Search Results (57)

Long-term manual material handling (MMH) work leads to the trend of the younger onset of work-related musculoskeletal disorders (WMSDs), with low back pain (LBP) being the most common, which causes great trouble for both society and patients. To effectively prevent LBP and provide support for workers engaged in MMH work, wearable lumbar assistive exoskeletons have played a key role in industrial scenarios. This paper divides wearable lumbar assistive exoskeletons into powered, unpowered, and quasi-passive types, systematically reviews the research status of each type of exoskeleton, and compares and discusses the key factors such as driving mode, mechanical structure, control strategy, performance evaluation, and human–machine interaction. It is found that many studies focus on the assistive performance, human–machine coupling coordination, and adaptability of wearable lumbar assistive exoskeletons. At the same time, the analysis results show that there are many types of performance evaluation indicators, but a unified and standardized evaluation method and system are still lacking. This paper analyzes current research findings, identifies existing issues, and provides recommendations for future research. This study provides a theoretical basis and design ideas for the development of wearable lumbar assistive exoskeleton systems. Full article

The article analyzes food rescue application reviews from around the world. The analysis was conducted using text mining methods, including LDA, which we used to extract the main topics in user-generated discussions of the applications. It identified eight main themes in user feedback on food rescue applications: (1) Application setup, features and general user experience, (2) User satisfaction and feedback, (3) Product management and organization, (4) Convenience, ease of use and time management, (5) Recipe discovery, product utilization and meal ideas, (6) Application updates and technical support, (7) Sustainability and environmental impact, and (8) Social interaction and sharing. All of them are indirectly related to factors derived from the unified theory of acceptance and use of technology (UTAUT). Our study aligns with a research trend concerning how digital technologies can support sustainable food consumption. Full article

The purpose of this study was to analyse educators’ decisions on the continued use of the virtual learning environment (VLE) Blackboard and its associated e-learning technologies in the classroom within the public school system. This cross-sectional descriptive quantitative research collected 306 responses from educators in 30 public schools in Gauteng Province, South Africa. The results revealed that the empirical data’s mean performance expectancy (PEY) was lower than the ‘agree’ range of the hypothesised population, implying that the educators’ assumption is that the deployed technology does not improve their work performance. Furthermore, the results showed that learning tradition (LTD) has a complementary partial mediation effect on the relationship between PEY and continued use intention (CUI). Additionally, facilitating conditions (FCCs) also have a complementary partial mediation effect on the relationship between PEY and CUI. Conditional mediation (CoMe) from the path SOI x PEY -> LTD -> CUI was statistically significant. In probing the conditional indirect effect, the results showed that, if the social influence (SOI) increased, the mediation effect of LTD decreases. On the contrary, if it decreased, the mediation effect of LTD increased. This was also evident in the Johnson-Neyman plot. SOI did not moderate the mediation effect of FCC on the relationship between PEY and CUI. This study concludes that social and operational factors highly influence the dynamics of continued use of VLE and its associated e-learning technologies and cannot be discounted by practitioners and policy-makers in their quest to increase technology use in the school system. This study contributes to the unified technology acceptance and use theory model (UTAUT), advancing the idea that facilitating conditions and learning traditions can be mediators and social influence moderators within certain contexts and research settings. Full article

