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Gloves are used at workplaces to protect hands and fingers from potential hazards. Three types of work gloves were assessed in terms of the strength of grip, carrying, and lifting. Thirty adults (14 males and 16 females) joined as human participants. The strength data were measured under bare hand and three gloved conditions. The grip spans in the grip strength measurements included 45 mm, 55 mm, 65 mm, and 75 mm. The carrying strength was measured for both dominant and non-dominant hands under leg straight and semi-squat postural conditions. The lifting strength was measured at a semi-squat posture. The results showed that glove (p < 0.0001), grip span (p = 0.001), gender (p < 0.0001), and handedness (p < 0.0001) all affected grip strength significantly. Wearing the gloves tested in this study led to a decrease of grip strength up to 22.9%, on average, depending on gender, grip span, and hand tested. Wearing the cotton gloves led to a decrease of one-handed carrying strength ranged from 3.5% to 9.7% for female participants. All the participants took advantages in carrying strength when wearing the cut-resistant gloves. The leg lifting strength data indicated that the effects of the gloves were insignificant. The information of this study is beneficial for practitioners in the design of manual materials handling tasks concerning the use of work gloves.

Internet technologies are emerging very fast nowadays, due to which web pages are generated exponentially. Web page categorization is required for searching and exploring relevant web pages based on users’ queries and is a tedious task. The majority of web page categorization techniques ignore semantic features and the contextual knowledge of the web page. This paper proposes a web page categorization method that categorizes web pages based on semantic features and contextual knowledge. Initially, the GloVe model is applied to capture the semantic features of the web pages. Thereafter, a Stacked Bidirectional long short-term memory (BiLSTM) with symmetric structure is applied to extract the contextual and latent symmetry information from the semantic features for web page categorization. The performance of the proposed model has been evaluated on the publicly available WebKB dataset. The proposed model shows superiority over the existing state-of-the-art machine learning and deep learning methods.

Points-of-interest (POIs) are an important carriers of location text information in smart cities and have been widely used to extract and identify urban functional regions. However, it is difficult to model the relationship between POIs and urban functional types using existing methods due to insufficient POIs information mining. In this study, we propose a Global Vectors (GloVe)-based, POI type embedding model (GPTEM) to extract and identify urban functional regions at the scale of traffic analysis zones (TAZs) by integrating the co-occurrence information and spatial context of POIs. This method has three main steps. First, we utilize buffer zones centered on each POI to construct the urban functional corpus. Second, we use the constructed corpus and GPTEM to train POI type vectors. Third, we cluster the TAZs and annotate the urban functional types in clustered regions by calculating enrichment factors. The results are evaluated by comparing them against manual annotations and food takeout delivery data, showing that the overall identification accuracy of the proposed method (78.44%) is significantly higher than that of a baseline method based on word2vec. Our work can assist urban planners to efficiently evaluate the development of and changes in the functions of various urban regions.

Abstract With the rapid growth of social media, sentiment analysis has received growing attention from both academic and industrial fields. One line of researches for sentiment analysis is to feed bag-of-words (BOW) text representation into classifiers. Usually, raw BOW requires weighting schemes to obtain better performance, where important words are given more weights while unimportant ones are given less weights. Another line of researches focuses on neural models, where distributed text representations are learned from raw texts automatically. In this paper, we take advantages of techniques in both lines of researches. We use words’ weights to guide neural models to focus on important words. Various supervised weighting schemes are explored in this work. We discover that better text features are learned for sentiment analysis when suitable weighting schemes are applied upon neural models.

Due to their geometry and thermal physiology, hands are most vulnerable to cold weather injuries and loss of dexterity. Gloves are the most common for hand protection during exposure to extreme thermal and hazardous environments. Although glove microclimate properties such as area factor, air gap thickness, and contact area play a significant role in thermal protection, identifying local (at individual hand segments) glove microclimate properties is still a research gap. For the first time, the glove-microclimate properties for 16 hand segments at high spatial resolution were analyzed by employing state-of-the-art hand-held 3D scanner and post-processing techniques for different glove types. Our results clearly indicate that the glove area factor for distal phalanges is significantly higher (by 49.8 %) than that for other hand segments, which increases the heat transfer from distal phalanges. In contrast, average air gap thickness was relatively uniform across all hand segments. The glove type had a pronounced effect on glove microclimate properties, e.g., bulky and heavy cold weather protective gloves had a larger average air gap thickness and glove area factor. Regression models are also developed to estimate the glove microclimate properties from simple measurement (i.e., ease allowance). Overall, this study provides essential information for the design and development of protective gloves that can help improve safety, comfort, and dexterity. Methods and mathematical models developed in this study also contribute to facilitating extremity (e.g., hand) focused thermoregulation modeling, hazard simulation, injury prediction, ergonomic design, optimum performance (dexterity and tactility) along with thermal protection., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (© 2023 The Authors.)

