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All socioeconomic classes are impacted by the COVID-19 epidemic, with stress being one of the biggest symptoms causing indigestion, breathlessness, heart disease, obesity, and many more. The most commonly used selective serotonin reuptake inhibitors (SSRIs) drugs include, escitalopram, sertraline, paroxetine, and fluoxetine are provided by doctors. If the medicines are not taken in the recommended dosage, at the correct intervals, which can occasionally be fatal because persons with depression are susceptible to mood fluctuations. There are no appropriate ways to monitor high dosages in clinical laboratories, the requirement for simple, novel material with cutting-edge technology to do so is essential. Herein, we report the application of a single fluorescent probe, Tyrosine-GQDs for the detection of SSRI drugs. Upon addition of SSRI drugs into the Tyrosine-GQDs, the fluorescence emission intensity of Tyrosine-GQDs decreased due to the complex formation through H-bonding which is evidenced by the rise of new peaks in fluorescence spectra and lifetime measurements. The calculated detection limit proves that the designed nanoprobe is highly sensitive and selective towards escitalopram, sertraline, paroxetine, and fluoxetine are 0.13 × 10−6 M, 0.15 × 10−6 M, 0.07 × 10−6 M, and 0.07 × 10−6 M, respectively. A paper-based fluorescent sensor is fabricated using the designed probe which is efficient in the detection of the dosage of SSRI drugs. [Display omitted] • Tyrosine-GQDs are employed for the spectrophotometric detection of SSRI drugs. • The limit of detection for the SSRI drugs is quantified in sub-micromolar concentration. • The effect of interference shows that the complex formed between the Tyrosine-GQDs and drugs is highly stable. • The H-bond formation between the drugs and Tyrosine-GQDs dominates the mechanism responsible for the fluorescence quenching. • The fluorescent paper sensor is effective in determining the SSRI drugs. [ABSTRACT FROM AUTHOR]

[Display omitted] • M-GQDs exhibit excellent performance in distinguishing and detecting antibiotics. • Special phenomena are presented when various kinds of antibiotics are added with different mechanisms. • The probe shows low LOD for DOX, PEF and LEV of 39.3 nM, 48.6 nM and 35.1 nM. • A 3D μPAD-based platform is proposed for real-time monitoring antibiotics. For antibiotics misuse since the global outbreak of COVID 19, a novel strategy for discriminating and detecting antibiotics is proposed based on the graphene quantum dots with multi-doped heteroatoms including F, N and P (M-GQDs), which exhibit blue emission (419.0 nm) under the excitation of 336.0 nm. Specifically, the fluorescence of M-GQDs is quenched by tetracyclines (TCs) owing to inner filter effect (IFE) and enhanced by alkane-modified fluoroquinolones (AFQs), which is attributed to restricted conformational rotation based on π-π stacking, hydrogen-bonding and electrostatic interactions. Meanwhile, the electron-accepting property of oxazine ring in oxazine-modified fluoroquinolones (OFQs) increases emission peak at 498.0 nm and decreases emission peak at 419.0 nm as the color changes from blue to cyan. Moreover, a cascade system integrated with 3D microfluidic paper‐based analytical device (3D-μPAD) is applied successfully for visually distinguishing three antibiotics, which shows great potential and versatility of M-GQDs for food safety monitoring. [ABSTRACT FROM AUTHOR]

Illustration of the assembly, distribution, and point-of-care use of a rapidly-deployable, cell-free COVID-19 biosensor: 1) Assemble: Assembling CFPS reagents by mixing E. coli lysate, murine RNase Inhibitor (mRI), energy sources, cofactors, and toehold switch riboregulator plasmid. 2) Print: aliquoting CFPS reagents onto paper substrates housed in a plastic test cassette. 3) Dehydrate: lyophilizing CFPS reagents on paper substrates. 4) Distribute. 5) Saliva sample: applying saliva samples onto cassette without pretreatments. 6) Reaction: bioluminescent protein expression in presence of target RNA (+), or ribosome detachment in absence of target RNA (-). 7) Visual result: bioluminescent output in the presence of target RNA and NanoLuc luciferase expression. [Display omitted] • Progress towards a rapid, low-cost, point-of-care Covid-19 biosensor. • Engineered toehold switches respond to saliva enriched with SARS-CoV-2 RNA. • mRI enables robust paper-based cell-free protein synthesis in the presence of saliva. • Plastic cassette houses paper-based diagnostic test for simple distribution and use. The COVID-19 pandemic has illustrated the global demand for rapid, low-cost, widely distributable and point-of-care nucleic acid diagnostic technologies. Such technologies could help disrupt transmission, sustain economies and preserve health and lives during widespread infection. In contrast, conventional nucleic acid diagnostic procedures require trained personnel, complex laboratories, expensive equipment, and protracted processing times. In this work, lyophilized cell-free protein synthesis (CFPS) and toehold switch riboregulators are employed to develop a promising paper-based nucleic acid diagnostic platform activated simply by the addition of saliva. First, to facilitate distribution and deployment, an economical paper support matrix is identified and a mass-producible test cassette designed with integral saliva sample receptacles. Next, CFPS is optimized in the presence of saliva using murine RNase inhibitor. Finally, original toehold switch riboregulators are engineered to express the bioluminescent reporter NanoLuc in response to SARS-CoV-2 RNA sequences present in saliva samples. The biosensor generates a visible signal in as few as seven minutes following administration of 15 μL saliva enriched with high concentrations of SARS-CoV-2 RNA sequences. The estimated cost of this test is less than 0.50 USD, which could make this platform readily accessible to both the developed and developing world. While additional research is needed to decrease the limit of detection, this work represents important progress toward developing a diagnostic technology that is rapid, low-cost, distributable and deployable at the point-of-care by a layperson. [ABSTRACT FROM AUTHOR]

According to a recent World Health Organization estimate, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, which originated in China in 2019, has spread globally, infecting nearly 100 million people worldwide by January 2021. Patients with chronic liver diseases (CLD), particularly cirrhosis, hepatobiliary malignancies, candidates for liver transplantation, and immunosuppressed individuals after liver transplantation appear to be at increased risk of infections in general, which in turn translates into increased mortality. This is also the case for SARS-CoV-2 infection, where patients with cirrhosis, in particular, are at high risk of a severe COVID-19 course. Therefore, vaccination against various pathogens including SARS-CoV-2, administered as early as possible in patients with CLD, is an important protective measure. However, due to impaired immune responses in these patients, the immediate and long-term protective response through immunisation may be incomplete. The current SARS-CoV-2 pandemic has led to the exceptionally fast development of several vaccine candidates. A small number of these SARS-CoV-2 vaccine candidates have already undergone phase III, placebo-controlled, clinical trials in healthy individuals with proof of short-term safety, immunogenicity and efficacy. However, although regulatory agencies in the US and Europe have already approved some of these vaccines for clinical use, information on immunogenicity, duration of protection and long-term safety in patients with CLD, cirrhosis, hepatobiliary cancer and liver transplant recipients has yet to be generated. This review summarises the data on vaccine safety, immunogenicity, and efficacy in this patient population in general and discusses the implications of this knowledge on the introduction of the new SARS-CoV-2 vaccines. [ABSTRACT FROM AUTHOR]

