Search

Spatially Augmented Reality (SAR), also known as projection mapping, uses multiple projectors to illuminate surfaces of arbitrary shape and size and create seamless, large-scale displays. Traditional SAR assumes that the projection surface is static and rigid. This restriction was partially addressed by Dynamic-SAR, where the surface is rigid and of known shape but can be moved around. However, no prior work has addressed SAR using multiple projectors on deformable surfaces, where the shape is unknown and constantly changing. Thus, multi-projector SAR on deformable surfaces introduces several challenges, including projector-camera calibration on a deformable surface, real-time surface shape recovery and real-time multi-projector warping and blending. My thesis is the first attempt to develop a comprehensive framework for achieving seamless multi-projector displays on deformable surfaces. Furthermore, I will also be presenting its applications in the medical domain to enable remote surgical guidance by using SAR to illuminate surgical stencils on a physical surgical site precisely.

Anjungan Tunai Mandiri, dikenal juga dengan ATM adalah perangkat komputerisasi yang digunakan oleh lembaga keuangan (bank), koperasi, dan asuransi, termasuk juga taruna/taruni bank guna untuk melayani nasabahnya melakukan transaksi keuangan tanpa bantuan pegawai atau karyawan bank. Adapun Tujuan Penelitian ini adalah memetakan Persebaran ATM di kota Samarinda. Metode dalam pembuatan peta persebaran ATM Ini, Studi Literatur, Pengumpulan Data, Pengolahan Data, Implementasi, dan Hasil Implementasi. Berdasarkan hasil sementara kajian ini, Hasil Penelitian menunjukan bahwa perangkat lunak QGIS menciptakan peta dengan gambar yang jelas dan Lengkap. Di samping itu pemanfaatan plugin OpenStreetMap yang terintegrasi di dalam QGIS memberi keunggulan dalam memvisualisasikan pemetaan untuk dapat sampai pada tingkat detail dan akurasi yang tinggi. Hasil implementasi ini akan menghasilkan peta di kota Samarinda.

Fertilizer spreaders play a crucial role in evenly distributing granule fertilizer across palm plantations. However, in specific areas where growth conditions are unsuitable, fertilizer application becomes unnecessary. Therefore, this study aims to improve granule fertilizer distribution efficiency through enhanced fertilizer blade design. Using Finite Element (FE) simulation, the stress deformation and deflection of the existing spreader blade were evaluated. Meanwhile, Computational Fluid Dynamics (CFD) simulation was used to investigate the influence of spreader design on fertilizer projection speed and direction in the case of open and closed side discharge. The study revealed that the applied forces increased both the critical stress deformation and deflection. To ensure the fertilizer spreads properly over the desired area, the initial velocity had to be increased proportionally with an increase in the angle of direction. These findings contributed to a deeper understanding of the relationship between fertilizer projection velocity, spreader blade strength, and flow behaviour, enabling the reduction of waste in granule fertilizer, while enhancing the operational efficiency and reliability of fertilizer spreader.

This research investigates the impact of Non-digital Gamification (NDG) techniques on students' math achievement, focusing on Fractions. Utilizing a quasi-experimental design, the study involved 100 primary school students in Perak. Two groups were formed: the control group, which followed traditional teaching methods, and the experimental group, which experienced NDG learning approaches. The findings indicate that students using NDG perform better in math (fractions) assessments compared to those using conventional methods. This study provides empirical evidence supporting the efficacy of NDG in teaching Fractions. The results underscore the potential for educators to innovate and enhance gamification tools, particularly in mathematics, contributing to educational advancements aligned with the goals of the Malaysian Ministry of Education.

Data and geographical trend of Salmonella serovars infecting poultry in Malaysia is limited. In this study, the trend of Salmonella serovars infection was presented for the past ten years from 2011 to 2020 and the predominant serovars were mapped based on geographical distribution. Analysis of passive surveillance data demonstrated a shift of Salmonella serovars that infected poultry in Malaysia. The Salmonella serovars varied within ten years of registered cases with the Veterinary Research Institute, Ipoh, Malaysia involving samples from live and dead birds. Total number of cases found from the year 2011 to 2020 were 391 cases, involving 73 Salmonella serovars with an additional one group of unclassified serovars known as Salmonella spp. Further analysis revealed that eight serovars were found predominant throughout the ten-year period. These included S. Albany, S. Braenderup, S. Brancaster, S. Corvallis, S. Enteritidis, S. Kentucky, S. Typhimurium and S. Weltevreden. Salmonella spp. (Salmonella that is incapable to be identified based on serotyping) were also one of the major groups observed throughout the years. This study could help the authorities to improvise policies for better disease control programs through the establishment of diagnostic tools for rapid Salmonella screening in poultry.

Due to the substantial scientific development in the economy and the increasingly dynamic competitive market, small- and medium-sized manufacturing enterprises (SMEs) face great difficulties in utilizing the available resource potentials. This requires SMEs to adopt non-traditional tools and techniques to compete in the market. This study aims to develop an empirical model to assess the role of SMEs dynamic capability through green absorptive capacity (GAC) in building a significant relationship between green supply chain management practices (GSCMP), relational capability (RC) and environmental performance (EP).The model was established through an extensive review of existing works of literature and was tested with 265 managers and entrepreneurs from manufacturing SMEs operating in Karachi, the largest city in Pakistan. In this paper, PLS structural equation modeling approach was employed to validate the proposed research hypotheses. The findings confirmed that GAC directly relates to EP. Moreover, the results significantly uncovered that GSCMP positively impacts the GAC and EP. It was also confirmed that RC has a positive significant effect on GAC and EP. Finally, the outcomes confirmed that GAC mediates the linkages among GSCMP, RC, and EP.Green absorptive capacity in the setting of a manufacturing organization as a dynamic capability is a novel approach explained in this study. The study focused on how small- and medium-sized enterprises can enjoy a competitive edge through acquiring and disseminating dynamic environmental knowledge throughout the value chain and by practicing strategically green supply chain management and relational capabilities to boost environmental performance. The outcomes will incorporate industry practitioners through practicing superior green dynamic capabilities like green SCM practice, relational capability, and green absorptive capacity to achieve high environmental goals and organizational excellence.

