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High-efficiency video coding (HEVC) is the latest video coding standard that has better coding efficiency than the H.264/advanced video coding (AVC) standard. The purpose of this paper is to design and develop an effective block search mechanism for the video compression-HEVC standard such that the developed compression standard is applied for the communication applications.

In the proposed method, an rate-distortion (RD) trade-off, named regressive RD trade-off is used based on the conditional autoregressive value at risk (CaViar) model. The motion estimation (ME) is based on the new block search mechanism, which is developed with the modification in the Ordered Tree-based Hex-Octagon (OrTHO)-search algorithm along with the chronological Salp swarm algorithm (SSA) based on deep recurrent neural network (deepRNN) for optimally deciding the shape of search, search length of the tree and dimension. The chronological SSA is developed by integrating the chronological concept in SSA, which is used for training the deep RNN for ME.

The competing methods used for the comparative analysis of the proposed OrTHO-search based RD + chronological-salp swarm algorithm (RD + C-SSA) based deep RNN are support vector machine (SVM), fast encoding framework, wavefront-based high parallel (WHP) and OrTHO-search based RD method. The proposed video compression method obtained a maximum peak signal-to-noise ratio (PSNR) of 42.9180 dB and a maximum structural similarity index measure (SSIM) of 0.9827.

In this research, an effective block search mechanism was developed with the modification in the OrTHO-search algorithm along with the chronological SSA based on deepRNN for the video compression-HEVC standard.

Denim fabric has become a wardrobe staple due to its versatility to be worn in a variety of fashions. This paper aims to study denim fabrics to understand their unique hand by developing a hand evaluation system using computational method. Also, the effect of various washes was studied on the hand and surface morphology of denim fabrics.

Five different denim samples were manufactured with various washing treatments. The Kawabata Evaluation System was used to measure the low stress mechanical properties. Computation method was used to develop hand equations using multiple regression technique in the MS Excel software. The correlation coefficient analysis was done to determine the authenticity of the developed equations. Five primary hand attributes such as softness, smoothness, fullness, flexibility and stretchability were shortlisted by a panel of judges that influence the fabric handle.

The correlation coefficient between subjective and computational total hand values with thermal properties and without thermal properties was 0.88 and 0.85, respectively. The enzymatic wash fabric has the highest total hand value followed by the acid, bleach and stone-washed fabrics.

Although the hand evaluation system is available for conventional textiles like suiting and shirting fabrics, the method to predict fabric hand of non-conventional textiles such as denim fabrics remains an unexplored topic. The stresses acting on denim fabrics are completely different. Therefore, to the best of the author’s knowledge, a novel attempt has been made in this research work to develop a computational model to predict the total hand value of denim fabrics.

The purpose of this paper is to describe sustainability of hollow and solid blocks in sub-Saharan Africa.

Indicators of stakeholder value are proposed for measuring block sustainability based on comparisons of user building value price and carbon emissions. Block manufacturing processes in Tanzania and Uganda are described and assessed in this context.

The results from Uganda indicate that there are economic and environmental advantages in using hollow blocks as long as they are produced to statutory compliance levels. However, where blocks are not produced to standard requirements, the results indicate that it is better to use solid blocks. This surprising result seems to indicate that blocks prepared using low additions of cement might have sufficient functional quality for simple residential building applications even though they might not meet current standard strength requirements and have low cement productivity. These results also indicate that the improvement potential indicated previously cannot be realised when hollow blocks are used for simple construction needs.

Clear benchmarks for the best practical level of cement block sustainability seem to be missing. The first reasons is that the lowest acceptable compressive strength has not been defined since standard requirements might not be relevant in the studied context. The second one is that the lowest possible practically achievable cement content with acceptable cement productivity has not been established.

Understanding sustainability can be very difficult and substantial work needs to be done to introduce operational sustainability indicators.

The results contribute to the discussion of understanding, defining and measuring sustainability.