Rough Sets provide a mathematical tool to handle decision making under uncertainty. One major domain that can be characterized with inherent ambiguity is natural language texts which often leads to uncertainty in understanding the intent and relative importance of a sentence with respect to its context in the whole text. As a consequence, the process of sentence selection for generation of extractive summary can logically be considered as a process of decision making under uncertainty. In this paper we use rough set based techniques to deal with this uncertainty. This paper's contribution is two-fold. Firstly, this paper proposes a novel Rough Set based uncertainty measure called span and define special Rough subsets of universe called spanning sets. Span is Rough Set based measure for salience of a subset of universe and spanning set is the subset that maximizes the span. This corresponds to the key elements representing a problem and can be used to solve various real-life applications. Secondly, the concepts are applied to determine extracts of text documents. The idea behind the present work is to determine the most suitable subset(s) of the universe of sentences under consideration. An optimization problem is formulated to generate the extract for the text under consideration using the proposed uncertainty measure of span and is solved using Particle Swarm Optimization. The experimental results on DUC2001, DUC2002 single document data sets and Enron Email datasets establish the effectiveness of the proposed technique. There has been substantial work on Rough Sets though considering a stochastic Rough-subset of the universe and determining its aptness as a representative of the universe is still unexplored. The proposed technique is a novel effort to fill this gap. [ABSTRACT FROM AUTHOR]

Injuries to the metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints of the hand are particularly disabling. However, current standards for hand protection from blunt impact are not based on quantitative measures of the likelihood of damage to the tissues. The aim of this study was to evaluate the probability of injury of the MCP and PIP joints of the human hand due to blunt impact. Impact testing was conducted on 21 fresh-frozen cadaveric hands. Unconstrained motion at every joint was allowed. All hands were imaged with computed tomography and dissected post-impact to quantify injury. An injury-risk curve was developed for each joint using a Weibull distribution with dorsal impact force as the predictive variable. The injury risks for PIP joints were similar, as were those for MCP joints. The risk of injury of the MCP joints from a given applied force was significantly greater than that of the PIP joints (p = 0.0006). The axial forces with a 50% injury risk for the MCP and PIP joints were 3.0 and 4.2 kN, respectively. This is the first study to have investigated the injury tolerance of the MCP and PIP joints. The proposed injury curves can be used for assessing the likelihood of tissue damage, for designing targeted protective solutions such as gloves, and for developing more biofidelic standards for assessing these solutions. [ABSTRACT FROM AUTHOR]

The barrier properties of personal protective equipment are vital to healthcare personnel to protect themselves from possible infectious body fluids. Intraoperative exposure of healthcare personnel to body fluids can be substantial in both inpatient and outpatient settings. The glove–gown interface is known as one of the weakest points of the whole personal protective equipment system. However, there is a lack of scientific research designed to investigate the problem. This paper reports the results of experiments using a new testing methodology developed to quantify fluid leakage through the glove–gown interface while simulating surgical settings in terms of operating room personnel activities, exposure types, exposure durations, and physical stresses applied on the interface. This study represents one of the first efforts investigating the amount of fluid leakage through the glove–gown interface for a number of surgical gown and glove models while considering glove material differences and single vs. double gloving. The test results showed that there is a significant difference in fluid leakage amounts between three gown models and four glove models studied. The results also demonstrated that double gloving significantly reduced the fluid leakage compared to single glove use. The mean fluid leakage was lower in the double synthetic glove configurations (M = 2.76g) compared with all other configurations (3GLV, M = 8.3g; 4GLV, M = 9.49g; 5GLV, M = 3.08g; 6GLV, M = 20.03g; double latex, M = 5.22g). Findings highlighted a significant interaction between glove and gown designs, which suggests that gown and gloves should be designed together as a system to minimize or eliminate the fluid leakage. [ABSTRACT FROM AUTHOR]