The global data center (DC) sector has expanded rapidly during the last decades, due to the rising demand for digital services. In the Nordic region, Sweden has emerged as a global hub, attracting leading technology companies like Amazon, Facebook, Microsoft, and Google. Server halls of DCs are energy intensive buildings, which puts pressure on local water resources and contributes to global greenhouse gas emissions. This study aims to, firstly, quantify the environmental impact of DCs, based on energy usage, water consumption, and greenhouse gas (GHG) emissions. Secondly, it develops a planning tool by employing a multi-criteria approach to optimally locate new DCs and to assess the site suitability of existing ones in Sweden. Data of various performance indicators (geographical data on renewable energy accessibility, free cooling conditions, excess heat receivers, and resilience to water shortages) of DCs was collected through different means, e.g., questionnaire surveys, permit applications, company websites, and other open online data repositories. ArcGIS Pro was employed for spatial analysis, and 68 DCs with a site suitability index (SSI) ≤ 45 % were identified as less ideally located. The principal findings are centered on Sweden, and thereby primarily benefit stakeholders engaged in decision-making for evaluating existing or strategic planning of new DCs by incorporating a comprehensive environmental perspective. Given the rapidly changing climate, strategically siting DCs will become crucial for minimizing the sector's environmental impact.

The article discusses the suitability of several existing empirical models for determining the safe separation distances (SSDs) for waste fuels. Contour plots of SSDs are generated based on isotropic thermal radiation model for safely storing municipal solid waste (MSW) bales under different storage settings and the plots can be employed for routine hazard assessment purposes. Further, experimental design technique was employed, and orthogonal test matrices were generated to conduct the experiments for studying the combustion dynamics of the primary fire under the influence of storage settings of surrounding fuel units. Main and interaction effects of various storage parameters (e.g., height of adjacent fuel sources, clearance between fuel sources and array size) on the response variables (flame height and burn out time) was studied. A list of appropriate measures for minimising the risk of secondary fires at MSW bale storage sites are provided., Prevention and Mitigation of Waste Fires

Micro Enterprises (MEs) occupy a central place in the economies of both developed and developing nations. Micro Enterprises are business with fewer than ten (10) employees. These enterprises have fundamental roles in the development of any economy. In addition, they make-up the vast majority of enterprises (between 70% and 95%) around the globe. Despite the importance and contributions of MEs to the economic growth of Nigeria, many constraints affects their development and the most formidable of these constraints is the lack of access to finance. However, the emergence of Islamic microfinance in the Nigeria is a positive innovation in the economy. Adoption of Islamic microfinance would significantly address the financing constraints faced by micro enterprises in the region. It is against this background that this study examined the financing constraints faced by micro enterprises in the north-eastern region and assess how Islamic microfinance can improve MEs access to finance. A structured questionnaire was used to collect data from microenterprises in two (2) states of the north-east region, namely, Gombe and Yobe states, respectively. Multiple sampling techniques were used to identify respondents in six (6) local government areas (3 from each state), including, Gombe, Akko, Funekaye, Potiskum, Bade and Damaturu LGAs. Out of the 440 copies of questionnaires administered, 426 were usable. The results of descriptive analysis revealed that microenterprises may eventually succeed in accessing external financing, but they might encounter difficulties in the process of obtaining these loans. The challenges MEs often face in accessing external funds include: religious prohibition, high interest rate, strict documentation requirement, high collateral, short loan duration, insufficient amount of loan, and complexity of loan application and procedure. The result also showed that Islamic Microfinance have high prospect in enhancing ME access to finance and majority of ME owne

Point cloud processing methods leverage local and global point features %at the feature level to cater to downstream tasks, yet they often overlook the task-level context inherent in point clouds during the encoding stage. We argue that integrating task-level information into the encoding stage significantly enhances performance. To that end, we propose SMTransformer which incorporates task-level information into a vector-based transformer by utilizing a soft mask generated from task-level queries and keys to learn the attention weights. Additionally, to facilitate effective communication between features from the encoding and decoding layers in high-level tasks such as segmentation, we introduce a skip-attention-based up-sampling block. This block dynamically fuses features from various resolution points across the encoding and decoding layers. To mitigate the increase in network parameters and training time resulting from the complexity of the aforementioned blocks, we propose a novel shared position encoding strategy. This strategy allows various transformer blocks to share the same position information over the same resolution points, thereby reducing network parameters and training time without compromising accuracy.Experimental comparisons with existing methods on multiple datasets demonstrate the efficacy of SMTransformer and skip-attention-based up-sampling for point cloud processing tasks, including semantic segmentation and classification. In particular, we achieve state-of-the-art semantic segmentation results of 73.4% mIoU on S3DIS Area 5 and 62.4% mIoU on SWAN dataset, Comment: 14 pages, 8 figures

Purpose: This research aims to explore the synchronization of planning and budgeting for development in Indonesia, focusing on governance and network management perspectives. It seeks to understand the complexity and effectiveness of networks in national development management and the impact of external control on task implementation. Theoretical Framework: The study adopts a post-positivist approach, emphasizing the importance of governance structures and network management in development planning. It highlights the roles of independent bodies such as the Legislative/Regional Assembly (DPR/DPRD), Audit Board of the Republic of Indonesia (BPK), and Financial and Development Supervisory Agency (BPKP) in overseeing development processes and preventing waste and misappropriation. Method: A qualitative methodology was used, incorporating data from various government institutions and professional organizations. Data collection involved in-depth interviews and document analysis. The study employed data reduction, data display, and conclusion drawing/verification techniques to ensure a robust analysis. Results: The findings indicate a critical need for external control in ensuring adherence to plans and preventing misappropriation in development management. A significant overlap between the duties and authorities of BPKP and BPK was identified, suggesting a need for regulatory reform to enhance the efficiency and effectiveness of state financial oversight. Conclusion: The study concludes that efficient and effective external control is essential for achieving transparency and integrity in state finances management. It recommends regulatory reforms to clarify the roles of BPKP and BPK, thereby improving the effectiveness of financial oversight. These changes are crucial for supporting the attainment of national development objectives and ensuring sustainable growth.

Large Language Models (LLMs) such as ChatGPT and LlaMA are advancing rapidly in generative Artificial Intelligence (AI), but their immense size poses significant challenges, such as huge training and inference costs, substantial energy demands, and limitations for on-site deployment. Traditional compression methods such as pruning, distillation, and low-rank approximation focus on reducing the effective number of neurons in the network, while quantization focuses on reducing the numerical precision of individual weights to reduce the model size while keeping the number of neurons fixed. While these compression methods have been relatively successful in practice, there is no compelling reason to believe that truncating the number of neurons is an optimal strategy. In this context, this paper introduces CompactifAI, an innovative LLM compression approach using quantum-inspired Tensor Networks that focuses on the model's correlation space instead, allowing for a more controlled, refined and interpretable model compression. Our method is versatile and can be implemented with - or on top of - other compression techniques. As a benchmark, we demonstrate that a combination of CompactifAI with quantization allows to reduce a 93% the memory size of LlaMA 7B, reducing also 70% the number of parameters, accelerating 50% the training and 25% the inference times of the model, and just with a small accuracy drop of 2% - 3%, going much beyond of what is achievable today by other compression techniques. Our methods also allow to perform a refined layer sensitivity profiling, showing that deeper layers tend to be more suitable for tensor network compression, which is compatible with recent observations on the ineffectiveness of those layers for LLM performance. Our results imply that standard LLMs are, in fact, heavily overparametrized, and do not need to be large at all., Comment: 5 pages, 4 figures, 2 tables, and supplementary information of 2 pages and 1 figure. Revised version with new benchmarks for LlaMA2-7B

