An analysis of safety control effectiveness

The cost of injuries and “accidents” to an organisation is very important in establishing how much it should spend on safety control. Despite the usefulness of information about the cost of a company's accidents, it is not customary accounting practice to make these data available. Of the two kinds of costs incurred by a company through occupational injuries and accidents, direct costs and indirect costs; the direct costs are much easier to estimate. However, the uninsured costs are usually more critical and should be estimated by each company. The authors investigate a general model to estimate the above costs and hence to establish efficient safety control. One construction company has been a pilot for this study. By analysing actual company data for three years, it is found that the efficient safety control cost should be 1.2–1.3% of total contract costs.     

Environmental science research in India: An analysis of publications

Investigates the trends in environmental science research in India with regard to its various branches, channels of communication used, authorship pattern of the papers, institution-wise output, rank of journals, extent of collaboration and scholarship of papers basing the entries noticed in theParyavaran Abstracts. Major areas of interest of Indian environmentalists are given and prolific investigators have been listed. Journals used by Indian workers for publication of their work are studied. Subject areas with number of papers, number of authors, and average authorship are tabulated.     

Author-level altmetrics for the evaluation of Chinese scholars

Scientometrics - Altmetrics has been widely recognized in the evaluation of articles, books, and journals. However, only a few of scholars have focused on author-level metrics which limits the wide...     

Using Bayesian networks to discover relationships between bibliometric indices. A case study of computer science and artificial intelligence journals

As they are used to evaluate the importance of research at different levels by funding agencies and promotion committees, bibliometric indices have received a lot of attention from the scientific community over the last few years. Many bibliometric indices have been developed in order to take into account aspects not previously covered. The result is that, nowadays, the scientific community faces the challenge of selecting which of this pool of indices meets the required quality standards. In view of the vast number of bibliometric indices, it is necessary to analyze how they relate to each other (irrelevant, dependent and so on). Our main purpose is to learn a Bayesian network model from data to analyze the relationships among bibliometric indices. The induced Bayesian network is then used to discover probabilistic conditional (in)dependencies among the indices and, also for probabilistic reasoning. We also run a case study of 14 well-known bibliometric indices on computer science and artificial intelligence journals.     

An evaluation of machine learning and artificial intelligence models for predicting the flotation behavior of fine high-ash coal

In this study, five different machine learning (ML) and artificial intelligence (AI) models: random forest (RF), artificial neural networks (ANN), the adaptive neuro-fuzzy inference system (ANFIS), Mamdani fuzzy logic (MFL) and a hybrid neural fuzzy inference system (HyFIS) were employed to predict the flotation behavior of fine high ash coal in the presence of a novel “hybrid” ash depressant consisting of polyacrylamide chains grafted onto aluminium hydroxide nanoparticles: Al(OH)₃-PAM (Al-PAM). A total of 51 flotation tests were conducted on coal samples with 38% ash-content and a P₈₀ of approximately 49?μm. Different influencing variables of coal flotation including polymer dosage, pH, polymer conditioning time, sodium metasilicate dosage (commercial dispersant), and the impeller speed were used as inputs for the models. The combustible recovery and ash content of coal reported to the concentrate were used as response variables (outputs). For AI model development, 80% of the total data was used for training phase and 20% was used for testing phase. Coefficient of determination (R²) and root-mean-square error (RMSE) were used as performance indicators of the models. The MFL model showed the best accuracy for the prediction of the combustible recoveries and the froth ash contents for this specific feed. However, in case of any significant change in the characteristics of the feed, these models would have to be re-trained using the data obtained through further physical experimentation and/or process model simulations. Moreover as these models are trained on laboratory scale data, these are only good for the predictions at laboratory scale.     

Citation regression analysis of computer science publications in different ranking categories and subfields

A number of bibliometric studies point out that the role of conference publications in computer science differs from that in other traditional fields. Thus, it is interesting to identify the relative status of journal and conference publications in different subfields of computer science based on the citation rates categorised by the China Computer Federation (CCF) classifications and venue types. In this research, we construct a dataset containing over 100,000 papers recommended by the CCF catalogue and their citation information. We also investigate some other factors that often influence a paper’s citation rate. An experimental study shows that the relative status of journals and conferences varies greatly in different subfields of computer science, and the impact of different publication levels varies according to the citation rate. We also verify that the classification of a publication, number of authors, maximum h-index of all authors of a paper, and average number of papers published by a publication have different effects on the citation rate, although the citation rate may have a different degree of correlation with these factors.     

Simulation embedded artificial intelligence search method for supplier trading portfolio decision

An electric power supplier in the deregulated environment needs to allocate its generation capacities to participate in contract and spot markets. Different trading portfolios will provide suppliers with different future revenue streams of various distributions. The classical mean?variance (MV) method is inappropriate to deal with the trading portfolios whose return distribution is non-normal. In order to consider the non-normal characteristics in electricity trading, this study proposes a new model based on expected utility theory (EUT) and employs a hybrid genetic algorithm (GA) -- Monte-Carlo simulation technique as solution approach. In the real market data-based numerical studies, the performances of the proposed method and the standard MV method are compared. It was found that the proposed method is able to obtain better portfolios than MV method when non-normal asset exists for trading. The simulation results also reveal the accumulation effect along trading period, which will improve the normality of the supplier trading portfolios. The authors believe the proposed method is a useful complement for the MV method and conditional value at risk (CVaR)-based methods in the supplier trading portfolio decision and evaluation.     

