People's Daily Activities Research Articles

Evaluation Of Passenger Services In White Water Flight Port, Bengkalis District, Riau Province

Based on their activities, ports in Riau Province have goals and objectives for improving freight and passenger services. The existence of these Riau ports has an impact on improving Indonesia's economy. Therefore, ferry transportation is important in the Riau Province because it connects with the surrounding area and also to help economic development which of course requires facilities and infrastructure to support the smooth running of people's daily activities. The Air Putih Ferry Port in Bengkalis Regency, Riau Province, serves the Air Putih - Sei Selari route. For this reason, it is necessary to provide services in accordance with statutory regulations in order to carry out good port activities. However, in fact there are still several indicators of service to passengers that are not in accordance with the provisions. Based on these conditions, the authors conducted a literature study on the problems found at the port. Then an analysis is carried out using a method in order to obtain the results of the analysis as well as the problem solving suggestions. The method used to analyze the existing problems is the calculation of the level of conformity of passenger service standards with existing conditions. This method is carried out with the support of surveys related to services to passengers at the port. After that the results of this method will be analyzed with the level of conformity with the Minister of Transportation Regulation No. 39 of 2015. After that obtained the results of the analysis along with the suggestions for solving the problem. Based on the analysis, it can be concluded that there are still services and facilities that are still lacking at the White Water Crossing Port. The percentage of the appropriate aspects is 33.33% while the remaining 66.66% is not suitable.

Evaluating effects of the Covid-19 pandemic period on energy consumption and enviro-economic indicators of Turkish road transportation

ABSTRACT With the effect of the lockdown policies that have appeared during the Covid-19 pandemic period, various socio-economic effects have emerged that limit the daily activities of people. While the effects of Covid-19 have had a negative impact on many sectors, a decrease in certain sectoral activities has affected environmental factors positively, as in the case of road transport. This is because, during the Covid-19 pandemic, people’s use of private vehicles has decreased. In the current paper, the environmental effects of Turkish road transportation fuel consumption are investigated in the lockdown period experienced due to the Covid-19 pandemic. For this purpose, the production of greenhouse gas emissions (GHG), their global warming potential (GWP), and social costs were estimated, dependent on road transport fuel consumption data corresponding to the months of January and October from 2017 to 2020. Because the Covid-19 lockdown process in Turkey an important were a good opportunity for the detection of road transportation-induced GHG emissions, and their effect on the enviro-economic indicators. According to the main findings of the study, the consumption of road transport fuels decreased significantly in the first 2 months following March 2020, (April and May), when the first Covid-19 case appeared in Turkey. On the other hand, GHG emissions, their GWP, and social cost values decreased, also dependent on the decrease in fuel consumption values for this time period.

New ‘ase’ in the hole for treatment of acute ischemic stroke

New ‘ase’ in the hole for treatment of acute ischemic stroke

From PM2.5 exposure to PM2.5 risks of inhaled dose in daily activities: Empirical evidence during workdays from guangzhou, China

PM2.5 pollution poses a serious health risk to residents. In recent years, individual exposure to PM2.5 in daily activities has received much attention, but the impact of different activities at different times and spaces on PM2.5-triggered individual health risks has been ignored. In this paper, the residents' activity diaries and the data on the spatial-temporal changes in urban PM2.5 concentration obtained through the land-use regression model (LUR) were coupled to dynamically evaluate and analyze the characteristics of Guangzhou residents' PM2.5 inhaled dose in 24 h during workdays. The study showed that the residents' PM2.5 inhaled dose in daily activities is about 940.2 ± 94.5 μg (95% CI). This can better reflect the individual differences than the exposure to PM2.5 in daily activities. For every increase of 1 μg/day in the exposure to PM2.5, the inhaled dose will increase by 1.5 μg/day. The obvious spatial-temporal characteristics of PM2.5 inhaled dose are closely related to people's daily activities. The inhaled dose is high during the day and low at night, and the individual differences in inhaled dose in the commuting and leisure period is significant but is small during the working and sleeping period; if the individual inhaled dose exceeds the population's average for accumulated 7 h in a day, the probability of a high risk of the all-day inhaled dose increases significantly. The inhaled dose at the workplace and residence as well as in light physical activities accounts for a large proportion of the total. However, although the inhaled dose on roads and in other places, and moderate physical activities account for only a small proportion, it has a great impact on the risk level of the individual total inhaled dose. Therefore, the characteristics of PM2.5 inhaled dose and the individual activity should be considered, to propose targeted policies and suggestions from the perspective of both the government and individuals to reduce the health risk caused by PM2.5 pollution.