In this study, the perceptions of Swedish funeral directors were elicited on physical environmental hazards associated with the five life stages of handling cadavers through interviews and questionnaires (The data was collected from 78 respondents across Sweden). We conclude that although the workers in the Swedish funeral industry are at high risk of developing health problems caused by exposure to various chemicals (e.g., formaldehyde ((FA)) based embalming fluids, heavy metals, pesticides, pathogens, insecticides, and fungicides) and the industry itself contaminating air, soil and groundwater, the health and environmental hazards are perceived low by the funeral directors. In the study, funeral homes are classified into four distinguished groups: (a) environmentally ardent and the epitome of sustainability-(G-1) (21 %); (b) environmental- and customer-oriented-(G-2) (64 %); (c) environmentally apathetic and customer-oriented-(G-3) (6 %); and (d) environmentally unconscious-(G-4) (8 %). Results show that most funeral directors under the G-1 & G-2 (32 out of 43) are acquainted with generational environmental goals (GGs) set by the Swedish government and are aware of the choices that promote environmental wellbeing. On the other hand, G-4 is not acquainted with GGs and comprises only small funeral homes (<10 employees). Poor safety culture, traditions, and prioritising economic returns hinder making environmentally sustainable funeral industry choices. The article provides a better understanding of the Swedish funeral industry and suggests appropriate short- and long-term strategies that could curtail the associated physical environmental hazards of the industry.

Due to socio-economic and climatic changes around the world, large outdoor fires in the built environment have become one of the global issues that threaten billions of people. The devastating effects of them are indicative of weaknesses in existing building codes and standard testing methodologies. This is due in part to our limited understanding of large outdoor fire exposures, including the ones from wildland to communities and within communities. To address this problem, the Ignition Resistance Committee (IRC) of the International Association of the Fire Safety Science working group ‘Large Outdoor Fires and the Built Environment’ was established. This manuscript is the result of one of the IRC's initiatives to review current knowledge on exposures associated with large outdoor fires, identify existing knowledge gaps, and provide recommendations for future research. The article consists of two sections: the wildland fire exposure to the built environment and the settlement fire exposure to structures. Each section presents a comprehensive review of experimental and numerical studies of exposure mechanisms (flame contact and convection, radiation, and firebrands). The review concludes with a discussion on data consistency and existing knowledge gaps to highlight future directions for each of the three fire exposure mechanisms., Filkov was supported by the Australian Research Council (Grant No. DP210102540), Muhammad Asim Ibrahim was supported by KK-stiftelsen [grant number 817-2.1.9], Åforsk [grant number 21-106] and Swedish Institute [grant number 00119/2022], Yu Wang was supported by National Key R&D Program of China (2022YFC3003100) and USTC Research Funds of the Double First-Class Initiative (YD2320002005), Felipe R. Centeno and Calisa K. Lemmertz were supported by the Brazilian National Council for Scientific and Technological Development (CNPq/Brazil) and Higher Education Personnel Improvement Coordination (CAPES/Brazil), David Rush was supported by Engineering and Physical Sciences Research Council (IRIS-Fire project of UK, Grant No. EP/P029582/1)., Peer Reviewed, Postprint (published version)

Since the end of December 2019, the outbreak of coronavirus disease 2019 (COVID-19) epidemic has occurred and spread rapidly throughout the world. WHO officially announced that the COVID-19 had become a global pandemic. During the pandemic, there were dearth of information regarding pregnant women with COVID-19 pneumonia and their infants. In this paper, we report a case of a COVID-19 pregnant woman with diabetes. The pregnant woman blood specimens were collected periodically during her stay in the isolation center for blood chemistry analysis, IL-6, MDA as well blood sugar measurements while receiving treatments, these parameters coupled with respiratory indices were evaluated to predict the disease severity. It is suggested that respiratory failure, hyperglycemia, cytokinemia and oxidative stress played significant role in the disease progression and may therefore can be used in predicting the clinical outcome of the COVID-19.

Due to socio-economic and climatic changes around the world, large outdoor fires in the built environment have become one of the global issues that threaten billions of people. The devastating effects of them are indicative of weaknesses in existing building codes and standard testing methodologies. This is due in part to our limited understanding of large outdoor fire exposures, including the ones from wildland to communities and within communities. To address this problem, the Ignition Resistance Committee (IRC) of the International Association of the Fire Safety Science working group ‘Large Outdoor Fires and the Built Environment’ was established. This manuscript is the result of one of the IRC's initiatives to review current knowledge on exposures associated with large outdoor fires, identify existing knowledge gaps, and provide recommendations for future research. The article consists of two sections: the wildland fire exposure to the built environment and the settlement fire exposure to structures. Each section presents a comprehensive review of experimental and numerical studies of exposure mechanisms (flame contact and convection, radiation, and firebrands). The review concludes with a discussion on data consistency and existing knowledge gaps to highlight future directions for each of the three fire exposure mechanisms.

Penggunaan kendaraan pribadi untuk Mahasiswa Teknik Universitas Mulawarman mengharuskan mereka mencari Pom Bensin terdekat untuk mengetahui apakah Pom Bensin sudah tersebar luas di daerah samarinda yang berdekatan dengan Fakultas Teknik Universitas Mulawarman. Penelitian ini menggunakan Sistem Informasi Geografis ini menggunakan metode buffer dan Multiple Ring Buffer memakai data sekunder yang telah tersedia. Buffer menggambarkan area tertutup (poligon) pada suatu jarak tertentu pada bentang kenampakan tertentu. Buffering mempunyai fungsi untuk Mengidentifikasi daerah yang berada di sekitar kenampakan geografis; Mengidentifikasi/memilih kenampakan yang termasuk di dalam atau berada di luar daerah buffer dan Untuk menyediakan ukuran perkiraan yang dekat dengan suatu kenampakan

Purpose: The research aims to analyze the impact of Relationship Quality between employees and the organization on individual and overall organizational performance at the Population and Civil Registration Office of Bone Regency. Theoretical Framework: The investigation is anchored in the paradigm that Relationship Quality—characterized by trust, commitment, and satisfaction—plays a crucial role in enhancing both individual and organizational efficacy. The study theorizes that a robust relationship quality leads to improved job satisfaction, loyalty, and professional development, thereby influencing performance outcomes. Method: A qualitative descriptive methodology was employed, segregating the collected data into primary and secondary categories. Primary data were gathered through observations and interviews, while secondary data were compiled from intermediary sources such as written documents. The data underwent a rigorous process of reduction, display, and verification to ensure comprehensive analysis and authenticity. Results: The study concludes that the Population and Civil Registration Office of Bone Regency exhibits strong and productive working relationships, with high levels of trust, professionalism, and adaptability among employees. Despite the prevalence of high job satisfaction, the need for revisiting the compensation scheme was identified as a factor for further improvement. Conclusion: The research advocates for the advancement of trust, adaptability, and professionalism through continuous training and effective communication. It also suggests that implementing a compensation satisfaction survey and a performance-based incentive scheme will enhance employee dedication and performance, fostering sustainable organizational growth. Originality/Value: This study contributes to the existing literature by providing empirical insights into the significance of Relationship Quality in public sector organizations, particularly within the Populatio

