Human Daily Activities Research Articles

Study on the Photodegradation of Dissolved Organic Matter in Urban Water Bodies in Nanjing

This paper reviews the DOM pollution of water resources in China, and complements the organic matter degradation characteristics of urban water bodies in Nanjing. In this study, the photodegradation experiment and UV-visible spectroscopy measurement of three types of water are conducted, analyzed and discussed around the degradation of DOC and aromatic substances, and the DOM degradation characteristics of different cities are studied. It can be identified in this study that urban water bodies near human living areas and non-circulation have higher DOM content and are less susceptible to decomposition, which is related to human needs and transformation of the environment and human daily activities.

Trust Management in Vehicular Ad-Hoc Networks: Extensive Survey

Over the past few decades, Intelligent Transportation System (ITS) has become a vital and extensive element of daily human life and activity. Vehicular Ad hoc Networks (VANETs) have become the most promising components of ITS, which promises to enhance transport efficiency, passenger safety, and comfort by exchanging traffic and infotainment information to intelligent vehicles. Moreover, VANETs have emerged with new paradigms (e.g., Cloud, SDN (Software-Defined Networking), Fog computing, Blockchain, and AI (Artificial Intelligence) techniques) to provide strategic and secure communications to increase their reliability. Therefore, efficient and robust mechanisms, such as trust management, are essential requirements in VANETs. This survey provides an extensive overview of the VANET and trust management’s main concepts. After that, we briefly review existing surveys, followed by the significant challenges of security and trust in VANETs. Then, we identify, review, classify, summarize, and compare related approaches. Finally, we give some future research directions.

Efficient Gait Analysis Using Deep Learning Techniques

Human Activity Recognition (HAR) has always been a difficult task to tackle. It is mainly used in security surveillance, human-computer interaction, and health care as an assistive or diagnostic technology in combination with other technologies such as the Internet of Things (IoT). Human Activity Recognition data can be recorded with the help of sensors, images, or smartphones. Recognizing daily routine-based human activities such as walking, standing, sitting, etc., could be a difficult statistical task to classify into categories and hence 2-dimensional Convolutional Neural Network (2D CNN) MODEL, Long Short Term Memory (LSTM) Model, Bidirectional long short-term memory (Bi-LSTM) are used for the classification. It has been demonstrated that recognizing the daily routine-based on human activities can be extremely accurate, with almost all activities accurately getting recognized over 90% of the time. Furthermore, because all the examples are generated from only 20 s of data, these actions can be recognised fast. Apart from classification, the work extended to verify and investigate the need for wearable sensing devices in individually walking patients with Cerebral Palsy (CP) for the evaluation of chosen Spatio-temporal features based on 3D foot trajectory. Case-control research was conducted with 35 persons with CP ranging in weight from 25 to 65 kg. Optical Motion Capture (OMC) equipment was used as the referral method to assess the functionality and quality of the foot-worn device. The average accuracy precision for stride length, cadence, and step length was 3.5 ± 4.3, 4.1 ± 3.8, and 0.6 ± 2.7 cm respectively. For cadence, stride length, swing, and step length, people with CP had considerably high inter-stride variables. Foot-worn sensing devices made it easier to examine Gait Spatio-temporal data even without a laboratory set up with high accuracy and precision about gait abnormalities in people who have CP during linear walking.

Amperometric Measurements by a Novel Aerosol Jet Printed Flexible Sensor for Wearable Applications

In the last years, increasing interest has been addressed on wearable devices for the continuous monitoring of biochemical profiles of patients and athletes. Among various markers, lactate represents one of the most interesting to deepen the knowledge on fatigue processes, in combination with traditional electromyographic features. In this work, we propose a new, low-cost, rapid, and flexible electrochemical amperometric lactate carbon biosensor fabricated by the novel technique of aerosol jet printing (AJP). The developed biosensors were combined with a paper-based microfluidic system and a chitosan-based stable functionalization to allow a long-term continuous monitoring of human daily activities. The sensor performances were evaluated by static and dynamic electrochemical tests in buffered media. The limit of detection is 11 ± 2 mM for the static setup and of 15 ± 6 mM for the dynamic setup, while sensitivity is 0.02 and 0.04 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{A}$ </tex-math></inline-formula> /mM, respectively, with single sigma standard deviation lower than 8%. Increasing and decreasing concentration steps of lactate induces comparable response times below 30 s. These results obtained in vitro under controlled laboratory conditions represent a promising starting point to pursue with ex-vivo and in vivo validation for the future development of stand-alone wearable patches for noninvasive lactate measurements.