The study aims to investigate the effects of delivery modalities on psychometric characteristics and student performance on cognitive tests. A first study assessed the inductive reasoning ability of 715 students under the supervision of teachers. A second study examined 731 students' performance on the application of the control-of-variables strategy in basic physics but without teacher supervision due to the COVID-19 pandemic. Rasch measurement showed that the online format fitted to the data better in the unidimensional model across two conditions. Under teacher supervision, paper-based testing was better than online testing in terms of reliability and total scores, but contradictory findings were found in turn without teacher supervision. Although measurement invariance was confirmed between two versions at item level, the differential bundle functioning analysis supported the online groups on the item bundles constructed of figure-related materials. Response time was also discussed as an advantage of technology-based assessment for test development. • The psychometric properties were comparable across two testing conditions. • The DBF analyses supported the online groups on the rich-visualization items. • Test results were inconsistent in the two groups through dual testing conditions. • Items with average response time under 30 s seemed to lack discrimination. [ABSTRACT FROM AUTHOR]

This paper aims to analyze and understand the impact of the corona virus disease (COVID-19) on aviation and also the role aviation played in the spread of COVID-19, by reviewing the recent scientific literature. We have collected 110 papers on the subject published in the year 2020 and grouped them according to their major application domain, leading to the following categories: Analysis of the global air transportation system during COVID-19, the impacts on the passenger-centric flight experience, and the long-term impacts on broad aviation. Based on the aggregated reported findings in the literature, this paper concludes with a set of recommendations for future scientific directions; hopefully helping aviation to prepare for a post-COVID-19 world. • We give an overview of the COVID-19 impact on air transportation. • In total, 110 papers have been carefully studied, categorized, and reported. • We investigate airlines, passenger experience, and long-term impact. • We conclude our review paper with future research directions. [ABSTRACT FROM AUTHOR]

Since the early 2010s, the production of industrial wood pellet for overseas exports has grown considerably in the Southern United States (U.S.). In order to meet the increasing demand for woody biomass feedstock, the wood pellet mills in the U.S. South consume roundwood pulpwood along with low-valued logging residues and sawmill wood wastes. By estimating empirical roundwood pulpwood market models based on the bi-monthly time-series data, this study estimates the impacts of wood pellet production, precipitation, and the COVID-19 Pandemic on the hardwood and softwood pulpwood stumpage prices in 12 southeastern Atlantic coastal micro-markets. Results suggest that the wood pellet industry has led to an increase in pine pulpwood prices, but no statistically significant impact is found on the hardwood pulpwood prices. Rainfall is found to be a positive determinant of the roundwood pulpwood prices. Furthermore, since March 2020, the COVID-19 Pandemic is estimated to lead about 6% decline in both pine and hardwood pulpwood prices. This study provides empirical evidence and additional insights on the timber market dynamics and how small roundwood markets have reacted to the additional wood demand from a growing bioenergy industry in the U.S. South. • The wood pellet industry competes with pulp and paper mills for woody biomass. • The wood pellet industry has led to an increase in pine pulpwood prices. • No significant impact of wood pellets is found on the hardwood pulpwood prices. • The COVID-19 Pandemic has led to decline pulpwood stumpage prices. [ABSTRACT FROM AUTHOR]

• We examined panic buying and hoarding at the onset of the COVID-19 outbreak. • Perceived scarcity was the strongest predictor of panic buying. • Intolerance of uncertainty was the strongest predictor of hoarding. • Buying restrictions imposed by retailers may reduce panic buying. The COVID-19 pandemic led to panic buying in many countries across the globe, preventing vulnerable groups from accessing important necessities. Some reports inaccurately referred to the panic buying as hoarding. Although hoarding is a separate issue characterised by extreme saving behaviour, the two problems may be influenced by similar factors. Participants from Australia and the United States (final N = 359) completed online self-report measures of panic buying, hoarding, shopping patterns, perceived scarcity, COVID-19 illness anxiety, selfishness, and intolerance of uncertainty. Our findings showed that panic buying was related to hoarding symptoms (r 's =.23 -.36), and yet, both were uniquely associated with different psychological factors. Whilst panic buying was most strongly related to greater perceived scarcity (r 's =.38 -.60), hoarding was most related to a general intolerance of uncertainty (r 's =.24 -.57). Based on our findings, future strategies to prevent panic buying should focus on reducing perceived scarcity cues in the community, as this seems to be the primary driver of panic buying. Another preventative strategy to reduce excessive acquiring and saving may be to implement educational programs to increase people's ability to tolerate distress and uncertainty. [ABSTRACT FROM AUTHOR]

Objective: Communication in healthcare has influenced and been influenced by the COVID-19 pandemic. In this position paper, we share observations based on the latest available evidence and experiential knowledge that have emerged during the pandemic, with a specific focus on policy and practice.Methods: This is a position paper that presents observations relating to policy and practice in communication in healthcare related to COVID-19.Results: Through our critical observations as experts in the field of healthcare communication, we share our stance how healthcare communication has occured during the pandemic and suggest possible ways of improving policy and professional practice. We make recommendations for policy makers, healthcare providers, and communication experts while also highlighting areas that merit further investigation regarding healthcare communication in times of healthcare crises.Conclusion: We have witnessed an upheaval of healthcare practice and the development of policy on-the-run. To ensure that policy and practice are evidence-based, person-centred, more inclusive and equitable, we advocate for critical reflection on this symbiotic relationship between COVID-19 and the central role of communication in healthcare.Practice Implications: This paper provides a summary of the key areas for development in communication in healthcare during COVID-19. It offers recommendations for improvement and a call to review policies and practice to build resilience and inclusive and equitable responsiveness in communication in healthcare. [ABSTRACT FROM AUTHOR]

The importance of user-friendly, inexpensive, sensitive, and selective detection of viruses has been highlighted again due to the recent Coronavirus disease 2019 (COVID-19) pandemic. Among the analytical tools, paper-based devices (PADs) have become a leading alternative for point-of-care (POC) testing. In this review, we discuss the recent development strategies and applications in nucleic acid-based, antibody/antigen-based and other affinity-based PADs using optical and electrochemical detection methods for sensing viruses. In addition, advantages and drawbacks of presented PADs are identified. Current state and insights towards future perspectives are presented regarding developing POC diagnosis platform for COVID-19. This review considers state-of-the-art technologies for further development and improvement in PADs performance for virus detection. [Display omitted] • Reviewed developments in PADs for optical and electrochemical detection of viruses. • Highlighted recent applications in antibody/antigen-, nucleic acid-, aptamer- and CRISPR-based PADs for viral detection. • Emphasized low-cost POC diagnostic tools to control the global COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The microfluidic paper-based analytical devices (μPADs) have grown-up swiftly over the decade due to its low cost, simple fabrication procedure, resource-limitedness, non-toxicity and their environmentally benign nature. The μPADs, also identified as point-of-care devices or health care devices have successfully applied in several fields such as diagnostics, biological, food safety, environmental, electrochemical and most importantly colorimetric/fluorimetric sensors, owing to the attractive passive motions of analyte without any external forces. In recent years, a large number of colorimetric and fluorimetric probes have been reported that can selectively recognize the analytes in μPADs. However, there is no organized review on its structure-activity relationship. In this review, we have focused to summarize the colorimetric and fluorimetric probes utilized in μPADs. This review discuss about the relationships between the structure and functions of various probes as signaling units of the efficient μPADs. The probes including nanomaterials, nanozymes, polymers and organic molecules, their structural activity with regard to sensing performances along with their limit of detection are also discussed. This review is expected to assist readers for better understanding of the sensing mechanisms of various chemo and bio-probes utilized in μPADs, as well as promote their advancement in the field. On the other hand, this review also helps the researchers for enhancement of μPADs and paves way for synergistic application of existing molecular probes as an effective diagnostic tool for the worldwide pandemic novel corona virus COVID-19. [Display omitted] • Traditional fabrications methods for µPADs are summarized. • Discusses about the hindrances of commercial wax printing method for fabrication of µPADs. • Various sensory materials are categorized and discussed along with their structure activity properties. • Highlights about the most promising material that could be a probable probe for µPADs. • The vision towards employing µPADs for COVID19 diagnosis is also discussed. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has highlighted the need for diversity in the market and alternative materials for personal protective equipment (PPE). Paper has high coatability for tunable barrier performance, and an agile production process, making it a potential substitute for polyolefin-derived PPE materials. Bleached and newsprint papers were laminated with polyethylene (PE) coatings of different thicknesses, and characterised for their potential use as medical gowns for healthcare workers and COVID-19 patients. Thicker PE lamination improved coating homogeneity and water vapour resistance. 49 GSM bleached paper with 16 GSM PE coating showed high tensile and seam strength, and low water vapour transmission rate (WVTR). Phi-X174 bacteriophage testing revealed that paper laminated with 15 GSM coating hinders virus penetration. This research demonstrates that PE laminated paper is a promising material for low cost viral protective gowns. [Display omitted] • Polyethylene (PE) laminated bleached paper was assessed as a medical gown material. • High tensile and seam strength were achieved for the laminate composites. • Viral penetration was hindered by a 49 GSM paper sheet with a 15 GSM PE coating. • The laminated paper met medical gown material testing standards. • Prototypes were praised by medical professionals when worn in a clinical setting. [ABSTRACT FROM AUTHOR]

