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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)

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.

The next generation of manufacturing system calls for feasible solutions with high efficiency and flexibility. Thus in recent years, Human-Robot Collaboration (HRC) research attracts many attentions worldwide since it unites the repeatability and accuracy of robots and the adaptivity and intelligence of human operators. In this paper, the system architecture of an HRC solution is presented, together with the novel method of vision sensing and controlling towards a smart solution with high accuracy and fast speed. The proposed method is demonstrated through implementations in the real robot cell, and evaluated by quantifiable measurements. Future research outlooks are addressed at the end of the paper., QC 20210309

Context: To reduce manual effort of extracting test cases from natural-language requirements, many approaches based on Natural Language Processing (NLP) have been proposed in the literature. Given the large amount of approaches in this area, and since many practitioners are eager to utilize such techniques, it is important to synthesize and provide an overview of the state-of-the-art in this area. Objective: Our objective is to summarize the state-of-the-art in NLP-assisted software testing which could benefit practitioners to potentially utilize those NLP-based techniques. Moreover, this can benefit researchers in providing an overview of the research landscape. Method: To address the above need, we conducted a survey in the form of a systematic literature mapping (classification). After compiling an initial pool of 95 papers, we conducted a systematic voting, and our final pool included 67 technical papers. Results: This review paper provides an overview of the contribution types presented in the papers, types of NLP approaches used to assist software testing, types of required input requirements, and a review of tool support in this area. Some key results we have detected are: (1) only four of the 38 tools (11%) presented in the papers are available for download; (2) a larger ratio of the papers (30 of 67) provided a shallow exposure to the NLP aspects (almost no details). Conclusion: This paper would benefit both practitioners and researchers by serving as an “index” to the body of knowledge in this area. The results could help practitioners utilizing the existing NLP-based techniques; this in turn reduces the cost of test-case design and decreases the amount of human resources spent on test activities. After sharing this review with some of our industrial collaborators, initial insights show that this review can indeed be useful and beneficial to practitioners. © 2020 Elsevier B.V., open access

Context: Continuous integration (CI) is a practice that aims to continuously verify quality aspects of a software intensive system both for functional and non-functional requirements (NFRs). Functional requirements are the inputs of development and can be tested in isolation, utilising either manual or automated tests. In contrast, some NFRs are difficult to test without functionality, for NFRs are often aspects of functionality and express quality aspects. Lacking this testability attribute makes NFR testing complicated and, therefore, underrepresented in industrial practice. However, the emergence of CI has radically affected software development and created new avenues for software quality evaluation and quality information acquisition. Research has, consequently, been devoted to the utilisation of this additional information for more efficient and effective NFR verification. Objective: We aim to identify the state-of-the-art of utilising the CI environment for NFR testing, hereinafter referred to as CI-NFR testing. Method: Through rigorous selection, from an initial set of 747 papers, we identified 47 papers that describe how NFRs are tested in a CI environment. Evidence-based analysis, through coding, is performed on the identified papers in this SLR. Results: Firstly, ten CI approaches are described by the papers selected, each describing different tools and nine different NFRs where reported to be tested. Secondly, although possible, CI-NFR testing is associated with eight challenges that adversely affect its adoption. Thirdly, the identified CI-NFR testing processes are tool-driven, but there is a lack of NFR testing tools that can be used in the CI environment. Finally, we proposed a CI framework for NFRs testing. Conclusion: A synthesised CI framework is proposed for testing various NFRs, and associated CI tools are also mapped. This contribution is valuable as results of the study also show that CI-NFR testing can help improve the quality of NFR testing i

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.

The hydrolate, or hydrosol, is the residue of essential oil (EO) distillation traditionally used in perfumery. Evidence that also this product exerts anti-microbial activity promoted a study on Monarda citriodora EO and hydrolate against thirty representative strains of bacteria and fungi. Additionally, the sole hydrolate was tested in four moulds responsible for the spoilage of paper artworks. The micro-broth dilution test served to assess EO (concentrations between 0.0078% and 4%) and hydrolate (concentrations between 12.5% and 50%) suppressiveness vs untreated control. EO and hydrolate were analysed (GC–MS), resulting in thymol being the major terpenic compound of both products (19.6% and 66.4% in the respective spectra). EO suppressed all micro-organisms, although resistant Pseudomonas aeruginosa strains required up to 4% EO as minimum inhibitory concentration (MIC). Hydrolate suppressed all micro-organisms, except three P. aeruginosa strains where even the highest concentration (50% hydrolate) failed to reach the MIC. In each strain, the concentration needed to obtain 50% inhibition (IC50) was calculated by means of an exponential decay function. Median IC50 was 0.06% (EO) and 7.35% (hydrolate), while six IC50 EO data were shown to be outliers. In non-outlier data, IC50 hydrolate and EO were significantly correlated (r = 0.73**), and IC50 hydrolate was approximately a hundred times higher than IC50 EO. Despite higher concentration needed for the same inhibitory effect, the amount of terpenes supplied with hydrolate was lower than with EO. This means a higher hydrolate effectiveness that was likely due to the hydrophilic environment promoting higher terpene availability. Lastly, hydrolate exhibited promising results in the control of fungal growth on paper artworks, suppressing the four tested strains at concentrations of 25–50%.