Aplikasi Klasifikasi Sampah Organik dan Non Organik dengan Metode GLCM Dan LS-SVM

Garbage is an item that no longer has any benefits for its users, which is the residue from the results of daily human activities or is the result of natural processes that have a solid form. The existing waste processing is only limited to conventional waste processing, which is only transported from the waste-producing place to the Temporary Disposal Site (TPS) and then just dumped into the TPS without processing it first, even though the rules for waste management procedures that must be carried out are: waste collection is then recycled and disposed of to the TPS, then the waste is transported to be disposed of at the Final Disposal Site. Therefore, in the process of waste management, it is necessary to separate waste into organic and non-organic waste. However, most people still have difficulty in sorting organic and non-organic waste, so an application is needed to help socialize waste sorting to the community. In this study, the Least Square Support Vector Machine (LS-SVM) method will be used to classify the types of waste. Meanwhile, to perform the texture extraction process from the included garbage image, the Gray Level Co-occurrence Matrix (GLCM) method will be used. The result of this research is a waste classification application that can provide knowledge and add insight for users, especially in distinguishing the types of organic waste and inorganic waste. The application of the GLCM and LS-SVM methods in the built application can detect types of organic and inorganic waste with a success rate of 97%.

Analysis of Leachate Water Distribution in Bakung Final Waste Disposal Location (TPA) Using Resistance Geoelectric Methods in Keteguhan Permai Housing Bandar Lampung

Poor waste management is one of the most frequent environmental problems in Indonesia. We know that the waste generated as a result of daily human activities will be transported, then disposed of at the Final Disposal Site (TPA). The lack of attention to WWTP maintenance at the Daffodil TPA can be seen from the thickness of the sediment at the WWTP dam. The thickness of the sediment in the WWTP causes leachate from the TPA not to fully enter the WWTP but flows directly into the river and seeps into the ground. Thus, this research was conducted as an aim to explain the spread of leachate pollution in shallow groundwater in the Keteguhan Permai housing complex. The method is quantitative. The results of this study indicate that groundwater is shallow in the study area, namely, Keteguhan Permai housing has been polluted by leachate. The average leachate intrusion depth in this study area is less than 13 meters with a resistivity value of less than 10?m.

Anxiolytic and Antioxidant Effect of Phytoecdysteroids and Polyphenols from Chenopodium quinoa on an In Vivo Restraint Stress Model.

The variety of stressful conditions in daily human activity requires nutritional support with safe, specialized food products containing functional food ingredients (FFIs) enriched with biologically active plant substances with proven adaptogenic properties. In this in vivo study, by evaluating a set of physiological parameters and biochemical markers, we investigated the effectiveness of the developed FFIs from Chenopodium quinoa grains in stress conditions induced by daily episodes of immobilization for 36 days. The results of the evaluation of the anxiety-like functions, locomotor, and search activity of rats in the "open field" and "elevated plus maze" tests demonstrated the ability of FFIs to reduce stressful behavior induced by immobilization. The improvement in the long-term memory of animals treated with FFIs was noted in the passive avoidance test. Together with the hypolipidemic effect and compensation of transaminase levels, FFIs normalized the excretion of catecholamines in the urine and reduced the levels of malondialdehyde to values of the control group. According to the results of the assessment of FFI acute oral toxicity, the LD50 value exceeded 5000 mg/kg of body weight, which categorizes the FFIs under hazard class 5-substances with low hazard. The conducted experiment demonstrated the effectiveness of nutritional support with FFIs on the selected stress model. The positive safety profile of FFIs makes them reasonable to study on other stress models and to conduct clinical testing as part of specialized food products in various categories of people exposed to chronic stress.

Implementasi Malware Security and Proxy Layer 7 Protocol at Routerboard Mikrotik

In today's era along with the times, the internet network is needed in finding information because the internet is used as a source of information and exchange data in real time. Therefore, the internet is used in daily human activities, especially in the world of education. In the world of education, the internet is used as a means of online learning and as a source for finding learning materials related to education. In an educational institution, the number of users connected to the internet network is very large, making it possible for infiltration by malware and distribution. Malware activity is closely related to computer memory, computer performance and also network activity on computer systems making it difficult to access learning media sites in the network because there is no network security management that regulates the data exchange traffic of each user. The right solution to overcome this problem is to implement malware security and proxy server configurations as the method used to manage traffic for data exchange activities. The methodology used is dynamic analysis by analyzing malware on a system and looking at the activities or processes activated by the malware. By applying the security malware method and proxy server configuration, the quality of the network will be better because it minimizes malware traffic on the network system according to the configuration that is applied and also users will be restricted from accessing certain sites.

