. (Climate Change & Energy, Entrepreneurship and Innovation, Sustainability and, Social Media & Internet of Things). It is observed that these unique SI clusters have received disproportionate attention during the last decade and are likely to influence the future trajectory of the journal.

The paper presents a brief review of the rail defect coding systems, which were recommended by the International Union of Railways during the period from 1959 to today. The recommended classification and numbering of RCF rail defects, defined in the document IRS 70712 from 2018, were critically analysed. In addition, harmonisation of European standards and IRS 70712 was found necessary by the authors. The paper points out that appropriate attention was paid neither to the combination of several types of RCF rail defects nor to the occurrence of these defects in the sensitive track zones such as switches, crossings, expansion joints, and insulated rail joints. Authors propose that Infrastructure Managers should supplement the Handbook of rail defects with RCF defects that occur on the railway network under their control. Moreover, the authors present the experience regarding RCF defects on the Serbian railway network.

The condition of the rails affects the comfort and safety of railway transport. Managing the rail life cycle on modern railway networks has been playing an increasingly important role. Inspection, preventive and corrective maintenance of rails are significant activities in the integral maintenance of the railway superstructure and substructure. The competitiveness of railway transport on the traffic market requires the realization of maintenance activities in short periods of line closure. The paper presents guidelines for rail reprofiling based on the experiences of railway Infrastructure Managers and the recommendations of European technical regulations. The need for grinding new rails after laying the track was pointed out. The advantage of preventive over corrective reprofiling is emphasized. Different types of target profiles of the reprofiled railhead are presented. This paper aims to improve the knowledge of experts in the field of railway infrastructure maintenance.

In tribological applications, the degradation of metallic coatings due to oxidation and thermal softening at high temperatures is an issue of increasing concern. Recently, researchers have focused on the development of durable hard coatings that can perform well under elevated temperatures. The alloying of ternary TiAlN coatings with various elements has received considerable attention due to its ability to improve coating properties at high temperatures by solid solution hardening, grain refinement, formation of new phases, diffusion barriers, and self-lubricious tribo-oxides. This paper reviews the microstructure, thermal stability, oxidation behaviour, and mechanical and tribological properties of resultant quaternary TiAlXN coatings (X = Si, Cr, V, Ta and B). The effects of the deposition parameters, chemical composition, high-temperature annealing, and coating architecture on the coating properties are discussed in depth. The properties of quinary TiAlCrSiN coatings are also reviewed to provide a better understanding of the synergistic effects of Si and Cr additions to TiAlN. The maximum hardness and plastic deformation resistance (H/E and H3/E2) of TiAlXN coatings produced by various deposition techniques are compared. This paper provides useful insights into the challenges and future research perspectives of the reviewed coatings.

This paper proposes an automated tomato fruit detection and counting process without a need for any human intervention. First of all, wide images of whole tomato plant rows were extracted from a 360-degree video taken in a greenhouse. These images were utilized to create a new object detection dataset. The original tomato detection methodology uses a deep CNN model with slicing-aided inference. The process encompasses two stages: first, the images are cut into patches for object detection, and consequently, the predictions are stitched back together. The paper also presents an extensive study of post-processing parameters needed to stitch object detections correctly, especially on the patch's borders. Final results reach 83.09% F1 score value on a test set, proving the suitability of the proposed methodology for robotic farming.

The spectrum cartography finds applications in several areas such as cognitive radios, spectrum aware communications, machine-type communications, Internet of Things, connected vehicles, wireless sensor networks, and radio frequency management systems, etc. This paper presents a survey on state-of-the-art of spectrum cartography techniques for the construction of various radio environment maps (REMs). Following a brief overview on spectrum cartography, various techniques considered to construct the REMs such as channel gain map, power spectral density map, power map, spectrum map, power propagation map, radio frequency map, and interference map are reviewed. In this paper, we compare the performance of the different spectrum cartography methods in terms of mean absolute error, mean square error, normalized mean square error, and root mean square error. The information presented in this paper aims to serve as a practical reference guide for various spectrum cartography methods for constructing different REMs. Finally, some of the open issues and challenges for future research and development are discussed. © 2021 The Authors

