Markers of Alzheimer’s disease in handwriting

The aim of the research. Alzheimer’s disease is the most common form of dementia. One of the potential tools for early detection of the onset of the disease is the handwriting analysis. It can be a warning signal for a serious medical investigation. The dynamics of handwriting changes are also a good indicator of the progression of the disease and the eff ectiveness of therapy. Methods. The authors have developed two corresponding tests. The fi rst (AD-HS) allows the assessment of handwriting markers of cognitive impairment and Alzheimer’s disease from an available handwriting sample. The second (ADHC) is designed to assess dynamics by comparing two handwritten documents written at diff erent times. Results. The pilot study includes 16 patients who were found to be at diff erent stages of the disease by medical examination. They all provided old handwriting samples dated 10–20 years ago and new handwriting samples specifi cally written as part of the experiment. Evaluation of 36 handwriting characteristics showed that both tests were eff ective in identifying Alzheimer’s disease and its stage. The correlation between the handwriting analysis and the medical test result was 0.62. Conclusion. Further refi nement of the proposed tests and expansion of the research base will enable handwriting exercises to be incorporated into supportive therapy to slow the progression of the disease.

Stock Market Volatility

Researchers examine stock volatility in emerging (E7) nations prior to and during COVID-19 announcements using multiple volatility estimations. The correlation coefficient matrix indicates that there is a strong positive correlation between the specified volatility estimators in the pre-COVID-19 and post-COVID-19 periods. Rogers-Satchell standard deviation has the first rank, and Garman-Klass has the last position in the pre-post-COVID-19 analysis volatility estimators. However, the authors discover a considerable influence of pre-post COVID-19 on the world's E7 countries. The findings' primary implication is that post-COVID-19 volatility is greater than pre-COVID-19 volatility. This means that investors' financial portfolios should be rebalanced to favor industries that are less impacted by COVID-19. Additionally, it serves as an early warning signal for investors and the government to take preventative measures in the event that it occurs again in the future.

Time Synthesis in Organization of Sensorimotor Action

Time synthesis of sensorimotor action is reviewed as a process of a coherence setting action duration (expected duration), time sequence of required operations and significant changes in conditions. Aim: to experimentally set up the connection of time synthesis success and efficiency of realization sensorimotor action in changeable conditions. Hypothesis: successful time synthesis of the setting duration and the temporal sequence of operations in the mental organization of sensorimotor action in changing conditions is realized in accordance with the corresponding operational meaning and is allowed by anticipatory effects and an increase in the effectiveness of the action, materials and methods. An experimental study involved 152 male and female students. Participants of the investigation solved experimental tasks, implemented in a computer version, according to schemes of a simple visual-motor reaction and a choice reaction (separately and in combination), according to a scheme of sensorimotor action with a warning signal when the apperceptive scheme, setting duration and sequence of required operations were changed promptly. Results were reviewed in the aspect of disclosing the features of the subject's elimination of the uncertainty of the moment of achieving the goal in the future and the construction of a sequence of operations of sensorimotor actions in a connection with changes in external conditions, typical for the time deficit regime. The conditionality of the time synthesis of sensorimotor action by the actual operational meaning was established revealing that the successful temporal synthesis of sensorimotor action in changing conditions is associated with the fastest acceptance of an adequate apperceptive scheme, with effective anticipation of the moment of achieving the goal and the formation of a detailed setting duration of action, with the formation of a temporal sequence of required operations. Conclusions. The levels of success of the time synthesis of sensorimotor action in changing conditions are highlighted: “quite successful; moderately successful; unsuccessful.”