A multi-view feature fusion approach for effective malware classification using Deep Learning

The number of malware infected machines from all over the world has been growing day by day. New malware variants appear in the wild to evade the malware detection and classification systems and may infect with ransomware or crypto miners for adversary financial gain. A recent colonial pipeline ransomware attack is an example of these attacks that impacted daily human activities, and the victim had to pay ransom to restore their operations. Windows-based systems are the most adopted systems across different industries for running applications. They are prone to get targeted by installing the malware. In this paper, we propose a Deep Learning (DL)-based Convolutional Neural Network (CNN) model to perform the malware classification on Portable Executable (PE) binary files using the fusion feature set approach. We present an extensive performance evaluation of various DL model architecture and Machine Learning (ML) classifier i.e. Support Vector Machine (SVM), on multi-aspect feature sets covering the static, dynamic, and image features to select the proposed CNN model. We further leverage the CNN-based architecture for effective classification of the malware using different combinations of feature sets and compare the results with the best-performed individual feature set. Our performance evaluation of the proposed model shows that the model classifies the malware or benign files with an accuracy of 97% when using fusion feature sets. The proposed model is robust and generalizable and showed similar performances on completely unseen two malware datasets. In addition, the embedding features of the CNN model are visualized, and various visualization methods are employed to understand the characteristics of the datasets. Further, large-scale learning and stacked classifiers were employed after the penultimate layer to enhance the CNN classification performance.

Heterogeneous chemistry of ozone with floor cleaning agent: Implications of secondary VOCs in the indoor environment

Human daily activities such as cooking, and cleaning can affect the indoor air quality by releasing primary emitted volatile organic compounds (VOCs), as well as by the secondary product compounds formed through reactions with ozone (O3) and hydroxyl radicals (OH). However, our knowledge about the formation processes of the secondary VOCs is still incomplete. We performed real-time measurements of primary VOCs released by commercial floor-cleaning detergent and the secondary product compounds formed by heterogeneous reaction of O3 with the constituents of the cleaning agent by use of high-resolution mass spectrometry. We measured the uptake coefficients of O3 on the cleaning detergent at different relative humidities in dark and under different light intensities (320 nm < λ < 400 nm) relevant for the indoor environment. On the basis of the detected compounds we developed tentative reaction mechanisms describing the formation of the secondary VOCs. Intriguingly, under light irradiation the formation of valeraldehyde was observed based on the photosensitized chemistry of acetophenone which is a constituent of the cleaning agent. Finally, we modeled the observed mixing ratios of three aldehydes, glyoxal, methylglyoxal, and 4-oxopentanal with respect to real-life indoor environment. The results suggest that secondary VOCs initiated by ozone chemistry can additionally impact the indoor air pollution.

Larvacidal Effect of Methanol Extract from Soursop Leaves Against 3rd Instar Larvae of Culex sp. Mortality

The Culex genus is widely distributed in Indonesia. It is responsible for disrupting and interfering of human daily activities and also transmitting some diseases for human. Vector control strategies normally involve the use of synthetic larvacide however excessive use causes adverse effect on environmental quality and resistance to larvae. Thus, extract from soursop (Annona muricata L.) leaves may be an alternative sources of larval control agents. The present study reveals the larvicidal effect of methanol extract from soursop leaves against 3rd instar larvae of Culex sp. mortality. Experimental method with Completely Randomized Design (CRD) consisting of 6 treatments (0 ppm, 125 ppm, 250 ppm, 500 ppm, 1000 ppm and 2000 ppm) and 4 replications was carried out with Culex sp. 3rd instar larvae and the larval mortality was recorded after 48 hours since the exposure. Data were analyzed using one way ANOVA which was continued by Tukey HSD and followed by Probit analyze to determine LC50 and LC90. The results showed that the methanol extract of soursop leaves exhibited significant larvicidal activity with LC50 and LC90 values are 586,099 and 2119,645 ppm. The highest concentration increases the mortality rate of larvae. In conclusion, the soursop leaves methanol extract has a larvacide effect on the 3rd larvae of Culex sp.

