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The purpose of the present study is to identify the active and dead links of uniform resource locators (URLs) associated with web references and to compare the effectiveness of Chrome, Google and WayBack Machine in retrieving the dead URLs.

The web references of the Library Hi Tech from 2004 to 2008 were selected for analysis to fulfill the set objectives. The URLs were extracted from the articles to verify their accessibility in terms of persistence and decay. The URLs were then executed directly in the internet browser (Chrome), search engine (Google) and Internet Archive (WayBack Machine). The collected data were recorded in an excel file and presented in tables/diagrams for further analysis.

From the total of 1,083 web references, a maximum number was retrieved by the WayBack Machine (786; 72.6 per cent) followed by Google (501; 46.3 per cent) and the lowest by Chrome (402; 37.1 per cent). The study concludes that the WayBack Machine is more efficient, retrieves a maximum number of missing web citations and fulfills the mission of preservation of web sources to a larger extent.

A good number of studies have been conducted to analyze the persistence and decay of web-references; however, the present study is unique as it compared the dead URL retrieval effectiveness of internet explorer (Chrome), search engine giant (Google) and WayBack Machine of the Internet Archive.

The web references of a single journal, namely, Library Hi Tech, were analyzed for 5 years only. A major study across disciplines and sources may yield better results.

URL decay is becoming a major problem in the preservation and citation of web resources. The study has some healthy recommendations for authors, editors, publishers, librarians and web designers to improve the persistence of web references.

The study aims to drive conceptual clarity around resistance to information technology projects, integrating multiple facets of the phenomenon from earlier studies.

The study conducts a meta-synthesis of qualitative studies on resistance to technology projects; it analyzes those studies at a case-specific level, compares and contrasts emergent concepts against each other, and “translates” those to the rest of the studies. The study uses the seven-step meta-ethnography method by Noblit and Hare to reciprocally translate emergent concepts to construct the conceptual model.

Through meta-synthesis, the study derives a new conceptual model for resistance to information technology projects, exemplifying how the identified antecedents create user resistance and how the phenomenon progresses within organizations.

This study enriches the observations and conclusions of past individual studies while explicating various facets of the mechanisms that generate and progress technology resistance within organizations. It offers fresh insights into the equivocal nature of the phenomenon and the distinctive ways it progresses from individual to group level.

Many ambitious and costly digital transformation efforts do not succeed due to user resistance. Understanding the mechanisms that create user resistance can help organizations manage technology projects better, thereby reducing the technology assimilation gap and protecting returns on related investments.

There have been extensive studies on technology acceptance (enablers) within organizations, while those relating to technology inhibitors are somewhat limited. However, the symmetry of understanding between enablers and inhibitors is vital for organizations to assimilate promising technologies and transform their business models. This model uses a new lens of sensemaking theory to explain how the antecedents trigger perceived threats and resistance behavior; it highlights the nuances around the development of resistance within individuals and its progression to groups. The resultant model offers better generalizability in organizational contexts.

The purpose of this paper is to predict the machining performance of electrical discharge machining of Ti-13Nb-13Zr (TNZ) alloy, a promising biomedical alloy, using artificial neural networks (ANN) models.

In the research, three major performance characteristics, i.e. the material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR), were chosen for the study. The input parameters for machining were the voltage, current, pulse-on time and pulse-off time. For the ANN model, a two-layer feedforward network with sigmoid hidden neurons and linear output neurons were chosen. Levenberg–Marquardt backpropagation algorithm was used to train the neural networks.

The optimal ANN structure comprises four neurons in input layer, ten neurons in hidden layer and one neuron in the output layer (4–10-1). In predicting MRR, the 60–20-20 data split provides the lowest MSE (0.0021179) and highest R-value for training (0.99976). On the contrary, the 70–15-15 data split results in the best performance in predicting both TWR and SR. The model achieves the lowest MSE and highest R-value for training in predicting TWR as 1.17E-06 and 0.84488, respectively. Increasing the number of hidden neurons of the network further deteriorates the performance. In predicting SR, the authors find the best MSE and R-value as 0.86748 and 0.94024, respectively.