The paper considers the development of the Northern Sea Route (NSR) in modern Russia and China. The analysis of cargo turnover and the advantages of the NSR is carried out. The importance of the nuclear icebreaker fleet in the development of the NSR is determined and specified. The route is currently considered as transcontinental and it is planned to become part of the “Silk Road”. It belongs to the number of Russian national projects. As an international transit highway, the Northern Sea Route has a huge potential for international cooperation and development of the North and East of Russia. The cluster-modular approach in the organization of NSR is considered. © 2022 The Authors. Published by ELSEVIER B.V.

The spectrum cartography finds applications in several areas such as cognitive radios, spectrum aware communications, machine-type communications, Internet of Things, connected vehicles, wireless sensor networks, and radio frequency management systems, etc. This paper presents a survey on state-of-the-art of spectrum cartography techniques for the construction of various radio environment maps (REMs). Following a brief overview on spectrum cartography, various techniques considered to construct the REMs such as channel gain map, power spectral density map, power map, spectrum map, power propagation map, radio frequency map, and interference map are reviewed. In this paper, we compare the performance of the different spectrum cartography methods in terms of mean absolute error, mean square error, normalized mean square error, and root mean square error. The information presented in this paper aims to serve as a practical reference guide for various spectrum cartography methods for constructing different REMs. Finally, some of the open issues and challenges for future research and development are discussed. © 2021 The Authors

Reduction of iron ore is central to iron and steel making process. The reaction kinetics are generally studied using classical models that are based on the mechanism of interface control, nucleation control, or diffusion control. This paper presents a different approach in which physical governing equations for the solid phase are solved simultaneously with the gas phase transport equations. The reaction rate is calculated using shrinking core model and the parameters for kinetic term are estimated using genetic algorithm. The model is validated using data collected from experiments performed using iron ore pellets in a packed bed reactor and iron ore powder in a TGA apparatus. The reduced samples are subjected to SEM analysis to observe the microstructural changes that occur as a result of high temperature reduction. The paper further discusses, the applicability of the classical models in describing the different stages of reduction. © 2022 Elsevier B.V.

The spectrum cartography finds applications in several areas such as cognitive radios, spectrum aware communications, machine-type communications, Internet of Things, connected vehicles, wireless sensor networks, and radio frequency management systems, etc. This paper presents a survey on state-of-the-art of spectrum cartography techniques for the construction of various radio environment maps (REMs). Following a brief overview on spectrum cartography, various techniques considered to construct the REMs such as channel gain map, power spectral density map, power map, spectrum map, power propagation map, radio frequency map, and interference map are reviewed. In this paper, we compare the performance of the different spectrum cartography methods in terms of mean absolute error, mean square error, normalized mean square error, and root mean square error. The information presented in this paper aims to serve as a practical reference guide for various spectrum cartography methods for constructing different REMs. Finally, some of the open issues and challenges for future research and development are discussed. © 2021 The Authors

The spectrum cartography finds applications in several areas such as cognitive radios, spectrum aware communications, machine-type communications, Internet of Things, connected vehicles, wireless sensor networks, and radio frequency management systems, etc. This paper presents a survey on state-of-the-art of spectrum cartography techniques for the construction of various radio environment maps (REMs). Following a brief overview on spectrum cartography, various techniques considered to construct the REMs such as channel gain map, power spectral density map, power map, spectrum map, power propagation map, radio frequency map, and interference map are reviewed. In this paper, we compare the performance of the different spectrum cartography methods in terms of mean absolute error, mean square error, normalized mean square error, and root mean square error. The information presented in this paper aims to serve as a practical reference guide for various spectrum cartography methods for constructing different REMs. Finally, some of the open issues and challenges for future research and development are discussed. © 2021 The Authors