Background: Plants possess a vast array of bioactive compounds with strong antioxidant properties, which can be explored for their potential pharmacological activities and utilized as novel therapeutic agents. Objective: The current study was designed to rationally justify the use of Cordia rothii (CR) and Viola serpens (VS) as antioxidants, antibacterials, and antifungals, as they have traditionally been used for such purposes. Methods: Phytochemical screening of aqueous methanolic extracts from CR and VS was conducted, followed by measuring their antioxidant potentials, including total phenolic and flavonoid content, 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH) and reducing power capacity. Additionally, in vitro antibacterial and antifungal tests, as well as in vivo acute toxicity assessment were carried out. Results: The phytochemical screening of the CR and VS extracts revealed the presence of saponins, tannins, anthraquinones, and flavonoids. Both extracts exhibited antioxidant scavenging potential with IC50 values of 0.039 and 0.05 mg/ml, respectively. The CR extract displayed bactericidal activity against S.aureus ATCC 6538, S.epidermidis ATCC 12228, and E.coli ATCC 8739, while no antifungal activity was observed in either extract. The LD50 of either extract was calculated to be greater than 10 g/kg/bw. In the acute toxicity study, no toxic effects were observed on body weight, water intake, liver weight, or abnormal liver enzyme levels. Histopathological studies showed no significant abnormalities in the control or extract-treated groups. Conclusion: It can be concluded that the traditional uses of plant extracts possess potent antibacterial and antioxidant properties, which could potentially be utilized as a novel drug.

Agriculture plays an important role in the food and economy of Bangladesh. The rapid growth of population over the years also has increased the demand for food production. One of the major reasons behind low crop production is numerous bacteria, virus and fungal plant diseases. Early detection of plant diseases and proper usage of pesticides and fertilizers are vital for preventing the diseases and boost the yield. Most of the farmers use generalized pesticides and fertilizers in the entire fields without specifically knowing the condition of the plants. Thus the production cost oftentimes increases, and, not only that, sometimes this becomes detrimental to the yield. Deep Learning models are found to be very effective to automatically detect plant diseases from images of plants, thereby reducing the need for human specialists. This paper aims at building a lightweight deep learning model for predicting leaf disease in tomato plants. By modifying the region-based convolutional neural network, we design an efficient and effective model that demonstrates satisfactory empirical performance on a benchmark dataset. Our proposed model can easily be deployed in a larger system where drones take images of leaves and these images will be fed into our model to know the health condition., Comment: 23 pages

The performance of convolutional neural networks (CNN) depends heavily on their architectures. Transfer learning performance of a CNN relies quite strongly on selection of its trainable layers. Selecting the most effective update layers for a certain target dataset often requires expert knowledge on CNN architecture which many practitioners do not posses. General users prefer to use an available architecture (e.g. GoogleNet, ResNet, EfficientNet etc.) that is developed by domain experts. With the ever-growing number of layers, it is increasingly becoming quite difficult and cumbersome to handpick the update layers. Therefore, in this paper we explore the application of genetic algorithm to mitigate this problem. The convolutional layers of popular pretrained networks are often grouped into modules that constitute their building blocks. We devise a genetic algorithm to select blocks of layers for updating the parameters. By experimenting with EfficientNetB0 pre-trained on ImageNet and using Food-101, CIFAR-100 and MangoLeafBD as target datasets, we show that our algorithm yields similar or better results than the baseline in terms of accuracy, and requires lower training and evaluation time due to learning less number of parameters. We also devise a metric called block importance to measure efficacy of each block as update block and analyze the importance of the blocks selected by our algorithm., Comment: 9 pages

Precise localization is critical for autonomous vehicles. We present a self-supervised learning method that employs Transformers for the first time for the task of outdoor localization using LiDAR data. We propose a pre-text task that reorganizes the slices of a $360^\circ$ LiDAR scan to leverage its axial properties. Our model, called Slice Transformer, employs multi-head attention while systematically processing the slices. To the best of our knowledge, this is the first instance of leveraging multi-head attention for outdoor point clouds. We additionally introduce the Perth-WA dataset, which provides a large-scale LiDAR map of Perth city in Western Australia, covering $\sim$4km$^2$ area. Localization annotations are provided for Perth-WA. The proposed localization method is thoroughly evaluated on Perth-WA and Appollo-SouthBay datasets. We also establish the efficacy of our self-supervised learning approach for the common downstream task of object classification using ModelNet40 and ScanNN datasets. The code and Perth-WA data will be publicly released., Comment: Accepted in IEEE International Conference on Robotics and Automation (ICRA), 2023

In this study, the effects of goat manure fertilization (2, 4, 8, and 12 Tn/ha) on the grain yield, organic compounds, and mineral composition of two quinoa varieties (CICA-17 and Regalona Baer) were evaluated under field conditions in Northwest Argentina. The results indicate that fertilization improved the quinoa grain yield and total protein content. Low manure doses positively affected the fatty acid (FA) profile, and significant changes were determined for the monounsaturated (MUFA) and polyunsaturated (PUFA) fatty acid contents of CICA-17 and on the saturated fatty acid (SFA) contents of R. Baer seeds. The amino acid contents were positively affected in CICA-17 and negatively in R. Baer. Soluble sugars (glucose, fructose, and sucrose), major elements (K, Si, P, Mg, Ca, and Na), minor elements (Fe, Mn, Al, Zn, and Cu), and ultratrace elements (Cr and Li) were detected and discussed in terms of their impact on human nutrition and health. Conclusively, manure addition affected some essential amino acids, the desaturase activity, the n6:n3 and SFA/UFA ratios, the atherogenic index, soluble sugars, and mineral content, and the fatty acid metabolism of each variety was differently affected, especially the C16 and C18 desaturase activity, which responded differently to various manure doses. Manure addition is a promising alternative to improve the nutritional quality and functionality of quinoa grains, but the response is not linear.

