Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Activity Recognition With Smartphone and Wearable Sensors Using Deep Learning Techniques: A Review

Human Activity Recognition (HAR) is a field that infers human activities from raw time-series signals acquired through embedded sensors of smartphones and wearable devices. It has gained much attraction in various smart home environments, especially to continuously monitor human behaviors in ambient assisted living to provide elderly care and rehabilitation. The system follows various operation modules such as data acquisition, pre-processing to eliminate noise and distortions, feature extraction, feature selection, and classification. Recently, various state-of-the-art techniques have proposed feature extraction and selection techniques classified using traditional Machine learning classifiers. However, most of the techniques use rustic feature extraction processes that are incapable of recognizing complex activities. With the emergence and advancement of high computational resources, Deep Learning techniques are widely used in various HAR systems to retrieve features and classification efficiently. Thus, this review paper focuses on providing profound concise of deep learning techniques used in smartphone and wearable sensor-based recognition systems. The proposed techniques are categorized into conventional and hybrid deep learning models described with its uniqueness, merits, and limitations. The paper also discusses various benchmark datasets used in existing techniques. Finally, the paper lists certain challenges and issues that require future research and improvements.
Finite-Time Stability and Stabilization of Switched Linear Time-Varying Systems with Time-Varying Delay

This paper deals with the finite-time stability (FTS) of switched linear time-varying (SLTV) systems with time-varying delay. Firstly, based on Lyapunov–Krasovskii functional technique and average dwell time (ADT) approach, a sufficient criterion on FTS for SLTV systems with time-varying delay is obtained. For the SLTV system with delay and control input, based on the criterion, a state feedback controller is designed such that the closed-loop system is finite-time stable (FTS). Finally, an example is employed to illustrate the validity of our results.
Digitalization Strategy and Convergence of Media Adaptation in the Digital Era (case study on TVRI Yogyakarta)

Recently, the digital era is evidenced by new technologies that contribute to the development of all activities of human life. Currently, the television industry is affected by the presence of digital television and media convergence due to the rapid technological development and the existence of the 4.0 industry revolution. The media industry is required to move on with the updated technology to provide its content in a good and rapid way. This study aims to analyze the issues facing TVRI Yogyakarta while trying to adapt the digitalization strategy and convergence of media in the digital era. In this study, the researcher used a qualitative method. The results found out that digital technology uses a spectrum of higher quality in terms of video, audio, and television data sets, Digital Television Broadcasting (DVB) can support more than one program on the same bandwidth channel. Shifting from analog to digital and competition are some of the problems faced by TVRI Yogyakarta while trying to adapt the digitalization strategy. To avoid facing digitalization strategy challenges, all Public Broadcasting Institutions engaged in television need to prepare themselves well to remain competitive and show their quality.
An improved bio-inspired based intrusion detection model for a cyberspace

Abstract Bio-inspired intrusion detection solutions provide better detection accuracy than conventional solutions in securing cyberspace. However, existing bio-inspired anomaly-based intrusion detection systems are still faced with challenges of high false-positive rates because the algorithms were tuned with unpredictable user-defined parameters, which led to premature convergence, exploration and exploitation discrepancies, algorithm complexity, and unrealistic results. In this paper, an intrusion detection system based on the foraging behavior of the social spider was developed. It employed signal transmission variables such as frequency of vibration to achieve a system that can evaluate real-life signals transmitted by computers and computing devices in the cyberspace to detect intrusion. This intrusion detection system was formulated using a social spider colony optimization model to generate a classifier that was tested using the standard NSL-KDD and live network traffic OAUnet datasets. The performance of the proposed intrusion detection system was evaluated by benchmarking it with existing classifiers using detection accuracy, sensitivity, and specificity as performance metrics. Results showed that the proposed model was more effective in terms of higher detection accuracy, sensitivity, specificity, and f-measure with a low false-positive rate. This showed that the spider model is a robust computational scheme that improves intrusion detection with a minimal false-positive rate in cyberspace.
Environmental Impacts of Milking Cows in Latvia

