Coffee has become a significant commodity for the people of Indonesia, including in Bener Meriah Regency. However, the quality of coffee remains low, partly due to deficiencies in the post-harvest process. This study aimed to analyze coffee farmers' post-harvest process in the Permata Sub-district and identify strategies to enhance coffee quality through farmers' institutions. The research method was qualitative, with data collection techniques comprising in-depth interviews and observation. The findings indicated that most coffee farmers in the Permata Sub-district lacked comprehensive post-harvest processing, impacting the coffee quality. The recommendations of this study emphasize the importance of strengthening coffee farmers' institutions, such as coffee farmer groups, to improve farmers' knowledge and skills in conducting optimal post-harvest processing following specialty coffee standards.

Past research suggests a continuity between perception and memory, as reflected in influences of orienting of spatial attention by cues presented after a visual target offset (post-target cues) on target perception. Conducting two experiments, we tested and confirmed this claim. Our study revealed an elevated reliance on post-target cues for target detection with diminishing target visibility, leading to better performance in validly versus invalidly cued trials, indicative for contrast gain. We demonstrated this post-target cueing impact on target perception without a postcue response prompt, meaning that our results truly reflected a continuity between perception and memory rather than a task-specific impact of having to memorize the target due to a response prompt. We further showed an influence of attention by the post-target cues with cues presented away from a clearly visible target, meaning that visual interactions at the target location provided no better explanation of our results. Our results generalize prior research with liminal targets and confirm the view of a perception-memory continuum, so that visual target processing is not shielded against visuospatial orienting of attention elicited by events following offset of the visual target.

One of the main challenges of the globalization became the increased destabilization and frequent economic crises. In general, managing an economic crisis is a particularly difficult task for any economy (developed or developing, and regardless the size). In the context of globalization, the process faces additional challenges. On the one hand, the world economy witnessed a shift from traditional purely economic instruments to more financial mechanisms of economic development. Indeed, the liberalization of the economy since the 1990s complicated the traditional trends of the world market development, resulting in significant fluctuation of the growth rates between different countries and regions. To respond to this evolution, governments and international organizations changed well-established strategies. For example, financial sector found itself given expanded role in economic development, with, different financial mechanisms used as key variables to evaluate an economic growth. On the other hand, the increased role of financial sector in a globilised world, increased financial relations among individual states and thus, interdependences. This “financial globalization” causes important fluctuations in the system and, as a result, causes instability in socio-economic development. Thus, an economic crisis of any important economy soon becomes a global issue (financial crisis of 2008, economic crisis of 2020).The recent global economic crisis due to the Covid-19 world pandemic, appeared to be unique for several reasons. First, it highlighted the transnational and origin-specific nature of the crisis. Second, it appeared to be the first case when non-economic factors caused a major economic crisis. As a result, this crisis turned out to be particularly difficult to predict and to manage. In this case, the main strategy of the global post-crisis rehabilitation became the restoration of the economy's predictability and ensuring its ability to obey the classical principles. According to the Organic Law on "Economic Freedom" of the European Union (2011), any factor which increases the equilibrium amount of output is reflected in the economic rehabilitation plan. Such exogenous factors are primarily fiscal mechanisms, i.e. - Reduction of taxes and increase of state expenditures. This crisis caused important structural problems with their effects on public expenditures (especially health expenditures, to the public expenditures, economic growth, price stability and budget balance) in both, developed and developing countries. Nearly every state declared an increase in public debt, inflation, and unemployment rates, while an important decrease was shown in growth rates and capital outflows. As the crisis due to the world pandemic was radically different from previous economic crises, it was difficult to manage it efficiently at the initial stage. It was complicated to forecast the scenarios of the unknown crisis with non-standard nature which was more linked to epidemiologic factors than objective economic law. The dynamics of crisis resolution depended on correct forecasting and effective implementation of global, but especially country-based reforms. More importantly, this crisis showed the important role of individual states to manage the global crisis. The traditional function of a government to ensure country’s socio-economic growth and security, increases in an extraordinary environment, such as sharp economic fluctuations and crises. In such contexts, the centralized management functions are sharply activated. The global pandemic processes have updated the economic essence of the state. The large scale of the latest economic crisis due to the world pandemic has determined the need for mutually coordinated development of anti-crisis plans of different countries. In today's dynamic landscape, modern global systems are redefining the standards and priorities of national economic policies. The economic status of countries is changing significantly as a result of their participation in these systems. As a result of these changes, today's global economy is marked by the proliferation of regionalism and the emergence of spatial economic frameworks. Thus, classical economic concepts are evolving, giving rise to a new model known as economic nationalism. This paradigm shift is generating new risks, which are increasing in severity and scope. The overriding objective of economic policy is now to ensure and predict stable development. Reflections are underway to meet the challenges of economic equilibrium and its social and institutional sustainability.In addition to the decentralization of the unified governance system, the global economy is characterized by the activation of centralized governance elements within individual countries. This requires the development of new norms for global cooperation and the careful balancing of national integration and development factors. The question of government's influence on the economy and the delegation of its powers becomes relevant once again. Effective coordination of public administration is based on the principle of decentralized government, which is a key element of a democratic model of society and facilitates the transfer of management tools to the public. The key objectives of this research are focused on the optimal distribution of state functions and the study of hierarchical principles of territorial organization within the country, fiscal mechanisms for the distribution of powers, as well as the particularities of the structuring of the spatial economy and its budgetary provisioning mechanisms.The example of the last two economic crisis showed that contemporary anti-crisis strategies should reflect several considerations. First, it becomes impossible to develop a single unified model of crisis management. Even in the context of important economic and financial globalization, the current decentralization and new spatial economic processes make it necessary to find a balance between the local priorities and global interests. Second, the role of non-economic crisis-causing factors increases compared to economic determinants. That is why international and country-based reforms should aim at not only economic rehabilitation, but also on the social, cultural and environmental restorations. Third, it becomes difficult to correctly predict the consequences of the crisis and thus, the effects of the anti-crisis strategy. Consequently, the success of the post-crisis rehabilitation of the countries' economies depends significantly not only on the effective realization of their own plan, but also, in general, on the specificities of the global economy and on the ability of the world markets to overcome the negative consequences of a crisis. In this context, the development of complex post-crisis platforms and state post-crisis strategies is the priority. The most effective component in the anti-crisis strategy is the monetary instruments of economic management. As for the determinant of successful functioning of economy, the priority should be given to the regulation of exchange rate, interest rate and inflationary processes. A state anti-crisis programs should reflect the crisis prevention strategy, which, on its turn, should differentiate the economic functions of the state and crisis management mechanisms according to the specific trends of given processes. It should aim at gaining stable development of the economy. Additionally, an anti-crisis plan should reflect the role of the society in the development process and its mental changes under crisis conditions. The state should be responsible for establishing and monitoring anti-crisis management mechanisms, financial recovery of the country and formation of correct relations. It is especially important to ensure economic security, to stabilize the development process and standard of living, to support the private sector and small entrepreneurship, to increase economic freedom, to ensure effective distribution of labor and capital, and others. All strategies of the anti-crisis platform should be focused on social results, as it should contribute to a fair distribution of income and to poverty reduction.

The COVID-19 pandemic necessitated a profound shift to online education, impacting students across various educational levels. This research, drawing from a comprehensive survey of 1,000,000 students, delves into the surge in demand for online education during the pandemic and its potential enduring implications. The study scrutinizes factors steering this transition, the perceived effectiveness of online education, challenges encountered by students, and their anticipated preferences for the future. Results unveil nuanced perspectives among K-12, undergraduate, and postgraduate students, with safety, flexibility, and accessibility emerging as pivotal influencers. The findings offer valuable insights into challenges faced and future preferences, enlightening educational stakeholders about the dynamic needs of students and contributing to the ongoing evolution of the post-pandemic educational landscape.

COVID-19 generated a scenario for global health with multiple systemic impairments. This retrospective study evaluated the clinical, radiological, and pulmonary functional evolution in 302 post-COVID-19 patients. Regarding post-COVID-19 pulmonary symptoms, dry cough, dyspnea, and chest pain were the most frequent. Of the associated comorbidities, asthma was more frequent (23.5%). Chest Tomography (CT) initially showed a mean pulmonary involvement of 69.7%, and the evaluation in the subsequent months showed an improvement in the evolutionary image, and with less than six months post-pathology, there was a commitment of 37 .7%, from six to twelve months, 20% and after 12 months, 9.9%. And as for most of the sample, 50.3% of the patients presented CT normalization in less than six months after infection, 23% normalized between six and twelve months, and 5.2% normalized the images after twelve months, with one remaining. Percentage of 17.3% who maintained post-COVID-19 pulmonary residual sequelae. Regarding spirometry, in less than six months after the pathology, 59.3% of the patients already showed a regular exam; 12.3% normalized their function within six to twelve months, and 6.3% concluded a normal exam after twelve months of post-pathology evaluation. Only 3.6% of the patients still showed some alteration in this period.

Huntington’s Disease (HD) is a devastating neurodegenerative disorder caused by a CAG trinucleotide repeat expansion in the HTT gene, for which no disease modifying therapies are currently available. Much of the recent research has focused on developing therapies to directly lower HTT expression, and while promising, these therapies have presented several challenges regarding administration and efficacy. Another promising therapeutic approach is the modulation of HTT post-translational modifications (PTMs) that are dysregulated in disease and have shown to play a key role in HTT toxicity. Among all PTMs, modulation of HTT phosphorylation has been proposed as an attractive therapeutic option due to the possibility of orally administering specific kinase effectors. One of the kinases described to participate in HTT phosphorylation is Protein Kinase CK2. CK2 has recently emerged as a target for the treatment of several neurological and psychiatric disorders, although its role in HD remains controversial. While pharmacological studies in vitro inhibiting CK2 resulted in reduced HTT phosphorylation and increased toxicity, genetic approaches in mouse models of HD have provided beneficial effects. In this review we discuss potential therapeutic approaches related to the manipulation of HTT-PTMs with special emphasis on the role of CK2 as a therapeutic target in HD.

Proteins are the primary effectors of function in biology, and thus complete knowledge of their structure and properties is fundamental to deciphering function in basic and translational research. The chemical diversity of proteins is expressed in their many proteoforms, which result from combinations of genetic polymorphisms, RNA splice variants and post-translational modifications. This knowledge is foundational for the biological complexes and networks that control biology, yet remains largely unknown. We propose here an ambitious initiative to define the human proteome; that is to generate a definitive reference set of the proteoforms produced from the genome. Several examples of the power and importance of proteoform-level knowledge in disease-based research are presented, along with a call for improved technologies in a two-pronged strategy to accomplish the Human Proteoform Project.

The coronavirus pandemic will deeply affect the tourism and travel sector. It is already clear now that its economic impact would be more severe that in the case of the Severe Acute Respiratory Syndrome (SARS) in 2002-2003. Although not as deadly as SARS, coronavirus infection has a longer incubation period and leaves about 85% of the infected without any (or with just mild) symptoms which makes it more difficult to track and to contain. Moreover, it appears to be much more contagious than its predecessor. The goods news is that most people recover from the disease and develop antibodies that can protect them from getting infected again (natural vaccination). Those cured might become the key element for the post-virus recovery strategies of tourism organisations. People with the acquired immunity to the virus would be capable of travelling freely without spreading the disease. Airlines, hotels and gastronomy should aim at this group offering them discounts and special offers. However, the problem is how to effectively ensure that everyone who claims to be cured from COVID-19 is telling the truth. Health tracking bracelets, apps, and other advanced technological solutions should be put in place. Recent best practices from Hong Kong, mainland China, or India might be applied.

We employ the solution concept of ex post Nash equilibrium to predict the interaction of a finite number of agents competing in a finite number of basic games simultaneously. The competition is called a multi-game. For each agent, a specific weight, considered as private information, is allocated to each basic game representing its investment in that game and the utility of each agent for any strategy profile is the weighted sum, i.e., convex combination, of its utilities in the basic games. Multi-games can model decision making in multi-environments in a variety of circumstances, including decision making in multi-markets and decision making when there are both material and social utilities for agents as, we propose, in the Prisoner's Dilemma and the Trust Game. Given a set of pure Nash equilibria, one for each basic game in a multi-game, we construct a pure Bayesian Nash equilibrium for the multi-game. We then focus on the class of so-called uniform multi-games in which each agent is constrained to play in all games the same strategy from an action set consisting of a best response per game. Uniform multi-games are equivalent to multi-dimensional Bayesian games where the type of each agent is a finite dimensional vector with non-negative components. A notion of pure type-regularity for uniform multi-games is developed and it is shown that a multi-game that is pure type-regular on the boundary of its type space has a pure ex post Nash equilibrium which is computed in constant time with respect to the number of the types and is independent of prior probability distributions. We then develop an algorithm, linear in the number of types of the agents, which tests if a multi-game is pure type-regular on the boundary of its type space in which case it returns a pure ex post Nash equilibrium for the multi-game.