This is a novel approach in performance prediction of electrical discharge machining in terms of new workpiece material (TNZ alloys).

The purpose of this study is to explore numerical scrutinization of micropolar and Walters-B non-Newtonian fluids motion under the influence of thermal radiation and chemical reaction.

The two fluids micropolar and Walters-B liquid are considered to start flowing from the slot to the stretching sheet. A magnetic field of constant strength is imposed on their flow transversely. The problems on heat and mass transport are set up with thermal, chemical reaction, heat generation, etc. to form partial differential equations. These equations were simplified into a dimensionless form and solved using spectral homotopy analysis method (SHAM). SHAM uses the basic concept of both Chebyshev pseudospectral method and homotopy analysis method to obtain numerical computations of the problem.

The outcomes for encountered flow parameters for temperature, velocity and concentration are presented with the aid of figures. It is observed that both the velocity and angular velocity of micropolar and Walters-B and thermal boundary layers increase with increase in the thermal radiation parameter. The decrease in velocity and decrease in angular velocity occurred are a result of increase in chemical reaction. It is hoped that the present study will enhance the understanding of boundary layer flow of micropolar and Walters-B non-Newtonian fluid under the influences of thermal radiation, thermal conductivity and chemical reaction as applied in various engineering processes.

All results are presented graphically and all physical quantities are computed and tabulated.

Light emitting diode (LED) has been the best resource for commercial and industrial lighting applications. However, thermal management in high power LEDs is a major challenge in which the thermal resistance (R_th) and rise in junction temperature (T_J) are critical parameters. The purpose of this work is to evaluate the R_th and T_j of the LED attached with the modified heat transfer area of the heatsink to improve thermal management.

This paper deals with the design of metal substrate for heatsink applications where the surface area of the heatsink is modified. Numerical simulation on heat distribution proved the influence of the design aspects and surface area of heatsink.

T_J was low for outward step design when compared to flat heatsink design (ΔT ∼ 38°C) because of increase in surface area from 1,550 mm² (flat) to 3,076 mm² (outward step). On comparison with inward step geometry, the T_J value was low for outward step configuration (ΔT_J ∼ 6.6°C), which is because of efficient heat transfer mechanism with outward step design. The observed results showed that outward step design performs well for LED testing by reducing both R_th and T_J for different driving currents.

This work is authors’ own design and also has the originality for the targeted application. To the best of the authors’ knowledge, the proposed design has not been tried before in the electronic or LED applications.

Three-dimensional (3D) printing is popular for many applications including the production of photocatalysts. This paper aims to focus on developing of 3D-printed photocatalyst-nano composite lattice structure. Digital light processing (DLP) 3D printing of photocatalyst composites was performed using photosensitive resin mixed with 0.5% Wt. of TiO₂ powder and varying amounts (0.025% Wt. to 0.2% Wt.) of graphene nanoplatelet powder. The photocatalytic efficiency of DLP 3D-printed photocatalyst TiO₂ composite was investigated, and the effects of nano graphite powder incorporation on the photocatalytic activity, thermal and mechanical properties were investigated.

Methods involve 3D computer-aided design modeling, printing parameters and comprehensive characterization techniques such as structural equation modeling, X-ray diffraction, thermogravimetric analysis, Fourier-transform infrared (FTIR) and mechanical testing.

Results highlight successful dispersion and characteristics of TiO₂ and graphene nanoplatelet (GNP) powders, intricate designs of 3D-printed lattice structures, and the influence of GNPs on thermal behavior and mechanical properties.

The study suggests applicability in wastewater treatment and environmental remediation, showcasing the adaptability of 3 D printing in designing effective photocatalysts. Future research should focus on practical applications and the long-term durability of these 3D-printed composites.