Examining financial distress of the Vietnamese listed firms using accounting-based models.

Financial distress is generally considered the most severe consequence for firms with poor financial performance. The emergence of the Covid-19 pandemic has adversely impacted the global business system and exacerbated the number of financially distressed firms in many countries. Only firms with strong financial fundamentals can survive extreme events such as the Covid-19 pandemic and the ongoing Russia-Ukraine conflict. Vietnam is no exception. However, studies examining financial distress using accounting-based indicators, particularly at the industry level, have largely been ignored in the Vietnamese context, particularly with the emergence of the Covid-19 pandemic. This study, therefore, comprehensively examines financial distress for 500 Vietnamese listed firms during the 2012-2021 period. Our study uses interest coverage and times-interest-earned ratios to proxy a firm's financial distress. First, our findings confirm the validity of Altman's Z"- score model in Vietnam only when the interest coverage ratio is used as a proxy for financial distress. Second, our empirical findings indicate that only four financial ratios, including EBIT/Total Assets, Net Income/Total Assets, Total Liabilities/Total Assets, and Total Equity/Total Liabilities, can be used in predicting financial distress in Vietnam. Third, our analysis at the industry level indicates that the "Construction & Real Estates" industry, a significant contributor to the national economy, exhibits the most significant risk exposure, particularly during the Covid-19 pandemic. Policy implications have emerged based on the findings from this study.

The role of lignin and lignin-based materials in sustainable construction - A comprehensive review.

The construction industry in the 21st century faces numerous global challenges associated with growing concerns for the environment. Therefore, this review focuses on the role of lignin and its derivatives in sustainable construction. Lignin's properties are defined in terms of their structure/property relationships and how structural differences arising from lignin extraction methods influence its application within the construction sector. Lignin and lignin composites allow the partial replacement of petroleum products, making the final materials and the entire construction sector more sustainable. The latest technological developments associated with cement composites, rigid polyurethane foams, paints and coatings, phenolic or epoxy resins, and bitumen replacements are discussed in terms of key engineering parameters. The application of life cycle assessment in construction, which is important from the point of view of estimating the environmental impact of various solutions and materials, is also discussed.

Early Impacts of the COVID-19 Pandemic on the United States Construction Industry.

The COVID-19 pandemic has been the largest global health crisis in decades. Apart from the unprecedented number of deaths and hospitalizations, the pandemic has resulted in economic slowdowns, widespread business disruptions, and significant hardships. This study focused on investigating the early impacts of the COVID-19 pandemic on the U.S. construction industry since the declaration of the national emergency on 13 March 2020. The study objectives were achieved through 34 telephone interviews with project managers, engineers, designers, and superintendents that represented different states and distinct industry sectors in the United States (U.S.). The interviewees offered information on their experience with the pandemic, including the general and adverse effects experienced, new opportunities created, and risk management efforts being undertaken. The reported adverse effects included significant delays on projects, inability to secure materials on time, reduction in productivity rates, material price escalations, and others. The new opportunities that were created included projects involving the fast-track construction of medical facilities, construction of residential buildings, transportation-related work, and opportunities to recruit skilled workers. The risk management measures that were widely adopted included measures to enhance safety and reduce other project risks. The safety measures adopted included requiring employees to wear cloth face masks, adoption of social distancing protocols, staggering of construction operations, offering COVID-19-related training, administering temperature checks prior to entry into the workplace, and others. Measures to manage other project risks included the formation of a task force team to review the evolving pandemic and offer recommendations, advocating that construction businesses be deemed essential to combat delays and taking advantage of government relief programs. The study findings will be useful to industry stakeholders interested in understanding the early impacts of the pandemic on the construction industry. Industry stakeholders may also build upon the reported findings and establish best practices for continued safe and productive operations.

Automated technique for high-pressure water-based window cleaning and accompanying parametric study.

