Specific Skill Requirements within Prefabricated Residential Construction: Stakeholders’ Perspectives

Skill availability is an important component in the uptake of prefabrication and plays a crucial role in housing supply. However, the challenge is that the demand for housing has outgrown the availability of specifically trained workers. This challenge is not unique to New Zealand; many developed countries worldwide are facing similar issues. Therefore, the purpose of this study is to determine relevant skills in the prefabricated residential construction sector in New Zealand (NZ) and suggest improvement measures from the standpoint of industry stakeholders. The study adopted a semi-structured online survey and administered it to multiple construction industry practitioners. The study found the training of the construction workforce as one significant area of focus. In addition, external sourcing of international prefabrication-specific skilled workers could improve the issues of skill shortages in the residential prefabrication sector. Furthermore, the study revealed that the barriers to healthier prefabrication uptake are closely linked to shortages in management, digital architecture and design, and vocational skills related to residential construction. The study has contributed to the current pool of knowledge by identifying skill issues in NZ’s prefabricated residential construction sector, classifying the major restraints limiting prefabrication implementation, and determining measures for increasing industry uptake. It is anticipated that this will help construction organizations and the wider industry develop strategic goals and a roadmap for meeting the skill requirements in NZ. Training policies and programmes can be developed with focus on crucial prefabrication skill requirements at governmental level. Curriculum reviews are recommended for uptake by academic and vocational institutions.

New risk measures for variance distortion and catastrophic financial risk measures

In recent years, expectation distortion risk measures have been widely used in financial and insurance applications due to their attractive properties. The author introduced two new classes of financial risk measures “VaR raised to the power of t” and “ES raised to the power of t” in his works and also investigated the issue of the belonging of these risk measures to the class of risk measures of expectation distortion, and described the corresponding distortion functions. The aim of this study is to introduce a new concept of variance distortion risk measures, which opens up a significant area for investigating the properties of these risk measures that may be useful in applications. The paper proposes a method of finding new variance distortion risk measures that can be used to acquire risk measures with special properties. As a result of the study, it was found that the class of risk measures of variance distortion includes risk measures that are in a certain way related to “VaR raised to the power of t” and “ES raised to the power of t” measures. The article describes the composite method for constructing new variance distortion functions and corresponding distortion risk measures. This method is used to build a large set of examples of variance distortion risk measures that can be used in assessing certain financial risks of a catastrophic nature. The author concludes that the study of the variance distortion risk measures introduced in this paper can be used both for the development of theoretical risk management methods and in the practice of business risk management in assessing unlikely risks of high catastrophe.

Strategic Potential of Agricultural Waste as a Feedstock for Biofuels Production in Ukraine

Ukraine’s agriculture is a leading sector of the national economy. Ukraine has a significant area (603628 km2), 70.9% of which are agricultural lands. Quality soil and good climatic conditions create favorable conditions for the development of crop and livestock production. The generation of a large amount of organic waste from agriculture opens wide opportunities for the development of the biogas technologies. The aim of the paper is to identify the main waste management trends in Ukraine based on data on waste generation and waste management and to calculate the strategic potential of agricultural waste as a feedstock for biofuels production. The resource potential of crop, livestock and processing waste has been considered and the necessity of its use for energy purposes has been substantiated. It has been determined that the greatest potential of agricultural waste that can be used for biogas production in Ukraine is concentrated in crop production. The livestock industry and processing enterprises also have a powerful feedstock base for biogas production. It has been determined that the agroindustrial sector of Ukraine produces significant amount of waste. As a result of the study, it has been found that the potential volume of biogas production from agricultural waste can replace 36.1% of natural gas consumption in Ukraine.

