bluuebunny/crossref_metadata_2025 huggingface dataset:
[Retracted] An Exploration: Alzheimer’s Disease Classification Based on Convolutional Neural Network
Authors: Sethi Monika , Ahuja Sachin , Rani Shalli , Koundal Deepika , Zaguia Atef , Enbeyle Wegayehu
Published: January 2022

Alzheimer’s disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning models have shown outstanding performance in the diagnosis of AD, and these models do not need any handcrafted feature extraction over conventional machine learning algorithms. Since the 2012 AlexNet accomplishment, the convolutional neural network (CNN) has been progressively utilized by the medical community to assist practitioners to early diagnose AD. This paper explores the current cutting edge applications of CNN on single and multimodality (combination of two or more modalities) neuroimaging data for the classification of AD. An exhaustive systematic search is conducted on four notable databases: Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed in June 2021. The objective of this study is to examine the effectiveness of classification approaches on AD to analyze different kinds of datasets, neuroimaging modalities, preprocessing techniques, and data handling methods. However, CNN has achieved great success in the classification of AD; still, there are a lot of challenges particularly due to scarcity of medical imaging data and its possible scope in this field.
Clairvoyant: AdaBoost with Cost-Enabled Cost-Sensitive Classifier for Customer Churn Prediction
Authors: Thakkar Hiren Kumar , Desai Ankit , Ghosh Subrata , Singh Priyanka , Sharma Gajendra
Published: January 2022

Customer churn prediction is one of the challenging problems and paramount concerns for telecommunication industries. With the increasing number of mobile operators, users can switch from one mobile operator to another if they are unsatisfied with the service. Marketing literature states that it costs 5–10 times more to acquire a new customer than retain an existing one. Hence, effective customer churn management has become a crucial demand for mobile communication operators. Researchers have proposed several classifiers and boosting methods to control customer churn rate, including deep learning (DL) algorithms. However, conventional classification algorithms follow an error-based framework that focuses on improving the classifier’s accuracy over cost sensitization. Typical classification algorithms treat misclassification errors equally, which is not applicable in practice. On the contrary, DL algorithms are computationally expensive as well as time-consuming. In this paper, a novel class-dependent cost-sensitive boosting algorithm called AdaBoostWithCost is proposed to reduce the churn cost. This study demonstrates the empirical evaluation of the proposed AdaBoostWithCost algorithm, which consistently outperforms the discrete AdaBoost algorithm concerning telecom churn prediction. The key focus of the AdaBoostWithCost classifier is to reduce false-negative error and the misclassification cost more significantly than the AdaBoost.
Privacy-Preserving Query Scheme (PPQS) for Location-Based Services in Outsourced Cloud
Authors: Yang Guangcan , He Yunhua , Xiao Ke , Tang Qifeng , Xin Yang , Zhu Hongliang
Published: January 2022

Pervasive smartphones boost the prosperity of location-based service (LBS) and the increasing data prompt LBS providers to outsource their LBS datasets to the cloud side. The privacy issues of LBS in the outsourced cloud scenario have attracted considerable interest recently. However, current schemes cannot provide sufficient privacy preservation against practical challenges and are little concerned about the data retrieval efficiency of the cloud side. Therefore, we present an efficient Privacy-Preserving LBS Query Scheme (i.e.,

PPQS

). In our scheme, two cloud entities are employed to store the sensitive information of the outsourced data and provide the query service, which enhances the ability of privacy preservation for sensitive information. Besides, by using the techniques of homomorphic encryption and searchable symmetric encryption, the proposed scheme supports both the type query and the range query, which can significantly improve the data retrieval efficiency of the cloud side and reduce the computation burden on the cloud side and the user side. Through detailed analysis on security and computation cost, we show the enhanced ability of privacy preservation and the lower computation cost compared to previous schemes. Based on a real dataset, extensive simulations are performed to validate the effectiveness and performance of our scheme.
White-Light Endoscopic Colorectal Lesion Detection Based on Improved YOLOv5
Authors: Gao Junbo , Xiong Qilin , Yu Chang , Qu Guoqiang
Published: January 2022

As an effective tool for colorectal lesion detection, it is still difficult to avoid the phenomenon of missed and false detection when using white-light endoscopy. In order to improve the lesion detection rate of colorectal cancer patients, this paper proposes a real-time lesion diagnosis model (YOLOv5x-CG) based on YOLOv5 improvement. In this diagnostic model, colorectal lesions were subdivided into three categories: micropolyps, adenomas, and cancer. In the course of convolutional network training, Mosaic data enhancement strategy was used to improve the detection rate of small target polyps. At the same time, coordinate attention (CA) mechanism was introduced to take into account channel and location information in the network, so as to realize the effective extraction of three kinds of pathological features. The Ghost module was also used to generate more feature maps through linear processing, which reduces the stress of learning model parameters and speeds up detection. The experimental results show that the lesion diagnosis model proposed in this paper has a more rapid and accurate lesion detection ability, and the AP value of polyps, adenomas, and cancer is 0.923, 0.955, and 0.87, and mAP@50 is 0.916.
Risk assessment in fleet management system using OCTAVE allegro
Authors: Alfarisi Salman , Surantha Nico
Published: January 2022