A developing energy-saving approach of cold sintering in a pure aqueous medium was applied to the preparation of α-Al2O3 ceramics and performed on spark plasma sintering equipment. The initial γ-Al(OH)3 and γ-AlOOH powders and the cold-sintered ceramics were studied by X-ray diffraction analysis, infrared spectroscopy, thermal analysis, and scanning electron microscopy to reveal the chemical and structural transformations they experienced during the cold sintering. At 450°C and 70 MPa, initial γ-AlOOH transformed into a fragile α-Al2O3 material. Porous α-Al2O3 ceramics with about 60% porosity was obtained after cold sintering of γ-Al(OH)3 in the same conditions combined with subsequent annealing at 1250°C for 3 h. The role of water molecules in the studied processes was considered as the enhancement of structural mobility in the cold-sintered material due to its reversible hydroxylation similar to earlier investigated supercritical water action on the precursors during α-Al2O3 formation. Further improvement of the cold sintering setup and regimens would open prospects in α-Al2O3 ceramics manufacturing by an ecologically benign route.

There is a strong need and demand from the United Nations, public institutions, and private sector for classifying government publications, policy briefs, academic literature, and corporate social responsibility reports according to their relevance to the Sustainable Development Goals (SDGs). It is well understood that the SDGs play a major role in the strategic objectives of various entities. However, linking projects and activities to the SDGs has not always been straightforward or possible with existing methodologies. Natural language processing (NLP) techniques offer a new avenue to identify linkages for SDGs from text data. This research examines various machine learning approaches optimized for NLP-based text classification tasks for their success in classifying reports according to their relevance to the SDGs. Extensive experiments have been performed with the recently released Open Source SDG (OSDG) Community Dataset, which contains texts with their related SDG label as validated by the community volunteers. Results demonstrate that especially RoBERTa achieves very high performance in the attempted task, which is promising for automated processing of large collections of sustainability reports for detection of relevance to SDGs.

Frass generated during the production of black soldier fly larvae is taking the interest of scientists and horticultural producers because it is a material from the biotransformation of organic waste, it has several nutrients that can be used by plants, and its recent focus on its biostimulant capacity. The thermal-composting process is a stabilization that improves physical and chemical properties of treated wastes, allowing better performance in plants compared to its fresh state. In this research, thermocomposted frass was evaluated as a germination substrate for kale seeds (Brassica olerasea). To achieve this, it was evaluated the phytotoxicity in different concentrations on the seeds, and seedlings were grown in a germination substrates mixed with 20, 40, 60, 80 and 100% of frass. The treatment with 20% frass showed the highest values of seedling height, stem diameter, number of leaves, length, and width of first true leaf and length and width of cotyledons, reduced the content of phenols and tannins antioxidants but the content of flavonoids increased compared to the control and the rest of mixtures.

Informal caregivers are essential in supporting stroke survivors' recovery, but it is unclear whether community interventions can promote healthy eating behaviors and social participation among stroke survivors and their caregivers. The Train-Your-Brain (TYB) program's impact on adherence to national dietary guidelines and social participation among informal caregivers and stroke survivors were assessed in a quasi-experimental study. The TYB program had nine sessions, with one focusing on healthy lifestyle. Participants were prompted to discuss the topics covered, leading to social participation. Dietary habits were assessed using fruits and vegetables consumption questions, while social participation was measured using the Social Impact Measurement Framework. Mann-Whitney U-test and Wilcoxon Signed Rank Test were utilized to analyze the data. Eighteen informal caregivers and 27 stroke survivors participated in the program. Results showed a significant increase in fruit and vegetable intake and social participation in informal caregivers after the program, but there were no significant changes in stroke survivors. Informal caregivers play a crucial role in stroke survivors' lifestyle habits and in encouraging social connections with other stroke survivors. Thus, healthcare professionals should prioritize caregiver support and education to facilitate long-term dietary and social changes that can positively impact stroke survivor's recovery and quality of life.

Refugee subject position is an evolving topic of contention in the world today with increased migrant and refugee mobilities. Urban refugee spaces are often segregated in the form of colonies, ethnic villages, even ghettos, embodying institutionalised discourses of apathy and violence. These spaces only occupy the cracks and margins of the normative, formal city, as appropriations of inhospitable natural terrains and urban systems. The paper discusses how refugees compete for resources for survival as “bio-political” subjects and are often held summarily responsible for causing ecological stress in host environments. After the 1947 Partition of the Indian subcontinent, millions of Hindu Bengali refugees from East Pakistan flooded the Eastern Indian states of West Bengal, Assam and Tripura. Kolkata particularly drew millions for better livelihood prospects. Facing government apathy and local violence, East-Bengali refugees appropriated the urban fringes of Kolkata and claimed their right to urban space through the political act of squatting or Jabar-Dakhal. The intent of this paper is to investigate and map the spatial distribution of East-Bengali refugee squatters and elaborate on how they transformed the terrain and distributed resources through self-management tactics. This spatial history case-study attempts to uncover locational data from archival government records, existing academic literature and fieldwork to visualise where the 145 pre-1950 and the 123 post-1950 Jabar-Dakhal colonies were located in the Kolkata Metropolitan Area. This case of successful refugee self-settlement is qualitatively read in relation to the major areas of ecological stress in Kolkata. One of the UN sustainable development goals is to make cities and human settlements ‘inclusive, safe, resilient and sustainable’. This paper hopes to encourage further studies of urban refugee self-settlement and local integration as a viable but complex socio-political-environmental process.

In recent decades, large-scale forest disturbances such as windthrow and bark beetle infestations have significantly impacted the carbon balance of Earth and forest ecosystems, mainly by altering soil respiration (SR) in addition to decreased gross primary productivity. SR is considered the second highest flux on Earth, emitting 78-98 Gt C yr-1 to the atmosphere, thus contributing to global warming. Post-disturbance development is marked by changes in the size of SR. To investigate the impact of bark beetle (Ips typographus) infestations in mountain spruce forests on SR, we measured SR at infested sites by bark beetles and adjacent undisturbed stands in the High Tatra Mountains (Slovakia). The measurements were conducted during the vegetation period (May-September) in 2016 and 2017, five to six years after the initial attack by bark beetles, at an altitudinal gradient (1100-1400 m a.s.l.). SR varied throughout different months and altitudes, and we observed that SR at infested sites reached higher values (in most cases) than at uninfested stands. Average monthly and altitude SR rates during both vegetation periods showed higher values at infested sites, but not all of them were statistically significant. We observed an insignificant change in SR rate between 2016 and 2017 at both infested and uninfested sites. The highest rates of SR were observed in 2016 from July to August at both infested and undisturbed sites. On the other hand, in 2017 the highest SR rates were observed in June and July at undisturbed sites and from June to August at infested sites. However, yearly SR from May-September at infested sites showed significantly higher rates than uninfested ones in both years. SR showed a decreasing pattern with elevation gain in 2016 at infested sites. However, this pattern was not observed in 2017. Based on our observation and results we postulate that SR rates at infested sites are boosted by needle fall and debris from dead and dying trees, which increases heterotrophic SR and compensates the decrease of autotrophic SR from tree roots. This study provides important insights into the impact of bark beetle infestations on SR and highlights the need for further research on the long-term effects of forest disturbances on carbon cycling.

Rheocasting is getting more and more attention from the electronics and automotive industries. Mechanical properties and thermophysical properties such as thermal and electrical conductivity are affected by the amount of solid phase generated in the slurry process. The RheoMetal process specifically has a significant deviation from the expected solid fraction. This deviation is discussed, explained and a way to control the excess generation of solid phase is proposed.

Climacteric fruits are characterized by a dramatic increase in autocatalytic ethylene production, which is accompanied by a spike in respiration, at the onset of ripening. The change in the mode of ethylene production from autoinhibitory to auto-stimulatory is known as the system 1 (S1) to system 2 (S2) transition. Existing physiological models explain the basic and overarching genetic, hormonal, and transcriptional regulatory mechanisms governing the S1 to S2 transition of climacteric fruit. However, the links between ethylene and respiration, the two main factors that characterize the respiratory climacteric, have been largely understudied at the molecular level. Results of recent studies indicate that the AOX respiratory pathway may play an important role in mediating cross talk between ethylene response, carbon metabolism, ATP production, and ROS signaling during climacteric ripening. New genomic, metabolic, and epigenetic information sheds light on the interconnectedness of ripening-associated metabolic pathways, necessitating expanding the current, ethylene-centric physiological models. Understanding points at which ripening responses can be manipulated may reveal key, speciesand cultivar-specific targets for regulation of ripening enabling superior strategies for reducing postharvest wastage.

In this review paper, we discuss the use of BERT, one of the most well-liked deep learning-based language models. The model's operation mechanism, key areas of applicability to text analytics tasks, comparisons with related models for each activity, and a description of certain proprietary models are all covered in this work. The data from several dozen original scientific studies that have been published in the last few years and have garnered the greatest interest from the scientific community were systematized in order to prepare this review. All researchers and students who wish to learn about the most recent developments in the field of natural language text analysis may find this survey helpful. Bidirectional Encoder Representations from Transformers, or BERT for short, is the subject of a thorough investigation that we present. The area of natural language processing holds significant importance in the creation of intelligent systems. To do a variety of activities, one must comprehend the sentence's correct meaning in order to produce the desired result. Computers have a hard time understanding languages because of how context constantly changes. The main challenge in natural language processing tasks is getting computers to understand text context, and BERT is seen as a revolution in this regard. It picks up the language and its meaning in a manner that is quite similar to how the human brain processes meaning from sentences. Its capacity to identify a word from both the left and right contexts of a sentence makes it special. A new era in the perception and comprehension of natural languages has been ushered in with the development of BERT, which could help computers understand natural languages more fully. This study aims to provide readers with a deeper knowledge of the BERT language model and how it is applied to different NLP tasks.

Brassica vegetables has demonstrated many health benefits over the years due to its composition of phenolic, flavonoid, and glucosinolate content. However, these bioactive molecules can be easily depleted during gastronomic operations. Therefore, a sustainable method which improves the phenolic content and antioxidant activity is required at large for the processors and consumers. Thermal processing has demonstrated as a method to improve the phenolic content and antioxidant status of Brassica vegetables. In the current study four different thermal processing methods, including freeze drying, sauteing, steam and air frying, were employed for five different Brassica vegetables, including kale, broccoli sprout, brussels sprout, red cabbage, and green cabbage. Total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activities were assessed using radical scavenging activity (DPPH and ABTS•+), reducing power (FRAP), and chelating ability of the metal ions. Among tested, air frying at 160oC for 10 minutes showed the highest TPC, TFC, and antioxidant activity of the Brassica vegetables, while sautéing showed the lowest. Steam treatment was preferred over the freeze-drying treatment. Within the vegetables tested, both kale and broccoli sprout contained higher antioxidants properties in most processing treatment employed. Results also indicated that there is a strong correlation between TPC, TFC, and the antioxidant activity (p<0.05). This study indicates that air frying could be a choice of sustainable thermal processing method for improving biomolecules for Brassica vegetables.

The manufacturing parameters influence the properties of thermo-mechanically processed met-als such as Mg alloys. This paper presents how the mechanical properties, the microstructure and the degradation rate of extruded Mn-containing Mg-Gd-alloys can be modified. Gd as repre-sented REE-element is particularly interesting due to the influence on the texture development in Mg and therefore studied as a base alloy system. The contents of Gd were investigated be-tween 2 to 9 wt.% with Mn-addition of 0.5 and 1.0 wt.%. The grain sizes and the corresponding textures were modified by the extrusion parameters and the alloy content. It was shown that modification by Mn can decrease the grain size, but increase the degree of recrystallization and decrease the degradation rate in the biological medium compared to the binary Mg-Gd system from previous studies. The results suggest that the binary Mg-Gd system can be produced more efficiently by adding Mn and that the resulting properties are more robust.

Determining the vibration environment is crucial to analyzing a design of any mechanical system, especially such dynamic systems as sounding rockets. Accuracy of measurement using accelerometers could be improved by application of mechanical vibration filtering and amplifying devices. This work presents a theoretical description of a tunable filter and amplifier. Principle of work is provided as well as results from application of the device on a sounding rocket are provided. It is shown that implementation of such devices allowed for enhancing the accuracy of acceleration measurements. Conclusions on future implementations are also provided.

Multi-wire saw machining (MWSM) used for slicing hard-brittle materials in semiconductor, is an important material removal process that uses free abrasives. Cutting model of single-wire saw machining (SWSM) is the basis of MWSM, and the material removal mechanism of SWSM can better understand than MWSM. Mathematical model (includes brittle fracture and plastic deformation) is presented in this paper for SWSM ceramic with abrasives. This paper determines the effect of various machining parameters on the removal of hard-brittle materials. For brittle fracture of SWSM ceramics, the minimum of the strain energy density is used as a fracture criterion. The material removed of SWSM ceramics due to plastic deformation is calculated using the equations of motion. Actual wire-sawing experiments are conducted to verify the results from the developed mathematical model. Theoretical results agree with experimental data and practical experience. The developed mathematical model shows that brittle fracture plays a major concern role in material removed of SWSM ceramics. Wire speed and working load have positively correlated with material removed of SWSM ceramics. The coefficient of friction is low, a lateral crack, which propagates almost parallel to the working surface, leads to more brittle fracture and material removed is increased on SWSM ceramics.