The maintenance of buildings has become an important issue with the construction of many high-rise buildings in recent years. However, the cleaning of the outer walls of buildings is performed in highly hazardous environments over long periods, and many accidents occur each year. Various robots are being studied and developed to reduce these incidents and to relieve workers from hazardous tasks. Herein, we propose a method of spraying high-pressure water using a pump and nozzle, which differs from conventional methods. The cleaning performance parameters, such as water pressure, spray angle, and spray distance, were optimized using the Taguchi method. Cleaning experiments were performed on window specimens that were contaminated artificially. The cleaning performance of the proposed method was evaluated using the image-evaluation method. The optimum condition was determined based on the results of a sensitive analysis performed on the image data. In addition, the reaction force due to high pressure and impact force on the specimens were investigated. These forces were not sufficient to affect the propeller thrust or cause damage to the building's surface. We expect to perform field tests in the near future based on the output of this research.

Impacts of sediment derived from erosion of partially-constructed road on aquatic organisms in a tropical river: The Río San Juan, Nicaragua and Costa Rica.

Throughout the humid tropics, increased land disturbance and concomitant road construction increases erosion and sediment delivery to rivers. Building road networks in developing countries is commonly a priority for international development funding based on anticipated socio-economic benefits. Yet the resulting erosion from roads, which recent studies have shown result in at least ten-fold increases in erosion rates, is not fully accounted for. While effects of road-derived sediment on aquatic ecosystems have been documented in temperate climates, little has been published on the effects of road-induced sediment on aquatic ecosystems in developing countries of the tropics. We studied periphyton biomass and macroinvertebrate communities on the deltas of Río San Juan tributaries, comparing north-bank tributaries draining undisturbed rain forest with south-bank tributaries receiving runoff from a partially-built road experiencing rapid erosion. Periphyton biomass, richness and abundance of macroinvertebrates overall, and richness and abundance of Ephemeroptera, Plecoptera and Trichoptera were higher on the north-bank tributary deltas than the south-bank tributary deltas. These findings were consistent with prior studies in temperate climates showing detrimental effects of road-derived fine sediment on aquatic organisms. A Non-Metric Multidimensional Scaling (NMDS) analysis showed the impacted community on the south-bank deltas was influenced by poorly-sorted substrate with greater proportions of fine sediment and higher water temperatures.

Method for extraction of airborne LiDAR point cloud buildings based on segmentation.

The LiDAR technology is a means of urban 3D modeling in recent years, and the extraction of buildings is a key step in urban 3D modeling. In view of the complexity of most airborne LiDAR building point cloud extraction algorithms that need to combine multiple feature parameters, this study proposes a building point cloud extraction method based on the combination of the Point Cloud Library (PCL) region growth segmentation and the histogram. The filtered LiDAR point cloud is segmented by using the PCL region growth method, and then the local normal vector and direction cosine are calculated for each cluster after segmentation. Finally, the histogram is generated to effectively separate the building point cloud from the non-building.Two sets of airborne LiDAR data in the south and west parts of Tokushima, Japan, are used to test the feasibility of the proposed method. The results are compared with those of the commercial software TerraSolid and the K-means algorithm. Results show that the proposed extraction algorithm has lower type I and II errors and better extraction effect than that of the TerraSolid and the K-means algorithm.

Hardhat-Wearing Detection Based on a Lightweight Convolutional Neural Network with Multi-Scale Features and a Top-Down Module.

Construction sites are dangerous due to the complex interaction of workers with equipment, building materials, vehicles, etc. As a kind of protective gear, hardhats are crucial for the safety of people on construction sites. Therefore, it is necessary for administrators to identify the people that do not wear hardhats and send out alarms to them. As manual inspection is labor-intensive and expensive, it is ideal to handle this issue by a real-time automatic detector. As such, in this paper, we present an end-to-end convolutional neural network to solve the problem of detecting if workers are wearing hardhats. The proposed method focuses on localizing a person's head and deciding whether they are wearing a hardhat. The MobileNet model is employed as the backbone network, which allows the detector to run in real time. A top-down module is leveraged to enhance the feature-extraction process. Finally, heads with and without hardhats are detected on multi-scale features using a residual-block-based prediction module. Experimental results on a dataset that we have established show that the proposed method could produce an average precision of 87.4%/89.4% at 62 frames per second for detecting people without/with a hardhat worn on the head.