Blockchain-assisted handover authentication for intelligent telehealth in multi-server edge computing environment

Abstract Intelligent telehealth system (ITS) provides patients and medical institutions with a lot of convenience, medical institutions can achieve medical services for patients in time through monitored health data. However, as the scope of people's daily activities extends, the traditional single-server architecture is no longer applicable. To deal with this problem, a multi-server architecture has been proposed recently while there remains security and privacy challenges, including handover authentication. In this paper, we investigate a blockchain-assisted handover authentication and key agreement scheme for ITS in a multi-server edge computing environment. Specifically, we first propose a novel handover authentication model of ITS with multi-server edge computing architecture. Second, the proposed handover authentication scheme allows the authenticated server to assist users subsequently authenticate with other server, thereby achieving interactions with the server anytime and anywhere with low overhead. Finally, blockchain technology and strong anonymity mechanism are introduced to protect users’ privacy strictly. To our best knowledge, the proposed scheme is the first in the literature to provide efficient authentication, strict anonymity and computational load transfer simultaneously. The security analysis and performance evaluation show that our scheme can not only satisfy the security requirements but also achieve higher efficiency in computation and communication cost.

ANALISISTINGKATKEPUASANKONSUMEN PADA RESTORAN CEPATSAJI MENGGUNAKAN FUZZY MAMDANI

The development of technology is the root of the civilization of society in carrying out their daily activities. In its application, technology can make it easy to access information that helps people to find out certain information, one of which is the level of consumer satisfaction with fast food restaurants.Fast food restaurants have become an important factor in people's daily activities. To fulfill its role in people's lives, fast food restaurants serve food and services for their consumers. Fast food restaurants with decent service, as well as good food prices and quality will certainly result in a high level of satisfaction from consumers with these fast food restaurants.The Fuzzy Mamdani method is one part of the Fuzzy Inference System which is useful for drawing conclusions or the best decisions in uncertain problems. In its application, the criteria for fast food restaurants become variables to determine the level of consumer satisfaction with these fast food restaurants. The Fuzzy Mamdani method forms a fuzzy set of input variables, then implements the implication function, then composes the rules, then defuzzy to get the level of consumer satisfaction with fast food restaurants. In determining the level of customer satisfaction, Fuzzy Mamdani obtained an accuracy rate of 100%.

Feasibility of a home-based environmental enrichment paradigm to enhance purposeful activities in adults with traumatic brain injury: a case series

Purpose To assess the feasibility, safety, and functional recovery of an Environmental Enrichment (EE) inspired paradigm for enhancing daily activities in people with traumatic brain injury. Methods Two TBI-Caregiver dyads participated in the six-month study. A preinstalled harness provided the support structure that enabled the family to perform task-specific functional and cognitive goals. The pre- and post-intervention evaluations included the safety, feasibility, and clinical outcomes such as the 10-m walk test, the Timed Up and Go test, the Jebsen Hand Function test, the Six-Minute Walk test, and the Trail Making Test. The Actigraph GT9X recorded the Caregiver-TBI step count during days spent in harness and out of harness. Results The study was feasible, safe, and both TBI subjects improved in functional outcomes. Analysis of Actigraph GT9X yielded mixed results. Conclusion A partnership with caregivers and adults with TBI to design an EE-focused community program could improve functional activities in real life. To optimize this intervention, caregivers will likely need an alternate approach to record time spent in the harness. IMPLICATIONS FOR REHABILITATION The complexities of the real world encourage meaningful activities and participation. Autonomy in everyday activities is an important long-term objective for adults with brain injury. Community-tailored harnesses designed to prevent falls encourages physical activity and social interaction.