The coronavirus disease 2019 (COVID-19), instigated by the zoonotic Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), rapidly transformed from an outbreak in Wuhan, China, into a widespread global pandemic. A significant post-infection condition, known as ‘long- COVID’, emerges in a substantial subset of patients, manifesting with a constellation of over 200 reported symptoms that span multiple organ systems. This condition, also known as 'post-acute sequelae of SARS-CoV-2 infection' (PASC), presents a perplexing clinical picture with far-reaching implications, often persisting long after the acute phase. While initial research focused on the immediate pulmonary impact of the virus, the recognition of COVID-19 as a multiorgan disruptor has unveiled a gamut of protracted and severe health issues. This review provides a summary of the primary effects of long COVID on the cardiovascular, nervous, and respiratory systems. It also delves into the mechanisms underlying these impacts and underscores the critical need for a comprehensive understanding of long COVID's pathogenesis.

At present, there are no hardware or biochemical systems allow to assess the severity of post-COVID syndrome in vivo. The hardware of the proposed biotechnical system is based on routine transthoracic electrical impedance rheography, which makes it possible to register the frequency characteristics of the patient's bioimpedance response to controlled stress stimulation, thereby simultaneously fixing the characteristics of his productive heart, the state of the hemomicrocirculatory bed, the efficiency of the gas transport function of his blood, and also reliably assess personal reactivity and adaptive potential. Subsequent mathematical approximation of the obtained biometric data by an original neural network makes it possible to rank the results obtained and automatically generate a program of medical rehabilitation for a particular patient, depending on the severity of his post-COVID syndrome. The study results proved two reliable physiological signs confirming the presence of latent post-COVID complications: a decrease in the base impedance value for light exercise and an increase in the length of the systolic arc of the rheocardiogram.

The utilization of tempered blast furnace (BF) slag through the direct fiber forming process to create high-value thermal insulation materials offers a dual benefit: it efficiently harnesses the latent heat within unused slag and substantially enhances the value of blast-furnace slag utilization. However, gauging the melting properties of iron slag under high temperatures is a challenge. In this study, we explore the melting behavior of SiO2 within a high-temperature molten pool. We employ dynamic visual data (video stream) captured via a non-contact charge coupled device (CCD) video recording system to extract SiO2 contours through image processing. The change in image centroid characteristics is used to establish a convolution function relationship, and MATLAB's traversal search algorithm determines SiO2's centroid position. Given that SiO2 is proportionate to crucible pixels, the area of SiO2 is calculated through pixel statistics within these contours. Subsequently, we propose a new indirect method to process image information, yielding SiO2 volume and mass at different time points. An exponential fitting yields the melting rate function of SiO2. Finally, we compare this indirect method with shape from shading (SFS), quantitative characterization, and dimensional analysis techniques. We also discuss the strengths and limitations of each method. Our findings reveal that the indirect solution method presented here boasts straightforward calculation steps and imposes minimal image format requirements. This research provides theoretical and technical support for blast-furnace slag's direct fiber forming process.

Localizing the source of a signal is often as important as deciphering the signal’s message. Localization mechanisms must cope with the challenges of representing the spatial information of weak, noisy signals. Comparing these strategies across modalities and model systems allows a broader understanding of the general principles shaping spatial processing. In this review we focus on the electrosensory system of knifefish and provide an overview of our current understanding of spatial processing in this system, in particular, localization of conspecific signals. We argue that many mechanisms observed in other sensory systems, such as the visual or auditory systems, have comparable implementations in the electrosensory system. Our review therefore describes a field of research with unique opportunities to provide new insights into the principles underlying spatial processing.

Polygonatum cyrtonema Hua. is an important medicinal and edible food in China and the traditional processing method of nine-steaming and nine-drying has been used to process its rhizome. However, the global dynamic changes in secondary metabolites during the processing and whether this method could enhance the accumulation of functional components remain unclear. This study aimed to explore the processing-induced mechanisms involved in the accumulation of secondary metabolites and provide theoretical support for the pharmacological study on P. cyrtonema. Results showed the first 6 processing cycles induced drastic changes in the accumulation of secondary metabolites, while the last 3 did not induce further changes. The accumulation of most secondary metabolites significantly increased after the first 3 cycles and stabilized after 6 cycles. The first 3 cycles led to numerous newly added metabolites while the last 6–9 cycles led to a small number of disappeared metabolites. Processing enhanced the biosynthesis of various secondary metabolites and significantly affected the metabolisms of amino acids. Together, a short period of processing was helpful for enhancing the biosynthesis of secondary metabolites. However, the enhancing effects will be reversed or weakened under continuous processing. The processing cycles should be reduced to 3–6 to maintain the enhancing effects.

Cardiac monitoring based on wearable photoplethysmography (PPG) is widespread because of its usability and low cost. Unfortunately, PPG is negatively affected by various types of disruptions, which could introduce errors to the algorithm that extracts Pulse Rate Variability (PRV). This study aims to identify the nature of such artifacts caused by various types of factors under the conditions of precisely planned experiments. We also propose methods for their reduction based solely on the PPG signal while preserving the frequency content of PRV. The accuracy of PRV derived from PPG was compared to Heart Rate Variability (HRV) derived from the accompanying recorded ECG. The results indicate that filtering PPG signals using the Discrete Wavelet Transform and its inverse (DWT/IDWT) is suitable for removing slow components and high-frequency noise. Moreover, the main benefit of amplitude demodulation is better preparation of the PPG to determine the duration of pulse cycles and reduce the impact of some other artifacts. Postprocessing applied to HRV and PRV indicates that the correction of outliers based on local statistical measures of signals and the AR model is only important when PPG is of low-quality and has no effect under good signal quality. The main conclusion is that DWT/IDWT, followed by amplitude demodulation, enables the proper preparation of the PPG signal for the subsequent use of PRV extraction algorithms. However, postprocessing in the proposed form should be applied more in the situations of observed strong artifacts than in motionless laboratory experiments.

Crowdsourced Data (CSD) generated by citizens is becoming more popular as its potential utilisation in many applications is increasing due to its currency and availability. However, the quality of CSD, including its relevance, is often questioned as the data is not generated by professionals nor follows standard data collection procedures. The quality of CSD can be assessed according to a range of attributes including its relevance. Information relevance has been explored through using in Geographic Information Retrieval (GIR) techniques to identify relevant information. This research tested a relevance assessment approach for CSD by adapting relevance assessment techniques available in the GIR domain. The thematic and geographic relevance were assessed using the Term Frequency-Inverse Document Frequency (TF-IDF), Vector Space Model (VSM) and Natural Language Processing (NLP) techniques. The thematic and geographic specificities of the queries were calculated as 0.44 and 0.67 respectively, which indicates the queries used were more geographically specific than thematically specific. The Spearman's rho value of 0.62 indicated that the final ranked relevance lists showed reasonable agreement with a manually classified list and confirmed the potential of the approach for CSD relevance assessment for other possible crowdsourced data analysis.

The effectiveness of forest resources depends on the comprehensiveness and rationality of their consumption and processing into finished products. This article discusses a problem of utilising solid fir wood greenery residues generated during the industrial production of essential oils. Bioconversion is considered to be the most promising utilization method. The objective of this research was to study the chemical composition of bioconversion products of fir wood greenery-based substrates. The РР-3.2 strain of Pleurotus pulmonarius (Fr.) Quél was used as a biodestructor. In the process of bioconversion, the contents of polysaccharides and lignin substances is reduced to 38 and 28 % respectively. Up to 20 % of protein accumulates in bioconversion products of fir wood greenery. The amount of nucleic acids is not more than 1.5 g per 1 kg, the contents of heavy metals, such as mercury, cadmium, arsenic, lead, do not exceed maximum permissible concentration standards. The substrate weight loss reaches 15 %. When fallen leaves and post-extraction poplar bud residues are added to the substrate, the substrate-destroying activity of fungi rises, as well as the protein content increases by 3 %. The digestibility of products as a result of bioconversion increases 1.6–2.8 times depending on the substrate composition. The obtained data enable to recommend post-fermented substrates based on fir wood greenery and balsam poplar biomass for use as a protein feed additive.

Processing of the RNA polymerase I pre-rRNA transcript into the mature 18S, 5.8S, and 25S rRNAs requires removing the “spacer” sequences. The canonical pathway for the removal of the ITS1 spacer, located between 18S and 5.8S rRNAs in the primary transcript, involves cleavages at the 3’ end of 18S rRNA and at two sites inside ITS1. The process generates a long and a short 5.8S rRNA that differ in the number of ITS1 nucleotides retained at the 5.8S 5’ end. Here we document a novel pathway that generates the long 5.8S for ITS1 while bypassing cleavage within ITS1. It entails a single endonuclease cut at the 3’-end of 18S rRNA followed by exonuclease Xrn1 degradation of ITS1. Mutations in RNase MRP increase the accumulation of long relative to short 5.8S rRNA; traditionally this is attributed to a decreased rate of RNase MRP cleavage at its target in ITS1, called A3. In contrast, we report here that the MRP induced switch between long and short 5.8S rRNA formation occurs even when the A3 site is deleted. Based on this and our published data, we propose that the switch may depend on RNase MRP processing RNA molecules other than pre-rRNA.

We show that combining vibrational spectroscopy with signal processing can result in a scheme for ultrasensitive detection of molecules. We consider the vibrational spectrum as a signal on the energy axis and apply a matched filter on that axis. On the example of a nerve agent molecule, we show that this allows detecting a molecule by its vibrational spectrum even when the recorded spectrum is completely buried in noise, when conventional spectroscopic detection is impossible. Detection is predicted to be possible with signal-to-noise ratios in recorded spectra as low as 0.1. We study the importance of spectral range used for detection as well as of the quality of the computed spectrum used to program the filter, specifically, the role of anharmonicity, of the exchange correlation functional, and of the basis set. The use of the full spectral range rather than of a narrow spectral window with key vibrations is shown to be advantageous, as well as accounting for anharmonicity.

This study aimed to explore the experiences of teachers in the recovery of educational skills. Specifically, ten teachers of Andap National High School are selected for the in-depth interview. This study employed a qualitative-phenomenological research design of which in-depth interview (IDI) was used in gathering the data. The study utilized phenomenological approach to study a particular phenomenon concerning the post-pandemic experiences of teachers in the implementation of the face-to-face classes. After gathering sufficient amount of data through the conduct of in-depth interviews, thematic analysis was used to analyse further the results and to check the disparities and similarities of all responses gathered. This was the time wherein responses were categorized and organized into themes. The findings of the study revealed that the challenges encountered of educational skills recovery were sources of learning materials, learning gaps, learners characteristics, difficulty with comprehension and lack of interest. It was also revealed that the most challenging teaching experiences in the recovery of educational skills were catching attention, learning gaps, communicating to students, need for remedial instruction, retroactive interference and difficulty in understanding English Language. They frequently have to quickly convert lesson material they had intended to teach in a classroom setting to any learning modality implemented. Further, adjust and learn new trend, provide opportunities to develop new skills, helps to manage stress, improved educational outcomes, become well-rounded teacher, networking and learning and continue doing responsibilities helped in these insights had in confronting challenges brought by the pandemic.

Protein phosphorylation and ADP-ribosylation (ADPr), as two types of post-translational modifications (PTM), are the process of adding phosphate group and ADP-ribose moieties to proteins, respectively. Although both PTM types can occur on many amino acid types, serine is the most common. Serine phosphorylation (pS), serine ADPr (SADPr), and their in situ crosstalks (pSADPr) play essential roles in biological processes. Although in silico classifiers have been developed for predicting pS and SADPr sites, the classifier for predicting pSADPr sites is unavailable. In this study, we developed classifiers to predict pSADPr sites. Specifically, we collected 3250 human pSADPr, 7520 SADPr, 151,227 pS and 80,096 unmodified serine sites. Based on them, we investigated the characteristics of pSADPr sites and constructed three classifiers to predict pSADPr sites from the pS dataset, the SADPr dataset and the protein sequences separately. We built and evaluated five deep-learning classifiers in ten-fold cross-validation and independent test datasets. Three of them (e.g. Convolutional Neural Network with the One-Hot encoding, dubbed CNNOH) performed better than the rest two. For instance, CNNOH had the AUC values of 0.700, 0.914 and 0.954 for recognizing pSADPr sites from the SADPr, pS and unmodified serine sites.Therefore, it is challenging to distinguish pSADPr sites from SADPr sites compared to the other two. It is consistent with our observation that pSADPr's characteristics are more similar to those of SADPr than the rest. Furthermore, we used the classifiers as base classifiers to develop a few stacking-based ensemble classifiers to improve performance. However, none of the ensemble classifiers showed better performances, suggesting that the base classifiers have good enough performances. Finally, we developed an online tool for extensively predicting human pSADPr sites based on the CNNOH classifier, dubbed EdeepSADPr. It is freely available through http://edeepsadpr.bioinfogo.org/.

