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It is generally recognised that numeric databases (NDBs) have become essential in information retrieval (IR). NDBs differ from traditional bibliographic databases (BDBs) with respect to their content, structural complexity, data manipulation capabilities, and the complexity of the user interfaces and user charging schemes. Recent trends in user interfaces and user charging for all online IR are towards charging for the information actually retrieved from the database rather than for the connect time. However, the viability of such charging schemes depends on the user's ability to estimate the charges in advance, during the query negotiation phase. This paper presents a systematic and general method for estimating user charges for retrieved information in advance, in the context of NDBs based on the relational data model (RDM). The method accepts relational algebra (RA) queries of any complexity, estimates the sizes of their results, and charges for them on the basis of the descriptions of the original database files. The method is a novel one and is directly applicable to any RDM‐based NDB. Tools based on the method are required in the query interfaces to NDBs in order to make query formulation and reformulation meaningful.

This study examines short selling as one external determinant of corporate tax avoidance. Prior research suggests that short sellers have information advantages over retail investors, and high short-interest levels are a bearish signal of targeted stock prices. As a result, when short-interest levels are high, managers have been shown to take actions to minimize the negative effect of high short interest on firms’ stock prices. Tax-avoidance activities may convey a signal of bad news (i.e., high stock price crash risk). We predict that, when short-interest levels are high, managers possess incentives to reduce firm tax avoidance in order to reduce the associated stock price crash risk. Consistent with this prediction, we find that short interest is negatively associated with subsequent tax-avoidance levels. This effect is incremental to other factors identified by prior research. We conclude that short selling significantly constrains corporate tax avoidance.

This paper aims to investigate auditors’ pricing of excess cash holdings and the variation in their pricing decisions in light of the precautionary motives of cash holdings and certain firm-specific conditions and during periods of crisis.

The authors conduct the two-stage-least-squares multivariate analysis using a sample of publicly listed non-financial US firms for the period 2003 to 2021 (42,413 firm-year observations).

The findings show a significant positive relationship between excess cash and audit fee. Next, the authors find that audit pricing of excess cash is significantly higher for firms with lower financial constraints. However, the authors do not find evidence to suggest that auditors price excess cash significantly higher for firms with lower hedging needs. In additional analysis, the authors find evidence to suggest that auditors charge significantly less for excess cash in firms that report financial loss and firms operating in industries with high litigation risk. The additional analysis also reveals excess cash is not positively and significantly priced by auditors as a result of the global financial crisis and Covid-19 pandemic.

Most researchers have analyzed excess cash holding from the perspective of managers, i.e. agency conflict or managerial prudence, while somewhat neglecting auditors’ perception of the embedded risk of excess cash holdings. The authors provide new insights on auditors’ perspective of excess cash holding and identify certain factors/situation/conditions that cause variation in the audit fee premium. The findings offer useful insights for managers and shareholders who are interested in assessing the effects of excess cash holdings policies on the audit fee premium.

The disjunctive graph is a network representation of the job‐shop scheduling problem, while the longest path problem (LPP) is one of the most important subjects in this research field. This paper aims to study the special topological structure of the disjunctive graph, and proposes a suite of quick value‐setting algorithms for solving the LPPs commonly encountered in job‐shop scheduling.

The topological structure of the disjunctive graph is analyzed, and some properties and propositions regarding LPPs are presented. Subsequently, algorithms are proposed for solving LPPs encountered in job‐shop scheduling.

The proposed algorithms significantly improve the efficiency of the shifting‐bottleneck procedure, making it practicable to realise real‐time scheduling and hence effective operations of modern manufacturing systems.

The paper demonstrates that it is possible to develop very efficient algorithms by imposing a special topological structure on the network.

This chapter proposes three different definitions for the market power in the antitrust case, such as dynamic monopoly power, static monopoly power and market power.

The chapter presents simple economic models to analyse which definition of the three market powers is consistent with predatory pricing or tying.

The prerequisite market power is simply market power in the predatory pricing case or static monopoly power in the tying case.

Dynamic monopoly power defined as the market power from an antitrust perspective by the Antitrust Modernization Commission should not be the prerequisite market power in the case of the abuse of dominance or the violation of Section 2 of the Sherman Act.

A possession of substantial market power or monopoly power is typically understood as a prerequisite in abuse of dominance in Korea and EU or violation of Section 2 of the Sherman Act in the United States. However, the antitrust law does not clearly indicate the meaning of market power or monopoly power. This chapter proposes three different definitions for the market power in the antitrust case and analyses which definition of the three market powers is consistent with predatory pricing or tying.