The quality of sheet metal formed parts is strongly dependent on the tribology and friction conditions that are acting in the actual forming process. These friction conditions are then dependent on the tribology system, i.e. the applied sheet material, coating and tooling material, the lubrication and process conditions. Although friction is of key importance, it is currently not considered in detail in sheet metal forming simulations. The current industrial standard is to use a constant (Coulomb) coefficient of friction, which limits the overall simulation accuracy. Since a few years, back there is an ongoing collaboration on friction modelling between Volvo Cars, Tata Steel, TriboForm Engineering, AutoForm Engineering and the University of Twente. In previous papers by the authors, results from lab scale studies and studies of body parts at Volvo Cars, both parts in early tryout for new car models as well as parts in production have been presented. However, the introduction of a new friction model in the sheet metal forming simulations forces the user to gain knowledge about accurate values for new input parameters and question current modeling assumptions. This paper presents results from studies on the influence on the sheet metal forming simulation results from stamping die surface roughness and introduction of strain rate sensitivity in the sheet material model. The study will use a FE-model of a part presented in previous papers. © 2019 The Authors. Published by Elsevier B.V., Open access

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations w ithout numerical difficulties. ©2019 Hie Authors. Published by Elsevier B.V., open access

The quality of sheet metal formed parts is strongly dependent on the tribology and friction conditions that are acting in the actual forming process. These friction conditions are then dependent on the tribology system, i.e. the applied sheet material, coating and tooling material, the lubrication and process conditions. Although friction is of key importance, it is currently not considered in detail in sheet metal forming simulations. The current industrial standard is to use a constant (Coulomb) coefficient of friction, which limits the overall simulation accuracy. Since a few years, back there is an ongoing collaboration on friction modelling between Volvo Cars, Tata Steel, TriboForm Engineering, AutoForm Engineering and the University of Twente. In previous papers by the authors, results from lab scale studies and studies of body parts at Volvo Cars, both parts in early tryout for new car models as well as parts in production have been presented. However, the introduction of a new friction model in the sheet metal forming simulations forces the user to gain knowledge about accurate values for new input parameters and question current modeling assumptions. This paper presents results from studies on the influence on the sheet metal forming simulation results from stamping die surface roughness and introduction of strain rate sensitivity in the sheet material model. The study will use a FE-model of a part presented in previous papers. © 2019 The Authors. Published by Elsevier B.V., Open access

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations w ithout numerical difficulties. ©2019 Hie Authors. Published by Elsevier B.V., open access

The main focus of this paper is how a manufacturing organization can secure the capabilities of effectively executing a series of improvements of various sizes and innovativeness from small to large and incremental to more radical at the manufacturing plant, so that the speed of improvement as a whole at the plant can be kept high. A recent study on a Toyota plant in Japan has shown that a group of production engineers located at the plant plays an important role in ensuring the mentioned kind of capabilities. Those engineers are considered trusted experts in improvement of various sizes and innovativeness. They also coordinate those improvements so that the speed of improvement at the plant level can be kept high. This study raises a question of how other manufacturing companies do to ensure these capabilities, given that the group of the production engineers playing the role is likely a specific organization design to this specific company. For the question, three manufacturing plants producing vehicles in Swedish are studied. The paper discusses implications drawn from a cross-case analysis of those plants including the Toyota plant, in terms of the main focus of this paper. © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Whether leaders and their teams agree or not on perceptions of leadership has been found to impact follower well-being and performance. Less is known about how agreements or disagreements play a role in relation to safety and leadership training. The present study examined the effects of leaders’ and followers’ perceptual distance on safety leadership prior to a leadership safety training. Forty-eight leaders and a total of 211 followers from the paper industry completed surveys before and after training. Polynomial regression with response surface analyses revealed that the agreement between leaders and their followers regarding safety leadership before training was positively related to training outcomes including safety leadership and followers’ safety self-efficacy. Line managers who overrated themselves on safety leadership before training had less favorable training outcomes. Our findings suggest that 360-degree feedback may not be sufficient for motivating leaders to change their behaviors during leadership training.