Anxiety disorders are among the most common mental disorders worldwide, and often respond incompletely to existing treatments. Selenium, a micronutrient that is a component of several biologically active selenoproteins, is also involved in several aspects of brain functioning, and may exert antidepressant and anxiolytic effects through multiple pathways. The current paper is a scoping review of translational, observational and interventional evidence on the potential role of selenium and its compounds in the management of anxiety and related disorders. Evidence from animal models suggests that this approach may be promising. Though evidence from observational studies in humans is inconsistent and affected by several confounding factors, the available evidence from randomized controlled trials suggests that selenium supplementation may be beneficial in the management of certain anxiety-related conditions, such as anxiety in medically ill patients, prevention of anxiety following exposure to traumatic stress, and obsessive-compulsive disorder. This paper provides a critical evaluation of the existing evidence base, including unanswered questions that could serve as the focus of further research, and outlines the potential benefits and risks associated with the use of selenium in anxiety disorders.

Techniques and tools meant to aid fire management activities in the Cerrado, such as accurately determining fuel load and composition spatially and temporally, are pretty scarce. The need to have fuel information for more efficient management in a considerably heterogeneous, biodiverse, and fire-dependent environment makes a constant search for the improvement of the use of remote sensing techniques in determining fuel characteristics. This study presents the following objectives: (1) to assess the use of data from Landsat 8 OLI images to estimate the fine surface fuel load of the Cerrado during the dry season by adjusting multiple linear regression equations; (2) to estimate fuel load through Random Forest and k-Nearest Neighbor (k-NN) algorithms in comparison with re-gression analyses; and (3) to evaluate the importance of predictor variables from satellite images. Therefore, 64 sampling units were collected, and the pixel values associated with the field plots were extracted in a 3 x 3 pixel window surrounding the reference pixel. For the multiple linear regression analyses, the R² values ranged from 0.63 to 0.78, and the models fitted by the Random Forest al-gorithm ranged from 0.52 to 0.83, while those fitted by k-NN ranged from 0.30 to 0.68. Adopting the Random Forest algorithm resulted in improvements in the statistical metrics of evaluation of the fuel load estimates for Cerrado grassland relative to the multiple linear regression analyses.

Demand for niobium and tantalum is increasing exponentially as these are essential ingredients for the manufacture of, among others, capacitors in technological devices and ferroniobium. Mine tailings rich in such elements could constitute an important source of Nb and Ta in the future and so alleviate potential supply risks. This paper evaluates the possibility of recovering niobium and tantalum from the slags generated during the tin beneficiation process of mine tailings from the old Penouta mine, located in Spain. To do so, a simulation of the processes that would be required to beneficiate and refine both elements is carried out. After tin carbothermic reduction, the slags are sent to a hydrometallurgical process where at the end niobium oxide and tantalum oxide are obtained. Reagents, water and energy consumption, in addition to emissions, effluents and product yields are assessed. Certain factors were identified as critical, and recirculation was encouraged in the model to maximize production and minimize reagents use and wastes. With this simulation, considering 3000 production hours per year, the metal output from the tailings of the old mine could cover around 1% and 7.4% of the world annual Nb and Ta demand, respectively.

Lyme disease and associated co-infections are increasing worldwide and approximately 20% of individuals develop chronic Lyme disease (CLD)/Post-Treatment Lyme Disease Syndrome (PTLDS) despite early antibiotics. A 7–8-week protocol of double dose dapsone combination therapy (DDDCT) for CLD/PTLDS results in symptom remission in approximately 50% of patients for one year or longer, with published culture studies indicating higher doses of dapsone demonstrate efficacy against resistant biofilm forms of Borrelia burgdorferi. The purpose of this study was therefore to evaluate higher doses of dapsone in the treatment of resistant CLD/PTLDS and associated co-infections. Twenty-five patients with a history of Lyme and associated co-infections, most of whom had ongoing symptoms despite several courses of DDDCT, took one or more courses of high dose pulsed dapsone combination therapy (200 mg dapsone X 3-4 days and/or 200 mg BID x 4 days), depending on persistent symptoms. The majority of patients noticed sustained improvement in 8 major Lyme symptoms, including fatigue, pain, headaches, neuropathy, insomnia, cognition, and sweating, where dapsone dosage, not just treatment length, positively affected outcomes. High dose pulsed dapsone combination therapy may represent a novel therapeutic approach for the treatment of resistant CLD/PTLDS, and should be confirmed in randomized, controlled clinical trials.

Robot Operating System 2 (ROS 2) is a robotic software that uses a set of Quality of Service (QoS) policies to manage the quality of robot data transmissions in a network, such as the RELIABLE and KEEP_LAST options. In ROS 2 node communication, the RELIABLE connection guarantees that all message data can be properly sent from the publisher to the subscriber. However, strict reliability is not guaranteed if the RELIABLE connection uses the KEEP_LAST option to transmit the robot data in the publish-subscribe communication. This study aims to analyze the efficiency of local cache, cache control, and QoS balancing optimization to improve ROS 2 node communication when using the RELIABLE and KEEP_LAST options to transmit multi-robot data in Aggregated Robot Processing (ARP) architecture. Our idea in local cache and cache control is to streamline the sensor data output before processing it when the sensor device produces the data with the same value in a row. Furthermore, QoS balancing optimization aims to balance the DEPTH and DEADLINE QoS configuration to determine the rates and buffer size in ROS 2 node communication. This study shows that combining local cache and QoS balancing optimization improves multi-robot data transmission and cooperation in ARP architecture.

The modern diet, which consists of food produced with high level of industrial processing, is associated with an increased risk of the development of lifestyle diseases. Current nutritional science is, however, focused on chemical composition of food, and not on the type and degree of processing used during the food production. Here, we take a fresh perspective on the relationship between the extent and type of food processing, and evaluate its impact on consumer health. We argue that the preference for consumption of minimally processed foods, and restriction of ultra-processed foods should be an essential part of a healthy lifestyle, disease prevention, and even treatment. We also present a simple, user-friendly consumer guide, which is intended to be used as a practical, ready-to-go identifier of ultra-processed foods.

The menopausal transition often accompanies distressing manifestations, including vasomotor symptoms, sleep disruptions, and depressive syndrome. Omega-3 fatty acids have emerged as a potential intervention to alleviate these symptoms. This review aimed to comprehensively assess the impact of omega-3 supplementation on vasomotor symptoms, sleep quality, and depression among postmenopausal women. We conducted a systematic search focusing on randomized control trials across the Cochrane Library, Web of Science, PubMed, CINAHL, EMBASE, and SCOPUS databases from inception to August 2023. Among the initial pool of 163 studies, 9 studies met the inclusion criteria and were incorporated into this review. Four studies suggested potential benefits of omega-3 intake for improving hot flashes and night sweats. Sleep quality outcomes displayed heterogeneity across the studies. Incorporating diverse scales such as the Hamilton Depression Rating Scale-21, the Patient Health Questionnaire depression scale, and Generalized Anxiety Disorder-7 for depression outcomes, the review found inconclusive evidence on omega-3's impact on depression. Overall, the combined analysis of these studies did not provide substantial evidence to support the efficacy of omega-3 fatty acids in improving vasomotor symptoms, sleep quality, and depression. Further well-designed RCTs with larger participant groups are crucial to validate and generalize these results.

Media coverage of Lyme disease (LD) has led to an increase in consultations for presumed LD in Europe. However, LD is confirmed in only 10-20% of patients, with a significant number remaining in a diagnostic dead-end. The objective of our study was to understand the genesis of the LD hypothesis in care pathways. In 2019, 30 patients from a prospective cohort consulting in the infectious diseases department at University Hospital in Marseille for presumed LD were recruited for semi-structured interviews. The inclusion criteria were: suffering from subjective symptoms for 6 months, no clinical or paraclinical argument suggesting current LD. The patients’ medical trajectories were collected using a biographical approach. A majority of participants were convinced they had LD despite the lack of medical evidence and the scepticism of their referring GP. The diagnosis of Lyme disease was primarily triggered by identification with clinical stories circulating in the media. Most of participants had conducted the diagnostic investigation themselves. Diagnostic wandering in the diagnostic process suggests a failure of modern medicine to propose solutions for medically unexplained symptoms. Clinicians should systematically explore patients’ etiologic representations in a patient-centred care approach.

The rapid spread of COVID-19 worldwide was accompanied by intense fears, confusion, worries, anger and stress threatening people’s mental health. Unprecedented measures to slow down and prevent the transmission of COVID-19 have had various impacts on the population’s health behaviour and mental health. The main purpose of the present study is to investigate the lockdown’s effects on university students’ mental health in Romania. Based on a cross-sectional design, the survey data were collected from a sample of 722 participants (247 males; M = 21.1 years; SD ± 1.73). A path analysis was performed to verify the hypothesised direct and indirect effects included in the multiple mediation model. The findings showed a positive association between stress and boredom proneness, missing daily social interactions, spending more time on phone conversations and the increasing interest in following news about the pandemic. The path analysis revealed an excellent fit between the proposed multiple mediation model and the sample data. Boredom proneness and missing daily social interactions both affected stress, directly and indirectly, through more time spent on phone conversations. In addition, it was found that the increased interest in following news about the pan-demic mediated the relationship between boredom proneness and perceived stress. In terms of gender differences, our findings revealed that female students experienced significantly higher stress levels than male students, perceived to a greater extent the lack of daily social interactions and spent more time on phone conversations. Overall, the findings further extend the empirical evidence on university students’ mental health in the context of the COVID-19 pandemic. Therefore, universities need to organise support programmes focused on developing university students’ coping strategies to maintain their mental health even in adverse contexts.

The traditional processing of Dendrobium officinale (DO) is manufactured through five necessary processing steps: fresh strips, drying at 85 °C, curling, molding, and drying at 35 °C (Fengdou), and the antioxidant activity of DO were increased after processing into Fengdou. To comprehensively analyses the changing functional components, a plant-wide target metabolomics were employed. In total, 739 differential ingredients were identified in five processing treatments, mainly highlighting differences in variation with phenolic acids, flavonoids, lipids, and amino acids and their derivatives, and glycosylation of aglycone result in the up-regulation in flavonoid glycosides levels. Temperature is a key step in DO processing production. In addition, the main enrichment of specific differential ingredients was found in five different metabolic pathways, including glucosinolate biosynthesis, linoleic acid metabolism, flavonoid biosynthesis, phenylpropanoid biosynthesis, ubiquinone and other terpene quinone biosynthesis. Correlation analysis clarified that total phenols and flavonoids showed a significant positive correlation with antioxidant capacity. This study provides new insights into the influence of processing processes on DO quality, which may lead to guidance for the high-quality production of DO.

Objective tools to track medication adherence are lacking. A tool to monitor pill intake that can be implemented in mHealth apps without the need for additional devices was developed. We propose a pill intake detection tool that uses digital image processing to analyze images of a blister to detect the presence of pills. The tool uses the circular Hough transform as a feature extraction technique and is therefore primarily useful for the detection of pills with a round shape. This pill detection tool is composed of two steps. First, the registration of a full blister and storing of reference values in a local database. Second, the detection and classification of taken and remaining pills in similar blisters, to determine the actual number of untaken pills. In the registration of round pills in full blisters, 100% of pills in gray blisters or blisters with a transparent cover were successfully detected. In counting of untaken pills in partially opened blisters, 95.2% of remaining and 95.1% of taken pills were detected in gray blisters, while 88.2% of remaining and 80.8% of taken pills were detected in blisters with a transparent cover. The proposed tool provides promising results for the detection of round pills. However, the classification of taken and remaining pills need to be further improved, in particular for the detection of pills with non-oval shapes.

The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that affected the lifestyle and economy. Various studies have been focused on the identification of COVID-19 impact to mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the change of mood state of children and adolescents during the first lockdown. Therefore, to this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best performing model and (v) a post-hoc interpretation of the features’ impact on the best performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of COVID-19 pandemic on the mood state of children and adolescents.

Requirements are predominantly written in Natural Language (NL), which makes them accessible to stakeholders with varying degrees of experience, as compared to a model-based language which requires special training. However, despite its benefits, NL can introduce ambiguities and inconsistencies in requirements, which may eventually result in system quality degradation and system failure altogether. The system complexity that characterizes current systems warrants an integrated and comprehensive approach to system design and development. This need has brought about a paradigm shift towards Model-Based Systems Engineering (MBSE) approaches to system design and a departure from traditional document-centric methods. While, MBSE shows great promise, the ambiguities and inconsistencies present in NL requirements hinder their conversion to models directly. The field of Natural Language Processing (NLP) has demonstrated great potential in facilitating the conversion of NL requirements into a semi-machine-readable format that enables their standardization and use in a model-based environment. A first step towards standardizing requirements consists of classifying them according to the ``type'' (design, functional, performance, etc.) they represent. To that end, a language model capable of classifying requirements needs to be fine-tuned on labeled aerospace requirements. This paper presents the development of an annotated aerospace requirements corpus and its use to fine-tune BERT$_\text{BASE-UNCASED}$ to obtain aeroBERT-Classifier: a new language model for classifying aerospace requirements into design, functional, or performance requirements. A comparison between aeroBERT-Classifier and bart-large-mnli (zero-shot text classification) showcased the superior performance of aeroBERT-Classifier on classifying aerospace requirements despite being fine-tuned on a small labeled dataset.