Human factors are the most important factor in road traffic crashes. Human-caused traffic crashes can be reduced through the active safety system of vehicles. Road hypnosis is an unconscious driving state caused by the combination of external environmental factors and the driver’s psychological state. When drivers fall into a state of road hypnosis, they cannot clearly perceive the surrounding environment and make various reactions in time to complete the driving task, and driving safety is greatly affected. Therefore, road hypnosis identification is of great significance for the active safety of vehicles. A road hypnosis identification model based on XGBoost—Hidden Markov is proposed in this study. Driver data and vehicle data related to road hypnosis are collected through the design and conduct of vehicle driving experiments. Driver data, including eye movement data and EEG data, are collected with eye movement sensors and EEG sensors. A mobile phone with AutoNavi navigation is used as an on-board sensor to collect vehicle speed, acceleration, and other information. Power spectrum density analysis, the sliding window method, and the point-by-point calculation method are used to extract the dynamic characteristics of road hypnosis, respectively. Through normalization and standardization, the key features of the three types of data are integrated into unified feature vectors. Based on XGBoost and the Hidden Markov algorithm, a road hypnotic identification model is constructed. The model is verified and evaluated through visual analysis. The results show that the road hypnosis state can be effectively identified by the model. The extraction of road hypnosis-related features is realized in non-fixed driving routes in this study. A new research idea for road hypnosis and a technical scheme reference for the development of intelligent driving assistance systems are provided, and the life identification ability of the vehicle intelligent cockpit is also improved. It is of great significance for the active safety of vehicles. Full article

High-dimensional data have attracted considerable interest from researchers, especially in the area of variable selection. However, when dealing with time-to-event data in survival analysis, where censoring is a key consideration, progress in addressing this complex problem has remained somewhat limited. Moreover, in microarray research, it is common to identify groupings of genes involved in the same biological pathways. These gene groupings frequently collaborate and operate as a unified entity. Therefore, this study is motivated to adopt the idea of a penalized semi-parametric Bayesian Cox (PSBC) model through elastic-net and group lasso penalty functions (PSBC-EN and PSBC-GL) to incorporate the grouping structure of the covariates (genes) and optimally perform variable selection. The proposed methods assign a beta process prior to the cumulative baseline hazard function (PSBC-EN-B and PSBC-GL-B), instead of the gamma process prior used in existing methods (PSBC-EN-G and PSBC-GL-G). Three real-life datasets and simulation scenarios were considered to compare and validate the efficiency of the modified methods with existing techniques, using Bayesian information criteria (BIC). The results of the simulated studies provided empirical evidence that the proposed methods performed better than the existing methods across a wide range of data scenarios. Similarly, the results of the real-life study showed that the proposed methods revealed a substantial improvement over the existing techniques in terms of feature selection and grouping behavior. Full article

Nowadays, much of the attention paid to building construction is focused on sustainability and environmental protection. The materials applied in construction should be safe and free of toxins, but they should also follow the idea of circular construction. Quests for materials with an appropriate structure and composition, unifying features of a construction, insulation (thermally and acoustically), and environmentally friendly material turned our attention in this paper toward expanded perlite (EP). This study aimed to analyze the results of the experimental determination of the basic physical and mechanical parameters of expanded perlite and pure perlite concrete blocks (PPC), i.e., containing 100% EP instead of sand, while in contrast, most existing studies focus only on the partial replacement of sand with EP. This research aims to confirm that PPC containing 100% EP is the product that meets the requirements for load-bearing walls in single-family buildings in European countries such as Poland. The study aimed to determine the procedure for preparing the samples of PPC, i.e., the mixing procedure, the displacement speed during compaction, and the maximum loading force during compaction. It was determined that the appropriate speed of compaction to form the samples is 15 mm per minute, i.e., the same as during the compressive strength tests. The maximum compaction force of 10,000 N during the preparation of samples at a speed of displacement of 15 mm per minute guarantees a compressive strength greater than 3 MPa for dry density class 650, and the method of forming the samples in a single layer, i.e., solid samples. Full article

COVID-19 is characterized by a striking similarity to premature aging. Its clinical manifestations range from asymptomatic to critical illness. No single central agent has been demonstrated so far. We present Klotho, an antiaging protein, as a key factor in COVID-19 pathophysiology. There is epidemiological evidence that both acute and chronic uses of Klotho agonists have a beneficial effect in reducing COVID-19 severity and mortality. A review of the PubMed epidemiological, clinical, and mechanistic evidence supports a role for Klotho deficit as a central determinant of severe COVID-19. Clinical data support the idea that chronic use of Klotho agonists protects against severe COVID-19 and that its acute use may be beneficial. We propose a unifying hypothesis that low Klotho levels play a key role in severe COVID-19, while increasing Klotho levels can have a beneficial effect through the prevention of acute kidney injury (AKI) and potential antiviral effects. Further research is needed. Full article