Intervenciones educativas para la prevención del pie diabético

Introducción: El pie diabético es una de las complicaciones con mayor impacto en la calidad de vida de las personas con Diabetes Mellitus; en este sentido, las intervenciones educativas son fundamentales en la prevención de esta complicación, por lo cual este artículo tiene el objetivo de caracterizar las intervenciones educativas para el autocuidado y prevención de pie diabético en personas con Diabetes Mellitus. Metodología: Revisión narrativa de la literatura. Se realizó la búsqueda con las siguientes palabras clave: “Diabetes Mellitus and Educative intervention and Diabetic foot and Self-care” en español, inglés y portugués, en las bases de datos Ovid Nursing, LILACS, Google Scholar, Medline y fuentes de literatura gris. Se analizaron 56 artículos que cumplieron los criterios de selección, publicados entre los años 2008 al primer trimestre del 2020. Resultados: Surgieron las siguientes categorías de análisis: 1. Tipos de intervenciones (educación de grupo, educación entre pares, visitas presenciales y telefónicas, redes sociales y comunicación participativa); 2. Ítems a tratar en cada intervención (inspección diaria de los pies, ejercicios y demás cuidados); 3. Duración de las Intervenciones; y, 4. Impacto de las intervenciones (resultados obtenidos). Conclusiones: Las diferentes intervenciones educativas al usarse adecuadamente mejoran el nivel de autocuidado de las personas con Diabetes Mellitus y por ende mitigan el desarrollo del pie diabético.

Manfaat Pemisahan Sampah Organik dan Anorganik Melalui Media Buku Ilustrasi Anak

<p><em>Garbage was defined as the residue of people's daily activities. In the social environment, to facilitate decomposition, garbage was generally grouped into two simple parts; namely organic garbage and inorganic garbage. </em><em>garbage management was needed to reduce the possibility of positive and negative impacts to the environment. Economic value was grouped into positive impact, while the source of odors and disease clusters were grouped as negative impact. </em><em>In this study, the focus of the problem is on the negative group. Because if garbage is allowed to pile up in one place in community settlements, it will become a source of unpleasant odors so that it can then become an area for germs to develop. </em><em>This condition is an indication that the garbage had not been managed properly and could worsen the surrounding environment. Therefore, proceeded from the scientific visual communication design, then at least hazard information and proper garbage processing method needed to be made. Knowledge of garbage separation to reduce environmental pollution would be better if known or understood since childhood. </em><em>The choice of work form for dissemination of this information was children illustration book. The process of realizing this illustration book work used several stages, the first stage was the process of collecting data about garbage and its processing, then data on the type of book that match the target audience. </em><em>The second stage was the process of making books, in this stage it went through several processes from pre-production to production.</em></p><p><em> </em></p><p><strong><em>Keywords:</em></strong><em> Illustration, Children, Health, Garbage</em><em></em></p>

Using Perforated Liners to Combat the Detrimental Effects of Excessive Sweating in Lower Limb Prosthesis Users.

Excessive sweating of the residual limb has a substantial effect on the daily activities of people with lower limb amputation. Prosthetic liners offer protection and comfort to sensitive areas but often exacerbate perspiration. They act as insulators, trapping sweat on the skin's surface to the detriment of skin health. Recently, liners with perforations have been developed, allowing the moisture to escape. The goal of this study was to assess the impact of such liners. A sample group of 13 patients with unilateral transtibial amputation, who wore a perforated liner (PL) as part of their current prescription, was compared to 20 control patients who wore non-perforated liners (NPL). During their routine appointments, they completed a survey of scientifically validated outcome measures relating to their limb health, pain and the impact on daily life over a 12-month period. Patients using the PL had healthier residual limbs, reporting higher scores on questions relating to limb health, experiencing fewer skin issues (p<0.001) and estimating a 61.8% lower rating in perceived sweat (p=0.004). Perhaps consequentially, there was a lower incidence of residual (p=0.012) and phantom (p=0.001) limb pain when compared to the control group. The prevalence of individual issues affecting the residual limbs of PL users was also lower. Of the issues that remained, only 23% were attributed to sweating in PL users, compared to 49% for the NPL group (p=0.066). PL users missed fewer days of work in the year (2.4 vs 11.6, p=0.267) and were also limited on fewer days (1.4 vs 75.4, p=0.009). The use of perforated liners shows much promise within prosthetic care, significantly improving the health of the residual limb. The observed effects on perceived sweat reduction, residual skin health, pain levels and patient limitation suggest that perforated liners are highly beneficial to patients.