Highlights • Improved Word Vectors (IWV) increases the accuracy of existing pre-trained word embeddings. • We propose a proper combination of lexicons to improve sentiment classification. • IWV improved the accuracy of Google's pre-trained Word2Vec to over 2%. Abstract Sentiment analysis is a fast growing area of research in natural language processing (NLP) and text classifications. This technique has become an essential part of a wide range of applications including politics, business, advertising and marketing. There are various techniques for sentiment analysis, but recently word embeddings methods have been widely used in sentiment classification tasks. Word2Vec and GloVe are currently among the most accurate and usable word embedding methods which can convert words into meaningful vectors. However, these methods ignore sentiment information of texts and need a large corpus of texts for training and generating exact vectors. As a result, because of the small size of some corpora, researcher often have to use pre-trained word embeddings which were trained on other large text corpora such as Google News with about 100 billion words. The increasing accuracy of pre-trained word embeddings has a great impact on sentiment analysis research. In this paper, we propose a novel method, Improved Word Vectors (IWV), which increases the accuracy of pre-trained word embeddings in sentiment analysis. Our method is based on Part-of-Speech (POS) tagging techniques, lexicon-based approaches, word position algorithm and Word2Vec/GloVe methods. We tested the accuracy of our method via different deep learning models and benchmark sentiment datasets. Our experiment results show that Improved Word Vectors (IWV) are very effective for sentiment analysis. [ABSTRACT FROM AUTHOR]

Abstract Question processing is a natural language processing task which is more complex and requires proper understanding of the question. In this work, we aimed to carry out a deep analysis to determine what types of information can be extracted from various question types. The extracted information is of two types: directly available information like entity types and indirectly inferred information like question type, intent, and domain. Named Entity Recognition (NER) methods can be applied for extracting direct entities. There are predominantly two approaches for NER, one is based on Conditional Random Field (CRF) and other is based on Bi-directional Long Short-Term Memory (Bi-LSTM) network. For identifying intent, domain and type, a Convolutional Neural Network (CNN) based model and a Bi-LSTM based model were used. Global Vector Representation (GloVe) is uesd for word embedding. In addition, the initial GloVe embeddings are enriched with synonyms and antonyms by using the counter fitting method. Experiments were conducted over Quora question collection dataset which is annotated manually. Results are promising for both tasks and the word embedding enrichment using counter-fitting method indicate significant importance in capturing semantics. [ABSTRACT FROM AUTHOR]

Regret is a universal emotion that arises from sadness, disappointment, or remorse about something that has occurred or that one has done or failed to do in the past. It typically involves wishing that a different decision had been made or that a different action had been taken. Although regret has been studied in various contexts such as psychology, neuroscience, and philosophy, its expression and analysis on social media using natural language processing techniques is a relatively recent research topic that warrants further investigation. In this paper, we present a study of regret and its expression on social media platforms. Specifically, we present a novel dataset of Reddit texts that have been classified into three classes: Regret by Action, Regret by Inaction, and No Regret. We then use this dataset to investigate the language used to express regret on Reddit and to identify the domains of text that are most commonly associated with regret. Our findings show that Reddit users are most likely to express regret for past actions, particularly in the domain of relationships. We also found that deep learning models using GloVe embedding outperformed other models in all experiments, indicating the effectiveness of GloVe for representing the meaning and context of words in the domain of regret. Overall, our study provides valuable insights into the nature and prevalence of regret on social media, as well as the potential of deep learning and word embeddings for analyzing and understanding emotional language in online text. These findings have implications for the development of natural language processing algorithms and the design of social media platforms that support emotional expression and communication. • Regret is an emotion that has a significant impact on behavior and decision-making. • Regret is often triggered by actions or inactions in the past. • Reddit's users often regret their actions about relationships in the past. • Effectiveness of GloVe in representing the meaning and context of words. [ABSTRACT FROM AUTHOR]