The purpose of this study is to use the OCTAVE allegro methodology to identify risks in fleet management system (FMS), determine prioritized risks to be mitigated, provide mitigation recommendations for these prioritized risks, and shows how effective the recommendation is. The result of this study is expected to become an input for FMS service provider of possible risks in FMS services, and risk mitigation approaches that can be used to handle those risks. This risk assessment has successfully identified 6 critical information assets, 10 risks in total, and 4 risks that need to be mitigated, followed by proposed mitigation approaches for those risks. Some of the recommendation has been applied by the company and contribute to SLA achievement of the system. The result also showed that application and simulation software provide most prominent risks in FMS service, thus securing these two will eliminate most risk in FMS service.
An improved deep bagging convolutional neural network classifier for efficient intrusion detection system
Authors: Ramasamy Mathiyalagan , Eric Pamela Vinitha
Published: January 2022

In the current trend, the network-based system has substantial jobs, and they have become the targets of attackers. When an intrusion occurs, the security of a computer system is compromised. As a result, we must seek out the best methods for ensuring frameworks. A crucial component of the security management architecture is the intrusion detection system (IDS). To maintain effective network security, the design and implementation of IDS remain an important assessment topic. For intrusion detection, the previous system created an enhanced relevance vector machine (ERVM) classifier. However, intrusion detection is not robust for large-scale intrusion datasets, resulting in a high attack rate. The suggested work developed an improved deep bagging based convolutional neural network (DBCNN) for intrusion detection to address this issue. Preprocessing, feature selection, and classification are three processes included in the proposed framework. The KDD dataset is preprocessed in this stage using the kalman filter method. The feature selection is then carried out using the inertia weight based dragonfly method (IWDA). Finally, the DBCNN classifier successfully identifies interruption assaults. The KDD dataset is used to test the new model. The test results show that the proposed work accomplishes better execution contrasted and the current framework as far as accuracy, precision, recall and f-measure.
Studying performance evaluation of hybrid e-bike using solar photovoltaic system
Authors: Hamoodi Safwan A. , Abdullah Al-Karakchi Ahmed A. , Hamoodi Ali N.
Published: January 2022

Hybrid e-bike system is a bicycle included electric hub motor used to aid propulsion. A solar package with main components is built with it. This study included the travelled distance divided by time under only the batteries and batteries with photovoltaic (PV) modules (at different hours during the day). A comparison between two methods is made and documented in this paper. The paper aims to captivate the fettle and experiences with the use of e-bike. Commuting distance per hour was approximately 6.8 km/h. The current limitations must not exceed (10.4 A) and the big challenge, no shading plops on solar panels due to rider. Finally, depending on the solar irradiance with time curve, hybrid e-bike gave longer travelled distance with respect to time as compared with the batteries case study.
Header of death: security implications of IPv6 extension headers to the open-source firewall
Authors: Gamilla Anazel P. , Naagas Marlon A.
Published: January 2022

IPv6 extension headers (EHs) contain additional information utilized by network devices (such as routers and firewalls) to determine how to direct or process an IPv6 packet. However, the use of excessive and unknown EHs can lead to the security implications such as evasion and denial of service (DoS) of the target firewall. Study revealed that there is no permanent remediation that prevents the IPv6 EHs attack from invading the open-source firewalls by default. Using IPv6 packet manipulations technique, the attacker can evade the target network including the firewall and target host that can lead to a complete unavailability of network service. The common vulnerability scoring system (CVSS) also indicates that the base, temporal, and environment metric groups of IPv6 EHs vulnerabilities were in the critical level of severity. Quick and dirty solutions such as denying and allowing packets and IP addresses as preventive measures is still one of the effective ways of defending against the EHs packet manipulation attacks, as a temporary solution to date.
Hybrid algorithm for cloud-fog system based load balancing in smart grids
Authors: Saoud Afaf , Recioui Abdelmadjid
Published: January 2022

Energy management is among the key components of smart metering. Its role is to balance energy consumption and distribution. Smart devices integration results in a huge data exchange between different parts of the smart grid causing a delay in the response and processing time. To overcome this latency issue, the cloud computing has been proposed. However, cloud computing does not perform well when there are large distances from the cloud to the consumers. Fog computing solves this issue. In this paper, a cloud-fog computing system is presented to achieve an accurate load balancing. The hybridization of whale optimization algorithm with bat algorithm (WOA-BAT) is proposed for load balancing. The model performance is compared to state of art load balancing techniques as throttled, round robin, whale and particle swarm optimization algorithms in terms of processing and the response time. The results reveal that the proposed WOA-BAT has better results in terms of response time than the three algorithms with 4.3% improvement compared to RR and TH. It also outperforms all the algorithms in terms of processing time by at least 22.3%.
Hybrid multistage framework for data manipulation by combining cryptography and steganography
Authors: Osman Omnia Mohammed , Kanona Mohammed Eltayeb Ahmed , Hassan Mohamed Khalafalla , Elkhair Afra Adil Elsir , Mohamed Khalid Sheikhidris
Published: January 2022