Increasing pressures surrounding efficiency and sustainability are key global drivers in dairy farm management strategies. However, for numerous resource-based, social, and economic reasons sustainable intensification strategies are herd-size dependent. In this study, we investigated the environmental impacts of Latvia’s dairy farms with different management practices. The herd size-dependent management groups varied from extensively managed small herds with 1–9 cows, extending to stepwise more intensively managed herds with 10–50, 51–100, 100–200, and over 200 milking cows. The aim is to compare the environmental impacts of different size-based production strategies on Latvia’s dairy farms. The results show that the gross greenhouse gas emissions differ by 29%: from 1.09 kg CO2 equivalents (CO2e) per kg of raw milk for the farms with 51–100 cows, down to 0.84 kg CO2e/kg milk for farms with more than 200 cows. However, the land use differs even more—the largest farms use 2.25 times less land per kg of milk than the smallest farms. Global warming potential, marine eutrophication, terrestrial acidification, and ecotoxicity were highest for the mid-sized farms. If current domestic, farm-based protein feeds were to be substituted with imported soy feed (one of the most popular high-protein feeds) the environmental impacts of Latvian dairy production would significantly increase, e.g., land use would increase by 18% and the global warming potential by 43%. Environmental policy approaches for steering the farms should consider the overall effects of operation size on environmental quality, in order to support the best practices for each farm type and steer systematic change in the country. The limitations of this study are linked to national data availability (e.g., national data on feed production, heifer breeding, differences among farms regards soil type, manure management, the proximity to marine or aquatic habitats) and methodological shortcomings (e.g., excluding emissions of carbon sequestration, the use of proxy allocation, and excluding social and biodiversity impacts in life-cycle assessment). Further research is needed to improve the data quality, the allocation method, and provide farm-size-specific information on outputs, heifer breeding, manure storage, and handling.
Dimension-Reduced Rx Beamforming Optimized for Simultaneous Detection and Estimation

Phased array radar systems are indispensable in many applications requiring robust sensing of the environment. To achieve sensitive target detection and accurate direction-of-arrival (DOA) estimation, a large number of receiving antenna elements are needed. The high dimension of the element-level data inevitably leads to a large computational burden for digital signal processing. This problem can be overcome by transforming the element-level data into a lower dimensional beamspace. In this article, we present a novel parameter-controlled design method to construct this transformation. If the dimension reduction is not too drastic, it jointly achieves optimal detection and DOA estimation performance. Otherwise, it meets predefined performance criteria by exploiting an acceptable tradeoff between detection and DOA estimation performance. We propose a general design tool, which is not limited to a specific array configuration. It comprises a precalculated set of plots, providing the radar designer an overview of possible performance for a given scenario. We describe a straightforward method to construct the corresponding transformation. Numerical studies highlight the superiority of the proposed design method.
Image Scale Estimation Using Surface Textures for Quantitative Visual Inspection

In this study, a learning-based scale estimation technique is proposed to enable quantitative evaluation of inspection regions. The underlying idea is that surface texture of structures (i.e. bridges or buildings) captured on images contains the scale information of the corresponding images, which is represented by pixel per physical dimension (e.g., mm, inch). This allows training a regression model that provides a relationship between surface textures on images and their corresponding scales. Deep convolutional neural network is used to extract scale-related features from the texture patches and estimate their scales. The trained model can be exploited to estimate scales for all images captured from structure surfaces that have similar textures. The capability of the proposed technique is fully demonstrated using data collected from surface textures of three different structures and achieves an overall average scale estimation error of less than 15%.
On the Robustness of Diffusion in a Network Under Node Attacks

How can we assess a network's ability to maintain its functionality under attacks? Network robustness has been studied extensively in the case of deterministic networks. However, applications such as online information diffusion and the behavior of networked public raise a question of robustness in probabilistic networks. We propose three novel robustness measures for networks hosting a diffusion under the Independent Cascade or Linear Threshold model, susceptible to attacks by an adversarial attacker who disables nodes. The outcome of such a process depends on the selection of its initiators, or seeds, by the seeder, as well as on two factors outside the seeder's discretion: the attacker's strategy and the probabilistic diffusion outcome. We consider three levels of seeder awareness regarding these two uncontrolled factors, and evaluate the network's viability aggregated over all possible extents of an attack. We introduce novel algorithms from building blocks found in previous works to evaluate the proposed measures. A thorough experimental study with synthetic and real, scale-free and homogeneous networks establishes that these algorithms are effective and efficient, while the proposed measures highlight differences among networks in terms of robustness and the surprise they furnish when attacked. Last, we devise a new measure of diffusion entropy, and devise ways to enhance the robustness of probabilistic networks.
Current Solutions and Future Trends for Robotic Prosthetic Hands

The desire for functional replacement of a missing hand is an ancient one. Historically, humans have replaced a missing limb with a prosthesis for cosmetic, vocational, or personal autonomy reasons. The hand is a powerful tool, and its loss causes severe physical and often mental debilitation. Technological advancements have allowed the development of increasingly effective artificial hands, which can improve the quality of life of people who suffered a hand amputation. Here, we review the state of the art of robotic prosthetic hands (RPHs), with particular attention to the potential and current limits of their main building blocks: the hand itself, approaches to decoding voluntary commands and controlling the hand, and systems and methods for providing sensory feedback to the user. We also briefly describe existing approaches to characterizing the performance of subjects using RPHs for grasping tasks and provide perspectives on the future of different components and the overall field of RPH development.
Richness patterns of endemic and threatened conifers in south-west China: topographic-soil fertility explanation