Energy self-sufficient households with photovoltaics and electric vehicles are feasible in temperate climate.

The idea that households produce and consume their own energy, that is, energy self-sufficiency at a very local level, captures the popular imagination and commands political support across parts of Europe. This paper investigates the technical and economic feasibility of household energy self-sufficiency in Switzerland, which can be seen as representative for other regions with a temperate climate, by 2050. We compare sixteen cases that vary across four dimensions: household type, building type, electricity demand reduction, and passenger vehicle use patterns. We assume that photovoltaic (PV) electricity supplies all energy, which implies a complete shift away from fossil fuel based heating and internal combustion engine vehicles. Two energy storage technologies are considered: short-term storage in lithium-ion batteries and long-term storage with hydrogen, requiring an electrolyzer, storage tank, and a fuel cell for electricity conversion. We examine technological feasibility and total system costs for self-sufficient households compared to base cases that rely on fossil fuels and the existing power grid. PV efficiency and available rooftop/facade area are most critical with respect to the overall energy balance. Single-family dwellings with profound electricity demand reduction and urban mobility patterns achieve self-sufficiency most easily. Multi-family buildings with conventional electricity demand and rural mobility patterns can only be self-sufficient if PV efficiency increases, and all of the roof plus most of the facade can be covered with PV. All self-sufficient cases are technically feasible but more expensive than fully electrified grid-connected cases. Self-sufficiency may even become cost-competitive in some cases depending on storage and fossil fuel prices. Thus, if political measures improve their financial attractiveness or individuals decide to shoulder the necessary investments, self-sufficient buildings may start to become increasingly prevalent.

Data-Driven Living Spaces' Heating Dynamics Modeling in Smart Buildings using Machine Learning-Based Identification.

Modeling and control of the heating feature of living spaces remain challenging tasks because of the intrinsic nonlinear nature of the involved processes as well as the strong nonlinearity of the entailed dynamic parameters in those processes. Although nowadays, adaptive heating controllers represent a crucial need for smart building energy management systems (SBEMS) as well as an appealing perspective for their effectiveness in optimizing energy efficiency, unfortunately, the leakage of models competent in handling the complexity of real living spaces' heating processes means the control strategies implemented in most SBEMSs are still conventional. Within this context and by considering that the living space's occupation rate (i.e., by users or residents) may affect the model and the issued heating control strategy of the concerned living space, we have investigated the design and implementation of a data-driven machine learning-based identification of the building's living space dynamic heating conduct, taking into account the occupancy (by the residents) of the heated space. In fact, the proposed modeling strategy takes advantage, on the one hand, of the forecasting capacity of the time-series of the nonlinear autoregressive exogenous (NARX) model, and on the other hand, from the multi-layer perceptron's (MLP) learning and generalization skills. The proposed approach has been implemented and applied for modeling the dynamic heating conduct of a real five-floor building's living spaces located at Senart Campus of University Paris-Est Créteil (UPEC), taking into account their occupancy (by users of this public building). The obtained results assessing the accuracy and addictiveness of the investigated hybrid machine learning-based approach are reported and discussed.

Incorporating Worker Awareness in the Generation of Hazard Proximity Warnings.

Proximity warning systems for construction sites do not consider whether workers are already aware of the hazard prior to issuing warnings. This can generate redundant and distracting alarms that interfere with worker ability to adopt timely and appropriate avoidance measures; and cause alarm fatigue, which instigates workers to habitually disable the system or ignore the alarms; thereby increasing the risk of injury. Thus, this paper integrates the field-of-view of workers as a proxy for hazard awareness to develop an improved hazard proximity warning system for construction sites. The research first developed a rule-based model for the warning generation, which was followed by a virtual experiment to evaluate the integration of worker field-of-view in alarm generation. Based on these findings, an improved hazard proximity warning system incorporating worker field-of-view was developed for field applications that utilizes wearable inertial measurement units and localization sensors. The system's effectiveness is illustrated through several case studies. This research provides a fresh perspective to the growing adoption of wearable sensors by incorporating the awareness of workers into the generation of hazard alarms. The proposed system is anticipated to reduce unnecessary and distracting alarms which can potentially lead to superior safety performance in construction.