Enteric methane (CH4) is one of the main greenhouse gases emitted in livestock production systems with ruminants. Among the options to reduce such emissions, animal genetics is one of the factors that is taking relevance in recent years. The aim of the present study was to assess the emission of enteric CH4 in dairy cows with different genetic backgrounds. Sixteen cows belonging to the following three genetic groups were selected for this study: seven F1 (50% Jersey × 50% Gyr), five Triple cross (50% Jersey × 31% Holstein × 19% Sahiwal) and four Jersey. Enteric CH4 emissions were measured in all cows for 15 months, at the middle of each month, using the SF6 technique. Enteric CH4 emissions did not differ (p > 0.05) among genetic groups, although it varied with the stage of lactation, due to differences in milk yield and dry matter intake (DMI). Pasture DMI and the intensity of CH4 emissions (g kg−1 DMI) differed (p < 0.05) between dry and lactating cows, with higher DMI in the lactation period, while CH4 emission intensity was higher for dry cows. Cows with the highest proportion of Bos taurus genes presented a higher annual mean methane conversion factor (Ym), with 7.22, 7.05 and 5.90% for the Triple cross, purebred Jersey and F1, respectively. In conclusion, non-significant differences in enteric CH4 emissions and Ym were detected among dairy cows with different genetic backgrounds. However, F1 cows tended to show lower enteric CH4 emission and Ym, compared to those with more Bos taurus genes.

This study focused on the exploration of microbial communities inhabiting extreme cold environments, such as the Passu and Pisan glaciers of Pakistan, and their potential utilization in industrial applications. Among the 25 initially screened strains, five were found to be suitable candidates for exopolysaccharide (EPS) production, with strain CUI-P1 displaying the highest yield of 7230.5 mg/L compared to the other four strains. The purified EPS from CUI-P1 was tested for its ability to protect probiotic bacteria and E. coli expressing green fluorescence protein (HriGFP) against extreme cold temperatures, and it exhibited excellent cryoprotectant and emulsification activity, highlighting its potential use in the biotechnological industry. Furthermore, the genome of Acinetobacter sp., CUI-P1 comprised 199 contigs, with a genome size of 10,493,143bp and a G + C content of 42%, and showed 98.197% nucleotide identity to the type genome of Acinetobacter baumannii ATCC 17978. These findings offer promising avenues for the application of EPS as a cryoprotectant, an essential tool in modern biotechnology.

With the growth of machine learning techniques, privacy of data of users has become a major concern. Most of the machine learning algorithms rely heavily on large amount of data which may be collected from various sources. Collecting these data yet maintaining privacy policies has become one of the most challenging tasks for the researchers. To combat this issue, researchers have introduced federated learning, where a prediction model is learnt by ensuring the privacy of data of clients data. However, the prevalent federated learning algorithms possess an accuracy and efficiency trade-off, especially for non-IID data. In this research, we propose a centralized, neural network-based federated learning system. The centralized algorithm incorporates micro-level parallel processing inspired by the traditional mini-batch algorithm where the client devices and the server handle the forward and backward propagation respectively. We also devise a semi-centralized version of our proposed algorithm. This algorithm takes advantage of edge computing for minimizing the load from the central server, where clients handle both the forward and backward propagation while sacrificing the overall train time to some extent. We evaluate our proposed systems on five well-known benchmark datasets and achieve satisfactory performance in a reasonable time across various data distribution settings as compared to some existing benchmark algorithms., Comment: 48 pages

Learning-to-rank is an applied domain of supervised machine learning. As feature selection has been found to be effective for improving the accuracy of learning models in general, it is intriguing to investigate this process for learning-to-rank domain. In this study, we investigate the use of a popular meta-heuristic approach called simulated annealing for this task. Under the general framework of simulated annealing, we explore various neighborhood selection strategies and temperature cooling schemes. We further introduce a new hyper-parameter called the progress parameter that can effectively be used to traverse the search space. Our algorithms are evaluated on five publicly benchmark datasets of learning-to-rank. For a better validation, we also compare the simulated annealing-based feature selection algorithm with another effective meta-heuristic algorithm, namely local beam search. Extensive experimental results shows the efficacy of our proposed models., Comment: 29 pages

Community question answering (CQA) forums are Internet-based platforms where users ask questions about a topic and other expert users try to provide solutions. Many CQA forums such as Quora, Stackoverflow, Yahoo!Answer, StackExchange exist with a lot of user-generated data. These data are leveraged in automated CQA ranking systems where similar questions (and answers) are presented in response to the query of the user. In this work, we empirically investigate a few aspects of this domain. Firstly, in addition to traditional features like TF-IDF, BM25 etc., we introduce a BERT-based feature that captures the semantic similarity between the question and answer. Secondly, most of the existing research works have focused on features extracted only from the question part; features extracted from answers have not been explored extensively. We combine both types of features in a linear fashion. Thirdly, using our proposed concepts, we conduct an empirical investigation with different rank-learning algorithms, some of which have not been used so far in CQA domain. On three standard CQA datasets, our proposed framework achieves state-of-the-art performance. We also analyze importance of the features we use in our investigation. This work is expected to guide the practitioners to select a better set of features for the CQA retrieval task., Comment: 20 pages

We propose a Digit-Serial Left-tO-righT (DSLOT) arithmetic based processing technique called DSLOT-NN with aim to accelerate inference of the convolution operation in the deep neural networks (DNNs). The proposed work has the ability to assess and terminate the ineffective convolutions which results in massive power and energy savings. The processing engine is comprised of low-latency most-significant-digit-first (MSDF) (also called online) multipliers and adders that processes data from left-to-right, allowing the execution of subsequent operations in digit-pipelined manner. Use of online operators eliminates the need for the development of complex mechanism of identifying the negative activation, as the output with highest weight value is generated first, and the sign of the result can be identified as soon as first non-zero digit is generated. The precision of the online operators can be tuned at run-time, making them extremely useful in situations where accuracy can be compromised for power and energy savings. The proposed design has been implemented on Xilinx Virtex-7 FPGA and is compared with state-of-the-art Stripes on various performance metrics. The results show the proposed design presents power savings, has shorter cycle time, and approximately 50% higher OPS per watt., Comment: Presented at 2023 26th Euromicro Conference on Digital System Design (DSD)

Localization is a fundamental task in robotics for autonomous navigation. Existing localization methods rely on a single input data modality or train several computational models to process different modalities. This leads to stringent computational requirements and sub-optimal results that fail to capitalize on the complementary information in other data streams. This paper proposes UnLoc, a novel unified neural modeling approach for localization with multi-sensor input in all weather conditions. Our multi-stream network can handle LiDAR, Camera and RADAR inputs for localization on demand, i.e., it can work with one or more input sensors, making it robust to sensor failure. UnLoc uses 3D sparse convolutions and cylindrical partitioning of the space to process LiDAR frames and implements ResNet blocks with a slot attention-based feature filtering module for the Radar and image modalities. We introduce a unique learnable modality encoding scheme to distinguish between the input sensor data. Our method is extensively evaluated on Oxford Radar RobotCar, ApolloSouthBay and Perth-WA datasets. The results ascertain the efficacy of our technique., Comment: UnLoc: A Universal Localization Method for Autonomous Vehicles using LiDAR, Radar and/or Camera Input has been accepted for publication in the Proceedings of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)