Understanding the relationships between species richness patterns and environment constitutes a key issue in biogeography and conservation strategies. To our knowledge, this is the first integrative study that incorporates soil and human-influence data into species richness modelling. Our aims were to (a) estimate the richness patterns of four conifers groups (all conifers species, endemics, threatened, and endemic-threatened species) in south-west China, (b) assess the relative importance of environmental predictors (energy, water, climate, topography, and soil) and the human-influence on the conifers richness patterns and (c) identify hotspot ecoregions, nature reserves, or important plant areas as priority conservation areas. Generalized linear models and hierarchical partitioning were used by correlating 8962 distributional records of 97 conifer species with different environmental drivers. Results indicated that central Sichuan, northern Sichuan, northern Yunnan, and the southern areas of the Hengduan mountains were identified as distinct centres of conifers richness in China. Topographic heterogeneity and soil fertility were the strongest drivers of conifer richness patterns, while climate, energy, water, and human drivers were contributed to a lower degree. The identified conifers’ important areas were mostly located outside of the existing nature reserves but inside the ecoregions. Our findings emphasize that incorporating soil data into spatial modelling provides great insights for the conservation of conifers species. We recommend conservationists to use soil variables and other environmental data to generate a comprehensive understanding of the key drivers underlying the patterns of conifer diversity and distribution.
Non-pollen palynomorphs in deep time: unravelling the evolution of early eukaryotes

Abstract Most of the Precambrian (>538 Ma) fossil record, which includes the time before the onset of macroscopic multicellular life, consists of minute organically preserved remains of soft-bodied micro-organisms, i.e. non-pollen palynomorphs (NPP). These microfossils include single-celled prokaryotic or eukaryotic organisms, filamentous sheets and bacterial cellular aggregates, and occur in marine and lacustrine sediments through most of the Earth's history. Ancient NPP have informed our understanding of one of the biggest evolutionary steps in the history of life: the origin of the eukaryotic cell and the subsequent diversification of eukaryotic life before the evolution of macroscopic forms. The oldest widely accepted eukaryotic microfossils are large cells with opening structures and ornamentation from upper Paleoproterozoic units in China and India. NPP diversify through Meso- and Neoproterozoic Eras, notably recording the oldest multicellular life around 1 billion years ago. In the latest Neoproterozoic, during the advent of animal life, the NPP record allows us to study the cryptic evolution of animals while they were exclusively small and poorly or non-mineralized before the appearance of organisms with hard parts. We review the current understanding of the early eukaryotic evolution and major advances in Precambrian palaeontology through the lens of the NPP record in deep time.
The use of serum urea nitrogen concentrations for the determination of lysine requirements of gilts

Abstract Seven experiments were conducted to evaluate the serum urea nitrogen (SUN) method of estimating the lysine requirement of 20-, 35-, 50-, and 80-kg cross-bred barrows. Treatments within each experiment were replicated three times with four barrows per replicate (except Exp. 4, replicates 1 and 2 had three barrows per replicate; Exp. 6, replicates 1 and 2 had five barrows per replicate). Initial BW were 17.2 kg in Exp. 1; 35.2 and 37.0 kg in Exp. 2 and 3; 49.8 and 52.1 kg in Exp. 4 and 5; and 79.8 and 81.1 kg in Exp. 6 and 7. The lysine levels fed ranged from 0.35 to 1.25%, depending on the weight of the barrows. Within an experiment, all treatment diets contained the concentration of corn and soybean meal of the diet with the lowest level of lysine. With the exception of lysine, the diets met or exceeded the ideal amino acid pattern for all indispensable amino acids for the diet containing the highest level of lysine. All treatment diets were isonitrogenous and equal in electrolyte balance (Na+K-Cl). An initial blood sample was taken from all barrows for initial SUN determination. Barrows were fed treatment diets for a minimum of 6 d before blood samples were collected for final SUN determination. All experiments were randomized complete block designs and initial SUN was used as a covariate for final SUN. As dietary lysine increased, final SUN decreased (linear, P P
Comparison of the Effectiveness of Ultrasound-Guided Prolotherapy in Supraspinatus Tendon with Ultrasound-Guided Corticosteroid Injection of Subacromial Subdeltoid Bursa in Rotator Cuff-Related Shoulder Pain: A Clinical Trial Study