Image processing is a field of which its popularity increases and continues and, that grows dynamically with new technologies. Nowadays, image processing finds itself in use in almost every field. One of these uses is undoubtedly in the field of medicine, where diagnosis and treatment planning are made from images and, which is constantly changing with newly developed techniques. Of course, the most important factor in using this so widely in the medical field is the acquisition of images on every medical field. With the help of these images, the complaints can be seen more easily and the doctor can follow a path in the treatment of the disease. In our study, we used the Region Growing segmentation method to detect the intestinal mass. This study compares the area determined by the specialist with the area obtained with the segmentation process and, it is seen that the created software system can be used as an auxiliary system to specialist doctors.

The quasi-beta processing was considered as a promising processing technology to obtain a component with an excellent mechanical properties. To achieve an optimized quasi-beta processing parameter of TC21 alloy, the hot deformation behavior in near β phase region for the alloy with a forged structure was investigated by the thermal compression test and finite element (FEM) simulation. The obtained results indicated that the flow behavior of the samples was significantly influenced by the hot deformation parameters and it exhibited a flow hardening behavior at the start stage of deformation. Based on the experimental data, the constitutive equation and processing maps were obtained. The optimum hot processing parameters was 986℃/10^-3s^-1. Based on the FEM simulation results, the evolution of temperature field, strain field and stress field in the deformed samples at different strains exhibited a similar trend in the unstable region, which distributed symmetrically along the center line of the samples, and the center area of the samples was the highest and the center area of the section was the lowest.

Background: This study aims to explore children’s social information processing (SIP) as an ex-planatory mechanism in the link between parent-child relationship and children’s learning diffi-culties; (2) Methods: The sample included 115 kindergarteners (62 girls; 53 boys; Mage = 68.5 months, SD = 6.04), their parents and the school teacher. Parents reported on relationship quality with the child and teachers reported on children’s learning difficulties and school achievements. Children’s SIP was assessed with the social information processing interview – preschool version (3) Results: Mother and father relationship quality with the child associated with children’s SIP, however, only the father's but not the mother's quality of relationship with the child was associ-ated with children’s learning difficulties and school achievements. Children’s SIP mediated this latter link; (4) Conclusions: Parents’ relationship quality with the child and children’s SIP are pertinent factors in children’s learning in the early years. The father-child relationship seem to be a strong determinant of children approach to learning and achievement and may have long last-ing effect on children's mental health

Camel milk occupies a pivotal and essential position in the dietary customs of individuals residing in semi-arid and arid areas. Historically, the promotion and distribution of camel milk have been negligible, primarily due to the absence of processing facilities in areas where camels are raised. Consequently, the consumption of untreated camel milk has been predominantly limited to nomadic households. However, owing to its exceptional medicinal properties and health-enhancing effects, a substantial surge in the global demand for camel milk and its derivatives has been observed over the past two decades. This growing demand has prompted the dairy sector to introduce a diverse range of camel milk products, which are distinguished by their enhanced nutritional and functional properties. In contrast to products derived from bovine milk, the current market offers only a limited selection of food items sourced from camel milk. Recent advances in food processing technologies have enabled the production of an array of both dairy and non-dairy products derived from camel milk. This includes a variety of items such as powdered milk, cheese, yogurt, ice cream. Moreover, in certain regions, camel milk is incorporated into customary cuisine, serving as a key ingredient in local culinary practices like fermented milk, camel milk tea, or as a fundamental component in various meals. This review underscores the possibility of transforming camel milk into a range of dairy products by addressing its intrinsic functional constraints. This objective can be realized by adjusting the processing parameters and modifying its chemical composition through enrichment techniques. Furthermore, further research avenues may focus on enhancing product quality and exploring innovative processing techniques.

The rapid expansion of China's A-share market, coupled with the burgeoning volume of financial research reports, presents a unique challenge to traditional stock price prediction methodologies. This study introduces FAST-SCAN, a novel framework designed to leverage natural language processing (NLP) and time series analysis for the efficient and accurate prediction of stock market trends. By integrating advanced sentiment analysis, primarily through the utilization of the RoBERTa model, with dynamic time series forecasting, FAST-SCAN aims to distill actionable insights from the vast corpus of financial research reports. This approach not only enhances the speed and efficiency of financial analysis but also addresses the time-sensitive nature of market-influencing factors, thereby providing a competitive edge in stock price prediction. The framework demonstrates a notable improvement in predictive performance, achieving a 20% annualized return and a 7.19% RankIC, marking a significant advancement over conventional statistical analysis methods. Through its innovative combination of NLP and predictive analytics, FAST-SCAN paves the way for a more informed and strategic approach to investment in the A-share market, emphasizing the importance of timely, data-driven decision-making in the financial industry.

Image denoising has been one of the important problems in the field of computer vision, and it has a wide range of practical value in many applications, such as medical image processing, image enhancement, and computational photography. Traditional image denoising methods are usually based on hand-designed features and filters, but these methods perform poorly under complex noise and image structures. In recent years, the rapid development of neural network technology has revolutionized the image-denoising task. This paper introduces the knowledge about neural networks and image denoising, explores the impact of neural networks on image denoising, and how is it possible to denoise images by neural networks. It also summarises other image-denoising methods and finally points out the challenges and problems faced by image-denoising at present. Some possible new development directions are proposed to provide new solutions for image-denoising researchers and to promote the development of the field.

Spinal deformity refers to a range of disorders that are defined by anomalous curvature of the spine and may be classified as scoliosis, lordosis, or kyphosis. Among these, scoliosis stands out as the most common type of spinal deformity in human beings, and it can be distinguished by abnormal lateral spine curvature accompanied by axial rotation. Accurate identification of spinal deformity is crucial for a person's diagnosis, and numerous assessment methods have been developed by researchers. Therefore, the present study aims to systematically review recent works on spinal deformity as-assessment for scoliosis diagnosis, utilizing image processing techniques. To gather relevant studies, a search strategy was conducted on three electronic databases (Scopus, ScienceDirect, and PubMed) between 2012 and 2022, using specific keywords and focusing on scoliosis cases. A total of 17 papers fully satisfied the established criteria and were extensively evaluated. Despite variations in methodological designs across the studies, all reviewed articles obtained quality ratings higher than satisfactory. Various diagnostic approaches have been employed, including artificial intelligence mechanisms, image processing, and scoliosis diagnosis systems. These approaches have the potential to save time and, more significantly, can reduce the incidence of human error. While all assessment methods have potential in scoliosis diagnosis, they possess several limitations that can be ameliorated in forthcoming studies. Therefore, the findings of this study may serve as guidelines for the development of a more accurate spinal deformity assessment method that can aid medical personnel in the real diagnosis of scoliosis.

Reading is a complex cognitive task involving processes from different systems. The present work aims to identify some points of divergence reported in the reading literature and discuss them in a new experimental paradigm framework. Inspired by the paradigms of perceptual identification and rapid parallel presentation (RPVP), we emphasize that the originality of our experimental paradigm lies in the recruitment of multi-stable Arabic percepts within the region where low-level processing occurs (i.e., the visual span area). With good flexibility, the current paradigm has reached higher-order processing levels. In agreement with previous works highlighting the parafoveal-on-foveal effect, results suggest parallel word processing. Furthermore, they suggest a rapid extraction of syntactic and semantic information from words in sentences while attributing an advantage to semantic processing in the emergence of the sentence superiority effect.

Better auditory processing of musicians is observed in previous research. As musicians differentiate their practice method and performance environment, we aimed to assess auditory perception in Greek musicians with respect to their musical specialization. If there are differences, this may provide a basis for better shaping auditory training in individuals with auditory processing disorder. The auditory tests administered were speech in noise (Speech in Babble), with and without rhythmic advantage (Word Recognition—Rhythm Component), short-term and working memory (Digit Span - Forward and Backwards), temporal resolution (Gaps In Noise) and detection of frequency discrimination threshold (DFL). Groups consisted of classical musicians, Byzantine chanters, percussionists, and non-musicians (12 participants/group). Statistical analysis revealed significant difference in: (i) word recognition in noise with precursor synchronized pulse between classical musicians compared to Byzantine musicians, (ii) better frequency discrimination of Byzantine musicians compared to non-musicians for the 2000Hz region and (iii) working memory, an advantage detected in musicians. Considering all the above, we conclude that musicians have a superior auditory perception, regardless of musical specialization. Musical training enhances elements of auditory processing and may be used as an additional rehabilitation during auditory training, focusing on specific types of music for specific auditory processing deficits.

The major goal of this paper is to use image enhancement techniques for enhancing and extracting data in FANET applications to improve the efficiency of surveillance. The proposed conceptual system design can improve the likelihood of FANET operations in oil pipeline surveillance, and sports and media coverage with the ultimate goal of providing efficient services to those who are interested. The system architecture model is based on current scientific principles and developing technologies. A FANET, which is capable of gathering image data from video-enabled drones, and an image processing system that permits data collection and analysis are the two primary components of the system. Based on the image processing technique, a proof of concept for efficient data extraction and enhancement in FANET situations and possible services is illustrated

Muscle spindles are encapsulated sensory organs found in most of our muscles. Prevalent models of sensorimotor control assume the role of spindles is to reliably encode limb posture and movement. Here, I argue that the traditional view of spindles is outdated. Spindle organs can be tuned by spinal γ motor neurons that receive top-down and peripheral input, including from cutaneous afferents. A new model is presented, viewing γ motor activity as an intermediate coordinate transformation that allows multimodal information to converge on spindles, creating flexible coordinate representations at the level of the peripheral nervous system. That is, I propose that spindles play a unique overarching role in the nervous system: that of a peripheral signal-processing device that flexibly facilitates sensorimotor performance, according to task characteristics. This role is compatible with previous findings and supported by recent studies with naturalistically active humans. Such studies have so far shown that spindle tuning enables the independent preparatory control of reflex muscle stiffness, the selective extraction of information during implicit motor adaptation, and for segmental stretch reflexes to operate in joint space. Incorporation of advanced signal-processing at the periphery may well prove a critical step in the evolution of sensorimotor control theories.

Significant challenges exist globally regarding literacy teaching and learning, particularly in poor socio-economic settings in countries of the Global South. In this paper we argue that to address these challenges, major features of how the brain works that are currently ignored in the educational literature should be taken into account. First, perception is an active process based in detection of errors in hierarchical predictions of sensory data and action outcomes. Reading is a particular case. Second, emotions play a key role in underlying cognitive functioning. Innate affective systems underlie and shape all brain functioning, including oral and written forms of language and sign. Third, there is not the fundamental difference between listening/speaking and reading/writing often alleged on the basis of evolutionary arguments. Both are socio-cultural practices driven and learnt by the communication imperative of the social brain. Fourth, like listening, reading is not a linear, bottom-up process. Both are non-linear contextually shaped psycho-social processes of understanding, shaped by current knowledge and cultural contexts and practices. Reductionist neuroscience studies which focus on decontextualized parts of reading cannot access all the relevant processes. An integrated view of brain function reflecting this non-linear nature implies that an ongoing focus on personal meaning and understanding provides positive conditions for all aspects of literacy learning. Assessment of literacy teaching at all its stages should include indicators that take into account these foundational features relating reading and writing to neuroscience.

In order to solve the problems of waveform distortion and signal delay by many physical and electrical systems with linear low-pass transfer characteristics with multiple complex poles, a general digital-signal-processing (DSP)-based method of real-time recovery of the original source waveform from the distorted output waveform is proposed. From the convolution kernel representation of a multiple-pole low-pass transfer function with an arbitrary denominator polynomial with real valued coefficients, it is shown that the source waveform can be accurately recovered in real time using a particular moving average algorithm with real-valued DSP computations only, even though some or all of the poles are complex. The proposed digital signal recovery method is DC-accurate and unaffected by initial conditions, transient signals, and resonant amplitude enhancement. This method can be applied to most sensors and amplifiers operating close to their frequency response limits or around their resonance frequencies to accurately deconvolute the multiple-pole characteristics and to improve the overall performances of data acquisition systems and digital feedback control systems.

The dietary pattern that characterizes the Western diet is strongly associated with obesity and related metabolic diseases, but biological mechanisms supporting these associations remain largely unknown. We argue that the Western diet promotes inflammation that arises from both structural and behavioral changes in the resident microbiome. The environment created in the gut by ultra-processed foods, a hallmark of the Western diet, is an evolutionarily unique selection ground for microbes that can promote diverse forms of inflammatory disease. Recognizing the importance of the microbiome in the development of diet-related disease has implications for future research, public dietary advice as well as food production practices. Research into food patterns suggests that whole foods are a common denominator of diets associated with a low level of diet-related disease. Hence, by studying how ultra-processing changes the properties of whole foods and how these foods affect the gut microbiome, more useful dietary guidelines can be made. Innovations in food production should be focusing on enabling health in the super-organism of man and microbe, and stronger regulation of potentially hazardous components of food products is warranted.