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In the process of the operation and maintenance of secondary devices in smart substation, a wealth of defect texts containing the state information of the equipment is generated. Aiming to overcome the low efficiency and low accuracy problems of artificial power text classification and mining, combined with the characteristics of power equipment defect texts, a defect texts mining method for a secondary device in a smart substation is proposed, which integrates global vectors for word representation (GloVe) method and attention-based bidirectional long short-term memory (BiLSTM-Attention) method in one model. First, the characteristics of the defect texts are analyzed and preprocessed to improve the quality of the defect texts. Then, defect texts are segmented into words, and the words are mapped to the high-dimensional feature space based on the global vectors for word representation (GloVe) model to form distributed word vectors. Finally, a text classification model based on BiLSTM-Attention was proposed to classify the defect texts of a secondary device. Precision, Recall and F1-score are selected as evaluation indicators, and compared with traditional machine learning and deep learning models. The analysis of a case study shows that the BiLSTM-Attention model has better performance and can achieve the intelligent, accurate and efficient classification of secondary device defect texts. It can assist the operation and maintenance personnel to make scientific maintenance decisions on a secondary device and improve the level of intelligent management of equipment.

A common task in summarizing scientific articles is employing the rhetorical structure of sentences. Determining rhetorical sentences itself passes through the process of text categorization. In order to get good performance, some works in text categorization have been done by employing word embedding. This paper presents rhetorical sentence categorization of scientific articles by using word embedding to capture semantically similar words. A comparison of employing Word2Vec and GloVe is shown. First, two experiments are evaluated using five classifiers, namely Naïve Bayes, Linear SVM, IBK, J48, and Maximum Entropy. Then, the best classifier from the first two experiments was employed. This research showed that Word2Vec CBOW performed better than Skip-Gram and GloVe. The best experimental result was from Word2Vec CBOW for 20,155 resource papers from ACL-ARC, features from Teufel and the previous label feature. In this experiment, Linear SVM produced the highest F-measure performance at 43.44%.

To overcome the data sparseness in word embedding trained in low-resource languages, we propose a punctuation and parallel corpus based word embedding model. In particular, we generate the global word-pair co-occurrence matrix with the punctuation-based distance attenuation function, and integrate it with the intermediate word vectors generated from the small-scale bilingual parallel corpus to train word embedding. Experimental results show that compared with several widely used baseline models such as GloVe and Word2vec, our model improves the performance of word embedding for low-resource language significantly. Trained on the restricted-scale English-Chinese corpus, our model has improved by 0.71 percentage points in the word analogy task, and achieved the best results in all of the word similarity tasks.

• Emailed electronic survey to nursing group. • Glove use during patient transport and removal of linen from a patient room. • Overuse of gloves may lead to missed opportunities for hand hygiene. • New hospital protocol created to improve environmental hygiene. General guidance for personal protective equipment (PPE) is provided by the Occupational Safety and Health Administration and the United States Centers for Disease Control and Prevention. Previous research of PPE demonstrates the ability of gloves to harbor infectious pathogens. We surveyed health care workers to investigate current PPE practices during linen removal from patient rooms and during patient transport. The results suggest a possible risk for cross-contamination of the environment from overuse of gloves. [ABSTRACT FROM AUTHOR]

This study compared the effects of a hand cooling glove (∼16°C water temperature; subatmospheric pressure of −40 mmHg) and a cooling jacket (CJ) on post-exercise cooling rates (gastrointestinal core temperature, Tc; skin temperature, Tsk) and cognitive performance (the Stroop Colour-Word test). Twelve male athletes performed four trials (within subjects, counterbalanced design) involving cycling at a workload equivalent to 75% ⩒O2max in heat (35.7 ± 0.2°C, 49.2 ± 2.6% RH) until a Tc of 39°C or exhaustion occurred. A 30-min cooling period (in 22.3 ± 0.3°C, 42.1 ± 3.6% RH) followed, where participants adopted either one-hand cooling (1H), two-hand cooling (2H), wore a CJ or no cooling (NC). No significant differences were seen in Tc and Tsk cooling rates between trials; however, moderate effect sizes (d = 0.50-0.76) suggested Tc cooling rates to be faster for 1H, 2H and CJ compared to NC after 5 min; 1H and CJ compared to NC after 10 min and for CJ to be faster than 2H at 25-30 min. Reaction times on the cognitive test were similar between all trials after the 30 min cooling/no-cooling period (p > .05). In conclusion, Tc cooling rates were faster with 1H and CJ during the first 10 min compared to NC, with minimal benefit associated with 2H cooling. Reaction time responses were not impacted by the use of the glove(s) or CJ. [ABSTRACT FROM AUTHOR]