Evaluation of Feature Extraction and Classification for Lower Limb Motion Based on sEMG Signal.

The real-time and accuracy of motion classification plays an essential role for the elderly or frail people in daily activities. This study aims to determine the optimal feature extraction and classification method for the activities of daily living (ADL). In the experiment, we collected surface electromyography (sEMG) signals from thigh semitendinosus, lateral thigh muscle, and calf gastrocnemius of the lower limbs to classify horizontal walking, crossing obstacles, standing up, going down the stairs, and going up the stairs. Firstly, we analyzed 11 feature extraction methods, including time domain, frequency domain, time-frequency domain, and entropy. Additionally, a feature evaluation method was proposed, and the separability of 11 feature extraction algorithms was calculated. Then, combined with 11 feature algorithms, the classification accuracy and time of 55 classification methods were calculated. The results showed that the Gaussian Kernel Linear Discriminant Analysis (GK-LDA) with WAMP had the highest classification accuracy rate (96%), and the calculation time was below 80 ms. In this paper, the quantitative comparative analysis of feature extraction and classification methods was a benefit to the application for the wearable sEMG sensor system in ADL.

Privacy-Aware Data Fusion and Prediction With Spatial-Temporal Context for Smart City Industrial Environment

As one of the cyber–physical–social systems that plays a key role in people's daily activities, a smart city is producing a considerable amount of industrial data associated with transportation, healthcare, business, social activities, and so on. Effectively and efficiently fusing and mining such data from multiple sources can contribute much to the development and improvements of various smart city applications. However, the industrial data collected from the smart city are often sensitive and contain partial user privacy such as spatial–temporal context information. Therefore, it is becoming a necessity to secure user privacy hidden in the smart city data before these data are integrated together for further mining, analyses, and prediction. However, due to the inherent tradeoff between data privacy and data availability, it is often a challenging task to protect users’ context privacy while guaranteeing accurate data analysis and prediction results after data fusion. Considering this challenge, a novel privacy-aware data fusion and prediction approach for the smart city industrial environment is put forward in this article, which is based on the classic locality-sensitive hashing technique. At last, our proposal is evaluated by a set of experiments based on a real-world dataset. Experimental results show better prediction performances of our approach compared to other competitive ones.

Thermal display glove for interacting with virtual reality

Thermal perception is essential for the survival and daily activities of people. Thus, it is desirable to realize thermal feedback stimulation for improving the sense of realism in virtual reality (VR) for users. For thermal stimulus, conventional systems utilize liquid circulation with bulky external sources or thermoelectric devices (TEDs) on rigid structures. However, these systems are difficult to apply to compact wearable gear used for complex hand motions to interact with VR. Furthermore, generating a rapid temperature difference, especially cooling, in response to a thermal stimulus in real-time is challenging for the conventional systems. To overcome this challenge and enhance wearability, we developed an untethered real-time thermal display glove. This glove comprised piezoelectric sensors enabling hand motion sensing and flexible TEDs for bidirectional thermal stimulus on skin. The customized flexible TEDs can decrease the temperature by 10 °C at room temperature in less than 0.5 s. Moreover, they have sufficiently high durability to withstand over 5,000 bends and high flexibility under a bending radius of 20 mm. In a user test with 20 subjects, the correlation between thermal perception and the displayed object’s color was verified, and a survey result showed that the thermal display glove provided realistic and immersive experiences to users when interacting with VR.