As part of the Copernicus programme of the European Union (EU), the European Space Agency (ESA) has developed and is currently operating the Sentinel-2 mission that is acquiring high spatial resolution optical imagery. This paper provides a description of the calibration activities and the current status of the mission products validation activities. Measured performances, from the validation activities, cover both Top-Of-Atmosphere (TOA) and Bottom-Of-Atmosphere (BOA) products. Results presented in this paper show the good quality of the mission products both in terms of radiometry and geometry and provide an overview on next mission steps related to data quality aspects.

Whether and how P2X7 receptor knockout (KO) modulates central post-stroke pain (CPSP) induced by lesions of the ventrobasal complex (VBC) of the thalamus regarding behavioral, molecular, and electrical recording parameters remains unclear. Following the experimental protocols of a rat hemorrhage CPSP model, the VBC of the thalamus of wild-type and P2X7 receptor KO mice was injected with 10 mU/0.2 μl type IV collagenase to produce an animal model of stroke-like thalamic hemorrhage. Behavioral data showed that the CPSP group induced thermal and mechanical pain, while the P2X7 receptor KO group showed reduced thermal and mechanical pain behaviors. Molecular assessments revealed that the CPSP group had lower expression of NeuN and KCC2 and higher expression of GFAP, IBA1, and BDNF. The P2X7 KO group showed lower expression of GFAP, IBA1, and BDNF and higher expression of KCC2 than the CPSP group. The expression of NKCC1, GABAa receptor, and TrkB did not differ significantly between the control, CPSP, and P2X7 receptor KO groups. Muscimol application increased multiunit numbers and [Cl-] influx in the membrane in the CPSP group, while P2X7 receptor KO reduced multiunit activity and [Cl-] outflux. P2X4 receptor expression was significantly decreased in the 100 kb but not the 50 kb site in the P2X7 receptor KO group. Altogether, the P2X7 hypothesis of CPSP was proposed, wherein P2X7 receptor KO changed the CPSP pain behavior, numbers of astrocytes and microglia, CSD amplitude of the anterior cingulate cortex and the medial dorsal thalamus, BDNF and KCC2 expression, [Cl-], and P2X4 activation in 100 kb with P2X7 receptors. The present findings have implications for the clinical treatment of CPSP symptoms.

Objective: The primary objective of this study was to develop a logistic regression model to predict post-operative in-hospital mortality rates for patients undergoing emergency cardiac surgeries, with the aim of improving predictive accuracy over traditional risk assessment tools and enhancing patient outcomes and clinical decision-making. Methods: Data were collected from 4,855 patients who underwent emergency cardiac surgeries at a tertiary hospital between 2008 and 2017. The analysis incorporated demographic, anthropomet- ric, and clinical factors, including the ASA classification, emergency status, and various preoperative laboratory values. A logistic regression model was developed, and the Elixhauser Comorbidity Index was calculated using standard ICD-10 codes for its comprehensive assessment of comorbidities. Model performance was evaluated using metrics such as AUROC, AUPRC, accuracy, precision, recall, and F1 score. Results: The logistic regression model demonstrated strong predictive performance, with an AUROC of 0.939 and an AUPRC of 0.350. Key pre-operative factors identified included emergency operation status, ASA classification, and preoperative prothrombin time. The model significantly outperformed the traditional ASA classification system, which showed an AUROC of 0.524 and an AUPRC of 0.010. These findings suggest a substantial improvement in predicting post-operative mortality. Conclusion: The logistic regression model significantly improves the prediction of post-operative mortality in emergency cardiac surgeries compared to the ASA classification system. These findings highlight the potential of incorporating comprehensive pre-operative factors into predictive models to enhance clinical decision-making and patient outcomes. Implementing such models in routine clinical practice could lead to more accurate risk assessments, better resource allocation, and improved patient care.

Over the last few decades, the approach to geological and geomorphological surveys has changed remarkably. The advent of digital tools has allowed significant advances in the acquisition and management of survey data. In this paper, we demonstrate the development and testing of a new and effective digital survey method that allows for the fast acquisition and collaborative storage and management of data and information. This method was tested in collaboration with five universities for the mapping and classification of landslides in 249 survey areas in Central Italy and, more precisely, in the municipalities affected by the 2016 Central Italy Earthquake. Geological and geomorphological surveys were carried out in the field with tablet PCs, GPS, and cameras. The survey project for collecting field data was based on the structure of the Italian Landslide Inventory (IFFI) and the Territorial Resilience Central Apennines Earthquake Reconstruction (ReSTART) projects. The structure of the database and input forms were implemented for these aims. Moreover, the data and information were retrieved and organised in detailed records useful to the administrative entities.

Depressive disorder is a disease that causes a decrease in daily function, and the prevalence of depressive disorder in Korea is the highest among OECD countries. However, due to lack of manpower and negative social awareness, timely treatment is not being performed properly. As a solution to this, AI counseling chatbots are emerging, and it is essential to use labeled datasets to create chatbots. Currently, many countries around the world, including Korea, use the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition, DSM-5, but there are no nine labeled datasets according to the DSM-5 depression disorder diagnosis criteria. This study collects sympathetic datasets, analyzes morphemes using Kind Korean Morpheme Analyzer and augments and builds a word dictionary using Word2Vec. As a result, we have labeled the dataset with nine criteria for diagnosing DSM-5 depressive disorder, which will enhance the performance of the counseling chatbot.

Background: Healthcare workers are exposed to several hazardous chemicals in the workplace that can result in numerous adverse health effects. Several published studies from many different countries have documented the presence of metabolites from antineoplastic drugs in the urine of healthcare workers and demonstrated contaminated working surfaces with these agents. This study is a literature review that aims to compare the prevalence and degree of exposure to cyclophosphamide (CP), ifosfamide (IFO), and methotrexate (MTX) in physicians, nurses, and pharmacists in the hospital setting. Methods: An extensive search was conducted on PubMed, Google Scholar, HINARI, OARE, and Science Direct where applicable research about healthcare workers and antineoplastic drugs exposure were identified. Medical Subject Headings (MeSH) keywords: occupational exposure, antineoplastic drugs (cytotoxic drugs, cyclophosphamide, ifosfamide, methotrexate), hazardous chemicals, healthcare workers (healthcare professionals, nurses, pharmacists, physicians), hospital setting, and adverse health outcomes were used. Thereafter, relevant articles from 2000 to 2020 were selected to perform this literature review. Results: Review of literature demonstrated that cyclophosphamide was the most prevalent contaminant in the hospital setting. Additionally, pharmacists are among the group of healthcare workers with the highest prevalence of antineoplastic agents in urine samples while nursing stations had the highest rate of surface contamination. Conclusion: There is an urgent need to develop a system to monitor and detect the pathway of environmental exposure to antineoplastic drugs in the healthcare setting, and to evaluate the effectiveness of the existing protective measures. Well designed studies focusing on improving the standard safety precautions is a priority to protect healthcare workers.

Clothing consumers mostly purchase, use, and dispose of clothing products in a linear business model. This approach pollutes the environment and causes economic and social crises in the ecosystems. This study evaluates the amount of generated postconsumer clothing waste (PCCW) and proposes recycling as a sustainable waste management approach. Data were collected to understand disposal behavior of consumers with 474 respondents selected from households in Bahir Dar city. The findings show that all the determinant factors on the PCCW generation level except religion were significant. The most frequently disposed type of clothing waste is jeans-trouser (51%). The CEBM was used to develop alternative products from a jeans-based clothing waste. The jean's fiber and fabric-reinforced composite materials were developed using unsaturated polyester resin as a sustainable way of PCCW management strategy. The shredded form fiber-reinforced composite was found to have a smoother surface than that of the chopped or cut fabric-reinforced composite. The product was characterized by physical and mechanical properties. The study can be further extended to the scope of the entire consumers in Ethiopia and alternative recyclable products can be studied.

In the evolving landscape of higher education, St. Michael's College (SMC) recognizes the significant impact of the COVID-19 pandemic on student welfare. This paper emphasizes the critical importance of addressing mental health, time management, and stress in the college environment, underlining their influence on academic success and personal growth. The call to action urges students to prioritize their well-being. Diverse cultural and religious perspectives, including Islam, emphasize the importance of a balanced mind and body. The wide-ranging literature review uncovers empirically supported strategies for mental health, time management, and stress reduction. Personal autonomy emerges as crucial in addressing these challenges. This study underscores the importance of these dimensions, calls for student action, and highlights the diversity of perspectives contributing to holistic well-being.

This study delves into the dynamic intersection of artificial intelligence (AI) and English Language Teaching (ELT) within the post-pandemic educational landscape. Grounded in Constructivism, Connectivism, and Technological Pedagogical Content Knowledge (TPACK), this research explores how AI integration impacts language instruction efficiency, benefits and challenges perceived by St. Michael's College students, differences in learning outcomes, and its potential to address the digital divide. Employing a qualitative approach, the study employs a rigorous method that includes intensive in-depth reviews of literature, maximum purposeful sampling, informal interviews, observation, and thematic analysis. Through these methods, data was collected from 30 participants, leading to the identification of five key themes. Findings suggest that AI enhances instructional efficiency, offers personalized learning experiences, and bridges gaps in remote settings. However, challenges and the importance of pedagogical balance emerged. The study underscores the need for adaptive pedagogical strategies to fully harness AI's potential. Future directions and limitations are discussed, emphasizing the study's implications for shaping the future of ELT.

Amidst the transformative backdrop of the post-Covid-19 context, the researcher embarks on a qualitative exploratory study to investigate the intricate interplay of religiosity, educational engagement, and campus life among college students at St. Michael's College, Iligan City. Guided by a theoretical framework integrating Vygotsky's sociocultural theory, Fredricks et al.'s engagement theory, and Pascarella & Terenzini's educational impact theory, this study delves into the dynamic fruition of religiosity in response to the pandemic's impact. Employing purposeful and maximum variation sampling, the investigation unravels narratives from interviews, focus group discussions, and observations. Through thematic analysis, themes of "Adaptive Integration," "Religious Resilience," "Community Nexus," "Challenges of Synchronization," and "Identity Negotiation" emerge. In the discourse of these themes, strategies students employ to navigate challenges arising from this confluence are unearthed. Rooted in a deep understanding of the context, this study presents implications for fostering holistic growth within an inclusive educational environment. As the findings and insights are presented, stakeholders are invited to reimagine the role of religiosity, educational engagement, and campus life in shaping the holistic student experience in a world forever changed.

Fasting, a practice with historical roots in various cultures, has recently garnered significant interest in the field of medicine. In this article, we delve into the mechanisms underlying fasting-induced autophagy and its therapeutic applications for spike protein associated pathology. We explore the therapeutic potential of fasting on spike protein-related pathology and the role of interventions to upregulate autophagy, including compounds like spermidine, resveratrol, rapamycin, and metformin. In conclusion, fasting, coupled with an understanding of its nuances, holds promise as a therapeutic intervention for spike protein related diseases; with broad implications for human health. This review presents the therapeutic possibility of using autophagy to treat spike protein related diseases, and details the interventions to deploy this therapeutic modality.

Post-translational modifications (PTMs) represent a cornerstone in the complexity of the proteome, significantly contributing to diversifying protein structure and function. PTMs can significantly influence protein function, stability, localization, and interactions with other molecules. Therefore, it is important when choosing a protein expression system to ensure the precise incorporation of PTMs during protein synthesis, which is paramount for producing biologically active proteins. Cell-free protein synthesis (CFPS) system has emerged as a powerful protein synthesis platform and research toolkit in synthetic biology. The open nature of the system allows the reaction environment to be tailored to any protein of interest to promote specific PTMs, thus allowing for the production of a protein with the desired modifications. This review presents various PTMs achieved in the CFPS systems, providing insights into the current challenges, successes, and future prospects.

An adaptor signature can be viewed as a signature concealed with a secret value and, by design, any two of the trio yield the other. In a multiparty setting, an initial adaptor signature allows each party create additional adaptor signatures without the original secret. Adaptor signatures help address scalability and interoperabity issues in blockchain. They can also bring some important advantages to cryptocurrencies, such as low on-chain cost, improved transaction fungibility, and less limitations of a blockchain’s scripting language. In this paper, we propose a new two-party adaptor signature scheme that relies on quantum-safe hard problems in coding theory. The proposed scheme uses a hash-and-sign code-based signature scheme introduced by Debris-Alazard et al. and a code-based hard relation defined from the well-known syndrome decoding problem. To achieve all the basic properties of adaptor signatures formalized by Aumayr et al., we introduce further modifications to the aforementioned signature scheme. We also give a security analysis of our scheme and its application to the atomic swap. After providing a set of parameters for our scheme, we show that it has the smallest pre-signature size compared to existing post-quantum adaptor signatures.