The floors of Smolensk churches of the 12th–13th centuries

In 17 of 21 Smolensk’ 12-13 century buildings there were found floor remains with different state of preservation. Some were found quite intact in significant area in situ, and other are represented with single findings of glazed tiles. Neither smalt, nor slate slabs were used in that buildings. In two churches there were found sandstone slabs, imitating the slate. In another two churches was mainly used lime mortar grouting. The most widespread materials for the decoration of the floors were the plinth and the glazed tiles. The plinth was mostly used for decorating of the main area of the buildings, for example, in naos and galleries. The glazed tiles more often were found in the separated compartments: choirs, apses and in some special parts of galleries. There is no great variety of shapes and dimensions of ceramic tiles of that time. The square and triangle ones prevail. Their side dimension is normally 11-12 sm, the thickness is 1,8 – 2,8 sm. This proportion remains unchanged, unlike plinth size, which during 12 century becomes smaller and smaller. And also some figured tiles were found in Smolensk’ buildings of that time.

Capturing and Evaluating the Effects of the Expansive Species Ailanthus altissima on Agro-Ecosystems on the Ionian Islands

There is a significant number of agricultural systems with rich and special biodiversity, characterized as high-nature-value farming systems (HNV) in the Ionian Islands region. These agro-ecosystems cover a significant area in this region and are divided in olive groves and vineyards which, in some cases, cover a significant part of the protected areas (Natura 2000 and SPA). There are solid olive groves but also a large number of scattered trees or clusters, as well as vineyards, which are largely identified as high-quality wine producers. Finally, there are smaller but extremely important examples of HNV, such as the Englouvi plateau in Lefkada. In this study, we propose a method to survey the spread of Ailanthus altissima in olive groves and vineyards (HNV areas) with the scope of evaluating the considered agro-ecosystems, based on the importance of ecosystems and ecosystem services they provide, and preparing a management plan for HNV areas.

Features of organization of production and management of airport enterprises

Development of a market economy in Russia is accompanied by active development of the transport sector, a huge role in which civil aviation plays.A significant area of the country and its uneven settlement, geographic and climatic diversity, the underdevelopment in some regions of land transport infrastructure cause the value of the use of air transport. Strategic development of modern air transport enterprises takes under non- stationarity external environment. The study analyzes the current state of development of civil aviation, the main functional relationships of the air transport market were determined, the analysis of functioning of Russian airports, which enabled us to identify the factors system, affecting the air transport passenger traffic.

Nanotechnological Advancement in Antimicrobial Drug Designing

The global spread of multidrug-resistant (MDR) microbial infections is currently one of the most severe risks to global public health, with 10 million fatalities expected by 2050 unless action is taken. Nanotechnology has revolutionized science and medicine. The reliance on nanotechnology is growing. Nanoparticles have distinct properties that improve biological, chemical, and physical properties studied for various uses. A significant area of attention in the synthesis of nanoscale modulators is the utilization of crude formulations, retro-synthesized, and pure chemicals, mainly from herbal sources, with fewer adverse effects. Green chemistry has devised a tangential technique for synthesizing metals and metal oxides to produce nanoparticles. Plant extracts (leaves, stems, and shoots) and microorganisms (bacteria, fungus, and yeast) are used as reducing intermediates to make nanoparticles. Studies in microbiology have shown that nanoparticles kill bacteria, fungi, viruses, and protozoa. These green nanoparticles contain antibacterial, antifungal, and anti-inflammatory effects. Most nanoparticles have high antibacterial properties, indicating they may be used to combat diseases and biological contaminants. These nanoparticles have antibacterial action against pathogenic microorganisms that cause serious illnesses, including multidrug-resistant pathogens. The current research will pave the way for future applications and improved methods for producing nanoparticles, paving the way for an innovative route in nano-life sciences with widespread recognition.