An analysis and design of implantable blood heat exchangers for artificial hearts

The use of radioisotopic fueled power conversion systems in long lived artificial hearts are currently being studied by a number of AEC and NHLI contracts. All of these systems require a heat rejection subsystem to remove waste heat. A parametric analysis of an implantable blood heat exchanger was carried out to determine the problem areas and to arrive at a blood heat exchanger design.Biological and physiological design constraints were first established. The parametric analyses showed that the thoracic and abdominal aortic regions are needed for the transfer of relatively large amounts of heat (up to 50 W). Blood heat exchanger designs are presented for the rejection of 25 and 50 W of waste heat.     

Investigating the applications of artificial intelligence in cyber security

Scientometrics - Artificial Intelligence (AI) provides instant insights to pierce through the noise of thousands of daily security alerts. The recent literature focuses on AI’s application to...     

Evolutionary stages and multidisciplinary nature of artificial intelligence research

Scientometrics - This paper analyzed the growth and multidisciplinary nature of Artificial Intelligence research during the last 60 years. Web of Science coverage since 1960 was...     

The human-robot interface: the role of artificial intelligence

We introduce the concept of artificial intelligence and its possible contribution towards the human-robot interface by considering the conceptual support such an idealized interface might provide. In following this argument we see that integration of the various aspects of a product's life cycle is a convenient way of introducing the information and knowledge required for an artificially intelligent interface. The information is produced as a result of the earlier processes and lost in most conventional systems. We conclude that the knowledge of the task the robot is to perform is available not as an afterthought which would require manual input to the robot after the product has been designed but as part of a factory-wide integrated system concerned with the whole product life cycle.     

A conceptual framework for combining artificial intelligence and optimization in engineering design

Research in artificial intelligence and optimization (OR) has had significant impact on the formulation and solution of computational methods in engineering design. This paper presents a conceptual framework for understanding a more powerful technology that is evolving from a combination of these approaches. The paper first proposes generalized representations of engineering design models that involve quantitative and qualitative aspects. Second, it presents a general classification of AI and OR models in terms of model attributes, in order to establish mappings with generic solution techniques. Third, the requirements of solution methods are discussed, as well as several schemes for the integration of AI and optimization to identify future research directions. Several specific approaches are included to illustrate various ways in which AI and optimization can be combined for tackling computational design models.     

Comparing technology convergence of artificial intelligence on the industrial sectors: two-way approaches on network analysis and clustering analysis

Scientometrics - This study investigates technology convergence of AI considering both industrial sectors and technological characteristics with patent data in terms of two-way approaches:...     

Binder effectiveness for beads with high drug levels

Previous reports from these laboratories showed that microcrystalline cellulose (Avicel^R MCC, PH-101) formulations with low and medium drug levels (10 and 50%) produced very uniform beads whereas formulations containing MCC with high drug levels (80%) were difficult to process without special treatment or required the incorporation of alternate excipients. In this study, several binders, at a 2% level, specifically: Carbomer (Carbopol^R 934-P), Sodium carboxymethylcellulose (CMC 7MF), Hydroxypropylcellulose (Klucel^R HXF), Methylcellulose (Methocel^R K-15). Povidone, USP (PVP K29-32) and Pregelatinized starch NF (Starch 1500^R), were evaluated to determine whether they might impart advantages in processing and whether any differences in dissolution behavior would result. Spheres containing 80% anhydrous theophylline, the binders and MCC were manufactured by the extrusion/marumerization technique. In general, beads containing high drug levels produced with these binders are suitable for further processing (coating). Processing ease, bead shape, and bead hardness (friability) varied with the choice of binder. Beads with carbomer, hydroxypropylcellulose, and methylcellulose remained intact during dissolution testing; beads with starch, carboxymethylcellulose, PVP, and the control did not.     

An observation framework for retracted publications in multiple dimensions

Scientometrics - To describe the impact of the retracted publications on the scientific community more comprehensively, this paper introduces the impact of retracted publications on technology. It...     

Binder effectiveness for beads with high drug levels

Abstract

Previous reports from these laboratories showed that microcrystalline cellulose (Avicel^R MCC, PH-101) formulations with low and medium drug levels (10 and 50%) produced very uniform beads whereas formulations containing MCC with high drug levels (80%) were difficult to process without special treatment or required the incorporation of alternate excipients. In this study, several binders, at a 2% level, specifically: Carbomer (Carbopol^R 934-P), Sodium carboxymethylcellulose (CMC 7MF), Hydroxypropylcellulose (Klucel^R HXF), Methylcellulose (Methocel^R K-15). Povidone, USP (PVP K29-32) and Pregelatinized starch NF (Starch 1500^R), were evaluated to determine whether they might impart advantages in processing and whether any differences in dissolution behavior would result. Spheres containing 80% anhydrous theophylline, the binders and MCC were manufactured by the extrusion/marumerization technique. In general, beads containing high drug levels produced with these binders are suitable for further processing (coating). Processing ease, bead shape, and bead hardness (friability) varied with the choice of binder. Beads with carbomer, hydroxypropylcellulose, and methylcellulose remained intact during dissolution testing; beads with starch, carboxymethylcellulose, PVP, and the control did not.     

A low cost artificial intelligence vision system for piece part recognition and orientation

Traditional feature extraction vision techniques are well proven. However, in certain circumstances they suffer from some severe disadvantages such as processing time, image complexity, internal features and lighting levels. This paper describes the use of a hybrid system using artificial intelligence techniques in combination with traditional feature extraction techniques which overcome some disadvantages. This hybrid system is able to locate and orientate parts with complex internal features and to provide an indication of quality.