• Paper-based electrochemical sensors for hydrogen peroxide in aerosol phase. • Filter screen-printed electrode modified via drop casting with nanocomposite. • Electrochemical smartphone-assisted device to customize the disinfection process. The outbreak of COVID-19 is caused by high contagiousness and rapid spread of SARS-CoV-2 virus between people when an infected person is in close contact with another one. In this overall scenario, the disinfection processes have been largely improved. For instance, some countries have approved no-touch technologies by vaporizing disinfectants such as hydrogen peroxide, with the overriding goal to boost the safety of the places. In the era of sustainability, we designed an electrochemical paper-based device for the assessment of hydrogen peroxide nebulized by a cost-effective ultrasonic aroma diffuser. The paper-based sensor was fabricated by modifying via drop-casting a filter paper-based screen-printed electrode with a dispersion of carbon black-Prussian Blue nanocomposite, to assess the detection of hydrogen peroxide at −0.05 V vs Ag/AgCl. The use of paper-based modified screen-printed electrode loaded with phosphate buffer allowed for monitoring the concentration of hydrogen peroxide in aerosol, without any additional sampling instrument to capture the nebulized solution of hydrogen peroxide at a concentration up to 7% w/w. Hydrogen peroxide, a reconverted ultrasonic aroma diffuser, and the paper-based electrochemical sensor assisted by smartphone have demonstrated how different low-cost technologies are able to supply an useful and cost-effective solution for disinfection procedures. [ABSTRACT FROM AUTHOR]

Accurate and rapid detection and isolation become indispensable to restrict the spread of COVID-19. Since the start of COVID-19 pandemic in December 2019, many indisposal diagnostic tools are being developed incessantly. Out of all presently used tools, the gold standard rRT- PCR tool having very high sensitivity and specificity is a time consuming complicated molecular technique having requirements of special expensive equipment. Here, the main focus of this work is to develop rapid disposal paper capacitance sensor having simple and easy detection. We discovered a strong interaction between limonin and Spike-glycoprotein of SARS-COV-2 in comparison to its interaction with other similar viruses such as HCOV-OC43, HCOV-NL63, HCOV-HKU1, Influenza B and A viruses. The antibody free capacitive sensor having comb electrode structure was fabricated on whatman paper with drop coating of limonin (extracted using green method from pomelo seeds) and calibrated with known swab samples. The Blind test with unknown swab samples shows high sensitivity of 91.5% and high specificity of 88.37%. Requiring low sample volume and detection time and using biodegradable materials in the sensor fabrication assure the potential application as a point of care disposal diagnostic tool. [Display omitted] • A rapid COVID-19 detection kit based on capacitive sensor based has been developed. • Limonin extracted from plant seeds has been used as sensing material. • The sensor exhibited high sensitivity of 91.5%(clinical) with high specificity of 88.37%. • Detection time for the sensor is ∼182s with sample volume requirement of 10 μL. • This paper-based sensor assures inexpensive, easy, onsite detection of COVID-19. [ABSTRACT FROM AUTHOR]

• In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from the clinical needs to the design of AI-based systems, to the translation of the developed tools to the clinic. • We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease on the national level. • We focus on three specific use-cases for which AI systems can be built: from the early disease detection, the management of the disease in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical features. • Infrastructure considerations and population modeling in two European countries will be described. • This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead. In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from clinical requirements to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease at the national level. We focus on three specific use-cases for which AI systems can be built: early disease detection, management in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical data. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead. [ABSTRACT FROM AUTHOR]

The world has been encountered with COVID-19 pandemic since at the beginning of 2020 and the number of infected people by COVID-19 is increasing every day. Despite various studies conducted by researchers and doctors, no treatment has been developed until now, therefore self-protection and isolation are strongly recommended to stop the spread of the virus. The elderly population and people with chronic diseases such as hypertension, cardiovascular diseases, diabetes, and cancer are categorized as risk groups, however, we suggest that people with hemoglobinopathies or porphyria can be described as risk groups as well. Current in silico studies have revealed that the COVID-19 virus can attack heme and hemoglobin metabolisms which are responsible for the oxygen transport to the tissues, iron metabolism, elevated levels of oxidative stress, and tissue damage. Data of the in silico study have been supported with the biochemistry and hemogram results of the COVID-19 patients, for instance hemoglobin levels decreased and serum ferritin and C-reactive protein levels increased. Indicated biochemistry biomarkers are tightly associated with inflammation, iron overload, and oxidative stress. In conclusion, since people with hemoglobinopathies or porphyria have already impaired heme and hemoglobin metabolism, COVID-19 infection can enhance the adverse effects of impaired hemoglobin metabolism and accelerate the progression of severe symptoms in patients with hemoglobinopathies or porphyria compared to the normal individuals. Thus those people can be considered as a risk group and extra precautions should be applied for them to protect them. [ABSTRACT FROM AUTHOR]

SARS, a new type of respiratory disease caused by SARS-CoV, was identified in 2003 with significant levels of morbidity and mortality. The recent pandemic of COVID-19, caused by SARS-CoV-2, has generated even greater extents of morbidity and mortality across the entire world. Both SARS-CoV and SARS-CoV-2 spreads through the air in the form of droplets and potentially smaller droplets (aerosols) via exhaling, coughing, and sneezing. Direct detection from such airborne droplets would be ideal for protecting general public from potential exposure before they infect individuals. However, the number of viruses in such droplets and aerosols is too low to be detected directly. A separate air sampler and enough collection time (several hours) are necessary to capture a sufficient number of viruses. In this work, we have demonstrated the direct capture of the airborne droplets on the paper microfluidic chip without the need for any other equipment. 10% human saliva samples were spiked with the known concentration of SARS-CoV-2 and sprayed to generate liquid droplets and aerosols into the air. Antibody-conjugated submicron particle suspension is then added to the paper channel, and a smartphone-based fluorescence microscope isolated and counted the immunoagglutinated particles on the paper chip. The total capture-to-assay time was <30 min, compared to several hours with the other methods. In this manner, SARS-CoV-2 could be detected directly from the air in a handheld and low-cost manner, contributing to slowing the spread of SARS-CoV-2. We can presumably adapt this technology to a wide range of other respiratory viruses. • Direct bioaerosol capture and subsequent immunoassay on a single assay platform. • Handheld, rapid, low-cost, smartphone-based paper microfluidic assay detecting SARS-CoV-2 from the air. • No need for an air sampler, with the total capture-to-assay time of <30 min. • A smartphone microscope successfully confirmed the presence of SARS-CoV-2 from the air. • The device and method can slow the spread of SARS-CoV-2 and other emerging respiratory viruses. [ABSTRACT FROM AUTHOR]