Human activity classification based on sound recognition and residual convolutional neural network

Human activity recognition is crucial for a better understanding of workers in construction sites and people in the built environment. Previous studies have been proposed various ways in which sensing and machine learning techniques can be utilized to collect human activity data automatically. Sound recognition has the potential to be utilized in ways that complement the limitations of the previous methods because sound signals are easy to propagate in indoor environments where many physical obstacles exist, and this method can simultaneously recognize not only sounds from human activities but also sounds from related objects. Therefore, this study develops a sound recognition-based human activity classification model using a residual neural network. A sound data is collected based on ten classes representing people's daily activities in the indoor environment. Then, the features of the sound data were extracted using the Log Mel-filter bank energies method, and a residual neural network model with 34 convolutional layers was trained using the data. The results showed the following: the accuracy of the model was 87.6%, and the Precision score for each class ranged from 76.8% to 92.6%, the Recall scores ranged from 75.8% to 98.6%, and the F1-score ranged from 78.6% to 93.7%. The contribution of this study is to demonstrate that sound recognition can classify people's indoor activities successfully, but this study leaves the limitation that it is based on a monophonic method that only one activity can be classified at a time.

Everyday Technology to Support Leisure and Daily Activities in People with Intellectual and Other Disabilities

ABSTRACT Objective: The study evaluated a smartphone-aided program to support independent access to leisure events and performance of daily activities in seven participants with moderate intellectual disability, four of whom also had severe hearing loss. Method: The program relied on the use of a Samsung Galaxy J4 Plus smartphone with Android 9.0 operating system. The smartphone’s functioning was automated via the MacroDroid application. The program was to allow the participants to alternate access to leisure events (delivered via the smartphone) with performance of vocational activities (guided through smartphone-delivered pictorial or pictorial and verbal instructions) over sessions of about 30 min. Results: During the baseline, the participants failed to access leisure events and perform scheduled activities independently. When the program was used, however, all participants succeeded in both accessing leisure events and performing activities independently. Conclusions: The smartphone-aided program can be a useful tool for people like the participants of this study.

A novel multiple impact factors based accuracy analysis approach for power quality disturbance detection

Nowadays, power quality problems are affecting people's daily life and production activities. With the aim to improve disturbance detection accuracy, a novel analysis approach based on multiple impact factors is proposed in this paper. First, a multiple impact factors analysis is implemented in which two perspectives, i.e., the wavelet analysis and disturbance features are considered simultaneously. Five key factors, including wavelet function, wavelet decomposition level, redundant algorithm, event type and disturbance intensity, start and end moment of disturbance, have been considered. Next, an impact factors based accuracy analysis algorithm is proposed, through which each factor's potential impact on disturbance location accuracy is investigated. Three transforms, i.e., the classic wavelet, lifting wavelet and redundant lifting wavelet are employed, and their superiority on disturbance location accuracy is investigated. Finally, simulations are conducted for verification. Through the proposed method, the wavelet based parameters can be validly selected in order to detect power quality disturbance accurately.

Публичная власть: попытка концептуализации

The majority of interpretations of social authority is based upon its definition by M. Weber as an opportunity to exercise one’s will even against the will of other persons. In this context the authority constitutes a capacity of an individual or a community of people to force others (own members) to a certain type of conduct. In the primitive society the authority was natural, direct and detached neither from anindividual nor from a team, in general. The appropriation and xercise of authority was “interwoven” in daily activities of people and the observance (collective subordination to) of certain rules of conduct was ultimately essential for survival and thus natural and indisputable. The development of production activities of an individual and its sociality, in broad terms, led to the division of labor, inter alia, to segregation of the ruling and managerial activities and the consequent isolation of private and public areas of activity of an individual, attribution of the degree of publicity to social authority. Public authority is institutionalized authority; it is appropriated by artificial ways developed by the society and exercised through specially designated persons for these purposes (established entities) within the framework of the set procedures based on the territorial principle. Public authority is universal; it extends to all society members and to the entire area of their public activities. Once emerged in a socially stratified society, public authority inevitably acquires political nature – it is aimed at streamlining relationships between various groups and layers of the society and alignment (delineation, suppression) of their interests. This particular comprehension of public political authority underpins various definitions of the state which basically come down to the most efficient (sovereign) as well as (in a set of conceptions) proper (fair, legitimate, legal etc.) form of its organization.