Planar underactuated mechanical systems have been a popular research issue in the area of mechanical systems and nonlinear control. This paper reviews the current research status of control methods for a class of planar underactuated manipulator (PUM) systems containing a single passive joint. Firstly, the general dynamics model and kinematics model of the PUM are given, and its control characteristics are introduced; secondly, according to the distribution position characteristics of the passive joints, the PUM is classified into the passive first joint system, the passive last joint system, and the passive intermediate joint system, and the analysis and discussion are carried out in respect to the existing intelligent control methods. Finally, in response to the above discussion, we provide a brief theoretical analysis and summarize the challenges faced by PUM, i.e., uncertainty and robustness of the system, unified control methods and research on underactuated systems with uncontrollable multi-passive joints; at the same time, the practical applications have certain limitations that need to be implemented subsequently, i.e., anti-jamming, multi-planar underactuated robotic arm co-control and spatial underactuated robotic arm system development. Aiming at the above challenges and problems in the control of PUM systems, we elaborate on them in points, and put forward the research directions and related ideas for future work, taking into account the contributions of the current work. Full article

Modeling and simulation have been used to study tsunamis for several decades. We created a review to identify the software and methods used in the last decade of tsunami research. The systematic review was based on the PRISMA methodology. We analyzed 105 articles and identified 27 unique software and 45 unique methods. The reviewed articles can be divided into the following basic categories: exploring historical tsunamis based on tsunami deposits, modeling tsunamis in 3D space, identifying tsunami impacts, exploring relevant variables for tsunamis, creating tsunami impact maps, and comparing simulation results with real data. Based on the outcomes of this review, this study suggests and exemplifies the possibilities of system dynamics as a unifying methodology that can integrate modeling and simulation of most identified phenomena. Hence, it contributes to the development of tsunami modeling as a scientific discipline that can offer new ideas and highlight limitations or a building block for further research in the field of natural disasters. Full article

Despite much research being conducted within the scope of sustainable tourism, more progress has yet to be made in defining how close or far different countries are from achieving this goal. Consequently, this paper aims to evaluate and compare the commitment of citizens, as tourists, from the 27 member states of the European Union to sustainable tourism. A map of sustainability was developed through the use of machine learning algorithms. A cluster analysis was performed, followed by a sustainable rating. The main findings indicate the existence of three country segments among the European Union member states according to the involvement of its citizens as tourists with sustainable practices: highly committed, moderately committed, and uncommitted. Based on these segments, we proposed a recommendation system that suggests the top-five countries where tourists could exchange sustainable tourism habits based on the idea of contagion or imitation behaviours among individuals across an extensive set of everyday decisions. The results reveal significant variations in sustainable tourism practices across member states, highlighting both challenges and opportunities for harmonisation. By implementing this recommendation system, we facilitate the adoption of sustainable habits among tourists and stakeholders, driving a more unified approach to sustainability in the multiple tourism destinations. This study shows no convergence between the 27 European Union member states regarding sustainable tourism. Therefore, political policies are necessary so that all citizens converge on sustainable tourist habits and the European Union contributes, as a whole, to sustainable tourism. Full article