General physicians misclassify depression in more than half of the cases. Researchers have explored the feasibility of leveraging passively collected data points, also called digital biomarkers, to provide more granular understanding of depression phenotypes as well as a more objective assessment of disease. This paper provides a systematic review following the PRISMA guidelines (Page et al., 2021) to understand which digital biomarkers might be relevant for passive screening of depression. Pubmed and PsycInfo were systematically searched for studies published from 2019 to early 2024, resulting in 161 records assessed for eligibility. Excluded were intervention studies, studies focusing on a different disease or those with a lack of passive data collection. 74 studies remained for a quality assessment, after which 27 studies were included. The review shows that depressed participants' real-life behavior such as reduced communication with others can be tracked by passive data. Machine learning models for the classification of depression have shown accuracies up to 0.98, surpassing the quality of many standardized assessment methods. Inconsistency of outcome reporting of current studies does not allow for drawing statistical conclusions regarding effectiveness of individual included features. The Covid-19 pandemic might have impacted the ongoing studies between 2020 and 2022. While digital biomarkers allow real-life tracking of participant's behavior and symptoms, further work is required to align the feature engineering of digital biomarkers. With shown high accuracies of assessments, connecting digital biomarkers with clinical practice can be a promising method of detecting symptoms of depression automatically. • This paper provides a PRISMA-guided systematic review of studies (2019-2024), showing model accuracy ranges of 0.52-0.98. • The review summarises constructs related to mood, location, smartphone use, communication, sleep quality and heart rate. • To be integrated into clinical practice, captured data needs to be reduced to the most relevant features. [ABSTRACT FROM AUTHOR]

COVID-19 has seriously impacted China's real economy and directly threatened the survival and healthy development of enterprises. Based on the data of Chinese A-share listed enterprises in Shanghai and Shenzhen from 2017 to 2021, this paper empirically examines the impact and mechanism of COVID-19 on enterprises financial distress. The findings are as follows: (1) The impact of COVID-19 significantly increases the risk of enterprises financial distress. (2) There are differences in the effect of COVID-19 on the financial situation of enterprises with different characteristics, with significant negative impacts on enterprises in industries such as mining and manufacturing, as well as smaller enterprises and those located in the central and western regions. (3) Digital transformation and government subsidies can alleviate the negative effects of COVID-19 on enterprises financial distress. The paper provides a basis for enterprises to recover from financial difficulties as soon as possible, enhance competitiveness, and realize sustainable development. [ABSTRACT FROM AUTHOR]

Evidence on the impact of the COVID-19 pandemic on adolescent mental health is mixed and does not disentangle natural age-related changes. We compared depressive symptoms among 16-year-olds surveyed, at a fourth wave, before or during the pandemic, while accounting for expected trajectories of within-person change based on 3 prior waves. In this longitudinal cohort of 3,171 adolescents in Tokyo, Japan, adolescents were grouped based on their age 16 survey timing: pre-pandemic (February 2019 to February 2020) and during-pandemic (March 2020 to September 2021). Depressive symptoms were self-reported using the Short Mood and Feelings Questionnaire. Mixed-effect models were fitted to assess group differences while controlling for previous trends. Variations by sex, household income, and pandemic phase (early, late first-year, and second-year) were examined. Of 2,034 eligible adolescents, 960 (455 girls) were assessed before and 1,074 (515 girls) during the pandemic. Overall, depressive symptoms increased by 0.80 points (95% CI 0.28-1.31, 0.15 SD of the population average). This increase varied by sex and pandemic phase. For boys the increase emerged in the late first-year phase and enlarged in the second-year phase (mean difference from pre-pandemic: 1.69, 0.14-3.24), whereas for girls it decreased in the early school-closure phase (mean difference: –1.98, –3.54 to –0.41) and returned to the pre-pandemic level thereafter, with no additional increases during the pandemic. Into the second year of the COVID-19 pandemic, depressive symptoms of 16-year-olds worsened above the expected age-related change only in boys. Continuous monitoring and preventive approaches for adolescents at the population level are warranted. We worked to ensure that the study questionnaires were prepared in an inclusive way. We worked to ensure sex and gender balance in the recruitment of human participants. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. We actively worked to promote sex and gender balance in our author group. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work. [ABSTRACT FROM AUTHOR]

This paper reviews the basic issues involved in identifying and providing solutions to transport problems. The paper argues that solutions focusing solely on investment in capacity or second-best solutions to pricing such as road pricing and free public transport ignore the essential economic relationships involved in transport. The paper provides a critique of investment and pricing and argues for a more integrated approach in which optimal pricing is a core consideration in any investment decisions. This is seen as particularly important in a post Covid-19 world where many of the fundamental drivers of transport demand have changed, and public authorities face significant constraints on budgets whilst new pressures such as commitments to net zero in the face of global warming threats add additional pressures. The paper offers some thoughts on the principles involved in establishing a more suitable structure for dealing with the transport problem. • Identifies the core elements of the transport problem as pricing and investment. • Reviews problems in implementing first-best pricing. • Critiques investment decisions in the light of wider economic impacts. • Considers changes brought about by the Covid-19 pandemic and the move to net-zero. • Offers a set of principles for reforming transport policy. [ABSTRACT FROM AUTHOR]

COVID-19 has represented a watershed in people's lifestyle. One important consequence has been the change in how people buy groceries online/offline that represent the most frequent shopping-related trip any household performs. Since this has substantially different transport and environmental implications, it is crucial to investigate what are the COVID-19-induced variations. This paper estimates both travel and environmental impacts grocery channel choice implies by comparing, in a diachronic fashion, the pre and during COVID-19 situation in Norway, considering alternative scenarios describing different service configurations for each channel considered. Stated preferences are used to estimate market shares for grocery purchase distinguishing between different channels: in-store, home delivery and click&pick. The robust comparative analysis between the two time periods is guaranteed by the use of the same methodological approach in the same geographical context. Results show that total emissions are lower during the pandemic and adopting a free delivery strategy not only increases home delivery market shares but also produces the lowest level of CO2 emissions regardless of COVID-19. The results obtained are particularly relevant in the "new normal" the world is experiencing both from a public policymaking perspective, when promoting transport-related interventions to curb emissions, and from a private standpoint, when customising market strategies to increase sales and profits. • COVID-19 has represented a watershed in people's lifestyle. • Investigating changes in how people buy groceries online/offline is important. • Stated preferences are used to estimate grocery market shares for different channels. • The paper estimates travel and environmental impacts pre and during COVID-19 via scenario analysis. • Results are particularly relevant for both the public and private sector in the "new normal" conditions. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, the healthcare sector faced unprecedented challenges in effectively managing hospital resources. A crucial aspect of resource planning and allocation is the ability to predict the expected length of a patient's stay. Detecting whether a patient requires extended hospitalization or a shorter stay becomes vital for efficient hospital resource allocation and utilization. This paper aims to build a deep learning-based analytical model named LoSNet that predicts the length of stay for each patient at the time of admission to the Hospital. The early prediction of the length of stay requirement would aid healthcare professionals in optimizing the utility of hospital beds and other resources. In this direction, this paper compares the prediction ability of various machine-learning models including Random Forest, Decision Tree, Logistic Regression, and Naïve Bayes with a customized deep neural network model. The dataset used for this analysis includes ground truth on 3,18,438 patients' length of stay categorized into eleven classes such as 0-10 days being one class, 11-20 days being another class, and so on to more than 100 days. The methodology employed in this study involves data collection, data transformation, and training LoSNet, a deep neural network with no attention mechanisms. The results indicate impressive performance over other models and a random classifier, with a cross-entropy loss of 1.531 and an accuracy of 0.408 in predicting hospitalization durations in an 11-class classification setup, highlighting the framework's effectiveness in healthcare management. [ABSTRACT FROM AUTHOR]