Occupational Patterns of Structural Brain Health: Independent Contributions Beyond Education, Gender, Intelligence, and Age.

Occupational activity represents a large percentage of people’s daily activity and thus likely is as impactful for people’s general and cognitive health as other lifestyle components such as leisure activity, sleep, diet, and exercise. Different occupations, however, require different skills, abilities, activities, credentials, work styles, etc., constituting a rich multidimensional formative exposure with likely consequences for brain development over the lifespan. In the current study, we were interested in how different occupations with their different attributes relate to five variables: structural brain health, duration of early-life education, gender, IQ, and age, although the main focus was the relationship to brain health. To this end, we used the Occupation Information Network (O∗NET), which provides quantification of occupations along 246 items. Occupational patterns with different loadings for these 246 items were derived from 277 community-dwelling adults, ranging in age from 40 to 80, based upon the five subject measures. We found significant patterns underlying four of our variables of interest, with gender and education predictably showing the most numerous and strongest associations, while brain health and intelligence showed weaker associations, and age did not manifest any associations. For the occupational pattern associated with brain health, we found mainly positive associations on items pertaining to rigorous problem-solving, leadership, responsibility, and information processing. We emphasize that the findings are correlational and cannot establish causation. Future extensions of this work will assess the influence of occupation on future cognitive brain status and cognitive performance.

Applied cognitive strategy behaviours in people with parkinson's disease during daily activities: A cross-sectional study.

To explore the use of applied cognitive strategy behaviours during performance of daily activities in people with Parkinson's disease. Quantitative cross-sectional design. A total of 190 persons living at home with non-dementing Parkinson's disease were videotaped while performing a self-chosen activity in their natural environment. The videotaped performance was scored using the "Perceive, Recall, Plan and Perform System of Task Analysis" to measure: (i) performance mastery; and (ii) effective use of 34 cognitive strategy behaviours covering: attention and sensory processing (Perceive), accessing task-related knowledge (Recall), response planning and evaluation (Plan) and performance control (Perform). Mean performance mastery and a hierarchy of least to most effective applied cognitive strategy behaviours were determined for the total group and for 2 sub-groups based on disease severity. A multi-faceted Rasch model was used for data analysis. Mean performance mastery was 56% (standard deviation (SD) 28). Least efficient cognitive strategy behaviours were those used for planning, evaluating and controlling performance and most efficient strategies were those used for sensory discrimination and recalling factual information. More advanced disease indicated less efficient use of applied cognition. The results suggest that the efficiency of applied cognitive strategy behaviours is compromised in a certain pattern in people with Parkinson's disease, and that it declines with disease progression.

An innovative hybrid model based on outlier detection and correction algorithm and heuristic intelligent optimization algorithm for daily air quality index forecasting

Air pollution forecasting plays an important role in helping reduce air pollutant emission and guiding people's daily activities and warning the public in advance. Nevertheless, previous articles still have many shortcomings, such as ignoring the importance of outlier point detection and correction of original time series, and random initial parameters of models, and so on. A new hybrid model using outlier detection and correction algorithm and heuristic intelligent optimization algorithm is proposed in this study to address the above mentioned problems. First, data preprocessing algorithms are conducted to detect and correct outliers, excavate the main characteristics of the original time series; second, a widely used heuristic intelligent optimization algorithm is adopted to optimize the parameters of extreme learning machine to obtain the forecasting results of each subseries with improvement in accuracy; finally, experimental results and analysis show that the presented hybrid model provides accurate prediction, outperforming other comparison models, which emphasize the importance of outlier point detection and correction and optimization parameters of models, it also give a new feasible method for air pollution prediction, and contribute to make effective plans for air pollutant emissions.