Spatiotemporal characteristics and driving forces of construction land expansion in Yangtze River economic belt, China.

With rapid economic and population growth, construction land expansion in Yangtze River economic belt in China becomes substantial, carrying significant social and economic implications. This research uses Expansion Speed Index and Expansion Intensity Index to examine spatiotemporal characteristics of construction land expansion in the Yangtze River economic belt from 2000 to 2017. Based on a STIRPAT model, driving forces of construction land expansion are measured by Principal Component Analysis and Ordinary Least Square regression. The results show that: (1) there is a clear expansion pattern regarding the time sequence in provinces/cities of the Yangtze River economic belt, with rapid expansion in the initial stage, moderate expansion in the middle stage and rapid expansion in the later stage. (2) Spatial analysis demonstrates first expansion in the lower reaches in the early stage, rapid expansion of the upper reaches in the middle and later stage, and steady expansion of the middle reaches throughout the research period. (3)There are statistical significant correlations between construction land expansion and GDP, social fixed asset investments, population at the end of the year, population urbanization rate, per capita road area, and number of scientific and technological professionals as well as secondary and tertiary industry values. Of these factors, GDP, social fixed asset investments, population urbanization rate and second industry value are important common driving forces of construction land expansion in this region. The research findings have significant policy implications particularly on coordinated development of urban agglomerations and sustainable industry upgrading when construction land expansion is concerned.

Distinguishing Characteristics of Corruption Risks in Iranian Construction Projects: A Weighted Correlation Network Analysis.

The construction industry consistently ranks amongst the highest contributors to global gross domestic product, as well as, amongst the most corrupt. Corruption therefore inflicts significant risk on construction activities, and overall economic development. These facts are widely known, but the various sources and nature of corruption risks endemic to the Iranian construction industry, along with the degree to which such risks manifest, and the strength of their impact, remain undescribed. To address the gap, a mixed methods approach is used; with a questionnaire, 103 responses were received, and these were followed up with semi-structured interviews. Results were processed using social network analysis. Four major corruption risks were identified: (1) procedural violations in awarding contracts, (2) misuse of contractual arrangements, (3) neglect of project management principles, and, (4) irrational decision making. While corruption risks in Iran align with those found in other countries, with funds being misappropriated for financial gain, Iran also shows a strong inclination to champion projects that serve the government's political agenda. Root cause identification of corruption risks, namely, the noticeable impact of authoritarianism on project selection in Iran, over criterion of economic benefit or social good, is a significant outcome of this study.

Integrating continuous differential evolution with discrete local search for meander line RFID antenna design.

The automated design of meander line RFID antennas is a discrete self-avoiding walk (SAW) problem for which efficiency is to be maximized while resonant frequency is to be minimized. This work presents a novel exploration of how discrete local search may be incorporated into a continuous solver such as differential evolution (DE). A prior DE algorithm for this problem that incorporates an adaptive solution encoding and a bias favoring antennas with low resonant frequency is extended by the addition of the backbite local search operator and a variety of schemes for reintroducing modified designs into the DE population. The algorithm is extremely competitive with an existing ACO approach and the technique is transferable to other SAW problems and other continuous solvers. The findings indicate that careful reintegration of discrete local search results into the continuous population is necessary for effective performance.

Using Improved Principal Component Analysis to Explore Construction Accident Situations from the Multi-Dimensional Perspective: A Chinese Study.