Total 219 countries and territories globally suffering from the recent pandemic COVID-19 is now in its second wave with more brutality, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) . It has several symptoms like as persistent fever; respiratory illnesses; cough; fatigue; shortness of breath; loss of appetite; persistent pain or pressure in the chest; dysgeusia; acute respiratory distress syndrome (ARDS) etc., and here the things to worry about is the development of pulmonary fibrosis after COVID-19 in both peoples who had died of due to acute respiratory distress syndrome (ARDS) or those who survived. Due to COVID-19, dysregulated immune response and wound repair mainly in elderly patients causes this secondary pulmonary fibrosis. Thus using anti-fibrotic agents could be meaningful in these circumstances although their efficacy in treating COVID-19 is subject to more detailed laboratory research works. In this review article you will get to know about the lung fibrosis generation due to COVID-19 infection, about anti-fibrotic agents and the currents challenges of this field.

Cryptography has been used from time immemorial for preserving the confidentiality of data/information in storage or in transit. Thus, cryptography research has also been evolving from the classical Caesar cipher to the modern cryptosystems based on modular arithmetic to the contemporary cryptosystems based on quantum computing. The emergence of quantum computing imposes a major threat on the modern cryptosystems based on modular arithmetic whereby, even the computationally hard problems which constitute for the strength of the modular arithmetic ciphers could be solved in deterministic time. This threat triggered post-quantum cryptography research in order to design and develop post-quantum algorithms that can withstand quantum computing attacks. This paper provides a review of the various post-quantum cryptography and, in specific, code-based cryptography research dimensions. The research directions that are yet to be explored in code-based cryptography research is another key contribution of this paper.

The “tubulin code” combines different α/β-tubulin isotypes with several post-translational modifications (PTMs) to generate microtubule diversity in cells. During cell division, specific microtubule populations in the mitotic spindle are differentially modified, but only recently has the functional significance of these modifications started to be elucidated. In particular, α-tubulin detyrosination of stable microtubules in the spindle was shown to guide chromosomes during congression to the metaphase plate and allow the discrimination of mitotic errors, whose correction is required to prevent chromosomal instability (CIN), a hallmark of human cancers. Although alterations in certain tubulin PTMs have been reported in human cancers, it remains unclear whether and how tubulin PTMs have any functional implications for cancer cell properties. Here we review the role of the tubulin code in chromosome segregation during mitosis, together with the emerging cancer tubulin code and discuss possible links, as well as the respective diagnostic, prognostic and therapeutic implications for human cancers.

Since January, the disease caused by the Sars Cov-2 virus has spread and following pandemic. In a few months, the virus is seriously affecting the health systems of the various countries of the world and placing people in difficult psychological conditions. Few scientific resources on mental health have been published but still no one has proposed an action plan to cope the future psychological problems.This manuscript provides a plan to easily frame the priorities of mental health area related to COVID-19 to be taken into consideration which should be disseminated and that should be known by all health professionals and also by the major administrators of public health.

During the last two years the COVID-19 pandemic has affected the world population in several ways. An important increase in mental health problems is a consequence of this pandemic that is ubiquitous worldwide. In this work we study the effect of the pandemic on the mental health of a population of teenagers and youth based on the analysis of natural language processing, machine learning algorithms and expert knowledge. The data analysed was obtained from a chat helpline called Safe time from theIt Get’s Better Foundation in Chile. The data consists of 10,986 conversations gathered from 2018 until 2020 between volunteers from the foundation and users of the platform. We compared the conversationsbefore and during the pandemic in terms of their thematic content. Our analysis found: a significantdecrease in self-image appreciation during the pandemic; a significant decrease in the quality of personalrelationships during the pandemic, and a significant increase of performance appreciation.

This work aims to investigate systematically the tecno-economic feasibility of ther-mos-catalytic cracking process for two solid waste materials, a lipid-base material (re-sidual fat/scum from retention box of the University Restaurant of UFPA) and a lig-nin-cellulosic material Açaí seed (Euterpe oleracea. Mart). The thermo-catalytic process-es were carried out in pilot scale (THERMTEK/LEQ/UFPA/IME/RJ), and their economic feasibility analyzed. The yields of biofuels produced by fractional distillation were al-so studied. The physicochemical characteristics of the raw materials, the organic liquid product (bio-oil) and the chemical composition of kerosene, light-diesel and heavy-diesel from the lipid-base material, as well as those of kerosene and light-diesel from the Açaí seed were also determined. The economic indicators for the evaluation of the most viable cracking (pyrolysis) and distillation process of bio-oils were: a) the sim-ple payback criterion, b) discounted payback, c) net present value (NPV), d) internal rate of return (IRR), and e) index of profitability (IP). The analysis of the indicators showed the economic viability of crude palm oil (Elaeis guineensis, Jacq) and unfeasibil-ity for the palm oil neutralization. The minimum fuel selling price (MFSP) obtained is this work for the biofuels was of 1.34 US$/L) and the breakeven point obtained was of 1.28 US$/L. The sensibility analysis demonstrated that the pyrolysis and distillation yields are the most important variables to affect the minimum fuel selling price (MFSP).

The influences of doped hole transporting layer (HTL) on the performances of perovskite solar cells were studied. The influences of electrostatic and surface roughness effect by solvent additive doping were compared. The electrostatic effect of solvent additive doping was compared by addition of 1,8-diiodooctane and 1,8-dichlorooctane to the HTL layer. The surface roughness effect of solvent additive doping was examined using atomic force microscopy. The perovskite crystallinity by doping HTL was studied by X-ray diffraction. The conductivity of HTL was measured using four probe methods and was higher for doped devices. Enhanced conductivity of PEDOT:PSS film was also proved using fluorescence emission quenching. Long-term stability of the device was enhanced by doping HTL. The device with solvent additive exhibited much enhanced stability, retained over 80% of the initial PCE for 400 hours, while PCE of the device without solvent additive decreased to less than 80% after 200 hours. A very weak power microwave treatment was used for post-annealing of the fully fabricated devices. Post-annealing enhanced the lifetime of the device. Comparing different size of cells, the device lifetime was decreased as the cell area increased and as the size of the cell increased, the extent of lifetime enhancement by doping was increased.

Stroke represents one of the most important causes of morbidity ( eighty million patients with disabling of ongoing effects of stroke at a given time, globally) and mortality (the second leading cause of death) worldwide. Innovative systems biology-based approach is likely to increase the understanding of the underpinning of acute stroke promise to enhance stroke prevention, acute treatment, and neurorehabilitation. Recent growing body of evidence with shared pathobiology with COVID-19 and the critically important role of inflammation in the context of stroke points to far-reaching consequences of acute stroke, just as in the case of COVID-19 ( post-acute event issues as well as long term issues ) . So far, stroke typically defined by late-appearing disease manifestation by the range of stroke subtypes as defined by the WHO or American Stroke Association. This definition neglects the underlying pathobiological mechanisms such as low-grade chronic inflammation and already compromised vascular system. Diseases such as stroke is hardly a simple result of a single problem, but rather a complex cascade of pathobiological processes and interactions in a complex biochemical environment. The evidence of changes in innate immunity and adaptive immunity during the index event of acute stroke and recovery over next 3-12 months can be easily elicited with simple bedside blood tests such as neutrophil-lymphocyte ratio (NLR) with well over 300 published papers including several systematic reviews and meta-analyses confirming this. Global standard operating procedures (SOP) of stroke care dictated by the national and international stroke guidelines at present. It is imperative to explore the evidence of systems biology approach in current stroke guidelines. This is likely to be a key turning point in managing stroke across the continuum (prevention, management of acute event and rehabilitation).

Gastrodia elata Bl., one of the well-known edible plants in China, belongs to the family of Orchidaceae. In recent years, the consumption rate of this plant as food supplements or health products is gradually rising in China. However, the traditional industrialization processing procedures (TP) for this plant was found to be too complicated and not friendly to the environment with time and energy consuming. Hence, we investigated whether the processing procedures could be simplified without affecting its overall chemical profile and anti-oxidation activity. In this experiment, a new processing procedure (NP) was designed for processing this plant, and the chemical compositions of the extract for Gastrodia elata produced by two different processing procedures were identified by UPLC-Q-TOF/MS. It was found that phenols and parisins were the most abundant chemical components for this extract. Among all the identified components, the contents of gastrodin and its citric acid derivatives were obviously higher than that of other components. Therefore, the contents of six gastrodin derivatives and ethanol soluble extract rate for Gastrodia elata produced by two different processing procedures, traditional and newly designed processing procedures, were further comparatively investigated for the validation of the applicability. The DPPH radical scavenging activities of the extract from Gastrodia elata produced by two different processing procedures were also investigated. The results demonstrated that the contents of gastrodin and p-hydroxybenzyl alcohol in all samples were slightly decreased in NP samples compared with that of TP samples (except batches of S3 and S6 samples), but no significant difference was observed between these two groups (p = 0.8273 for gastrodin and p= 0.2320 for p-hydroxybenzyl alcohol). Additionally, the total contents of parisins were also in a decreased trend in NP samples but with no significant difference when comparing with that in TP samples (p = 0.1721). The results for ethanol soluble extract rate also had the same situation (p = 0.1094). Taken together, it was suggested that the chemical profiles and contents of gastrodin derivatives and ethanol soluble extract rate for NP samples was not significant different to that of TP samples. The DPPH radical scavenging activity of the extract of Gastrodia elata produced with NP method was not significant different from that of TP samples. As a result, the industrial processing procedures for the preservation of this material plant could be simplified to a more time and energy saving procedure without changing the quality of the products, which is also consistent with the concept of green and sustainable industrial production.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a frequent, debilitating and still enigmatic disease. There is a broad overlap in the symptomatology of ME/CFS and the Post-COVID Syndrome (PCS). A fraction of the PCS patients develops the full clinical picture of ME/CFS. New observations in microvessels and blood from patients suffering from PCS have appeared and include microclots and malformed pathological blood cells. Capillary blood flow is impaired not only by pathological blood components but also by prothrombotic changes in the vascular wall, endothelial dysfunction, and expression of adhesion molecules in the capillaries. These disturbances can finally cause a low capillary flow and even capillary stasis. A low cardiac stroke volume due to hypovolemia and the inability of the capacitance vessels to adequately constrict to deliver the necessary cardiac preload generate an unfavorable low precapillary perfusion pressure. Furthermore, a predominance of vasoconstrictor over vasodilator influences exists, in which sympathetic hyperactivity and endothelial dysfunction play a strong role, causing constriction of resistance vessels and of precapillary sphincters which leads to a fall in capillary pressure behind the sphincters. The interaction of these two precapillary cardiovascular mechanisms causing a low capillary perfusion pressure is hemodynamically highly unfavorable in the presence of a primary capillary stasis already caused by the pathological blood components and their interaction with the capillary wall, to severely impair organ perfusion. The detrimental coincidence of the microcirculatory with the precapillary cardiovascular disturbances may constitute the key disturbance of the Post-COVID-19 syndrome and finally lead to ME/CFS in pre-disposed patients because the interaction causes a particular kind of perfusion disturbance - capillary ischemia-reperfusion - which has a high potential of causing mitochondrial dysfunction by inducing sodium- and calcium-overload in skeletal muscles. The latter in turns worsens the vascular situation by the generation of reactive oxygen species to close a vicious cycle from which the patient can hardly escape.

The quality of yarn is essential in the control of the fabrics processes. There is some commercial equipment that measures the quality of yarn based on sensors, of different types, used for collecting data about some yarn characteristic parameters. The existing equipment, for the above-mentioned purpose, is characterized by its high size and cost, and for allowing the analysis of only few yarn quality parameters. Thus, this paper presents the development and results obtained with the design of a mechatronic prototype integrating a computer vision system that allows, among other parameters, the analysis and classification, in real time, of the hairs of the yarn using artificial intelligence techniques. The system also determines other characteristics inherent to the yarn quality analysis, such as: linear mass, diameter, volume, twist orientation, twist step, average mass deviation, coefficient of variation, hairiness coefficient, average hairiness deviation and standard hairiness deviation, as well as performs spectral analysis. A comparison of the obtained results with the designed system and a commercial equipment was performed validating the undertaken methodology.

In celIs, microtubule location, length, and dynamics are regulated by a host of microtubule-associated proteins and enzymes that read where to bind and act based on the microtubule “tubulin code,” which is predominantly encoded in the tubulin carboxy-terminal tail (CTT). Katanin is a highly conserved AAA ATPase enzyme that binds to the tubulin CTTs to remove dimers and sever microtubules. We have previously demonstrated that short CTT peptides are able to inhibit katanin severing. Here, we examine effects of CTT sequences on this inhibition activity. Specifically, we examine CTT sequences found in nature, alpha1A (TUBA1A), detyrosinated alpha1A, Δ2 alpha1A, beta5 (TUBB/TUBB5), beta2a (TUBB2A), beta3 (TUBB3), and beta4b (TUBB4b). We find that these natural CTTs have distinct abilities to inhibit, most noticeably beta3 cannot inhibit katanin. Two non-native CTT tail constructs are also unable to inhibit – despite having 94% sequence identity with alpha1 or beta5 sequences. Surprisingly, we demonstrate that poly-E and poly-D peptides are capable of inhibiting katanin significantly. An analysis of the hydrophobicity of the CTT constructs indicates that more hydrophobic polypeptides are less inhibitory than more polar polypeptides. These experiments not only demonstrate inhibition, but also likely interaction and targeting of katanin to these various CTTs when they are part of a polymerized microtubule filament.