Purpose: This study was driven by the need to better understand how consumer behaviour impacts online shopping. To examine the effects of consumer behaviors on online buying, this study has used specific consumer traits and customer loyalty. This search also helps businesses to identify needs, wants, and demands and remain loyal to their consumers.  Design/Methodology/Approach: Survey, using an instrument was used to collect data. In addition to the general sample check, the study also carried out a pilot check to evaluate the validity of the instrument used in the study. Findings: The consequences of this study show that there is a significant effect of consumer characteristics on online shopping. The discoveries are of advantage to both scholastics and experts and this examination is one of the first to show how to oversee computerized client connections on online networking brand groups.  Originality/Value: The results of this study would contribute as a guideline of online consumer Behaviour for businesses who want to do online business in Pakistan (Rawalpindi-Islamabad). These discoveries add to an exhaustive comprehension of purchaser conduct. Help organizations manage comparable circumstances and give counsel to the public authority to viably uphold organizations later.

Sistem Informasi berbasis website salah satu teknologi yang terdiri dari teks, gambar, file, dan suara animasi dan dengan kemampuan itu media informasi akan lebih menarik dan dimininati untuk dipergunakan sebagai media penyebaran informasi. Sistem Informasi berbasis website mampu mengolah data menjadi sebuah informasi dengan cara mengidentifikasi, mengumpulkan, mengelola dan menyediakan untuk diakses secara bersama-sama. Sistem informasi website Koordinator Kepegawaian Universitas Mulawarman merupakan sistem yang dipergunakan untuk memberikan kemudahan dalam mendapatkan informasi yang dibutuhkan pengguna dan selain itu, informasi yang di manajemenn oleh website akan tersimpan dengan baik dan tidak mudah hilang pada sistem sehingga memudahkan pegawai dalam mencari informasi. Sistem Informasi Koordinator Kepegawaian menggunakan sistem Content Management System (CMS) dalam implementasinya karena kemampuan dari CMS yang cocok dengan permasalahan sistem yang ada pada Koordinator Kepegawaian dan penelitian ini menggunakan metode waterfall dalam perencangan sistem Koordinator Kepegawaian. Hasil pada penilitan ini yaitu, informasi lebih mudah dicari dan tidak mudah hilang karena sistem CMS ini mampu membuat kategori-kategori berdasarkan kebutuhan Koordinator Kepegawaian.

Global resource limits and increasing demand for non-fossil energy sources have expanded the research on alternative fuels. Among them, algal biomass is designated as a third-generation feedstock with promising opportunities and the capability to be utilized for energy production in the long term. The paper presents the potential for converting beach wrack containing macroalgal biomass into gaseous fuel as a sustainable option for energy production, simultaneously improving the organic waste management that the coastline is facing. Beach wrack collected in the northern Baltic Sea region was converted by gasification technology applicable for carbon-based feedstock thermal recovery, resulting in syngas production as the main product and by-product biochar. Proximate and ultimate analysis, trace and major element quantification, detection of calorific values for macroalgal biomass, and derived biochar and syngas analysis were carried out. A higher heating value for beach wrack was estimated to be relatively low, 5.38 MJ/kg as received (or 14.70 MJ/kg on dry basis), but produced syngas that contained enough high content of CH4 (42%). Due to macroalgal biomass specifics (e.g., high moisture content and sand admixture), an adjusted gasification process, i.e., the combination of thermochemical procedures, such as mild combustion and pyrolytic biomass conversion, might be a better choice for the greater economic value of biowaste valorization

There exists a two-way detrimental relationship between climate change and several agricultural practices. Emissions from the agriculture sector (e.g. GHG emissions, and release of pesticides, herbicides, and nutrients) is causing global warming as well as damaging the environment, and in return climate change is stressing agriculture globally. The bewildering relationship between adaptative measures and risk of maladaptive outcomes demands a better understanding concerning “What, where and how to implement” adaptive measures. This article is one step towards a deeper understanding of “what”, “where” and “how” related questions on climate change adaptation in connection to constraints of carrying capacities and risks of maladaptation. A GIS based framework was employed for generating risk maps for identification of nutrient surplus and discharge hotspots “i.e. Where to implement?”. Moreover, extensive in-depth interviews and a questionnaire survey were conducted to explore perceptions on carrying capacities, resources management, climate change adaptation measures and risk of maladaptive results, i.e. “What and how to implement?”. Furthermore, the data collected from the questionnaire survey and interviews was employed to ameliorate the analytical model, previously developed, based on the initial work of Juhola et al, (2016) and Neset et al, (2019a), which could help guide planners, advisors, and farmers for better understanding of adaptation and maladaptive risks. The study is expected to assist in developing robust, sustainable and climate smart adaptive interventions in response to negative climate change impacts.

Background and objective: Interactions of long non-coding ribonucleic acids (lncRNAs) with micro-ribonucleic acids (miRNAs) play an essential role in gene regulation, cellular metabolic, and pathological processes. Existing purely sequence based computational approaches lack robustness and efficiency mainly due to the high length variability of lncRNA sequences. Hence, the prime focus of the current study is to find optimal length trade-offs between highly flexible length lncRNA sequences. Method: The paper at hand performs in-depth exploration of diverse copy padding, sequence truncation approaches, and presents a novel idea of utilizing only subregions of lncRNA sequences to generate fixed-length lncRNA sequences. Furthermore, it presents a novel bag of tricks-based deep learning approach “Bot-Net” which leverages a single layer long-short-term memory network regularized through DropConnect to capture higher order residue dependencies, pooling to retain most salient features, normalization to prevent exploding and vanishing gradient issues, learning rate decay, and dropout to regularize precise neural network for lncRNA–miRNA interaction prediction. Results: BoT-Net outperforms the state-of-the-art lncRNA–miRNA interaction prediction approach by 2%, 8%, and 4% in terms of accuracy, specificity, and matthews correlation coefficient. Furthermore, a case study analysis indicates that BoT-Net also outperforms state-of-the-art lncRNA–protein interaction predictor on a benchmark dataset by accuracy of 10%, sensitivity of 19%, specificity of 6%, precision of 14%, and matthews correlation coefficient of 26%. Conclusion: In the benchmark lncRNA–miRNA interaction prediction dataset, the length of the lncRNA sequence varies from 213 residues to 22,743 residues and in the benchmark lncRNA–protein interaction prediction dataset, lncRNA sequences vary from 15 residues to 1504 residues. For such highly flexible length sequences, fixed length generation using copy padding introdu, DEAL

