Green Measurements for Software Product Based on Sustainability Dimensions

Software is a central component in the modern world and vastly affects the environment’s sustainability. The demand for energy and resource requirements is rising when producing hardware and software units. Literature study reveals that many studies focused on green hardware; however, limited efforts were made in the greenness of software products. Green software products are necessary to solve the issues and problems related to the long-term use of software, especially from a sustainability perspective. Without a proper mechanism for measuring the greenness of a particular software product executed in a specific environment, the mentioned benefits will not be attained. Currently, there are not enough works to address this problem, and the green status of software products is uncertain and unsure. This paper aims to identify the green measurements based on sustainable dimensions in a software product. The second objective is to reveal the relationships between the elements and measurements through empirical study. The study is conducted in two phases. The first phase is the theoretical phase, where the main components, measurements and practices that influence the sustainability of a software product are identified. The second phase is the empirical study that involved 103 respondents in Malaysia investigating current practices of green software in the industrial environment and further identifying the main sustainability dimensions and measurements and their impact on achieving green software products. This study has revealed seven green measurements of software product: Productivity, Usability, Cost Reduction, Employee Support, Energy Efficiency, Resource Efficiency and Tool Support. The relationships are statistically significant, with a significance level of less than 0.01 (p = 0.000). Thus, the hypothesised relationships were all accepted. The contributions of this study revolve around the research perspectives of the measurements to attain a green software product.     

Transesterification of Palm-based Methyl Palmitate into Esteramine Catalyzed by Calcium Oxide Catalyst

The technology for transesterification reactions between methyl esters and alcohols is well established by using classical homogeneous alkaline catalysts, which provide high conversion of methyl esters to specialty or nonindigenous esters. However, in certain products where the purity of the esters is of concern, the removal of homogeneous catalysts after the completion of the reaction is a challenge in terms of production cost and water footprint. Therefore, a study to investigate the potential of heterogeneous catalysts was conducted on reactions between methyl palmitate and triethanolamine. The degree of basicity and active surface area of calcium oxide (CaO), zinc oxide (ZnO), and magnesium oxide (MgO) were first characterized by using temperature-programmed desorption (TPD-CO₂) and Brunauere–Emmett–Teller (BET), respectively. Among the metal oxides investigated, the CaO catalyst showed the best catalytic activity toward the transesterification process as it gave the highest conversion of methyl palmitate and yielded fatty esteramine compositions similar to the conventional homogeneous catalyst. The optimum transesterification condition by using the CaO catalyst utilized a lower vacuum system of approximately 200 mbar, which could minimize a considerable amount of energy consumption. Furthermore, low CaO dosage of 0.1% was able to give a conversion of 94.5% methyl ester and formed esteramine at 170 °C for 2 h. Therefore, the production of esterquats from esteramine may become more economically feasible through the methyl ester route by using the CaO catalyst, which can be recycled three times.     

Quantitative structure-properties relationship,molecular dynamic simulations and designs of some novel lubricant additives

Quantitative structure-properties relationship (QSPR) method was used to design some novel antioxidant lubricant additives, while molecular dynamics simulations were used to calculate their dynamic binding energies on steel and to hydrogen-containing DLC (a-C: H) crystal surfaces. 29 synthesized antioxidant lubricant additives were collected from literature and geometrically optimized by Spartan’14 version 1.1.2 software while Genetic Function Algorithm (GFA) method of the material studio version 8.0 software was used to build the predictive QSPR model. Four novel antioxidant lubricant additives were successfully designed out of which E)-3-(4-((3-amino-4-methylphenyl)diazenyl)-5-hydroxy-4H-pyrazol-3-yl)-2-argio-6,7-difluoroquinazolin-4(3H)-one with excellent property of 3.531295 (KOH/g) was found to be better than the one reported by other researchers. The dynamic binding energy results revealed that one of the designed additives was excellently bound to steel (?1120.11 kcal/mol) and to hydrogen-containing DLC (a-C: H) crystals surface (7814.156 kcal/mol) surfaces than its co-additives. This investigation shows that the entire studied antioxidant lubricant additive was found to be better bound to the steel surface than hydrogen-containing DLC (a-C: H) crystals surface. This study will help in synthesizing novel anti-oxidant lubricant additives with better additive properties that will slow the tendency of oil to oxidize and will not possess a threat to the environment as the structures do not contain zinc and phosphorus that could limit the operation of the catalytic converter in the exhaust pipe.     