PENINGKATAN KEBUGARAN JASMANI SISWA MELALUI AKTIVITAS FISIK RINGAN DALAM PEMBELAJARAN PJOK

During the Covid-19 pandemic, health is important for daily human activities. One of the ways to maintain health is to improve physical fitness in the body through physical activity, one of which is through PJOK learning. This study was to determine the effect of light physical activity in learning PJOK on the level of physical fitness of students. This study uses an experimental method with One Group Pretest-Posttest design which will be concluded later. The sampling technique used was purposive sampling with a population of all class IX students at SMP Negeri 2 Kawedanan Magetan for the 2021/2022 academic year. The selected class is class IX A which has the same criteria for physical activity in PJOK learning. This research consists of three stages, the first is pre-test, the second is giving treatment in the form of PJOK learning, and the third stage is post-test. The instrument to measure the pre-test and post-test scores in this study was the multi-stage fitness test. Data analysis in this study used normality, homogeneity and t-test techniques. The results of this study were an increase in light physical activity in learning physical education, sports and health on the level of physical fitness of students. It is evident from the tcount (40.96) &gt; ttable (1.720), the pre-test result was 25.89 ml/kg/minute and the post-test was 28.96 or an increase of 11%. The conclusion of this study is that there is an increase in students' physical fitness through light physical activity in PJOK learning.

Desain Media Filter Bioring dan Karbon Blok untuk mengurangi Kesadahan Zat Kapur Air Sumur Gali

Clean water is needed in fulfilling human needs to carry out all activities so it is necessary to know how water is said to be clean in terms of quality and can be used in adequate quantities in human daily activities. In terms of quality, there are several requirements that must be met, including physical quality consisting of odor, color, and taste, chemical quality consisting of pH, hardness, and so on as well as biological quality where the water is free from disease-causing microorganisms. One of the water sources that are still widely used by the people of Pulau Tenga Village, Kec. Menui Central Sulawesi Islands is dug well water. The high hardness value in the dug well water is 518 mg/L. It can be identified that the water contains lime so it is necessary to treat water in order to meet clean water quality standards. One of the efforts that must be done is to make water treatment by filtration. The design of the filter device is made of pipe measuring 3 inches. The filter material used for the bioring, water filter, zeolite, and activated carbon which is applied in a water filtration filter device with a filtration flow rate of 0.006 L/s.

Gaze Target Estimation Inspired by Interactive Attention

As an essential nonverbal cue, the human gaze reveals human intentions and plays a crucial role in human daily activities. Therefore, automatic detection of the person’s gaze target has drawn the interests of the computer vision community. This is useful not only for identifying whether children are attentive in class but also for locating items of interest to humans in retail settings. Existing gaze-following methods have only explored and exploited the scenes context and the head cues. Considering the significance of human-object interaction in understanding human intentions, we present the Visual-Spatial Graph and introduce a graph attention network to analyze the interaction probability between the human and elements in the scene. Then the interaction probability inferred from the visual-spatial information that is aggregated by the attention mechanism can be transformed into an interactive attention map that depicts the areas people care about. In addition, we construct a transformer as an encoder to integrate the features extracted by the scene and head pathways aiming to decode the gaze target. After introducing interactive attention, our proposed method achieves outstanding performance on two benchmarks: GazeFollow and VideoAttentionTarget. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/nkuhzx/VSG-IA</uri> .

A Single Smart Ring for Monitoring 20 Kinds of Multi‐Intensity Daily Activities––From Kitchen Work to Fierce Exercises

Human Activity Recognition In article number 2200204, Yuliang Zhao, Chao Lian, Wen Jung Li and co-workers propose a smart ring to monitor human daily activities driven by a hierarchical decision algorithm. With this portable, reliable, and high-accuracy human activity recognition solution, users only need to wear the ring on the index finger, and then 20 common activities can be accurately identified.

Deep learning on multi-view sequential data: a survey.

With the progress of human daily interaction activities and the development of industrial society, a large amount of media data and sensor data become accessible. Humans collect these multi-source data in chronological order, called multi-view sequential data (MvSD). MvSD has numerous potential application domains, including intelligent transportation, climate science, health care, public safety and multimedia, etc. However, as the volume and scale of MvSD increases, the traditional machine learning methods become difficult to withstand such large-scale data, and it is no longer appropriate to use hand-craft features to represent these complex data. In addition, there is no general framework in the process of mining multi-view relationships and integrating multi-view information. In this paper, We first introduce four common data types that constitute MvSD, including point data, sequence data, graph data, and raster data. Then, we summarize the technical challenges of MvSD. Subsequently, we review the recent progress in deep learning technology applied to MvSD. Meanwhile, we discuss how the network represents and learns features of MvSD. Finally, we summarize the applications of MvSD in different domains and give potential research directions.

Palladium/Graphene Oxide Nanocomposite for Hydrogen Gas Sensing Applications Based on Tapered Optical Fiber.