The improvement of the macro-level accident situation in the Chinese construction industry is currently an urgent task for the government due to the high accident rate. This study intends to use improved principal component analysis to explore the accident situations in the Chinese construction industry from a multi-dimensional perspective, aiming at providing targeted direction on the improvement of the accident situation for the government. Six composite indicators that can quantify the accident situation are firstly selected based on a wide review of the literature and interviews with safety experts, with the original data collected from China institutions. The classical principal component analysis is then improved to examine the correlations between indicators, and further to evaluate accident situations in China provinces. Finally, the features of accident situations are explored and analyzed from a multi-dimensional perspective. The findings show that the improved principal component analysis can retain more dispersion degree information of the original data. Meanwhile, three principal components including the accident frequency, trend, and severity were extracted to quantify the accident situation, and a hierarchical indicator system for the comprehensive evaluation of the accident situation was constructed to deeper understand multi-dimensional characteristics of China's accident situations. Furthermore, there exist great regional differences of accident situations in Chinese provinces. From the overall perspective, the accident situation shows a declining trend from the western backward region to the highly developed eastern coastal region. This study provides a multi-dimensional perspective for the government to formulate safety regulations and improve the accident situation.

Crowdsourcing architectural beauty: Online photo frequency predicts building aesthetic ratings.

The aesthetic quality of the built environment is of paramount importance to the quality of life of an increasingly urbanizing population. However, a lack of data has hindered the development of comprehensive measures of perceived architectural beauty. In this paper, we demonstrate that the local frequency of geotagged photos posted by internet users in two photo-sharing websites strongly predict the beauty ratings of buildings. We conduct an independent beauty survey with respondents rating proprietary stock photos of 1,000 buildings across the United States. Buildings with higher ratings were found more likely to be geotagged with user-uploaded photos in both Google Maps and Flickr. This correlation also holds for the beauty rankings of raters who seldom upload materials to the internet. Objective architectural characteristics that predict higher average beauty ratings of buildings also positively covary with their internet photo frequency. These results validate the use of localized user-generated image uploads in photo-sharing sites to measure the aesthetic appeal of the urban environment in the study of architecture, real estate, urbanism, planning, and environmental psychology.

Risk factors for neck pain among forklift truck operators: a retrospective cohort study.

BACKGROUND: No previous research has been performed into neck pain among forklift operators. This is a common complaint among these workers, who number around 150,000 in Sweden and six million in Europe. The aim of the study was to examine long-term exposure to unnatural neck positions among forklift operators as a risk factor for neck pain. METHODS: A retrospective cohort study was conducted of all eligible employees at a high-level warehouse. Forklift operators and office workers answered an 18-page questionnaire comprising questions about joint pain, work tasks, work postures and year of start for all items. By using person years in the exposed and less-exposed groups before start of neck pain we were able to calculate Incident Rate ratios for various exposures. RESULTS: Forty nine percent of the forklift operators reported having experienced neck pain compared to 30 % of office workers. Being a forklift operator was associated with an increased risk of neck pain (OR = 5.1, 95% CI 1.4-18.2). Holding the head in an unnatural position resulted in significantly increased risks for neck pain, irrespective of type of position. The risks for neck pain remained after taking other ergonomic exposures and psychosocial aspects into consideration. CONCLUSIONS: This is the first published study showing that forklift operators have an increased risk of neck pain. The results are therefore of significance for improving work schedules, the adjustment of work tasks for these workers and the design of the vehicles.

Factors associated with the prevalence of back pain and work absence in shipyard workers.

BACKGROUND: We conducted a questionnaire survey of shipyard workers to identify difficulties experienced due to orthopedic or musculoskeletal disorders. METHODS: The subjects were 375 workers (male, 361; female, 14) who worked for a single shipbuilding company. Questionnaire items covered the working environment, including work environment, working posture, and the weight of objects that the subject dealt with, as well as physical and lifestyle characteristics, namely smoking habits, drinking habits, sleeping hours, medications, exercise habits, and any weight gain of 20 kg or more since the age of 20. Subjects were also asked to indicate if they regularly experienced any of 17 listed difficulties in their daily lives, and to use an illustration of the human body to mark any body parts that were painful or hard to move. RESULTS: The mean age was 41.8 years (19-73 years). The lower and/or upper back was the most frequent site of pain (46.5%), followed by the shoulders (11.4%), knees (9.6%), and neck (5.3%). Maintaining a half-sitting posture was the most problematic activity of daily living. Back pain was less frequent in subjects who exercised regularly, and more common in those who worked with heavy loads or in narrow spaces. A multinomial logistic regression analysis showed that absence from work was more common in subjects with back pain who had gained weight since their youth, who smoked, who used fire while welding metal, or who worked in a lying posture. While 35.4% of subjects had experienced absence from work due to musculoskeletal pain, only 5.1% were permitted by their employer to alter their work content or reduce their workload. CONCLUSIONS: These results indicate that a large number of shipyard workers have difficulties in their work and daily life activities due to back pain. To prevent worsening of pain and to reduce work absence, it is important to provide appropriate training to minimize the risk factors for back pain that were identified in this study.