In today's rapidly growing communication and internet technologies, such as 5G, cloud computing, and blockchain, information security has become a critical component. When data is transmitted in its raw form, it is vulnerable to a variety of cybersecurity assaults. In this hybrid multi-stage data encryption architecture, which builds sequential and pseudo-random encoding/decoding algorithms with pre-stage text encryption discovered that image resolution and attributes were unaffected by the change in image size after testing several text sizes with the cover image and various image formats, it is suitable that the text size should be 15% smaller than the cover image. Furthermore, when compared to sequential encoding/decoding, the hybrid cryptography and steganography-pseudo-random encoding/decoding procedure is more efficient and time consuming.
Deep convolutional neural networks architecture for an efficient emergency vehicle classification in real-time traffic monitoring
Authors: Kherraki Amine , El Ouazzani Rajae
Published: January 2022

Nowadays, intelligent transportation system (ITS) has become one of the most popular subjects of scientific research. ITS provides innovative services to traffic monitoring. The classification of emergency vehicles in traffic surveillance cameras provides an early warning to ensure a rapid reaction in emergency events. Computer vision technology, including deep learning, has many advantages for traffic monitoring. For instance, convolutional neural network (CNN) has given very good results and optimal performance in computer vision tasks, such as the classification of vehicles according to their types, and brands. In this paper, we will classify emergency vehicles from the output of a closed-circuit television (CCTV) camera. Among the advantages of this research paper is providing detailed information on the emergency vehicle classification topic. Emergency vehicles have the highest priority on the road and finding the best emergency vehicle classification model in realtime will undoubtedly save lives. Thus, we have used eight CNN architectures and compared their performances on the Analytics Vidhya Emergency Vehicle dataset. The experiments show that the utilization of DenseNet121 gives excellent classification results which makes it the most suitable architecture for this research topic, besides, DenseNet121 does not require a high memory size which makes it appropriate for real-time applications.
 
Transfer learning for cancer diagnosis in histopathological images
Authors: Aneja Sandhya , Aneja Nagender , Abas Pg Emeroylariffion , Naim Abdul Ghani
Published: January 2022

Transfer learning allows us to exploit knowledge gained from one task to assist in solving another but relevant task. In modern computer vision research, the question is which architecture performs better for a given dataset. In this paper, we compare the performance of 14 pre-trained ImageNet models on the histopathologic cancer detection dataset, where each model has been configured as naive model, feature extractor model, or fine-tuned model. Densenet161 has been shown to have high precision whilst Resnet101 has a high recall. A high precision model is suitable to be used when follow-up examination cost is high, whilst low precision but a high recall/sensitivity model can be used when the cost of follow-up examination is low. Results also show that transfer learning helps to converge a model faster.
Systematic development of real-time driver drowsiness detection system using deep learning
Authors: Faisal Tarig , Negassi Isaias , Goitom Ghebrehiwet , Yassin Mohammed , Bashir Anees , Awawdeh Moath
Published: January 2022

Advancements in globalization have significantly seen a rise in road travel. This has also led to increased car accidents and fatalities, which become a global cause of concern. Driver's behavior, including drowsiness, contributes to many of the road deaths. The main objective of this study is to develop a system to diminish mishaps caused by the driver's drowsiness. Recently deep convolutional neural networks have been used in multiple applications, including identifying and anticipate driver drowsiness. However, limited studies investigated the systematic optimization of convolutional neural networks (CNNs) hyperparameters, which could lead to better anticipation of driver drowsiness. To bridge this gap, a holistic approach based on the deep learning method is proposed in this paper to anticipate the drivers' drowsiness and provide an alerting mechanism to prevent drowsiness related accidents. To ensure optimal performance achievement by the system, a database of real-time images preprocessed via Haar cascade's classifiers is used to systematically optimize the CNN model's hyperparameters. Different metrics, including accuracy, precision, recall, F1-score, and confusion matrix, are used to evaluate the performance of the model. The training evaluation results of the optimal model achieved an accuracy of 99.87%, while the testing results accurately classify the drowsy driver with 97.98%.
Prediction of diabetes disease using machine learning algorithms
Authors: Panda Monalisa , Mishra Debani Prashad , Patro Sopa Mousumi , Salkuti Surender Reddy
Published: January 2022

Diabetes mellitus is a powerful chronic disease, which is recognized by lack of capability of our body for metabolization of glucose. Diabetes is one of the most dangerous diseases and a threat to human society, many are becoming its victims and, regardless of the fact that they are trying to keep it from rising more, are unable to come out of it. There are several conventional diabetes disease health monitoring strategies. This disease was examined by machine learning (ML) algorithms in this paper. The goal behind this research is to create an effective model with high precision to predict diabetes. In order to reduce the processing time, K-nearest neighbor algorithm is used. In addition, support vector machine is also introduced to allocate its respective class to each and every sample of data. In building any sort of ML model, feature selection plays a vital role, it is the process where we select the features automatically or manually and it contributes most to our desired performance. Overall, four algorithms are used in this paper to understand which can easily evaluate the total effectiveness and accuracy of predicting whether or not a person will suffer from diabetes.
Indonesian part of speech tagging using maximum entropy markov model on Indonesian manually tagged corpus
Authors: Cahyani Denis Eka , Mustikaningtyas Winda
Published: January 2022