Determination of macroeconomic policies in real-time requires assessing the correct information regarding current economic conditions. This statement spurred researchers to develop methods involving high-frequency data for risk analysis. This paper extends the quarterly growth-at-risk (GaR) approach by involving a machine-learning approach based on the Mixed-Frequency Data Sampling Quantile Regression Neural Network (MIDAS-QRNN) model. This paper shows that the MIDAS-QRNN model has the best prediction accuracy and can show good PDB nowcasting. The monthly financial GaR can detect unusual economic growth movements during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Behavioral economic demand models quantify the extent to which an organism defends its consumption of a commodity. Commodity purchase tasks permit humans a quick yet psychometrically sound approach to assessing commodity demand for various retail products. Operant behavioral economic literature suggests economy type (open vs closed) can significantly alter demand, yet this effect is largely undocumented in the commodity purchase task literature. In this study, we leveraged the market pressures for retail goods (hand lotion and sanitizer; paper towels and toilet paper; soda and water) resulting from SARS-CoV-2 into a natural experiment comparing within-subject demand across two time-points during the pandemic using a crowdsourced approach. Results suggest that hypothetical commodity purchase tasks are sensitive to extra-experimental market pressures (e.g., scarcity due to the closing of economies), adding additional confidence to the self-report nature of purchase task responding and providing further construct validity to these approaches. • Reinforcer scarcity modulates operant demand. • The SARS-CoV-2 pandemic presented a natural experiment on scarcity. • Crowdsourced data shows effects of real-world scarcity on hypothetical demand. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and the Russian-Ukrainian war revealed vulnerabilities in supply chains, emphasizing the need for resilience in multiple industries. While Industry 4.0 is valuable, it cannot fully address complex supply chain challenges. Therefore, exploring additional drivers like Operations Management has become imperative. More clearly, the adoption of Operations Management tools and practices helps implement and use enabling technologies more effectively. In this context, the aim of the paper is to conduct a literature review to study supply chain resilience considering these two concepts. In other words, the objective of the work consists in examining the effectiveness of the mentioned combination. The paper presents a conceptual framework for enhancing supply chain resilience by exploring the potential of Industry 4.0 and Operations Management. The paper also underlines the positive impact issued from hybridizing both solutions to achieve resilience. As it discusses enhancing features of each concept aside the other. The paper provides a basic model for researchers to further detailed studies, and for managers to bringing the proposed framework from theory to practice. [ABSTRACT FROM AUTHOR]

• A new index is proposed to quantify the economic impact of COVID-19 on countries. • The index shows an extreme heterogeneity in the economic impact of COVID-19. • The factors behind this heterogeneity are explored using regression techniques. • The analysis includes pandemic-specific factors and structural factors. • Results stress the importance of industrial development for resilience. The socioeconomic impact of COVID-19 has been widely uneven across regions and countries, reflecting underlying differences in their resilience against shocks. This paper tries to explain this heterogeneity by identifying factors of resilience and vulnerability. To fully capture the impact of the crisis on economic activity, we propose a novel index of GDP loss that measures both the initial shock and recovery rate at the country level. With a dataset of 125 countries, we implement cross-sectional regression techniques to estimate the impact of pandemic-specific and structural factors on the index. The focus of the analysis is placed on a dimension that was not sufficiently explored yet in the specialized literature: the role of industrial capabilities. Results show that industrial capabilities were crucial in supporting countries' ability to absorb and resist the global shock. The paper thus provides new empirical evidence on the role played by manufacturing industries in strengthening resilience to face unexpected events. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a general concept for the analysis of temporal and multivariate data and the system LFPeers that applies this concept to temporal similarity search and results exploration. The conceptual workflow divides the analysis in two phases: a search phase to find the most similar objects to a query object before a time point t 0 in the temporal data, and an exploration phase to analyze and contextualize this subset of objects after t 0. LFPeers enables users to search for peers through interactive similarity search and filtering, explore interesting behavior of this peer group, and learn from peers through the assessment of diverging behaviors. We present the conceptual workflow to learn from peers and the LFPeers system with novel interfaces for search and exploration in temporal and multivariate data. An earlier workshop publication for LFPeers included a usage scenario targeting epidemiologists and the public who want to learn from the Covid-19 pandemic and distinguish successful and ineffective measures. In this extended paper, we now show how our concept is generalized and applied by domain experts in two case studies, including a novel case on stocks data. Finally, we reflect on the new state of development and on the insights gained by the experts in the case studies on the search and exploration of temporal data to learn from peers. [Display omitted] • Conceptual framework for temporal and multimodal similarity search and exploration. • Visual Analytics system for cause–effect analysis on temporal data. • Interactive user-defined search and exploration of data objects by similarity. [ABSTRACT FROM AUTHOR]

• This paper investigates the predictive ability of oil shocks for international stock market volatility based on a data-rich environment. • We further investigate the predictive ability of the best-performing oil shock by comparing its performance with that of traditional macroeconomic variables and uncertainty. • The LASSO method and regime-switching model can jointly deliver incremental improvement in forecasting accuracy. • This paper tries to provide new evidence for international stock market volatility prediction. This paper investigates the predictive ability of oil shocks for international stock market volatility based on a data-rich environment. Our empirical analysis shows that multiple oil shock measures contain valuable information for predicting stock market volatility, in addition to traditional economic variables and uncertainty indices. Moreover, based on the group 7 countries, the least absolute shrinkage and selection operator method and regime-switching model jointly deliver incremental improvement in forecasting accuracy from both statistical and economic perspectives. These results are confirmed by robustness checks under different business cycles and market conditions, including the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

This paper provides new evidence on the dynamic dependence and connectedness between investments in Financial Technology (FinTech) and green assets across different market conditions and investment horizons. The paper uses daily data and relies on wavelets coherency and quantile-based connectedness methods. First, our results indicate that the co-movement between FinTech and green bonds and clean energy stocks is mostly positive and strongest in the long-term but weak in the short-term, indicating a high probability of large joint losses for long-term investors and hedging opportunities of FinTech stocks for short-term investors in green financial assets. Second, the level of connectedness is stronger at both tails of the return distribution and similar in the short- and long-term while in the medium-term, normal market period total connectedness became stronger than the bearish market period connectedness during the COVID-19 pandemic. Lastly, results also indicate that across all market conditions and time scales, FinTech stocks dominate most of the green financial assets as demonstrated by the net pairwise directional risk spillover. This suggests that FinTech stocks may not offer good hedging opportunities for green financial indexes. The paper provides some crucial implications based on these findings. [ABSTRACT FROM AUTHOR]