Subcellular localization of Ribonucleic Acid (RNA) molecules provide significant insights into the functionality of RNAs and helps to explore their association with various diseases. Predominantly developed single-compartment localization predictors (SCLPs) lack to demystify RNA association with diverse biochemical and pathological processes mainly happen through RNA co-localization in multiple compartments. Limited multi-compartment localization predictors (MCLPs) manage to produce decent performance only for target RNA class of particular sub-type. Further, existing computational approaches have limited practical significance and potential to optimize therapeutics due to the poor degree of model explainability. The paper in hand presents an explainable Long Short-Term Memory (LSTM) network “EL-RMLocNet”, predictive performance and interpretability of which are optimized using a novel GeneticSeq2Vec statistical representation learning scheme and attention mechanism for accurate multi-compartment localization prediction of different RNAs solely using raw RNA sequences. GeneticSeq2Vec generates optimized statistical vectors of raw RNA sequences by capturing short and long range relations of nucleotide k-mers. Using sequence vectors generated by GeneticSeq2Vec scheme, Long Short Term Memory layers extract most informative features, weighting of which on the basis of discriminative potential for accurate multi-compartment localization prediction is performed using attention layer. Through reverse engineering, weights of statistical feature space are mapped to nucleotide k-mers patterns to make multi-compartment localization prediction decision making transparent and explainable for different RNA classes and species. Empirical evaluation indicates that EL-RMLocNet outperforms state-of-the-art predictor for subcellular localization prediction of 4 different RNA classes by an average accuracy figure of 8% for Homo Sapiens species and 6% for Mus Musculus species. EL-RMLocN

In this study, NASA’s VIIRS (Visible Infrared Imaging Radiometer Suite) fire hotspots and data of the Swedish Civil Contingencies Agency (MSB), collected between 2012 and 2018, was integrated to characterize waste fire incidents that were detected by VIIRS and reported to MSB (DaR), detected by VIIRS but not reported to MSB (DbNR) and that are reported to MSB but not detected by VIIRS (RbND). Results show that the average number of open waste fire incidents per million capita per year (AFIPMC) in Sweden, for the period 2012–2018, ranges from 2.4 to 4.7. Although a weak correlation exists (r = 0.44, P = 0.1563, one tailed) between years and number of fire incidents (MSB + VIIRS fires), a continuous increase in number of fire incidents was recorded between 2014 and 2018. It is concluded that the use of satellite data of fire anomalies, in-combination with the use of incident reports, will help in formalizing more reliable and comprehensive waste fire statistics. Another focus area of the article is to consolidate the recommendations and routines for safe storage of waste and biofuels and to present the lessons that can be learnt from past fire incidents. The article also discusses the technical, political, economic, social, and practical aspects of waste fires and provide a baseline for future research and experimentation.

nbsp Teks puisi nbsp adalah nbsp suatu nbsp bentuk nbsp pengekspresian nbsp kebahasaan nbsp dari pengalaman nbsp yang nbsp bersifat nbsp imajinatif nbsp yang nbsp kata-katanya nbsp disusun nbsp menurut syarat-syarat nbsp yang nbsp tertentu yaitu diksi majas citraan dan rima. Pengekspresian puisi bisa tentang apapun pengalaman hidup lagu novel atau keindahan alam. Dalam hal ini puisi-puisi yang ditulis bersumber dari keindahan alam atau tentang ekologi. Penelitian ini berfokus pada analisis diksi majas makna dan ekologi sastra. Hal ini ditinjau dari penggunaan setiap fokus yaitu pemilihan diksi majas pemaknaan dan ekologi sastra dalam teks puisi karya siswa kelas VIII SMP Negeri 2 Plandaan. Tahap awal penelitian ini diawali dengan menganalis data dan dilanjutkan dengan pengelompokan data sesuai dengan intrumen pengambilan data. Pada penelitian ini dianalisis 62 karya siswa kelas VIII SMP Negeri 2 Plandaan. Dari hasil analisis ditemukan diksi majas makna dan ekologi sastra yang digunakan. Dalam penelitian ini ditemukan 518 diksi denotatif 216 diksi konotatif. Kebanyakan siswa memilih diksi denotatif dalam hal ini siswa tidak banyak memilih kata-kata kiasan. Menggunakan Bahasa yang sederhana menjadi pilihan untuk dituliskan dalam puisi. Hasil analisis majas ditemukan 35 majas metafora 119 majas personifikasi 10 majas simile 11 majas hiperbola. Hasil penelitian ini menunjukkan penggunaan majas personifikasi yang sangat sering digunakan dalam puis--puisi karya siswa. Hal tersebut dapat dimaklumi bahwa majas metafora adalah majas yang paling mudah untuk dipahami bagi siswa maka siswa cenderung menggunakan sesuatu yang dianggap mudah. nbsp nbsp Hasil analisis makna pada karya siswa memberikan penggambaran lingkungan penulis dalam puisi. Makna dalam puisi menggunakan pilihan diksi yang sederhana sehingga pemaknaan menjadi mudah dengan secara langsung menggambarkan secara gamblang. Minimnya penggunaan kata kiasan menyebabkan pembaca mendapatkan makna dalam satu sudut pandang

This research study focuses on the design of variable rate liquid fertilizer applicator which can measure the NPK status of soil and applies N, P and K nutrients simultaneously at separate variable rates around the oil palm trees in the form of aqueous solutions of straight fertilizers having single nutrient. A fertilizer flow control and spray system was designed to apply liquid fertilizer around the oil palm tree on a 5 m × 5 m (25 m2) area of most effective roots. On the basis of simulation and calculations, six 8006 flat fan nozzles were selected to maintain the adequate swath coverage of fertilizer spray. The nozzles 1–3 were mounted vertically on the horizontal boom to spray on the machine side of an oil palm tree, i.e. the side of an oil palm where the machine is passing through. Whereas nozzles 4–6 were fixed at − 22°, − 21° and − 20° angles with respect to the horizontal plane on a 45° angled boom to spray across the tree using trajectory method. An average simulated liquid velocity of 14.05 m/s from each nozzle was found to spray at 2.5 m distance across the tree using trajectory method. Thus an improved distribution of fertilizer around the tree at 25 m2 area of most effective roots can be achieved using this system. Mechanical structure of the applicator which carries all the equipment and fertilizer tanks was designed by using the finite element analysis. Minimum safety factors of 3.13 and 11.34 were found for mechanical structure and fertilizer tank assembly respectively which were satisfactory to bear the necessary loads during field operation.