Luo Shen 洛神 (The Goddess of the Luo River), also known as Fu Fei 宓妃 (Consort Fu), governed the Luo River and was a deity with distinctive representativeness and influence in the Luoyang area of China. To date, research has been centered around literary works and paintings, particularly Luoshen Fu 洛神賦, with little exploration into the belief of the Goddess of the Luo River. In this paper, specific and detailed textual research is made on the origins and historical transformations, as well as functional shifts, of the Goddess of the Luo River from the perspective of belief in the deity. Based on extant ancient documents and stele inscriptions and combined with anthropological field research, five new ideas are described. First, rituals honoring the Luo River were present in ancient times, yet the deity of the Luo River was initially a male entity called Luo Bo 洛伯, not the goddess Consort Fu. Second, Consort Fu first appears in Li Sao 離騷 as a goddess from the Kunlun Mountains. Third, during the Eastern Han dynasty (25–220), driven by the political and cultural needs of rulers to maintain regime stability, the Goddess of Kunlun, Consort Fu, became the Goddess of the Luo River. Fourth, in the medieval period, the image of the Goddess of the Luo River underwent a historic transformation, evolving from a deity governing the Luo River to a beauty yearning for secular love, merging with the historical figure of Zhen Fei 甄妃 (Consort Zhen) from the Cao Wei dynasty (220–266), forming a unified literary and artistic figure that significantly broadened the social influence of the Goddess of the Luo River. Fifth, the state-sponsored worship of the Goddess of the Luo River reached its peak during the Tang (618–907) and Song (960–1279) dynasties, and afterward, it gradually declined to a local folk belief due to a shift in the political center. The transition of the belief in the Goddess of the Luo River from official to folk realms, deeply intertwining with people’s lives, is a historical reflection of the eastward shift in the imperial center after the Tang dynasty. It also signifies a transformation of the function of the belief in the Goddess of the Luo River, from a political guardian deity ensuring the nation’s peace and the government’s stability to a protective deity for ordinary people’s family stability and prosperity of descendants. Full article

Novel therapies for the treatment of familial dilated cardiomyopathy (DCM) are lacking. Shaping research directions to clinical needs is critical. Triggers for the progression of the disorder commonly occur due to specific gene variants that affect the production of sarcomeric/cytoskeletal proteins. Generally, these variants cause a decrease in tension by the myofilaments, resulting in signaling abnormalities within the micro-environment, which over time result in structural and functional maladaptations, leading to heart failure (HF). Current concepts support the hypothesis that the mutant sarcomere proteins induce a causal depression in the tension-time integral (TTI) of linear preparations of cardiac muscle. However, molecular mechanisms underlying tension generation particularly concerning mutant proteins and their impact on sarcomere molecular signaling are currently controversial. Thus, there is a need for clarification as to how mutant proteins affect sarcomere molecular signaling in the etiology and progression of DCM. A main topic in this controversy is the control of the number of tension-generating myosin heads reacting with the thin filament. One line of investigation proposes that this number is determined by changes in the ratio of myosin heads in a sequestered super-relaxed state (SRX) or in a disordered relaxed state (DRX) poised for force generation upon the Ca²⁺ activation of the thin filament. Contrasting evidence from nanometer–micrometer-scale X-ray diffraction in intact trabeculae indicates that the SRX/DRX states may have a lesser role. Instead, the proposal is that myosin heads are in a basal OFF state in relaxation then transfer to an ON state through a mechano-sensing mechanism induced during early thin filament activation and increasing thick filament strain. Recent evidence about the modulation of these mechanisms by protein phosphorylation has also introduced a need for reconsidering the control of tension. We discuss these mechanisms that lead to different ideas related to how tension is disturbed by levels of mutant sarcomere proteins linked to the expression of gene variants in the complex landscape of DCM. Resolving the various mechanisms and incorporating them into a unified concept is crucial for gaining a comprehensive understanding of DCM. This deeper understanding is not only important for diagnosis and treatment strategies with small molecules, but also for understanding the reciprocal signaling processes that occur between cardiac myocytes and their micro-environment. By unraveling these complexities, we can pave the way for improved therapeutic interventions for managing DCM. Full article