To compare psychiatric emergencies and self-harm at emergency departments (EDs) 1 year into the pandemic, to early pandemic and pre-pandemic, and to examine the changes in the characteristics of self-harm presentations. This retrospective cohort study expanded on the Pandemic-Related Emergency Psychiatric Presentations (PREP-kids) study. Routine record data in March to April of 2019, 2020, and 2021 from 62 EDs in 25 countries were included. ED presentations made by children and adolescents for any mental health reasons were analyzed. Altogether, 8,174 psychiatric presentations were recorded (63.5% female; mean [SD] age, 14.3 [2.6] years), 3,742 of which were self-harm presentations. Rate of psychiatric ED presentations in March to April 2021 was twice as high as in March to April 2020 (incidence rate ratio [IRR], 1.93; 95% CI, 1.60-2.33), and 50% higher than in March to April 2019 (IRR, 1.51; 95% CI, 1.25-1.81). Rate of self-harm presentations doubled between March to April 2020 and March to April 2021 (IRR, 1.98; 95% CI, 1.68-2.34), and was overall 1.7 times higher than in March to April 2019 (IRR, 1.70; 95% CI, 1.44-2.00). Comparing self-harm characteristics in March to April 2021 with March to April 2019, self-harm contributed to a higher proportion of all psychiatric presentations (odds ratio [OR], 1.30; 95% CI, 1.05-1.62), whereas female representation in self-harm presentations doubled (OR, 1.98; 95% CI, 1.45-2.72) and follow-up appointments were offered 4 times as often (OR, 4.46; 95% CI, 2.32-8.58). Increased pediatric ED visits for both self-harm and psychiatric reasons were observed, suggesting potential deterioration in child mental health. Self-harm in girls possibly increased and needs to be prioritized. Clinical services should continue using follow-up appointments to support discharge from EDs. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work. [ABSTRACT FROM AUTHOR]

What policy space does a country have for a short-term response to a catastrophic event? To quantify this space, the paper proposes a policy space index. The index combines a quantitative, albeit relatively limited and narrow, fiscal space concept with the indicators of nominal monetary space and reserve space. Each nominal policy space indicator is then adjusted for individual country's institutional features, such as the status of its currency, income group, access to capital markets, debt distress level, and the exchange rate regime. The final policy space index is derived as a composite of the three nominal policy space indicators, each adjusted for five institutional features. This index is different from the approach to measure fiscal space at the IMF and requires more work before it can be used operationally. The proposed index allows measuring the overall policy space in each country directly in percent of GDP. By way of illustration, the paper applies the index to the Covid-19 crisis. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to propose a novel SEIR stochastic epidemic model. A distinguishing feature of this new model is that it allows us to consider a setup under general latency and infectious period distributions. To some extent, queuing systems with infinitely many servers and a Markov chain with time-varying transition rate comprise the very technical underpinning of the paper. Although more general, the Markov chain is as tractable as previous models for exponentially distributed latency and infection periods. It is also significantly more straightforward and tractable than semi-Markov models with a similar level of generality. Based on stochastic stability, we derive a sufficient condition for a shrinking epidemic regarding the queuing system's occupation rate that drives the dynamics. Relying on this condition, we propose a class of ad-hoc stabilising mitigation strategies that seek to keep a balanced occupation rate after a prescribed mitigation-free period. We validate the approach in the light of the COVID-19 epidemic in England and in the state of Amazonas, Brazil, and assess the effect of different stabilising strategies in the latter setting. Results suggest that the proposed approach can curb the epidemic with various occupation rate levels if the mitigation is timely. • We propose queue-based stochastic epidemic model for viral epidemics. • The model is tractable yet general and applicable to large populations. • It describes epidemics with general latency and infectious periods. • We derive a control rule that ensure a shrinking epidemic. • Our experiments illustrate the effectiveness of the proposed control rule. [ABSTRACT FROM AUTHOR]

One of the unique symptoms of COVID-19 is chemosensory dysfunction. Almost three years since the beginning of the pandemic of COVID-19, there have been many studies on the symptoms, progress, and possible causes, and also studies on methods that may facilitate recovery of the senses. Studies have shown that some people recover their senses even within a couple of weeks whereas there are other patients that fail to recover chemosensory functions fully for several months and some never fully recover. Here we summarize the symptoms and the progress, and then review the papers on the causation as well as the treatments that may help facilitate the recovery of the symptoms. Depending on the differences in the levels of severity and the locations where the main pathological venues are, what is most effective in facilitating recovery can vary largely across patients and thus may require individualized strategies for each patient. The goal of this paper is to provide some thoughts on these choices depending on the differences in the causes and severity. [ABSTRACT FROM AUTHOR]

In recent years, the modeling of time-to-events has emerged as a highly promising and dynamic research area. This field has witnessed a surge of research studies dedicated to developing novel statistical methodologies aimed at effectively handling time-to-event phenomena. These studies are motivated by the increasing recognition of the importance of time-related factors in various fields such as medicine, epidemiology, finance, and engineering. Researchers have been actively engaged in proposing innovative approaches to address the complexities associated with time-to-event data. The overarching goal is to enhance our understanding of event occurrence and duration, enabling more accurate predictions and informed decision-making. This research encompasses a wide range of topics, including survival analysis, reliability modeling, and event prediction. The motivation behind these research efforts stems from the need to overcome traditional limitations in time-to-event analysis and to explore new avenues for modeling and interpretation. By introducing advanced statistical techniques, researchers seek to capture the intricate dynamics of event processes, considering factors such as censoring, competing risks, and time-varying covariates. The proliferation of research studies in this domain reflects a collective effort to push the boundaries of statistical modeling and analysis, paving the way for more comprehensive and robust methodologies. As researchers continue to delve deeper into the intricacies of time-to-event data, the impact of these advancements extends to diverse applications, ultimately fostering innovation and progress across interdisciplinary fields. This paper adopts and implements a new statistical approach to propose a family of flexible distributions, namely, a new generalized- O family of distributions. For the newly obtained family, certain mathematical properties such as identifiability, quantile function, r th non-central moment, Lorenz curve, incomplete moments, and the expression of the Bonferroni curve are obtained. Furthermore, an extension of the Weibull model is introduced using the newly developed approach, namely, a new generalized Weibull model. The parameters of the new generalized version of the Weibull model are estimated by adopting a well-known estimation approach. Finally, a data set consists of sixty (60) observations representing the times of the survival of some patients infected by the COVID-19 epidemic is analyzed to illustrate the new generalized Weibull model. [ABSTRACT FROM AUTHOR]