El sector ganadero de Costa Rica tiene 37.171 fincas distribuidas en fincas para carne (52,3), para doble propósito (31,3) y leche (16,2). La actividad contribuye con 2.445,35 Gg de CO2eq (22,5%) de las emisiones totales del país y de las cuales el 42% corresponden a las fincas productoras de leche. En ese sentido, es importante realizar una evaluación de las emisiones de gases de efecto invernadero (GEI) en los sistemas de producción de leche para identificar las oportunidades que contribuyan con la mitigación al cambio climático y la estimación de factores de emisión local de metano entérico en vacas lecheras con distinto perfil genético. La fermentación entérica fue la principal fuente de emisión de GEI (50%) en las fincas y la menor huella de carbono de leche se encontró en las fincas con mayor nivel de intensificación. La estructura del hato con una mayor proporción de vacas en ordeño (75-80%) y una tasa de reemplazo del 20% reduce entre 20 y 30% las emisiones de GEI en las fincas. Las vacas F1 (50% Bos taurus + 50% Bos indicus) mostraron una menor emisión de metano (diaria y anual) que las vacas con mayor genética europea. Las vacas lecheras presentaron una emisión de metano entérico y un factor de conversión de metano diferente entre el periodo de lactancia y seco. Además, ambos indicadores cambiaron a lo largo del periodo de lactancia. Este comportamiento tiene relación con el consumo y calidad de la materia seca de la dieta de las vacas. Dichas consideraciones contribuirán en una mejor estimación de metano entérico en vacas lecheras (diaria o anual). Este conocimiento y herramientas aportan al desarrollo de una ganadería competitiva con potencial de compensar sus emisiones de GEI.

Objective. The destructive activities of man have become obvious in the Northern parts of Nigeria where a terrorist group known as Boko Haram and Fulani herdsmen have engaged in destructive activities like burning down of churches, schools as well as destruction of lives and property in some of the higher institutions in Northern Nigeria. Libraries and their resources have also been affected at some points.Method. Based on the expository nature of this study the use of qualitative and quantitative research methodology was employed. The population of the study consists of all the librarians and library officer categories of personnel of the public libraries in North-west Nigeria. However, only 74 of these personnel that were accessible at the headquarters of the six public libraries were selected for the study by means of purposive sampling techniques. This sampling method is largely limited to exploratory research.Results. The reliability is as follows: Disaster Incident Experience= .551; Disaster Risk Management=.603; Disaster Management Facilities/equip=.775; Staff opinion= .533. Based on the above mentioned, it is noteworthy to say that it is only Disaster management facility that has higher score of reliability while others are satisfactorily reliable. Conclusions. It will therefore be proper for these libraries to be conscious of the adverse effects of disaster The overall findings have demonstrated that public libraries in the North have experienced disasters at one point in time or the other. However, they are not quite prepared to manage disaster as seen in the responses., Meta: As atividades destrutivas do homem tornaram-se óbvias nas partes do norte da Nigéria, onde um grupo terrorista conhecido como Boko Haram e os pastores Fulani se envolveram em atividades destrutivas, como queima de igrejas, escolas, bem como destruição de vidas e propriedades em algumas das regiões mais altas. instituições no norte da Nigéria. As bibliotecas e seus recursos também foram afetados em alguns pontos.Método. Com base na natureza expositiva deste estudo, o uso de metodologia de pesquisa qualitativa e quantitativa foi empregado. A população do estudo consiste em todas as categorias de bibliotecários e bibliotecários das bibliotecas públicas no noroeste da Nigéria.Resultados. A confiabilidade é a seguinte: Experiência de Incidente de Desastre = 0,551; Gestão de Risco de Desastres=0,603; Instalações/equipamento de gerenciamento de desastres=0,775; Opinião da equipe = 0,533. Com base no exposto acima, é importante dizer que apenas a instalação de gerenciamento de desastres possui maior pontuação de confiabilidade, enquanto outras são confiáveis satisfatoriamente.Conclusões. Portanto, será apropriado que essas bibliotecas estejam cientes dos efeitos adversos do desastre. Os resultados gerais demonstraram que as bibliotecas públicas no Norte sofreram desastres em um ponto no tempo ou outro. No entanto, eles não estão totalmente preparados para lidar com desastres, como visto nas respostas., Objetivo. Las actividades destructivas del hombre se han vuelto evidentes en las partes del norte de Nigeria, donde un grupo terrorista conocido como Boko Haram y pastores Fulani se han involucrado en actividades destructivas como el incendio de iglesias, escuelas y la destrucción de vidas y propiedades. Bibliotecas y sus recursos se han visto afectados.Métodos. Basado en la naturaleza expositiva de este estudio, se usó una metodología de investigación cualitativa y cuantitativa. La población del estudio consta de todas las categorías de bibliotecarios y oficiales de bibliotecas del personal de las bibliotecas públicas en el noroeste de Nigeria. Resultados. La confiabilidad es la siguiente: Experiencia en incidentes de desastres = .551; Gestión del Riesgo de Desastres=.603; Instalaciones de Manejo de Desastres/equipo=.775; Opinión del personal= .533.

Objective: Worldwide, millennials are taking over the online shopping since the first lockdown and the endemic has made changes in customer experience (CX) in the fashion retail industry. Based on the stimulus-organism-response (SOR) approach developed by Mehrabian and Russel (1974), the purpose of this study is to investigate the effect of experiential state (e.g., cognitive experiential state, affective experiential state) through attitudinal state (e.g., e-trust, e-satisfaction) to behavioural state (e.g., e-loyalty). Methodology: A self-administered questionnaire (SAQ) survey (e.g., e-questionnaire) is conducted using convenience sampling. This descriptive study includes the Malaysian millennials (i.e., age between 25 to 40 years old) who are residing in Klang Valley area and have at least one-time online shopping experience via the premium fashion brands’ mobile application (e.g., Zalora, Uniqlo, H&M, Adidas, Charles & Keith, Monki, etc). The structural equation modeling sampling formula are used to determine sample size and 250 e-questionnaires are collected. To data analysis and test the research hypotheses, SPSS 22.0 and Smart PLS 2.0 software are used. Findings: The findings in this study offer guidelines on how to utilize digitalization to leverage CX and the relevant suggestions are provided. Implications: This study contributes to academia and the industry, discussing both theoretical contributions and managerial implications.

Against many odds, the Hausa filer industry alias Kannywood has come of age. The film industry survives several pressing challenges from within and outside Nigeria, perhaps more than its counterparts anywhere else. Although there is no denying that the quality of its output has significantly improved, its survival has little or nothing to do with that. Many critics, including the Motion Pictures Practitioners Association of Nigeria (MOPPAN) leadership, call 'bad' films, are still being made. Romantic movies, laden with a typical and predictable pattern of the love triangle, song and dance sequences among other appropriated and plagiarized Bollywood modalities, remain the favourite of producers and arguably that of the audience. However, according to some surveys, such films lack merit in the realm of critical film discourse in Africa and beyond. This article is set out to discuss this issue through a content analysis of a recent film titled Sareena (Nuhu 2019). The movie, released in 2019, is a bloated, implausible melodrama and a direct mimicry of a famous Indian film, Kaabil (Gupta 2017).