This article argues that complexity scientists have been searching for a universal complexity in the form of a “theory of everything” since some important theoretical breakthroughs such as Bertalanffy’s general systems theory, Wiener’s cybernetics, chaos theory, synergetics, self-organization, self-organized criticality and complex adaptive systems, which brought the study of complex systems into mainstream science. In this respect, much attention has been paid to the importance of a “reductionist complexity science” or a “reductionist theory of everything”. Alternatively, many scholars strongly argue for a holistic or emergentist “theory of everything”. The unifying characteristic of both attempts to account for complexity is an insistence on one robust explanatory framework to describe almost all natural and socio-technical phenomena. Nevertheless, researchers need to understand the conceptual historical background of “complexity science” in order to understand these longstanding efforts to develop a single all-inclusive theory. In this theoretical overview, I address this underappreciated problem and argue that both accounts of the “theory of everything” seem problematic, as they do not seem to be able to capture the whole of reality. This realization could mean that the idea of a single omnipotent theory falls flat. However, the prospects for a “holistic theory of everything” are much better than a “reductionist theory of everything”. Nonetheless, various forms of contemporary systems thinking and conceptual tools could make the path to the “theory of everything” much more accessible. These new advances in thinking about complexity, such as “Bohr’s complementarity”, Morin’s Complex thinking, and Cabrera’s DSRP theory, might allow the theorists to abandon the EITHER/OR logical operators and start thinking about BOTH/AND operators to seek reconciliation between reductionism and holism, which might lead them to a new “theory of everything”. Full article

Atrial fibrillation (AF) is the most common type of cardiac rhythm disorder. Recent clinical and experimental studies reveal that environmental pollutants, including organophosphorus–organochloride pesticides and air pollution, may contribute to the development of cardiac arrhythmias including AF. Here, we discussed the unifying cascade of events that may explain the role of pollutant exposure in the development of AF. Following ingestion and inhalation of pollution-promoting toxic compounds, damage-associated molecular pattern (DAMP) stimuli activate the inflammatory response and oxidative stress that may negatively affect the respiratory, cognitive, digestive, and cardiac systems. Although the detailed mechanisms underlying the association between pollutant exposure and the incidence of AF are not completely elucidated, some clinical reports and fundamental research data support the idea that pollutant poisoning can provoke perturbed ion channel function, myocardial electrical abnormalities, decreased action potential duration, slowed conduction, contractile dysfunction, cardiac fibrosis, and arrhythmias including AF. Full article

The majority of the recent research on text similarity has been focused on machine learning strategies to combat the problem in the educational environment. When the originality of an idea is copied, it increases the difficulty of using a plagiarism detection system in practice, and the system fails. In cases like active-to-passive conversion, phrase structure changes, synonym substitution, and sentence reordering, the present approaches may not be adequate for plagiarism detection. In this article, semantic extraction and the quantum genetic algorithm (QGA) are integrated in a unified framework to identify idea plagiarism with the aim of enhancing the performance of existing methods in terms of detection accuracy and computational time. Semantic similarity measures, which use the WordNet database to extract semantic information, are used to capture a document’s idea. In addition, the QGA is adapted to identify the interconnected, cohesive sentences that effectively convey the source document’s main idea. QGAs are formulated using the quantum computing paradigm based on qubits and the superposition of states. By using the qubit chromosome as a representation rather than the more traditional binary, numeric, or symbolic representations, the QGA is able to express a linear superposition of solutions with the aim of increasing gene diversity. Due to its fast convergence and strong global search capacity, the QGA is well suited for a parallel structure. The proposed model has been assessed using a PAN 13-14 dataset, and the result indicates the model’s ability to achieve significant detection improvement over some of the compared models. The recommended PD model achieves an approximately 20%, 15%, and 10% increase for TPR, PPV, and F-Score compared to GA and hierarchical GA (HGA)-based PD methods, respectively. Furthermore, the accuracy rate rises by approximately 10–15% for each increase in the number of samples in the dataset. Full article