For the investigation of diseases and other harmful environmental influences (e.g., chemicals) epidemiological studies rely on high quality human samples, among others. Collecting samples and data in the field can pose an enormous challenge to the study team with regard to health protection and occupational safety, especially in the context of a pandemic where there was great uncertainty about the biological risks associated with SARS-CoV-2. The German Environmental Specimen Bank (German ESB) is a key element of environmental and human biomonitoring in Germany with the aim to document and assess trends of human and environmental exposure to chemicals over time and to provide scientific data for policy decision makers. Starting with a pilot study in 1978 human samples are now collected at four sampling locations annually, while sampling is carried out with a highly standardized mobile laboratory since 2013. Due to the corona pandemic 3 of 4 ESB sampling campaigns had to be cancelled in 2020. However, a continuous sampling is crucial to generate current policy relevant data on chemical exposure. Hence, a protection and hygiene concept has been developed including COVID-19 testing with the goal to protect the health of participants and employees during sampling and to meet legal requirements, while sustaining the standardized procedures of sampling and sample preparation. The concept is based on a flexible approach to allow adjustments to changing government regulations and recommendations in the course of the pandemic. By implementing this concept, all samplings were successfully carried out in 2021 & 2022, with the pandemic still ongoing. This paper provides an example of good practice and valuable insights in how to collect human samples during a pandemic. • Conducting epidemiological sampling and data collection for the German Environmental Specimen Bank during a pandemic. • Risk Assessment & development of an infection protection & hygiene concept to conduct safe sampling in a mobile laboratory. • Rapid sample handling, Point-of-Care analytics, and on-site cryopreservation. • Lessons learned during Covid 19 epidemic for future pandemic situations. [ABSTRACT FROM AUTHOR]

As the COVID-19 epidemic continues, concerns about long-term health impacts, specifically long COVID, persist. While the prevalence and symptomatology of long COVID have been explored in various global contexts, large-scale cohort studies in Japan remain limited, especially after the advent of the Omicron variant. In this observational study, 4,047 residents with a history of COVID-19 living in Toyonaka City, Osaka Prefecture, were assessed for long COVID symptoms using the VOICE mobile application and a paper survey. Respondents provided demographic and health information, as well as information regarding COVID-19 infection and subsequent symptoms. A Cox proportional hazard regression model was used to estimate the multivariable-adjusted hazard ratios and 95 % confidence intervals for overall morbidity of long COVID symptoms. The survey found that 5.2 % of participants reported the persistence of one or more symptoms at 30 days post-onset. Fatigue was the most commonly reported symptom (1.75 %), followed by hair loss (1.41 %), and cough (1.28 %). Factors associated with an increased risk of experiencing long COVID symptoms included BMI, severe illness during the acute phase, and infection with certain COVID-19 variant strains, including Alpha, Delta, and Omicron. However, the incidence rate of long COVID appears to be decreasing with the dominance of the Omicron variant. This large-scale study from Toyonaka City suggests a 5.2 % prevalence rate for persistent COVID-19 symptoms 4 weeks post-infection, potentially indicating a lower prevalence of long COVID in Japanese populations after the rise of the Omicron variant. [ABSTRACT FROM AUTHOR]

This paper argues that energy efficiency is a potent shield against oil price uncertainty in an increasingly interconnected world fraught with geopolitical tensions. By reducing dependence on oil, enhancing economic resilience, and improving energy security, energy efficiency measures offer multifaceted benefits for both national economies and global stability. Specifically, a wavelet coherence analysis is conducted to study the response of global energy efficiency to geopolitical oil price uncertainty. Quantile-on-quantile and quantile regressions are additionally employed to separate the impacts of various geopolitical oil price risk quantiles on the quantiles of energy efficiency. These methods are utilized in the examination of global time-series data covering the timeframe 2004:Q1–2020:Q4. The wavelet coherence outcomes indicate a positive correlation between geopolitical oil price uncertainty and energy efficiency, particularly during the energy crisis of the 2000s and the COVID-19 pandemic. The results also reveal that geopolitical oil price uncertainty leads to energy efficiency, indicating that an upsurge in geopolitical oil price uncertainty causes energy efficiency to increase. Moreover, the results from quantile-on-quantile and quantile regressions affirm the predominantly positive effects of geopolitical oil price uncertainty on global energy efficiency. Our conclusion therefore is that through strategic investments, innovative policies, and international collaborations relating to energy efficiency, nations can fortify themselves against the destabilizing effects of geopolitical conflicts on energy markets. This would ensure a more sustainable and secure energy future for all. [ABSTRACT FROM AUTHOR]

The research investigates the correlation between FinTech innovation and the index of renewable energy volatility from May 1, 2019, to October 28, 2022, by utilizing cutting-edge multiple wavelet analysis (MWA) techniques, including coherency and gain of partial wavelets. FinTech innovation and the green energy sector exhibit multiple coherencies. These coherencies suggest that three cycles, spanning from January 2020 to June 2020, December 2020 to April 2021, and January 2022 to May 2022, were focused on the high-frequency spectrum (3–68 days). Meanwhile, the other cycles were focused on low frequencies (69–130 days), which coexisted between June 2019 and January 2021 and January 2022 and October 2022. Green bonds and FinTech innovation's partial coherency indicate that modifications to the green bond markets caused shifts in FinTech innovation between June and November of 2019. FinTech innovation is in sync with green bonds pushing from December 2021 to October 2022. The partial consistency of FinTech innovation and sustainable energy demonstrates that both metrics align with the current FinTech innovation cycle, pushing from May 2019 to August 2019 and from December 2020 to July 2021, and both indicators are in sync with ARKF pushing. We also demonstrate how unexpected events like the Ukraine-Russia conflict and the COVID-19 epidemic affect these dynamic connectednesses. Hence, our paper calls for policy designs that promote the role of FinTech development in stabilizing the renewable energy market. [ABSTRACT FROM AUTHOR]

COVID-19 conspiracy theories have become widespread since the onset of the pandemic and compound the existing challenges of decisional capacity assessment. This paper aims to review the literature pertaining to decisional capacity assessment in the context of COVID-19 conspiracy beliefs and synthesize a practical approach with an emphasis on differential diagnosis and clinical pearls for the practicing physician. We reviewed papers on decisional capacity assessment and differential diagnosis in the context of COVID-19 conspiracy beliefs. A literature search was conducted using the US National Library of Medicine's PubMed.gov resource and Google Scholar. The resulting article content was utilized to synthesize a practical approach to decisional capacity assessment in the context of COVID-19 conspiracy beliefs. Specifically, aspects related to the history, taxonomy, evaluation, and management are reviewed. Appreciating the nuanced differences between delusions, overvalued ideas, and obsessions while with integrating the non-cognitive domains of capacity into the assessment are crucial to navigating the wide differential diagnosis of COVID-19 conspiracy beliefs. It is important to attempt to clarify and optimize patient decision-making abilities by addressing circumstances, attitudes, and cognitive styles specific to patients with seemingly irrational beliefs about COVID-19. [ABSTRACT FROM AUTHOR]

COVID-19 was declared a world pandemic in early-mid 2020. After about a year, several vaccines for this virus have been found and become alternative solutions to reduce the spread of the COVID-19 pandemic and build up herd immunity in society. Since early 2021, Indonesia has been one of the countries that participate in using vaccines for the public to fight the COVID-19 pandemic. However, there are a lot of positive and negative responses from Indonesian society related to these COVID-19 vaccines. Implementing a sentiment analysis model for a specific topic like a "vaccine" from social media could help us to see and understand the responses from society in Indonesia towards the vaccine program that is being conducted by the Indonesian government. Understanding society's response towards vaccines is expected to be able to support the Indonesian government, for example in formalizing the distribution strategy of vaccines in the future. This paper discusses how to develop a sentiment analysis model, by implementing several existing technologies such as Twitter API, TextBlob , and Googletrans Python libraries. The utilization of these existing technologies could show how a sentiment analysis model could be developed conveniently, for example in using cases to analyse Indonesian society's responses towards the COVID-19 vaccine program. Besides the solution design, this paper also shows a sample of data visualization of the sentiment analysis model in a meaningful infographics format. [ABSTRACT FROM AUTHOR]