This research discusses the development of a part of speech (POS) tagging system to solve the problem of word ambiguity. This paper presents a new method, namely maximum entropy markov model (MEMM) to solve word ambiguity on the Indonesian dataset. A manually labeled “Indonesian manually tagged corpus” was used as data. Furthermore, the corpus is processed using the entropy formula to obtain the weight of the value of the word being searched for, then calculating it into the MEMM Bigram and MEMM Trigram algorithms with the previously obtained rules to determine the part of speech (POS) tag that has the highest probability. The results obtained show POS tagging using the MEMM method has advantages over the methods used previously which used the same data. This paper improves a performance evaluation of research previously. The resulting average accuracy is 83.04% for the MEMM Bigram algorithm and 86.66% for the MEMM Trigram. The MEMM Trigram algorithm is better than the MEMM Bigram algorithm.
Bi-directional long short term memory using recurrent neural network for biological entity recognition
Authors: Siddalingappa Rashmi , Sekar Kanagaraj
Published: January 2022

Biomedical named entity recognition (NER) aims at identifying medical entities from unstructured data. A quintessential task in the supervision of biological databases is handling biomedical terms such as cancer type, DeoxyriboNucleic and RiboNucleic Acid, gene and protein name, and others. However, due to the massive size of online medical repositories, data processing becomes a challenge for a gazetteer without proper annotation. The traditional NER systems depend on feature engineering that is tedious and time-consuming. The research study presents a new model for Bio-NER using recurrent neural network. Unlike existing approaches, the proposed method uses bidirectional traversing with GloVe vector modelling performed at character and word levels. Bio-NER is performed in three stages; firstly, the relevant medical entities in electronic medical records from PubMed were extracted using the skip-gram model. Secondly, a vector representation for each word is created through the 1-hot method. Thirdly, the weights of the recurrent neural network (RNN) layers are adjusted using backward propagation. Finally, the long-short-term memory cells store the previously encountered medical entity to tackle context-dependency. The accuracy and F-score are calculated for each medical entity type. The MacroR, MacroP, and MacroF are equal to 0.86, 0.88, and 0.87. The overall accuracy achieved was 94%.
Evolução dos alimentos mais consumidos no Brasil entre 2008-2009 e 2017-2018
Authors: Rodrigues Renata Muniz , Souza Amanda de Moura , Bezerra Ilana Nogueira , Pereira Rosangela Alves , Yokoo Edna Massae , Sichieri Rosely
Published: January 2022

OBJETIVO: Descrever a evolução do consumo alimentar da população brasileira de 2008–2009 a 2017–2018. MÉTODOS: Foram utilizados dados dos Inquéritos Nacionais de Alimentação de 2008–2009 e 2017–2018, que estimaram o consumo alimentar de dois dias não consecutivos de indivíduos com 10 anos ou mais de idade. O primeiro inquérito colheu dados de consumo de 34.003 indivíduos por meio de registro alimentar; o segundo, de 46.164 indivíduos, por meio de recordatório de 24 horas. Identificaram-se os 20 grupos de alimentos mais frequentemente referidos nos dois inquéritos. A probabilidade de consumo de cada um dos grupos de alimentos nos dois inquéritos foi estimada segundo sexo, idade e renda. No presente estudo, são apresentados os alimentos que tiveram mudança igual ou superior a 5% na frequência de consumo entre os dois inquéritos. A probabilidade de consumo foi corrigida para a variabilidade intraindividual, utilizando método desenvolvido pelo National Cancer Institute. RESULTADOS: Arroz, feijão, café, pães, hortaliças e carne bovina permaneceram como base da alimentação dos brasileiros, sendo os seis itens mais consumidos em ambos os inquéritos. Alimentos ultraprocessados, como biscoitos doces/recheados, biscoitos salgados, carnes processadas e refrigerantes, também se mantiveram entre os 20 alimentos mais consumidos. As análises de tendência evidenciaram, independentemente de sexo, idade e faixa de renda, a diminuição do consumo de arroz, feijão, carne bovina, pães, frutas, laticínios, carnes processadas e refrigerantes, e o aumento da ingestão de sanduíches. CONCLUSÃO: A dieta do brasileiro permanece caracterizada pelo consumo de alimentos tradicionais, como arroz e feijão, e pela frequência elevada de ingestão de alimentos ultraprocessados, como biscoitos e refrigerantes. No entanto, entre os anos de 2008–2009 e 2017–2018, observou-se redução no consumo de arroz, feijão, carne bovina, pães, frutas, laticínios, carnes processadas e refrigerantes, mas aumento no consumo de sanduíches. Os resultados sinalizam piora na qualidade da alimentação do brasileiro.
Age and Medicare Insurance are Barriers to Telemedicine Access—A Quality Improvement Project
Authors: Madabhushi Vashisht , McLouth Christopher J. , King Robert , Bhakta Avinash , Beck Sandra , Patel Jitesh A.
Published: January 2022

Background

Telehealth use has had widespread expansion and adoption over the past two years. This study aims to evaluate access to telehealth essentials (TE) using a novel metric.