Rapid and direct method to extract SARS-CoV-2 RNA from municipal wastewater using the Chemagic™ 360 12-rod head platform v1

Wastewater based epidemiology (WBE) has emerged as a strategy to identify, locate, predict, and manage outbreaks of COVID-19, as an early warning signal to public health authorities of an expected surge in cases that may overwhelm local and global health care resources.. The WBE process is based on assaying municipal wastewater for molecular markers of the SARS-CoV-2 virus. The standard process for sampling municipal wastewater is time-consuming and requires the handling of large quantities of wastewater, which negatively affects throughput and timely reporting, and can increase safety risks. We report on a rapid and direct mostly automated method to assay multiple sub-samples of a bulk wastewater sample using a 75 minute run on the Chemagic™ 360 12 rod head platform. Including a preceding setup and incubation step, twelve 10 ml samples can be processed to purified RNA in 2.5 hrs. Up to 10 ml of wastewater from 12 different collection sites can be processed in 2.5 hrs.

Effect of Working Capital Management Decisions on Financial Performance

Collapse of companies in Kenya has been on the rise in the recent past. Far reaching endeavors to resuscitate these liquidating and ailing firms have generally been attributed on their corporate financial management decisions. Multinationals and KTDA managed tea firms in Kenya have been performing poorly in the recent past where audited financial statements and reports revealed a warning signal on its financial performance. Specific objectives of the study were to determine the effect of the accounts receivables period, accounts payables period, inventory conversion period, cash conversion cycle, financing policy, investing policy and moderating effect of ownership structure on financial performance. The study illustrated that accounts receivables collection period is negatively related to return on assets (? = -0.1299, p=0.0160), accounts payables payment period is negatively related to return on assets (? = -0.0843, p = 0.0070), inventory conversion period is negatively related to return on assets (?= -0.0623, p=0.0180), cash conversion cycle is negatively related to return on assets (? = -0.1107, p = 0.0030), financing policy is positively related to return on assets (? = 0.1589, p = 0.0000), investing policy is positively related to return on assets (? = 0.0291, p = 0.0000).

Early warning signal reliability varies with COVID-19 waves

Early warning signals (EWSs) aim to predict changes in complex systems from phenomenological signals in time series data. These signals have recently been shown to precede the emergence of disease outbreaks, offering hope that policymakers can make predictive rather than reactive management decisions. Here, using a novel, sequential analysis in combination with daily COVID-19 case data across 24 countries, we suggest that composite EWSs consisting of variance, autocorrelation and skewness can predict nonlinear case increases, but that the predictive ability of these tools varies between waves based upon the degree of critical slowing down present. Our work suggests that in highly monitored disease time series such as COVID-19, EWSs offer the opportunity for policymakers to improve the accuracy of urgent intervention decisions but best characterize hypothesized critical transitions.

INVESTIGATION OF ADDITIONAL LOSSES OF FIBER-OPTIC CONDUCTORS OF TYPE G-652 THAT OCCUR DURING BENDING

One of the problems of modern information transmission systems is the introduction of new methods of protecting information transmitted over fiber-optic transmission lines. Currently, new methods of unauthorized access are emerging, which are being improved and developed every year. Within the framework of this work, studies of additional losses during bending of the optical fiber were carried out in order to create an automatic control system for additional losses that occur during mechanical action leading to its bending. For this purpose, practical experiments were conducted to measure losses in optical fiber with multiple bends and a computer program was created based on the data obtained. With the help of this program, it is possible to estimate additional losses in the optical fiber when the wavelength changes from 1310 to 1625 nm and the bending angle indicators from 45 to 135 degrees. The program also allows you to automatically approximate the values of additional losses occurring in the optical fiber with different variations of different bending angles and their number. The study of additional losses will allow in the future to develop an automatic control system based on changes in the indicators of additional losses and, when they change, issue a warning signal about possible unauthorized connection to a fiber-optic cable.