Gaseous pollutants such as hydrogen gas (H2) are emitted in daily human activities. They have been massively studied owing to their high explosivity and widespread usage in many domains. The current research is designed to analyse optical fiber-based H2 gas sensors by incorporating palladium/graphene oxide (Pd/GO) nanocomposite coating as sensing layers. The fabricated multimode silica fiber (MMF) sensors were used as a transducing platform. The tapering process is essential to improve the sensitivity to the environment through the interaction of the evanescent field over the area of the tapered surface area. Several characterization methods including FESEM, EDX, AFM, and XRD were adopted to examine the structure properties of the materials and achieve more understandable facts about their functional performance of the optical sensor. Characterisation results demonstrated structures with a higher surface for analyte gas reaction to the optical sensor performance. Results indicated an observed increment in the Pd/GO nanocomposite-based sensor responses subjected to the H2 concentrations increased from 0.125% to 2.00%. The achieved sensitivities were 33.22/vol% with a response time of 48 s and recovery time of 7 min. The developed optical fiber sensors achieved excellent selectivity and stability toward H2 gas upon exposure to other gases such as ammonia and methane.

Modelling Demand for Energy Sources as Alternative Energy in the Province of North Sumatra

Energy is needed to support daily human activities. Energy usage for the necessities of life is always increasing every year. So far, the energy used comes from fossils, namely coal and oil. Every year fossil energy reserves are decreasing. To overcome these problems, a potential alternative energy source that can replace fossil energy is needed, namely renewable energy. Potential of renewable energy is one of the alternative sources of energy supply and security in the Province of North Sumatra. To ensure the resilience of renewable energy to run sustainably. It is necessary to analyze the potential of renewable energy in North Sumatra by using the Long-range Energy Alternatives Planning System. The results obtained show that North Sumatra has a great potential of energy sources as alternative energy, namely hydro energy and wind energy. It is forecasted that for the year 2027, hydro energy has a potential growth of 3,7 million megawatt-hours and wind energy has a potential growth of 2,2 million megawatt-hours.

A review on production and implementation of hydrogen as a green fuel in internal combustion engines

Huge and continuously growing non-renewable energy consumption due to human daily activities is accountable for the fossil fuel source crisis in recent decades. The growing concern about the emissions from internal combustion engines also impels the development of new energy sources to replace or reduce conventional non-renewable energy usage. In this context, hydrogen is found to be a promising solution in internal combustion engines to address these issues. The novelty of this review is to provide an overview of the use of hydrogen as internal combustion fuel, covering the operations in both spark-ignition (SI) and compression-ignition (CI) engines. Majority of the studies had shown that hydrogen enrichment fuels marked incredible engine performance in terms of thermal efficiency, fuel consumption and energy consumption. In addition, reductions in exhaust emissions such as smoke, soot, HC, CO, CO2 and NOx can be achieved in both SI and CI engines with proper operating conditions. Moreover, outstanding combustion behaviours were observed in both internal combustion engines with the application of hydrogen fuel. These enhancements were mainly attributed to the physicochemical properties of hydrogen, which exhibits higher calorific value and rapid flaming speed as discussed in this paper. To summarize, hydrogen utilisation in the IC and SI engines aided improvements in engine performance, exhaust emissions, and combustion behaviours under appropriate operating conditions and minor engine modifications such as ignition system and iridium spark plug for SI engines.

Room temperature operated hydrogen sensor using palladium coated on tapered optical fiber

Gaseous pollutants such as hydrogen gas (H2) are present in daily human activities and have been studied extensively due to their high explosive and widespread use in many fields. A common H2 gas detector is electrically based. Although these electrical or conductometric sensors attain high sensitivity, they suffer from drawbacks, including poor selectivity, high operating temperature, and susceptibility to electromagnetic interference, which the optical-based sensor can improve. This study describes the development of a palladium-coated (Pd) optical fiber for the room temperature (H2) hydrogen application process. To improve the evanescent light field that propagates through the fiber, a multimode fiber was used to fabricate a transducing channel with cladding and core diameters of 125 μm and 62.5 μm respectively. The multimode optical fibers were tapered from 125 μm cladding diameters to 20 μm diameter, 10 mm waist-length, 5 mm to the up and down tapered region, and coated with Pd by using the drop-casting technique. Various characterization techniques have been used to characterize palladium, such as Field Emission Scanning Electron Microscopy (FESEM), X-ray energy scattering (EDX), X-ray Diffraction (XRD), and atomic force microscopy (AFM). The fabricated Pd-based sensor operates safely at room temperature with a gas concentration of 0.125 % to 2.00 % H2. The measured sensitivity, response, and recovery time were 18,645 %, 50 s, and 230 s, respectively. The selectivity of the fabricated sensor toward H2 was affirmed with no response to other gases such as ammonia (NH3) and methane (CH4). However, this study demonstrates reliable, efficient, and reproducible H2 detection using a simple and cost-effective method.