Tool steel (AISI D3) is a preferred material for industrial usage. Some of the typical applications of D3 tool steel are blanking and forming dies, forming rolls, press tools and punches bushes. It is used under conditions where high resistance to wear or to abrasion is required and also for resistance to heavy pressure rather than to sudden shock is desirable. It is a high carbon and high chromium steel. Therefore, wire electric discharge machining (WEDM) is used to machine this tool steel. The paper aims to discuss these issues.

The present experimental investigation evaluates the influence of cryogenically treated wires on material removal rate (MRR) and surface roughness (SR) for machining of AISI D3 steel using the WEDM process. Two important process responses MRR and SR have been studied as a function of four different control parameters, namely pulse width, time between two pulses, wire mechanical tension and wire feed rate.

It was found that pulse width was the most significant parameter which affects the MRR and SR. Better surface finish was obtained with cryogenically treated zinc coated wire than brass wire.

The review of the literature indicates that there is limited published work on the effect of machining parameters in WEDM in cryogenic treated wires. Therefore, in this research work, it was decided to evaluate the effect of cryogenically treated wires on WEDM.

Correct and well-planned maintenance based on modern global methods directly affects efficiency, quality, direct production costs, reliability and profitability. The selection of an optimal policy for maintenance can be a good solution for industrial units. In fact, by managing constraints such as costs, working hours and human workforce causing sudden equipment failure, production and performance can increase.

Therefore, in this research a model was presented to select the best maintenance strategy at Kaghaz Kar Kasra Co of Iran. In this study, it was tried to integrate the two techniques of goal programming and the technique for order of preference by similarity to ideal solution (TOPSIS) to prioritize maintenance strategies. First, all factors affecting maintenance were identified, and based on the Best Worst Method (BWM) the degree of their importance was determined.

After the evaluation, only 14 criteria in the 4 dimensions of cost, added value, safety and feasibility were selected. The highest points were given to the criteria of equipment cost and depreciation, equipment and personnel performance, equipment installation time and technical feasibility, respectively. In the next stage, using the TOPSIS method the item of maintenance strategy was ranked, and the 3 strategies of preventive maintenance (PM), predictive maintenance (PDM) and corrective maintenance (CM) were chosen. Modeling was performed utilizing a goal programming approach to select the optimal maintenance strategy for 13 devices. All the technical specifications, cost limits and the device time were extracted. After the model was finished and solved the best item for each device was specified.

1. Developing a goal programming model and decision-making dashboard. 2. Identifying the criteria and factors affecting the selection of the maintenance strategy for paper production Industry

The purpose of this paper is to discuss the durability and flexibility characteristics of latex modified ferrocement in comparison with conventional ferrocement particularly when exposed to severe environmental conditions.

The research programme encompasses the laboratory investigation on the structural, the deformation behaviour and characteristic of latex modified ferrocement elements cured in air and salt‐water environments. The tests include determination of load and deflection characteristics, moments, crack widths, crack spacing, and the number of cracks when subjected to static flexure.

Test result indicates a significant improvement in reducing and bridging micro cracks, especially in the pre‐peak load region. Fracture toughness and deformability increased significantly. However, the post peak behaviour was quite similar to conventional ferrocement.

The results show that latex modification has improved the mechanical properties of cement mortars, particularly their flexural strength.

Electricity consumption prediction has been an important topic for its significant impact on electric policies. Due to various uncertain factors, the growth trends of electricity consumption in different cases are variable. However, the traditional grey model is based on a fixed structure which sometimes cannot match the trend of raw data. Consequently, the predictive accuracy is variable as cases change. To improve the model's adaptability and forecasting ability, a novel fractional discrete grey model with variable structure is proposed in this paper.

The novel model can be regarded as a homogenous or non-homogenous exponent predicting model by changing the structure. And it selects the appropriate structure depending on the characteristics of raw data. The introduction of fractional accumulation enhances the predicting ability of the novel model. And the relative fractional order r is calculated by the numerical iterative algorithm which is simple but effective.

Two cases of power load and electricity consumption in Jiangsu and Fujian are applied to assess the predicting accuracy of the novel grey model. Four widely-used grey models, three classical statistical models and the multi-layer artificial neural network model are taken into comparison. The results demonstrate that the novel grey model performs well in all cases, and is superior to the comparative eight models.

A fractional-order discrete grey model with an adaptable structure is proposed to solve the conflict between traditional grey models' fixed structures and variable development trends of raw data. In applications, the novel model has satisfied adaptability and predicting accuracy.