Methods

This single institute study surveyed outpatient surgical patients to determine their access to TE. Generalized linear mixed models were used to determine the relationship of demographic and county-level variables on access to four TE.

Results

138 patients were surveyed. Sixty-six (47.8%) were from Appalachian Kentucky. In the survey cohort, 122 (88.4%) had smart phones, 109 (80.7%) had devices with video messaging capabilities, 106 (80.9%) had cellular reception, and 112 (82.4%) had access to WiFi. Increasing age and Medicare insurance were the most consistent predictors of lack of access to TE.

Conclusion

Rural Appalachian Kentucky has access to TE. Telehealth has the potential to decrease the healthcare inequity in rural populations, but incompletely address this inequity for the aging population.
The Prognostic Utility of Mean Platelet Volume in Patients With Acute Coronary Syndrome: A Systematic Review With Meta-Analyses
Authors: Galimzhanov Akhmetzhan , Tenekecioglu Erhan , Rustamova Farida , Tun Han Naung , Mamas Mamas A.
Published: January 2022

Mean platelet volume (MPV) is a hematological index that is routinely measured in clinical settings. Although many studies have been conducted to investigate the prognostic significance of MPV in acute coronary syndromes (ACS), the findings have been inconsistent. The goal of this study was to systematically review all current evidence on the association between admission MPV and clinical outcomes after ACS. PubMed, Scopus, Web of Science, and other databases were searched. The primary endpoints were major adverse cardiovascular events (MACE) and mortality. We applied a Knapp and Hartung adjustment, prediction interval calculations and permutation tests during pairwise meta-analyses. A one-stage dose–response meta-analysis was also conducted. The meta-analysis consisted of 41 studies with 33443 participants. Mean platelet volume, as a continuous variable, was associated with the risk of long-term mortality (hazard ratio 1.33, 95% CI 1.19–1.48). After conducting permutation tests and calculation of prediction intervals, this association remained significant. The results for MACE were nonsignificant. Linear models were the best fitted models during dose–response meta-analyses, trends for nonlinearity were significant for long-term endpoints. Admission MPV was associated with long-term mortality in ACS patients, with nonlinear associations between MPV levels and long-term clinical outcomes.
Perceptions of Emotional Functionality: Similarities and Differences Among Dignity, Face, and Honor Cultures
Authors: Maitner Angela T. , DeCoster Jamie , Andersson Per A. , Eriksson Kimmo , Sherbaji Sara , Giner-Sorolla Roger , Mackie Diane M. , Aveyard Mark , Claypool Heather M. , Crisp Richard J. , Gritskov Vladimir , Habjan Kristina , Hartanto Andree , Kiyonari Toko , Kuzminska Anna O. , Manesi Zoi , Molho Catherine , Munasinghe Anudhi , Peperkoorn Leonard S. , Shiramizu Victor , Smallman Rachel , Soboleva Natalia , Stivers Adam W. , Summerville Amy , Wu Baopei , Wu Junhui
Published: January 2022

Emotions are linked to wide sets of action tendencies, and it can be difficult to predict which specific action tendency will be motivated or indulged in response to individual experiences of emotion. Building on a functional perspective of emotion, we investigate whether anger and shame connect to different behavioral intentions in dignity, face, and honor cultures. Using simple animations that showed perpetrators taking resources from victims, we conducted two studies across eleven countries investigating the extent to which participants expected victims to feel anger and shame, how they thought victims should respond to such violations, and how expectations of emotions were affected by enacted behavior. Across cultures, anger was associated with desires to reclaim resources or alert others to the violation. In face and honor cultures, but not dignity cultures, shame was associated with the desire for aggressive retaliation. However, we found that when victims indulged motivationally-relevant behavior, expected anger and shame were reduced, and satisfaction increased, in similar ways across cultures. Results suggest similarities and differences in expectations of how emotions functionally elicit behavioral responses across cultures.
Existential Meaninglessness Scale: Scale Development and Psychometric Properties
Authors: Li P. F. Jonah , Wong Y. Joel , McCullough Keiko M. , Jin Ling , Wang Chiachih DC
Published: January 2022

Grounded in a tripartite existential meaninglessness model, the authors developed the 18-item Existential Meaninglessness Scale (EMS) to assess one’s concern and anxiety of existential meaninglessness. Across three samples, the EMS’s factor structure and evidence of convergent, criterion-related, and incremental validity, internal consistency, and test–retest reliability were examined. Exploratory factor analyses demonstrated three dimensions of the EMS: incomprehension, purposelessness, and insignificance. Confirmatory factor analyses revealed that a bifactor model was a better fit to the data than other models. The bifactor model provided evidence for a general factor and measurement invariance. Ancillary bifactor indices indicated EMS’s unidimensionality. Findings of bivariate correlations and hierarchical regression analyses provided evidence for different aspects of construct validity and internal consistency. Both the Concern and Anxiety measures of the EMS positively predicted depressive symptoms and suicide ideation above and beyond the effects of general existential meaninglessness, general feelings of anxiety, and presence of meaning in life. Based on the findings, the authors discuss future research directions on existential meaninglessness using the EMS.
When 'the Happily Ever After Ends': The Grieving Process in Widowhoods
Authors: Bard Bárbara A. , Rodrigues Caroline S. M.
Published: January 2022