This study aims to achieve superior localization and mapping performance in point cloud degradation scenarios through the effective removal of dynamic obstacles. With the continuous development of various technologies for autonomous vehicles, the LIDAR-based Simultaneous localization and mapping (SLAM) system is becoming increasingly important. However, in SLAM systems, effectively addressing the challenges of point cloud degradation scenarios is essential for accurate localization and mapping, with dynamic obstacle removal being a key component.

This paper proposes a method that combines adaptive feature extraction and loop closure detection algorithms to address this challenge. In the SLAM system, the ground point cloud and non-ground point cloud are separated to reduce the impact of noise. And based on the cylindrical projection image of the point cloud, the intensity features are adaptively extracted, the degradation direction is determined by the degradation factor and the intensity features are matched with the map to correct the degraded pose. Moreover, through the difference in raster distribution of the point clouds before and after two frames in the loop process, the dynamic point clouds are identified and removed, and the map is updated.

Experimental results show that the method has good performance. The absolute displacement accuracy of the laser odometer is improved by 27.1%, the relative displacement accuracy is improved by 33.5% and the relative angle accuracy is improved by 23.8% after using the adaptive intensity feature extraction method. The position error is reduced by 30% after removing the dynamic target.

Compared with LiDAR odometry and mapping algorithm, the method has greater robustness and accuracy in mapping and localization.

The purpose of this study is to investigate the physical, ultraviolet (UV), colour appearance and colour fastness properties of selected fabrics dyed with natural dyes from Daboya and Ntonso communities of Ghana. The study further highlights the rich cultural heritage of traditional dyeing from these two communities. Craftsmen in West Africa especially Ghana, have sustained the traditional dyeing methods to produce textile products for consumers.

In this study, two sample fabrics were purchased from craftsmen at Ntonso and Daboya communities in Ghana. These fabrics were analysed at the laboratory under standard test methods for their physical, UV, colour appearance and colour fastness properties.

Results showed that all the sample fabrics have good UV shielding performance (ratings above 50+). Daboya sample fabrics (dyed with indigo dyes) produced more colour stains than the sample fabrics from Ntonso (dyed with black “kuntunkuni” dyes). The K/S_sum value or colour yield reduced after washing but that alternatively increased the calculated ultraviolet protection factor.

Findings from this study exposed the unique UV performance of dyed traditional fabrics (using natural dyes) from Ntonso and Daboya communities in Ghana. This inspires and enforces the need for craftsmen to improve their production cycle to produce these fabrics in different sizes which provides the necessary UV shielding abilities for consumers in the wake of climate changes.

This study demonstrated that the natural dyeing process at the two communities produced relatively good UV and colour fastness properties of the sample fabrics. These eco-friendly dyeing practices have survived over time to maintain and promote the concept of sustainability within the textile and fashion industry in Ghana.

Internet of Things (IoT) is a network, which provides the connection with various physical objects such as smart machines, smart home appliance and so on. The physical objects are allocated with a unique internet address, namely, Internet Protocol, which is used to perform the data broadcasting with the external objects using the internet. The sudden increment in the number of attacks generated by intruders, causes security-related problems in IoT devices while performing the communication. The main purpose of this paper is to develop an effective attack detection to enhance the robustness against the attackers in IoT.

In this research, the lasso regression algorithm is proposed along with ensemble classifier for identifying the IoT attacks. The lasso algorithm is used for the process of feature selection that modeled fewer parameters for the sparse models. The type of regression is analyzed for showing higher levels when certain parts of model selection is needed for parameter elimination. The lasso regression obtains the subset for predictors to lower the prediction error with respect to the quantitative response variable. The lasso does not impose a constraint for modeling the parameters caused the coefficients with some variables shrink as zero. The selected features are classified by using an ensemble classifier, that is important for linear and nonlinear types of data in the dataset, and the models are combined for handling these data types.

The lasso regression with ensemble classifier–based attack classification comprises distributed denial-of-service and Mirai botnet attacks which achieved an improved accuracy of 99.981% than the conventional deep neural network (DNN) methods.

Here, an efficient lasso regression algorithm is developed for extracting the features to perform the network anomaly detection using ensemble classifier.