Microwave assisted synthesis and antimicrobial activity of Fe3O4-doped ZrO2 nanoparticles

Composites play important role in dental filling by controlling shrinkage along with correction in teeth's shape and position. Rehabilitation of severely worn dentition can be achieved using mechanically strong composites. This study aims to synthesize zirconia-based composites to be used as dental fillers. Effect of microwave powers (100–900?W) along with Fe₃O₄ doping are studied on the structural, mechanical and magnetic properties of stabilized zirconia. SEM and TEM reveal formation of spherical nanoparticles with diameter of ～30?nm. XRD results shows phase pure tetragonal zirconia (t-ZrO₂) at microwave power of 500?W without any post heat treatment. Crystallite size calculated from XRD data (～23?nm) matches well with the previously reported value for stabilization of t-ZrO₂. Microwave energy dissipation results in stresses causing volume shrinkage leading to monoclinic to tetragonal phase transformation with higher X-ray density and hardness of ～1347HV. VSM results show ferromagnetic response with low coercivity (600Oe) value and saturation magnetization (～2emu/g). It is worth mentioning here that this is one of its kind study reporting synthesis of room temperature stabilized Fe₃O₄ doped zirconia composites at microwave power of 500?W. Antibacterial studies reveal inhibition zone of ～32?mm against bacillus bacteria suggesting their potential use as dental filler.     

SER and throughput analysis of space–time analog network coded relaying system over shadowed Rician fading channels

Wireless Networks - This research article presents an innovative approach based on analog network coding (ANC) in conjunction with space time block coding (STBC) which is termed as space time...     

Linguistic frequent pattern mining using a compressed structure

Traditional association-rule mining (ARM) considers only the frequency of items in a binary database, which provides insufficient knowledge for making efficient decisions and strategies. The mining of useful information from quantitative databases is not a trivial task compared to conventional algorithms in ARM. Fuzzy-set theory was invented to represent a more valuable form of knowledge for human reasoning, which can also be applied and utilized for quantitative databases. Many approaches have adopted fuzzy-set theory to transform the quantitative value into linguistic terms with its corresponding degree based on defined membership functions for the discovery of FFIs, also known as fuzzy frequent itemsets. Only linguistic terms with maximal scalar cardinality are considered in traditional fuzzy frequent itemset mining, but the uncertainty factor is not involved in past approaches. In this paper, an efficient fuzzy mining (EFM) algorithm is presented to quickly discover multiple FFIs from quantitative databases under type-2 fuzzy-set theory. A compressed fuzzy-list (CFL)-structure is developed to maintain complete information for rule generation. Two pruning techniques are developed for reducing the search space and speeding up the mining process. Several experiments are carried out to verify the efficiency and effectiveness of the designed approach in terms of runtime, the number of examined nodes, memory usage, and scalability under different minimum support thresholds and different linguistic terms used in the membership functions.

     

Destructive and non-destructive evaluation of concrete for optimum sand to aggregate volume ratio

Aggregates are the biggest contributor to concrete volume and are a crucial parameter in dictating its mechanical properties. As such, a detailed experimental investigation was carried out to evaluate the effect of sand-to-aggregate volume ratio (s/a) on the mechanical properties of concrete utilizing both destructive and non-destructive testing (employing UPV (ultrasonic pulse velocity) measurements). For investigation, standard cylindrical concrete samples were made with different s/a (0.36, 0.40, 0.44, 0.48, 0.52, and 0.56), cement content (340 and 450 kg/m³), water-to-cement ratio (0.45 and 0.50), and maximum aggregate size (12 and 19 mm). The effect of these design parameters on the 7, 14, and 28 d compressive strength, tensile strength, elastic modulus, and UPV of concrete were assessed. The careful analysis demonstrates that aggregate proportions and size need to be optimized for formulating mix designs; optimum ratios of s/a were found to be 0.40 and 0.44 for the maximum aggregate size of 12 and 19 mm, respectively, irrespective of the W/C (water-to-cement) and cement content.