The quality of sheet metal formed parts is strongly dependent on the tribology and friction conditions that are acting in the actual forming process. These friction conditions are then dependent on the tribology system, i.e. the applied sheet material, coating and tooling material, the lubrication and process conditions. Although friction is of key importance, it is currently not considered in detail in sheet metal forming simulations. The current industrial standard is to use a constant (Coulomb) coefficient of friction, which limits the overall simulation accuracy. Since a few years, back there is an ongoing collaboration on friction modelling between Volvo Cars, Tata Steel, TriboForm Engineering, AutoForm Engineering and the University of Twente. In previous papers by the authors, results from lab scale studies and studies of body parts at Volvo Cars, both parts in early tryout for new car models as well as parts in production have been presented. However, the introduction of a new friction model in the sheet metal forming simulations forces the user to gain knowledge about accurate values for new input parameters and question current modeling assumptions. This paper presents results from studies on the influence on the sheet metal forming simulation results from stamping die surface roughness and introduction of strain rate sensitivity in the sheet material model. The study will use a FE-model of a part presented in previous papers. © 2019 The Authors. Published by Elsevier B.V., Open access

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations w ithout numerical difficulties. ©2019 Hie Authors. Published by Elsevier B.V., open access

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations w ithout numerical difficulties. ©2019 Hie Authors. Published by Elsevier B.V., open access

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations w ithout numerical difficulties. ©2019 Hie Authors. Published by Elsevier B.V., open access

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations w ithout numerical difficulties. ©2019 Hie Authors. Published by Elsevier B.V., open access

The quality of sheet metal formed parts is strongly dependent on the tribology and friction conditions that are acting in the actual forming process. These friction conditions are then dependent on the tribology system, i.e. the applied sheet material, coating and tooling material, the lubrication and process conditions. Although friction is of key importance, it is currently not considered in detail in sheet metal forming simulations. The current industrial standard is to use a constant (Coulomb) coefficient of friction, which limits the overall simulation accuracy. Since a few years, back there is an ongoing collaboration on friction modelling between Volvo Cars, Tata Steel, TriboForm Engineering, AutoForm Engineering and the University of Twente. In previous papers by the authors, results from lab scale studies and studies of body parts at Volvo Cars, both parts in early tryout for new car models as well as parts in production have been presented. However, the introduction of a new friction model in the sheet metal forming simulations forces the user to gain knowledge about accurate values for new input parameters and question current modeling assumptions. This paper presents results from studies on the influence on the sheet metal forming simulation results from stamping die surface roughness and introduction of strain rate sensitivity in the sheet material model. The study will use a FE-model of a part presented in previous papers. © 2019 The Authors. Published by Elsevier B.V., Open access

The quality of sheet metal formed parts is strongly dependent on the tribology and friction conditions that are acting in the actual forming process. These friction conditions are then dependent on the tribology system, i.e. the applied sheet material, coating and tooling material, the lubrication and process conditions. Although friction is of key importance, it is currently not considered in detail in sheet metal forming simulations. The current industrial standard is to use a constant (Coulomb) coefficient of friction, which limits the overall simulation accuracy. Since a few years, back there is an ongoing collaboration on friction modelling between Volvo Cars, Tata Steel, TriboForm Engineering, AutoForm Engineering and the University of Twente. In previous papers by the authors, results from lab scale studies and studies of body parts at Volvo Cars, both parts in early tryout for new car models as well as parts in production have been presented. However, the introduction of a new friction model in the sheet metal forming simulations forces the user to gain knowledge about accurate values for new input parameters and question current modeling assumptions. This paper presents results from studies on the influence on the sheet metal forming simulation results from stamping die surface roughness and introduction of strain rate sensitivity in the sheet material model. The study will use a FE-model of a part presented in previous papers. © 2019 The Authors. Published by Elsevier B.V., Open access

The quality of sheet metal formed parts is strongly dependent on the tribology and friction conditions that are acting in the actual forming process. These friction conditions are then dependent on the tribology system, i.e. the applied sheet material, coating and tooling material, the lubrication and process conditions. Although friction is of key importance, it is currently not considered in detail in sheet metal forming simulations. The current industrial standard is to use a constant (Coulomb) coefficient of friction, which limits the overall simulation accuracy. Since a few years, back there is an ongoing collaboration on friction modelling between Volvo Cars, Tata Steel, TriboForm Engineering, AutoForm Engineering and the University of Twente. In previous papers by the authors, results from lab scale studies and studies of body parts at Volvo Cars, both parts in early tryout for new car models as well as parts in production have been presented. However, the introduction of a new friction model in the sheet metal forming simulations forces the user to gain knowledge about accurate values for new input parameters and question current modeling assumptions. This paper presents results from studies on the influence on the sheet metal forming simulation results from stamping die surface roughness and introduction of strain rate sensitivity in the sheet material model. The study will use a FE-model of a part presented in previous papers. © 2019 The Authors. Published by Elsevier B.V., Open access

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.