Strategies of employees in the construction industry to increase their sustainable employability.

BACKGROUND: The aging work force makes sustainable employability (SE) of workers a priority. However, it is unknown to what extent employees use implemented SE measures. OBJECTIVE: To determine the utilization of 1) SE measures offered by employers, 2) employee SE strategies, and 3) to identify barriers and facilitators of SE strategies. METHODS: Survey data were collected among 731 blue collar and 879 white collar workers to determine the utilization of employer SE measures. Focus groups were held with 16 blue collar and 17 white collar workers to identify employee SE strategies and their barriers and facilitators. RESULTS: Utilization of employer SE measures was highest for personal development measures. Strategies applied by blue collar workers included using equipment, suggesting improvements of their working conditions, and seeking promotion to a less physically demanding job. White collar workers named engaging in leisure time physical activity and seeking an adequate work-life balance. Implementation of these strategies was influenced by employee awareness and self-efficacy, the accessibility and costs and benefits of the strategy, management support and company culture. CONCLUSION: Usage of employer SE measures was generally low and recommendations are given for both blue and white collar workers to improve SE strategies.

Asbestos Consumption in Mongolia: 1996-2014.

Asbestos is still used in Mongolia in the energy and construction sectors, among others. However, limited data is available on asbestos consumption and asbestos-related disease in Mongolia. The purpose of this paper is to present the available information on the importation of asbestos into Mongolia. We used data on annual asbestos imports between 1996 and 2014 from Mongolian Customs Statistics and the National Council on Toxic and Hazardous Substances Affairs. The uses of this material are also presented with respect to chrysotile alone. Most asbestos is used for construction. Mongolia started using asbestos in the energy and construction industries as thermal insulation in 1961. Asbestos is still allowed for use in Mongolia under the Law on Toxic and Hazards Substances. There are no asbestos mines in Mongolia, and the manufacture of asbestos-containing materials does not take place there. Thus, asbestos is mainly imported from China and Russia. Mongolia used 44,422 metric tons of asbestos-containing materials between 1996 and 2014. In Mongolia, with the current use of asbestos, there will be a continuing risk of developing asbestos-related diseases from past use, and proper oversight of asbestos-involving activities and the safe removal and disposal of asbestos must be considered.

Automobile, construction and entertainment business sector influences on sedentary lifestyles.

Sedentary lifestyles contribute to premature death and health inequalities. Researchers have studied personal and community-level determinants of inactivity but few have analyzed corporate influences. To reframe the public health debate on inactivity and open new doors for public sector intervention, we conducted a scoping review of evidence from several disciplines to describe how the business and political practices of the automobile, construction, and entertainment sectors have encouraged sedentary lifestyles. In the last 50 years, these industries have found it profitable to produce motor vehicles, housing, and entertainment, which intentionally or unintentionally discourage physical activity. Ceding primary authority for policy decisions in these sectors to the market-based economy has enabled the growth of powerful lobbies that encourage and maintain sedentary lifestyles. To counteract these influences, public health and civil society need to confront more upstream economic and social determinants of sedentary lifestyles. Building on evidence from efforts to change harmful tobacco, alcohol and food industry practices, we propose the creation of research and policy agendas that contribute to public health practice that can modify corporate practices that contribute to physical, social and political environments that discourage physical activity.