Reduction of iron ore is central to iron and steel making process. The reaction kinetics are generally studied using classical models that are based on the mechanism of interface control, nucleation control, or diffusion control. This paper presents a different approach in which physical governing equations for the solid phase are solved simultaneously with the gas phase transport equations. The reaction rate is calculated using shrinking core model and the parameters for kinetic term are estimated using genetic algorithm. The model is validated using data collected from experiments performed using iron ore pellets in a packed bed reactor and iron ore powder in a TGA apparatus. The reduced samples are subjected to SEM analysis to observe the microstructural changes that occur as a result of high temperature reduction. The paper further discusses, the applicability of the classical models in describing the different stages of reduction. © 2022 Elsevier B.V.

Reduction of iron ore is central to iron and steel making process. The reaction kinetics are generally studied using classical models that are based on the mechanism of interface control, nucleation control, or diffusion control. This paper presents a different approach in which physical governing equations for the solid phase are solved simultaneously with the gas phase transport equations. The reaction rate is calculated using shrinking core model and the parameters for kinetic term are estimated using genetic algorithm. The model is validated using data collected from experiments performed using iron ore pellets in a packed bed reactor and iron ore powder in a TGA apparatus. The reduced samples are subjected to SEM analysis to observe the microstructural changes that occur as a result of high temperature reduction. The paper further discusses, the applicability of the classical models in describing the different stages of reduction. © 2022 Elsevier B.V.

Background: Emotional intelligence (EI) can help perceive, understand, and manage emotions and positively impact performance in any profession, including pharmacists, and consequently may have positive influence on patient-related outcomes. Although there is strong body of evidence suggesting that developing EI in health professionals (HPs) can increase their capacity to successfully communicate and build relationships with patients, thus increasing patient satisfaction, little is known about it in pharmaceutical care (PhC). Objectives: This review aimed to synthesize available data on the probable impact of EI on PhC. Methods: PubMed, Web of Science, and Embase databases were searched for papers in English dated between January 2000 and June 2021. Quantitative, qualitative, or mixed method studies on EI and PhC that involved practicing pharmacists were included. Results: The inclusion criteria were met by 4 papers only. One reported positive impact of EI in reducing the negative correlation between autistic-like traits and empathy among hospital pharmacists. One study demonstrated that EI levels can be significantly enhanced through pharmacy leadership programs. Another study established a positive correlation between EI and entrepreneurial orientation in practicing community pharmacists. Higher EI scores were predictors of increased work innovation, proactivity, and risk-taking levels. One study reported comparative EI data between different HPs and found pharmacists' superiority in the EI subdomains of self-awareness, self-motivation, and social skills. Conclusion: Additional research is required to provide evidence on how EI and EI development programs can add value to the provision of PhC. Processes and resources ought to be developed and secured to support the implementation and follow-up of such programs to bring long-term benefits to practicing pharmacists and consequently positively impacting patient-reported health outcomes.

This paper studies the enhanced heat transfer of adding longitudinal fins in a horizontal shell-and-tube heat storage unit. A two-dimensional numerical model is established and validated through comparing with experimental data in literature. Under the same ratio of fin volume to phase change materials (PCMs), the melting thermal performance is optimized by changing the fin thickness, interval and the number. Results demonstrate that adding longitudinal fins is a simple and effective method to enhance the thermal energy storage efficiency. The number of fins greatly affects the complete melting time, and the maximum time difference caused by the number of fins is as high as 72.85% under the same phase change material (PCM) filling mass. At the same time, increasing the number of fins will weaken the local natural convection. In this paper, the optimal number of fins in the limited research range is given, and the effectiveness of longitudinal fins in improving melting speed is quantified, which has certain practical significance for the engineering application research of phase change energy storage. © 2021 Elsevier B.V.

Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, suc, open access

Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, suc, open access

Context: Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. Objective: This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. Method: A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Results: Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Conclusions: Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, suc, open access