This paper examines the responses of credit and business cycle to various policy actions of the Government of Indonesia during the COVID-19 crisis. Specifically, the paper addresses two key questions (1) How do the credit and business cycle behave during the COVID-19 crisis in Indonesia? (2) Do the central bank and government policy responses effectively stabilize the credit and business cycle? Using the concordance Index and DCC-GARCH methodology, we found that the COVID-19 crisis increased Indonesia's credit and business cycle co-movements. Similarly, using the mixed data sampling regression technique, our findings suggest fiscal policy measures and government support help the business cycle revival during the COVID-19 pandemic. However, the monetary policy transmission is weak during the pandemic [ABSTRACT FROM AUTHOR]

This paper argues that the persistent inflation in the U.S. during the post-COVID economic recovery was mainly the result of the Fed's policy mistake caused by an overestimation of the negative output gap. The paper shows that after a two-quarter contraction, the U.S. economy quickly rebounded and outpaced its potential output, thus remaining in overheating territory. However, policymakers prolonged the monetary expansion beyond the necessary, which contributed to fuel inflation for a more prolonged time. The policy mistake was the result of an inaccurate estimation of potential output. Based on an alternative estimation that uses full employment as a condition, this paper shows that the U.S. economy has been running with a positive output gap since mid-2021. The results illustrate that the Federal Reserve was well-behind the curve in an economy in overexpansion and with a galloping inflation escalating well-above the target. [ABSTRACT FROM AUTHOR]

The British Journal of Anaesthesia (BJA) had an eventful 2021, following what was a cataclysmic 2020 for the whole world. Despite the tragic challenges of multiple waves of the COVID-19 pandemic and the unparalleled burdens this created for everyone working in anaesthesia and critical care, the BJA underwent a major transformation during 2021. The BJA strongly supported research and education relevant to the pandemic, and to the broader missions of anaesthesia, critical, and pain medicine. Innovations to the BJA in 2021 included a special section on COVID-19 and the Anaesthetist; a new open access journal in the BJA stable; creation of a new social media editor position; new webinar and author interview series; transition to a new manuscript management system; and a move away from paper to electronic publication. [ABSTRACT FROM AUTHOR]

The COVID-19 has become a global pandemic that dramatically impacted human lives and economic activities. Due to the high risk of getting affected in high-density population areas and the implementation of national emergency measures under the COVID-19 pandemic, both travel and transportation among cities become difficult for engineers and equipment. Consequently, the costly physical commissioning of a new manufacturing system is greatly hindered. As an emerging technology, digital twins can achieve semi-physical simulation to avoid the vast cost of physical commissioning of the manufacturing system. Therefore, this paper proposes a digital twins-based remote semi-physical commissioning (DT-RSPC) approach for open architecture flow-type smart manufacturing systems. A digital twin system is developed to enable the remote semi-physical commissioning. The proposed approach is validated through a case study of digital twins-based remote semi-physical commissioning of a smartphone assembly line. The results showed that combining the open architecture design paradigm with the proposed digital twins-based approach makes the commissioning of a new flow-type smart manufacturing system more sustainable. [ABSTRACT FROM AUTHOR]

There has been increasing recognition of vaccine access challenges in middle-income countries and the need for increased action, particularly in countries that are not eligible for or have transitioned out of Gavi, the Vaccine Alliance support. These countries' immunization systems are more vulnerable than ever as the COVID-19 pandemic exacerbates existing programme challenges, increasing the risk of delayed vaccine introductions, backsliding immunization coverage rates, and increased coverage inequity. The potential health and equity impact of improving immunization outcomes in middle-income countries is substantial. Modelling suggests that the introduction of pneumococcal conjugate vaccine and vaccines for rotavirus and human papillomavirus in this set of Gavi-transitioned and non-Gavieligible middle-income countries in 2020 could have saved an estimated 70,000 lives if 90 % coverage had been reached. Further, increasing coverage for already-introduced vaccines to 90 % could have saved an additional estimated 16,000 lives. Over the past decade, stakeholders have made considerable efforts to identify immunization challenges in middle-income countries as documented in the 2015 SAGE-endorsed Shared Partner Middle-Income Country Strategy. In the coming decade, new global platforms like Gavi 5.0 and the Immunization Agenda 2030 provide opportunities to align on MIC strategies and provide coordinated global support to middle-income countries. The international COVID-19 pandemic response has the potential to lay the foundation for long term support beyond the scope of COVID-19 to non-Gavi eligible middle-income countries. Meanwhile regional mechanisms to address immunization barriers in middle-income countries have grown in number and strength, offering sustainable platforms for cross-country collaboration and the provision of tailored technical support. To ensure that these opportunities are successfully acted upon and that middle-income countries achieve the Immunization Agenda 2030 goals, comprehensive, multi-stakeholder consultations were conducted to identify areas of action with the greatest potential to accelerate immunization progress. Stakeholders should work together to put these findings, highlighted in this paper, into action, adapting their approaches to specific country contexts and learning from and building on existing efforts. [ABSTRACT FROM AUTHOR]

Online health information seeking behavior (e-HISB) is becoming increasingly common and the trend has accelerated as a result of the COVID-19 pandemic when individuals strongly relied upon the Internet to stay informed by becoming exposed to a wider array of health information. Despite e-HISB having become a global trend, very few empirical investigations have analyzed its potential influence on healthcare access and individuals' health status. In this paper, we try to fill this gap. We use data from the second SHARE Corona Survey, supplemented with data from the previous 8th wave of SHARE, and estimate a recursive model of e-HISB, healthcare access, and individuals' health status that accounts for individuals' unobserved heterogeneity. Our findings suggest that e-HISB can empower individuals to better understand health concerns, facilitating improved health condition management. However, e-HISB can also trigger a chain reaction, as navigating vast amonts of online health information can heighten fear and anxiety. This increased anxiety may lead to higher utilization of medical services, adversely affecting individuals' perceptions of their health. [ABSTRACT FROM AUTHOR]

During the COVID pandemic many centres adopted e-learning as a tool to adhere to social distancing recommendations while continuing to provide ongoing postgraduate medical education. We aimed to complete this scoping review in order to map experience and perceptions amongst surgeons and surgical trainees to remote learning during the COVID pandemic. Using Arksey and O'Malley's five step model for conducting a scoping review, a systematic search strategy was undertaken across three online databases SCOPUS, MEDLINE and Web of Science. Only original articles in English in the field of postgraduate education in surgery were included. 44 studies were selected for review. Of these 44, 21 were studies of perception of a newly developed e-learning tool/platform. 17 were surveys of surgeon's attitudes towards e-learning during the COVID pandemic. The remaining 6 studies were studies of knowledge or skills acquired through new e-learning, which included a survey of experience. The United States was the most common country of origin with General Surgery the most represented surgical speciality. Response rates across all three study subgroups were on average >60%. Surgeon's experience of e-learning was reported in only positive terms in 86% (n = 38/44) of studies. This paper is informative in that it focuses specifically on surgeons' perceptions of a e-learning tools used in addition to skills or knowledge gained. Positive e-learning experience reported in these studies may lead to more blended learning curriculums being developed, deployed and evaluated going forward. • Scoping review of surgeons' perceptions towards e-learning during COVID. • Maps out the wide range of e-learning tools that were used during the pandemic. • Provides subgrouping framework of pandemic era e-learning studies. [ABSTRACT FROM AUTHOR]