The clinical correlation of findings obtained by fine needle fenestration biopsy of the canine spleen with visible ultrasound changes

The medical records databases (March 2016 to March 2021) of the Faculty of Veterinary Medicine, University of Zagreb, were examined to determine the frequency and clinical relevance of cytological diagnoses from fine-needle fenestration biopsy (FNFB) of the spleen in dogs with visible ultrasound changes. Seventy-five dogs were divided into clinically relevant and irrelevant groups, according to the clinical relevance of the diagnosis. The incidence of clinically relevant diagnoses was 28/75 (37.3%). Malignant diagnoses were over-represented (23/28; 82.1%), followed by hemorrhages/hematomas (3/28; 10.7%) and suppurative inflammation (2/28; 7.1%). The most common malignancy was lymphoma (12/28; 42.9%). There was no correlation between the ultrasound lesions examined and the relevant cytological diagnoses, except in cases of patchy echo texture (P = 0.010). Lesion size greater than 1.74 cm had the highest sensitivity/specificity values (91.3%; 42.1%) with a significant area under the curve (AUC) of 0.68 (P = 0.029) for predicting clinically relevant findings. The complication rate due to bleeding was 2/130 (1.5%). In conclusion, splenic FNFB can be safely performed in dogs as it carries a low risk of development of complications. Specific ultrasonographic findings, such as patchy echo texture pattern, may increase the suspicion of the presence of neoplastic disease in the form of lymphoma. According to ROC analysis, lesion size greater than 1.74 increases the possibility of predicting clinically relevant findings.

Multi-Ideology ISIS/Jihadist White Supremacist (MIWS) Dataset for Multi-Class Extremism Text Classification

Social media platforms are a popular choice for extremist organizations to disseminate their perceptions, beliefs, and ideologies. This information is generally based on selective reporting and is subjective in content. However, the radical presentation of this disinformation and its outreach on social media leads to an increased number of susceptible audiences. Hence, detection of extremist text on social media platforms is a significant area of research. The unavailability of extremism text datasets is a challenge in online extremism research. The lack of emphasis on classifying extremism text into propaganda, radicalization, and recruitment classes is a challenge. The lack of data validation methods also challenges the accuracy of extremism detection. This research addresses these challenges and presents a seed dataset with a multi-ideology and multi-class extremism text dataset. This research presents the construction of a multi-ideology ISIS/Jihadist White supremacist (MIWS) dataset with recent tweets collected from Twitter. The presented dataset can be employed effectively and importantly to classify extremist text into popular types like propaganda, radicalization, and recruitment. Additionally, the seed dataset is statistically validated with a coherence score of Latent Dirichlet Allocation (LDA) and word mover’s distance using a pretrained Google News vector. The dataset shows effectiveness in its construction with good coherence scores within a topic and appropriate distance measures between topics. This dataset is the first publicly accessible multi-ideology, multi-class extremism text dataset to reinforce research on extremism text detection on social media platforms.

Learning to Train CNNs on Faulty ReRAM-based Manycore Accelerators

The growing popularity of convolutional neural networks (CNNs) has led to the search for efficient computational platforms to accelerate CNN training. Resistive random-access memory (ReRAM)-based manycore architectures offer a promising alternative to commonly used GPU-based platforms for training CNNs. However, due to the immature fabrication process and limited write endurance, ReRAMs suffer from different types of faults. This makes training of CNNs challenging as weights are misrepresented when they are mapped to faulty ReRAM cells. This results in unstable training, leading to unacceptably low accuracy for the trained model. Due to the distributed nature of the mapping of the individual bits of a weight to different ReRAM cells, faulty weights often lead to exploding gradients. This in turn introduces a positive feedback in the training loop, resulting in extremely large and unstable weights. In this paper, we propose a lightweight and reliable CNN training methodology using weight clipping to prevent this phenomenon and enable training even in the presence of many faults. Weight clipping prevents large weights from destabilizing CNN training and provides the backpropagation algorithm with the opportunity to compensate for the weights mapped to faulty cells. The proposed methodology achieves near-GPU accuracy without introducing significant area or performance overheads. Experimental evaluation indicates that weight clipping enables the successful training of CNNs in the presence of faults, while also reducing training time by 4 X on average compared to a conventional GPU platform. Moreover, we also demonstrate that weight clipping outperforms a recently proposed error correction code (ECC)-based method when training is carried out using faulty ReRAMs.