The loss of the spouse causes several secondary losses, such as loss of family and individual identities, and for that, it is considered a risk factor for complicated grief. Our objective was to investigate the possible specific aspects of widowing. Data collection occurred via an online questionnaire, during the new Coronavirus pandemic and the mixed design (both qualitative and quantitative) research had the participation of 93 individuals. The loss of the spouse was considered the worst loss experienced by the participants. Loneliness, routine, bureaucracy, and rituals were the main difficulties, while spirituality, family, and work were mentioned as protective factors. Signs of complicated grief were associated with age and time since the death of the partner. There was no significant difference between gender nor between the type of death. The manifestations of widowing are similar to other forms of bereavement, but there are specifications that should be taken into consideration by professionals in order to promote quality of life.
Authorship and building psychological research in low and middle income countries: a view from the Pacific Island Nation of Fiji
Authors: Crookes Annie E , Warren Meg A
Published: January 2022

Psychological science has been biased towards Western, Educated, Industrialized, Rich, and Democratic contexts. One indicator of better globalization is increasing authorship from Indigenous populations and less economically developed countries. This article considers the nature of research development in these contexts by analysing authorship from Fiji, a middle-income country in the Pacific Islands region. A review of Fijian psychological research literature up to December 2020 identified 131 publications. The majority of publications (59.7%) had no author based in Fiji at the time of study. Of the Fiji-based authors, most were associated with a single Fijian sample study or were based in Fiji but mainly co-authoring studies on international populations. Despite an apparent growth in publications, there remains a lack of research development to benefit understanding of the Indigenous population or to increase local research capacity. The findings are discussed with respect to how existing initiatives to include and develop research in smaller, low- and middle-income countries may not be sufficiently serving longer-term purposes to support the Indigenous communities.
Exploring the relationship between church worship, social bonding and moral values
Authors: Brown Jennifer E , van Mulukom Valerie , Jong Jonathan , Watts Fraser , Farias Miguel
Published: January 2022

Religion is often understood to play a positive role in shaping moral attitudes among believers. We assessed the relationship between church members’ levels of felt connectedness to their respective congregations and perceived similarity in personal and congregational moral values, and whether there was a relationship between these and the amount of time spent in synchronous movement or singing during worship. The similarity between personal and perceived congregational moral importance (the importance assigned to different moral items) was correlated with feelings of closeness to one’s congregation but not by the amount of time spent in synchronous movement or singing. Differences in moral foundations scores and in moral importance of specific issues were found between different theological traditions. These findings demonstrate that, for churchgoers, there is a relationship between the use of music or synchronous movement in a church service and feelings of social bonding and there is also a relationship between the degree to which churchgoers identify with their church community and the degree to which they believe their priorities match those of their church. Furthermore, differences in theological tradition appear to be reflected in differences in moral values.
Self-referenced nanometric displacement sensing assisted by optical anapole mode
Authors: Wu Jingzhi , Yang Hengze , Wang Yanhong , Li Mengwei
Published: January 2022

Long range optical rulers measuring distance greater than 10 nm have important applications in monitoring biological processes, nanolithography, and nanometrology. In this work, we show that lateral displacement over 120 nm can be determined by measuring two reflection peaks of the proposed nanostructure. The optical anapole in the nanostructure is excited, resulting from interactions of plasmonic resonance modes of nanoholes perforated in metal films and an array of nanoblocks. Results show that a 120 nm relative displacement between the nanoblocks and nanoholes induces a negative correlated 90% variation of reflection spectrum which can be used for self-referenced measurement. The proposed design could find applications in nanometrology, biosensing, and nanofabrication.
Assessing the Implementation of On-Demand Transportation Services for People with Disabilities
Authors: Choi Jimin , Maisel Jordana L.
Published: January 2022

As there are many barriers to the effective operation of paratransit services required by the Americans with Disabilities Act (ADA), both riders and transit agencies have incentives to shift some paratransit ridership to more effective transit services. To address these barriers, transit agencies have started partnering with technology-enabled third-party companies (e.g., transportation network companies, taxi companies) to provide new on-demand mobility options for people with disabilities. Previous studies identified the benefits and challenges of implementing on-demand services through a public-private partnership, but did not fully investigate the implementation of this emerging on-demand service from the perspective of serving individuals with disabilities. This study conducted online surveys and semi-structured interviews with 10 transit agencies across the United States to understand how this new on-demand mobility option has complemented ADA paratransit services, and what challenges and outcomes the partnerships have experienced over time. Findings indicate that despite the positive impact of implementing the on-demand service on overall service operation and delivery, transit agencies still experienced challenges in providing accessible and reliable on-demand services for riders. Transit agencies and partnering mobility service companies should work together to increase technology adoption, availability and reliability of wheelchair accessible services, and driver sensitivity to the needs of individuals with disabilities, as well as to support the sustainability of the program through continuous evaluation.
Putting the Humanity Back Into Public Human Resources Management: A Narrative Inquiry Analysis of Public Service in the Time of COVID-19
Authors: Berry Sarah , Trochmann Maren B. , Millesen Judith L.
Published: January 2022