CLOUD-BASED TEMPERATURE MONITORING MECHANISM FOR COVID-19 PANDEMIC TRACKING

The unprecedented outbreak of novel coronavirus 2019 (COVID-19) globally has a huge impact to our daily life in numerous ways. To effectively minimize the spread of the virus, early symptom detection is crucial, especially in closed environment with high human traffic areas which post higher chances of human-to-human transmission. Body temperature measurement has been identified among the vital monitoring parameters. However, current available temperature monitoring mechanism is costly, limited to single individual and limited to locally without integrating to cloud and database. This led to difficulty in effective surveillance for suspicious COVID cases. Hence, the purpose of this paper is to introduce an end-to-end Internet of Things-enabled application for thermal monitoring as an early signal detection and screening method. This work integrates Raspberry Pi, thermal sensor, LCD display, buzzer, and LED light with Raspbian and Restful API for device-to-cloud communication. The system implemented is capable for user identification, body temperature remote monitoring and warning signal for fever symptoms. The result of this real-time system is capable to detect and screen the suspected contagious person in an organization effectively. Future works on integrating face recognition with machine learning and artificial intelligent enhancement.

A data-driven topsoil δ¹³C dataset and the drivers of spatial variability across the Tibetan Plateau

Soil carbon isotopes (δ13C) provide reliable insights at a long-term scale for studying soil carbon turnover. The Tibetan Plateau (TP), called “the third pole of the earth” is one of the most sensitive areas to global climate change and exhibits an early warning signal of global warming. Although many studies detected the variability of soil δ13C at site scales, a knowledge gap still exists in the spatial pattern of topsoil δ13C across the TP. To fill the substantial knowledge gap, we first compiled a database of topsoil δ13C with 396 observations from published literatures. Then we applied a Random Forest (RF) algorithm – a machine learning approach, to predict the spatial pattern of topsoil δ13C and β (indicating the decomposition rate of soil organic carbon (SOC), calculated by δ13C divided by logarithmically converted SOC). Finally, two datasets – topsoil δ13C and β with a fine spatial resolution of 1 km across the TP were developed. Results showed that topsoil δ13C varied significantly among different ecosystem types (p < 0.001). Topsoil δ13C was −26.3 ± 1.60 ‰ (mean ± standard deviation) for forests, 24.3 ± 2.00 ‰ for shrublands, −23.9 ± 1.84 ‰ for grasslands, −18.9 ± 2.37 ‰ for deserts, respectively. RF could well predict the spatial variability of topsoil δ13C with a model efficiency of 0.62 and root mean square error of 1.12 ‰, enabling to derive data-driven δ13C and β products. Data-driven topsoil δ13C varied from −28.26 ‰ to −16.95 ‰, with the highest topsoil δ13C in the north and northwest TP and the lowest δ13C in Southeast or South TP, indicating strong spatial variabilities in topsoil δ13C. Similarly, there were strong spatial variabilities in data-driven β, with the lowest β values at the east and middle TP, indicating a higher SOC turnover in the east and middle TP compared that of other regions in the TP. This study was the first attempt to develop a fine resolution product of topsoil δ13C and β across the TP, which could provide an independent data-driven benchmark for biogeochemical cycling models to study SOC turnover and terrestrial carbon-climate feedbacks over the TP under climate change. The data-driven δ13C and β datasets are public available at https://doi.org/10.6084/m9.figshare.16641292.v2 (Tang, 2021).

Multi-Angle Fusion-Based Safety Status Analysis of Construction Workers

Hazardous accidents often happen in construction sites and bring fatal consequences, and therefore safety management has been a certain dilemma to construction managers for long time. Although computer vision technology has been used on construction sites to identify construction workers and track their movement trajectories for safety management, the detection effect is often influenced by limited coverage of single cameras and occlusion. A multi-angle fusion method applying SURF feature algorithm is proposed to coalesce the information processed by improved GMM (Gaussian Mixed Model) and HOG + SVM (Histogram of Oriented Gradient and Support Vector Machines), identifying the obscured workers and achieving a better detection effect with larger coverage. Workers are tracked in real-time, with their movement trajectory estimated by utilizing Kalman filters and safety status analyzed to offer a prior warning signal. Experimental studies are conducted for validation of the proposed framework for workers’ detection and trajectories estimation, whose result indicates that the framework is able to detect workers and predict their movement trajectories for safety forewarning.