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

This paper aims to explore how accounting is fostering neoliberal citizenship through the participants of Australia’s National Disability Insurance Scheme (NDIS). More specifically, this paper aims to understand how accounting discourse and the management accounting technique of budgeting, when intertwined with automated administrative processes of the NDIS, are giving rise to a pastoral form of power that directs people’s behaviour toward certain ends.

Publicly available data has been crafted into an autoethnographic case study of one fictitious person’s experiences with the NDIS – Mina. Mina is an amalgam created from material submitted to the Joint Parliamentary Standing Committee on the NDIS. Mina’s experiences are then analysed through the lens of Foucault’s concept of pastoral power to explore how accounting has contributed to marketising and digitising public disability services.

Accounting rhetoric appears to be a central part of rationalising the decision to shift to individualised disability funding. Those receiving payments are treated as self-governable, financially responsible subjects and are therefore expected to have knowledge of management accounting techniques and budgeting. However, NDIS’s strong reliance on the accounting concepts of funds, budgets, cost and price is limiting people’s autonomy and subjecting them to intervention and control.

This paper addresses calls to explore the interplay between accounting and current disability policies. The analysis shows that incorporating accounting into the NDIS’s algorithms serves to conceal the underlying ideology of the programs, subtly driving behaviours towards neoliberal objectives. Further, this research extends the Foucauldian accounting literature by revealing the contribution of accounting to reinforcing the authority of digital pastors in contemporary times.

Family planning (FP) services through the lens of human rights are not well known in Nepal. This study aims to assess Family Planning 2020: Rights and Empowerment Principles for Family Planning and identify factors affecting contraceptive use among HIV-infected women living in rural Far Western Nepal.

This study conducted a cross-sectional survey using self-designed proforma. To assess the association between contraceptive use and independent variables, this study calculated adjusted odd ratio (AOR) with 95% confidence interval (CI) using statistical package for social sciences (SPSS) V.20.

Only 37.8% of participants had access to full range of contraceptive methods, and only 57.5% of participants received proper counseling. Agency/autonomy, transparency/accountability and voice/participation were practiced by 43.7%, 23.4% and 19.7% of participants, respectively. Husband’s support (AOR = 4.263; 95% CI: 1.640–11.086), availability of FP services in their locality (AOR = 2.497; 95% CI: 1.311–4.754), employment (AOR = 3.499; 95% CI: 1.186–10.328) and postpartum period (AOR = 0.103; 95% CI: 0.023–0.475) were significantly associated with contraceptive use.

Health-care providers’ and program managers’ perspectives were not examined.

Findings of this study will be useful for making strategic plan on human rights-based approach to FP.

Expanding access to contraceptive information and services and strengthening autonomy, accountability and participation are key to human rights-based approach to FP.

This study identified that inadequate counselling, nonavailability of full range of contraceptive methods, low level of autonomy, accountability and participation were key bottlenecks in fulfilling human rights-based approach to FP.

The purpose of this study is designed to achieve the learning process in producing studies involving economic issues and scenarios in business management in Malaysia. In addition, this study will provide exposure to the integration of managerial skills by using both microeconomics and macroeconomics concepts and theories to aid decision-making in a business environment.

The research method comprised qualitative methodology of literature review, case study and quantitative methodology of multiple linear regression (MLR). In this case, seven microeconomics and macroeconomics factors which are believed to significantly affect house price index (HPI) are taken into consideration which includes gross domestic product, consumer price index (CPI), government tax and subsidy on housing, overnight policy rate, unemployment rate (UNEMP), the median income (INC) and cost of production index.

This research has resulted in three significant factors affecting HPI from MLR, which include CPI, UNEMP and INC where the increase of these factors will cause a high increment of HPI. The other four factors are not significant.

Malaysia has been facing the stagnancy in house market these recent years due to issues such as massive oversupply, impacting Malaysia’s economy specifically focusing on domestic direct investment. To avoid oversupply issues, the vitality of future house demand and pricing forecast should be comprehended by involved bodies for more effective planning for the house development industry. To make a better and bigger impact, this research is intended to analyse the microeconomic and macroeconomic factors affecting the HPI to better understand the significance of each of these factors to the changes of HPI to resolve these economic issues.