Amid a global pandemic, unprecedented numbers of citizens relied on essential public employees as lifelines for their health, safety, and connectedness to the broader community. These public servants worked tirelessly through collective trauma to ensure their neighbors had what was needed to maintain some semblance of a routine in an otherwise unpredictable environment. This article uses narrative inquiry to examine the implications of the COVID-19 pandemic disruption on the public sector workplace, the quality of work life, and to investigate how employees coped during the crisis. Our research reports on interviews with 43 front-line and behind-the-scenes public employees who describe how they coped, maintained their public service motivation, and worked through increased demands for emotional labor in this new work-life environment. The findings suggest the need for human resources policies that allow for a flexible, reflective, holistic, and person-centered approach.
Survivors of domestic abuse and sexual violence within sexual health services, how we responded, who is missing and what should be done?
Authors: Heskin Joseph , Wyatt Rose , Hepworth Eleanor , Cassidy Sean , Melendez-Torres GJ , Cohen Charlotte
Published: January 2022

Background

Reports of domestic abuse (DA) and sexual violence (SV) continue to rise across the UK, and sexual health services are well situated to offer a safe setting for disclosure.

Methods

We performed a retrospective analysis of all safeguarding proformas completed between April and September 2019 across three sexual health clinics in London.

Results

158 patients reported current and/or historical DA and/or SV during the collection period. 70% of cohort identified as female, 28% as male and 2% as transgender. 25% of the cohort identified as gay men. Black and Asian attendees were disproportionately represented in the survivor cohort. 86% were offered a same-day appointment with the health advisor team and assessment for onward referral to specialist services. Almost half of the cohort were asymptomatic at the time of attendance and disclosure.

Discussion

With changes to access due to ongoing COVID-19 restrictions, asymptomatic survivors may not receive opportunities to disclose as they are directed to online platforms. Healthcare services must adapt routine enquiry practices to ensure safe disclosure is possible to make all contacts count and expand these practices into other healthcare specialities to provide further disclosure opportunities for survivors not attending sexual health services.
Impact of the COVID-19 Pandemic on Chronic Disease Care in India, China, Hong Kong, Korea, and Vietnam
Authors: Singh Kavita , Xin Yiqian , Xiao Yuyin , Quan Jianchao , Kim Daejung , Nguyen Thi-Phuong-Lan , Kondal Dimple , Yan Xinyi , Li Guohong , Ng Carmen S. , Kang Hyolim , Minh Nam Hoang , Mohan Sailesh , Yan Lijing L. , Shi Chenshu , Chen Jiayin , Thi Hong Hanh Hoa , Mohan Viswanathan , Kong Sandra , Eggleston Karen , Prabhakaran Dorairaj , Tandon Nikhil , Narayan KM Venkat , Ali Mohammed K , Ranjit Mohan Anjana , Mohan Deepa , Jagannathan Suganthi , Venkateshmurthy Nikhil Srinivasapura , Jarhyan Prashant , Gong Enying , Xiong Shangzhi , Chen Xinyue , Østbye Truls , Duman Ege K. , Cowling Benjamin J. , Ng Tiffany WY , Xiao Jingyi , Leung Gabriel M. , Chang Annie , Liang Richard
Published: January 2022

This study aims to provide evidence on how the COVID-19 pandemic has impacted chronic disease care in diverse settings across Asia. Cross-sectional surveys were conducted to assess the health, social, and economic consequences of the pandemic in India, China, Hong Kong, Korea, and Vietnam using standardized questionnaires. Overall, 5672 participants with chronic conditions were recruited from five countries. The mean age of the participants ranged from 55.9 to 69.3 years. A worsened economic status during the COVID-19 pandemic was reported by 19% to 59% of the study participants. Increased difficulty in accessing care was reported by 8% to 24% of participants, except Vietnam: 1.6%. The worsening of diabetes symptoms was reported by 5.6% to 14.6% of participants, except Vietnam: 3%. In multivariable regression analyses, increasing age, female participants, and worsened economic status were suggestive of increased difficulty in access to care, but these associations mostly did not reach statistical significance. In India and China, rural residence, worsened economic status and self-reported hypertension were statistically significantly associated with increased difficulty in access to care or worsening of diabetes symptoms. These findings suggest that the pandemic disproportionately affected marginalized and rural populations in Asia, negatively affecting population health beyond those directly suffering from COVID-19.
Differential Response and the Reduction of Child Maltreatment and Foster Care Services Utilization in the U.S. From 2004 to 2017
Authors: Johnson-Motoyama Michelle , Ginther Donna K. , Phillips Rebecca , Beer Oliver W. J. , Merkel-Holguin Lisa , Fluke John
Published: January 2022