Background: Shoulder pain is the third most common type of musculoskeletal disorder and rotator cuff (RC) tendinopathy is the most frequent diagnosis. Ultrasound is the most preferable guidance tool for diagnostic and interventional purposes. The aim of this study is to compare the effectiveness of the prolotherapy injection with corticosteroid injection in patients with RC dysfunction. Materials and Methods: Thirty to sixty-five-year-old patients with chronic RC disease were divided into two groups. Ultrasound-guided dextrose prolotherapy of supraspinatus tendon was done for one group and ultrasound-guided corticosteroid injection in the subacromial bursa was done for the other groups. Visual analog scale (VAS) and Shoulder Pain and Disability Index (SPADI) were evaluated for both groups at baseline, 3 and 12 weeks after injections. Results: Thirty-three patients were included in the result. Both the groups showed significant improvement in VAS and SPADI scores in 3 and 12 weeks after injections compared with preinjection times with no difference between two groups neither in 3 weeks nor in 12 weeks after injections. Conclusion: Both ultrasound-guided dextrose prolotherapy and CS injections are effective in the management of RC-related shoulder pain in both short-term and long-term with neither being superior to the other. Therefore, prolotherapy may be a safe alternative therapy instead of corticosteroid injection due to lack of its side effects.
Arthropod facilitation mediated by abandoned dead domatia

In ant–plant mutualistic interactions, plants provide shelter (domatia) and/or food to ants and in exchange, and ants provide protection against herbivores. After plant tissue senescence, ants are expected to abandon dead domatia, leaving these empty spaces available for other arthropods. In this study, we tested for the role of the mutualistic interaction between Cordia alliodora and Azteca pittieri in promoting new habitats for arthropods through abandoned dead domatia. We predicted that species richness, abundance, and colonization frequency of secondary arthropods would be greater in dead branch domatia (dead domatia) compared with live branch domatia (live domatia). During March 2019, we selected 38 C. alliodora trees in a Mexican tropical dry forest. For each tree, we collected five live and five dead domatia, for 380 domatia in total. We found six morphospecies of secondary arthropods colonizing live domatia, while 42 were present in dead domatia. Ants were the most species-rich group (10 species) in abandoned dead domatia and utilized them as nesting sites (25 nests). Secondary arthropod species richness, abundance, and colonization frequency were greater in dead domatia compared with live domatia. We concluded that the Azteca–Cordia mutualistic interaction is an important habitat facilitator by promoting new habitats for arthropods through abandoned dead domatia.
Imperative Dynamic Routing Between Capsules Network for Malaria Classification

Malaria is a severe epidemic disease caused by Plasmodium falciparum. The parasite causes critical illness if persisted for longer durations and delay in precise treatment can lead to further complications. The automatic diagnostic model provides aid for medical practitioners to avail a fast and efficient diagnosis. Most of the existing work either utilizes a fully connected convolution neural network with successive pooling layers which causes loss of information in pixels. Further, convolutions can capture spatial invariances but, cannot capture rotational invariances. Hence to overcome these limitations, this research, develops an Imperative Dynamic routing mechanism with fully trained capsule networks for malaria classification. This model identifies the presence of malaria parasites by classifying thin blood smears containing samples of parasitized and healthy erythrocytes. The proposed model is compared and evaluated with novel machine vision models evolved over a decade such as VGG, ResNet, DenseNet, MobileNet. The problems in previous research are cautiously addressed and overhauled using the proposed capsule network by attaining the highest Area under the curve (AUC) and Specificity of 99.03% and 99.43% respectively for 20% test samples. To understand the underlying behavior of the proposed network various tests are conducted for variant shuffle patterns. The model is analyzed and assessed in distinct environments to depict its resilience and versatility. To provide greater generalization, the proposed network has been tested on thick blood smear images which surpassed with greater performance.
Development of TPACK of chemistry teacher on electrolyte and non-electrolyte topic through lesson study

This research aims to develop the Technological Pedagogical Content Knowledge (TPACK) of chemistry teachers in teaching Electrolyte and Non-Electrolytes through Lesson Study. The research method used is a qualitative method with an interpretive paradigm. The sample in this study is the chemistry teachers at SMAN 1 Talaga and SMAN 1 Bantarujeg, Majalengka. The data collection instruments are TPACK questionnaires, observations, interviews, and reflective journals. Stages of research following the stages of the lesson study activities consist of 1) making lesson plans that are integrated with TPACK components, 2) implementing lesson plans in learning and observing the learning process, and 3) reflecting learning activities. The data analysis method used is the analysis method of Miles and Huberman. The results showed that the abilities of TPACK chemistry teachers before lesson study activities were at no perception level, where teachers could not implement learning technology. After doing the lesson study activities, TPACK teachers have developed from the perception level in the first cycle of lesson study to the conception level in the second cycle. So, it can be concluded that lesson study is one of the effective alternative methods to develop TPACK of chemistry teachers.