By 2014, the majority of U.S. states had implemented differential response (DR), a system policy that seeks to serve families of low-to moderate-risk for child maltreatment through family engagement, diversion from formal child protective services investigations, and service provision. However, the effects of DR programs on child welfare dynamics have yet to be evaluated nationally using causal methods. Using a quasi-experimental study design with data drawn from the National Child Abuse and Neglect Data System from 2004 to 2017, we found states with DR programs had approximately 19% fewer substantiated reports, 25% fewer children substantiated for neglect, and a 17% reduction in foster care services utilization when compared to states without DR programs. We find these estimates to be robust to the opioid epidemic and incarceration rates. Additional research is needed to better characterize DR programs and isolate the effects of DR programs geographically.
Intergenerational Pathways Linking Mothers’ Adverse Childhood Experiences and Children’s Social-Emotional Problems
Authors: Zhang Lixia , Mersky Joshua P. , Lee Chienti P.
Published: January 2022

Despite the persistent hypothesis that adverse childhood experiences (ACE) have intergenerational implications, empirical research documenting the effects and the mechanisms of transmission remains underdeveloped. This study examined the intergenerational effects of mothers’ adverse childhood experiences on their offspring’s social-emotional development and whether the association was mediated by mothers’ mental health, adult adversity, and perceptions of paternal involvement. The study sample included 831 mothers (19–49 years old, 47.5% White) with children aged 12–48 months who participated in a longitudinal investigation of low-income families in Wisconsin. ACEs were assessed by home visitors, and two waves of survey data were collected by researchers to assess demographics, mediators, and social-emotional outcomes. A path analysis showed that the association between maternal ACEs and children’s social-emotional problems was fully mediated, with postpartum mental health acting as a primary mechanism. Implications for intervention and future research directions are discussed.
Agitation, Alteration, and Transformation: A Deleuzean Knitting Adventure
Authors: Hoyden Jennifer Ruth
Published: January 2022

This narrative forms the closest thing to an articulation of how I came to park my car and place a handknit blanket upon a deer carcass on the side of the road. The experience upends expectations of intention and ideation, and instead centers me not as a creator but as an affective hub connecting environment to memory to material, and back again.
Effects of Perceived Placeness on Tourists’ Authenticity Experience Via the Mediating Role of Flow Experience
Authors: Yang Yang , Zhou Xing , Fan Lele , Yin Hongmei , Qu Hailin
Published: January 2022

Based on the case of Gaoshanliushui in China, our research empirically examines the mediating effect of tourists’ flow experience on the relationship between perceived placeness and satisfaction as well as their perceived authenticity from the perspective of existential authenticity in the ethnic tourism context. Moreover, we present a moderated mediation model and postulate the role that tourists’ cultural intelligence plays in improving satisfaction and perceived authenticity. We review the way it links perceived placeness to outcomes through the flow experience. The theoretical model and hypotheses were empirically tested using 509 questionnaires collected in July 2019. The theoretical and managerial implications are discussed.
Probing ‘instaworthiness’: Siting the selfie
Authors: Gupta Neha , Ray Avishek
Published: January 2022

This article examines ‘instaworthiness’ as a classificatory category in discourses of place-making. It engages in social-semiotic analyses of the selfies clicked at instaworthy cafes and pubs in Kolkata to highlight how the photographic performativity of selfie-taking reconfigures the notions of placemaking. It accounts for: how is an ‘instaworthy’ spot made and consumed? What ramifications does showcasing the self within these specific sites have upon perceptions of identity, both of the self and the space in question? In locating photographic practices of selfies within the wider shifts of the heuristics of ‘instaworthiness’, this article teases out how the self interacts with a diverse range of non-human actors toward conferring visual apartness upon certain spaces. Thus, producing and circulating selfies as a performance warrant thinking through how discourses of the self and that of spatiality co-constitute each other.
Technology-mediated tasks for the development of L2 pragmatics
Authors: González-Lloret Marta
Published: January 2022

Developing pragmatic competence implies the learning of the norms and principles that affect the behavior of participants in a culture (i.e. sociopragmatics) and the ability to choose the language to realize those norms (i.e. pragmalinguistics). Learning to be pragmatically appropriate in the second language (L2) is not easy, and although it is possible without instruction, research shows that instruction helps development (Plonsky & Zhuang, 2019). This article advocates that technology-mediated tasks are an excellent and effective pedagogic tool to promote L2 pragmatic development. The article will introduce some key findings of studies that incorporate technology and pragmatics as well as those that have investigated tasks and L2 pragmatics to then focus on those studies that incorporate the three elements: tasks, technology and L2 pragmatics. These studies are grouped by their main focus of investigation: (1) the task, (2) the technology, or (3) the L2 pragmatic feature. As a whole, these studies show the possibilities that tasks and technology-mediated contexts have to engage learners in discursive practices that may not be possible otherwise, exposing them to the cyberpragmatics of an ever-growing digital world. Finally, lines of new research to advance the field are suggested.