The COVID-19 pandemic has invited us to envision education anew, embracing a new era of instructional practices and learning environments. This study explores language teachers' and students' experiences in the post-pandemic landscape, providing valuable insights into the challenges, adaptations, and opportunities. Grounded in socio-cultural perspectives and educational theories, such as Vygotsky's socio-cultural theory and Deci and Ryan's self-determination theory, the study employs Interpretative Phenomenological Analysis (IPA) to comprehensively analyze secondary data sources. Findings reveal the significant role of social interaction and cultural context in language learning, amid reduced face-to-face contact and increased reliance on technology. The study uncovers the intricate interplay between individual, interpersonal, and environmental factors, shedding light on educators' and learners' challenges. Autonomy, competence, and relatedness emerge as crucial for intrinsic motivation and optimal learning outcomes. While acknowledging limitations related to secondary data sources, the study offers a comprehensive overview of the literature, informing language education practices in the post-pandemic era. It contributes to understanding the complex dynamics and interactions within language education and calls for future research using primary data collection methods to explore experiences and perspectives further. By understanding post-pandemic experiences, this study aims to shape education policies, practices, and pedagogical approaches, supporting effective language learning and the well-being of educators and learners in this transformed educational context.

In the era of rapid digitalisation, the widespread integration of digital technology into various aspects of daily life has sparked significant interest in understanding its impact on cognitive mental processes. While the emerging data suggests that its influence may be positive or negative, the depth of evidence regarding neurobiological mechanisms remains limited. This review aims to synthesise previously published studies and explore the implications of digital technology components, specifically those resembling mobile technology, internet search engines, and conversational artificial intelligence models, on cognitive functions embedded in the brain’s ability to reorganise itself, known as neuroplasticity. Despite acknowledged limitations, through an exhaustive approach, this paper intends to offer a dynamic perspective on the effects of digital media on the human brain, before the onset of addiction.

Kinetic modeling is an essential tool in systems biology research, enabling the quantitative analysis of biological systems and predicting their behavior. However, the development of kinetic models is a complex and time-consuming process. In this article, we propose a novel approach called KinModGPT, which generates kinetic models directly from natural language text. KinModGPT employs GPT-3 as a natural language interpreter and Tellurium as an SBML generator. We demonstrate the effectiveness of KinModGPT in creating SBML models from complex natural language descriptions of biochemical reactions. KinModGPT successfully generates valid SBML models from a range of natural language model descriptions of metabolic pathways, protein-protein interaction networks, and heat shock response. This article demonstrates the potential of KinModGPT in kinetic modeling automation.

Waste from fish cutting (heads, swim bladders, fins, skin, bones) is a high-value technological raw material for obtaining substances and products based on them with a wide range of properties. The possibility of using waste from cutting fish of the Gadidae family: the Alaska pollock (Gadus chalcogrammus) and the Pacific cod (Gadus macrocephalus), processed in the coastal zone, is scientifically substantiated. In this work, a technology has been developed for processing accumulated waste from fish cutting in order to obtain fish gelatin, which is characterized by high protein content (more than 80.0%) and a full set of essential and nonessential amino acids. We studied the quality of fish gelatin obtained from wastes from cutting the fish of the Gadidae family. The possibility of using fish gelatin as a component of fish products is shown; the dose of its introduction into the fish products is substantiated. The data obtained made it possible to recommend the use of fish processing waste products as a gelling component and a source of amino acids in multicomponent food systems.

In this study, the effect of laser processing (LP) on the thermoelectric (TE) properties of bismuth antimony telluride (BST) alloy pellets is investigated. The stoichiometric alloy used is Bi0.4Sb1.6Te3.0, which was prepared by mechanical alloying techniques (MA) and hot pressed (HP) into pellets under optimal pressing and temperature conditions. An innovative approach to HP was also developed to enhance the yield of BST samples per HP process while maintaining the TE profiles of the samples within a variance of less than 1%. The LP process was executed by employing a metal 3D printer, with a thorough analysis of beam parameters. Notably, the laser power (P) was held constant at 25 W, along with a fixed hatching distance (HD) of 50 μm, while the parameter adjusted was the laser's scanning speed (SS), which spanned from [600 - 1800] mm/s in increments of 200 mm/s. Examination via scanning electron microscopy (SEM) revealed two material states: melted and sintered. The melted state formed the surface crust of "canyon" patterns that were held on the surface of the samples, and which both size and quantity were altered with changes in SS. For thermoelectric characterization, two distinct shapes of LP samples were produced: rectangular (for electrical properties) and disk (for thermal properties). The results demonstrated that the electrical properties are profoundly influenced by SS, with the electrical conductivity peak at SS value of 1600 mm/s, while the Seebeck coefficient reaches a minimum at the same value. Consequently, within the temperature range of interest, a notable 12% increase in electrical conductivity and a 9% decrease in the Seebeck coefficient were observed compared to the non-LP material state. Overall, the ZT value experience an 6.9% decline within the same temperature interval.

Canola is the major oilseed crop of Canada. The de-oiled material is an important by-product due to its rich phenolic profile and high protein content. This co-processing stream from canola is primarily utilized as animal feed but represents an invaluable source of nutraceuticals. Microwave-assisted solvent extraction (MAE), as a green extraction method, has received considerable attention in recent times. The ease of use and application of many solvents at the same time makes the MAE one of the best methods for studying multiple solvents at the same time. The formation of canolol, from sinapine and sinapic acid, is primarily dependant on temperature which favors the decarboxylation reaction. Hence, MAE using green extractants can be used to enhance the yield of canolol. This study examined the effects of different pre-treatment temperature-time combinations of 140, 150, 160, and 170℃ for 5, 10, 15, 20 and 30 minutes on the extraction of canolol and other canola endogenous phenolic compounds. Three antioxidant assays assessed the antioxidant activity of the different extracts obtained by MAE confirming the microwave as a novel and versatile instrument for enhancing the yield of canolol. Improvements in the antioxidant activity of the different extracts further established the efficacy of the current method for isolating important natural phenolic derivatives for utilization by the nutraceutical industry.

Long Covid is a heterogeneous clinical condition in which Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and brain fog stand out among the different clinical symptoms and syndromes. The cerebral metabolic alterations and neuroendocrine disorders seem to constitute an important part of Long Covid. Given the substantial lack of drugs and effective therapeutic strategies, hypothalamic phospholipid liposomes which have been on the market for several years as adjuvant therapy of cerebral metabolic alterations resulting from neuroendocrine disorders, can be taken into consideration in an overall therapeutic strategy that aims to control the Long Covid associated symptoms and syndromes. Their pharmacological mechanisms and clinical effects strongly support their usefulness in Long Covid. Our initial clinical experience corroborates this rationale. Further research is imperative in order to obtain robust clinical evidence.

Weeds are unwanted and invasive plants that proliferate and compete for resources such as space, water, nutrients, and sunlight, affecting the quality and productivity of the desired crops. Weed detection is crucial for the application of precision agriculture methods and for this purpose machine learning techniques can be used, specifically convolutional neural networks (CNN). This study focuses on the search for CNN architectures and technology used to detect and identify weeds in different crops; 61 articles applying CNN architectures and technology were analyzed in the last five years (2019-2023). The results show the used of different devices to acquire the image for training, such as digital cameras, smartphones, and drone cameras. Additionally, the YOLO family and algorithms are the most widely adopted architectures, followed by VGG, ResNet, Faster R-CNN, AlexNet, and MobileNet, respectively. This study provides an update on CNNs that will serve as a starting point for researchers wishing to implement these weed detection and identification techniques.

In the last decade and a half, the world has experienced the outbreak of a range of viruses such as COVID-19, H1N1, flu, Ebola, Zika Virus, Middle East Respiratory Syndrome (MERS), Measles, and West Nile Virus, just to name a few. During these virus outbreaks, the usage and effectiveness of social media platforms increased significantly as such platforms served as virtual communities, enabling their users to share and exchange information, news, perspectives, opinions, ideas, and comments related to the outbreaks. Analysis of this Big Data of conversations related to virus outbreaks using concepts of Natural Language Processing such as Topic Modeling has attracted the attention of researchers from different disciplines such as Healthcare, Epidemiology, Data Science, Medicine, and Computer Science. The recent outbreak of the MPox virus has resulted in a tremendous increase in the usage of Twitter. Prior works in this field have primarily focused on the sentiment analysis and content analysis of these Tweets, and the few works that have focused on topic modeling have multiple limitations. This paper aims to address this research gap and makes two scientific contributions to this field. First, it presents the results of performing Topic Modeling on 601,432 Tweets about the 2022 Mpox outbreak, which were posted on Twitter between May 7, 2022, and March 3, 2023. The results indicate that the conversations on Twitter related to Mpox during this time range may be broadly categorized into four distinct themes - Views and Perspectives about MPox, Updates on Cases and Investigations about Mpox, MPox and the LGBTQIA+ Community, and MPox and COVID-19. Second, the paper presents the findings from the analysis of these Tweets. The results show that the theme that was most popular on Twitter (in terms of the number of Tweets posted) during this time range was - Views and Perspectives about MPox. It is followed by the theme of MPox and the LGBTQIA+ Community, which is followed by the themes of MPox and COVID-19 and Updates on Cases and Investigations about Mpox, respectively. Finally, a comparison with prior works in this field is also presented to highlight the novelty and significance of this research work.

In the age of social media, images come from unreliable sources that may change their content to fit a narrative. Automated detection of forged images is a complex task, especially considering recent technological advances in image manipulation software. However, in the existing literature, it is possible to identify two general approaches adopted by detection methods, active and passive. Active techniques preemptively act on an image by inserting structures before any manipulation is made that can verify its authenticity. In contrast, passive methods analyze the content of an image in search of traces of manipulation. In this way, this research proposes a novel approach to image manipulation detection, combining two passive methods through neural networks, creating a generalist approach capable of detecting with greater accuracy than the methods that compose it. Furthermore, this work used a combination of four datasets available in the literature for training and evaluation. After training, the merged approach obtained an accuracy of 89.59% in the set of validation images, significantly higher than the model trained with only unaltered images, which obtained 78.64%, and the two other models trained using images with a feature selection method applied to enhance inconsistencies that obtained 68.02% for Error-Level Analysis images and 50.70% for the method using Discrete Wavelet Transform. In addition to the performance improvement, the proposed approach’s accuracy variation was lower than the other models.

Face routing has been adopted in wireless sensor networks (WSNs) where topological changes occur frequently or maintaining full network information is difficult. For message forwarding in networks, a planar graph is used to prevent looping, and because long edges are removed by planarization and the resulting planar graph is composed of short edges, messages are forwarded along multiple nodes connected by them even though they can be forwarded directly. To solve this, face routing using information on all nodes within 2-hop range was adopted to forward messages directly to the farthest node within radio range. However, as the density of the nodes increases, network performance plunges because message transfer nodes receive and process increased node information. To deal with this problem, we propose a new face routing using the planar graphs of neighboring nodes to improve transfer efficiency. It forwards a message directly to the farthest neighbor and reduces loads and processing time by distributing network graph construction and planarization to the neighbors. It also decreases the amount of location information to be transmitted by sending information on the planar graph nodes rather than on all neighboring nodes. Simulation results show that it significantly improves transfer efficiency.

Smart living, a concept that has gained increasing attention in recent years, revolves around integrating advanced technologies in homes and cities to enhance the quality of life for citizens. Sensing and human action recognition are crucial aspects of this concept. Smart living applications span various domains, such as energy consumption, healthcare, transportation, and education, which greatly benefit from effective human action recognition. This field, originating from computer vision, seeks to recognize human actions and activities using not only visual data but also many other sensor modalities. This paper comprehensively reviews the literature on human action recognition in smart living environments, synthesizing the main contributions, challenges, and future research directions. This review selects five key domains: Sensing Technology, Multimodality, Real-time Processing, Interoperability, and Resource-Constrained Processing, as they encompass the critical aspects required for successfully deploying human action recognition in smart living. These domains highlight the essential role that sensing and human action recognition play in successfully developing and implementing smart living solutions. This paper serves as a valuable resource for researchers and practitioners seeking to explore further and advance the field of human action recognition in smart living.

The automatic classification of bird sounds is an ongoing research topic and several results have been reported for the classification of selected bird species. In this contribution we use an artificial neural network fed with pre-computed sound features to study the robustness of bird sound classification. We investigate in detail if and how classification results are dependent on the number of species and the selection of species in the subsets presented to the classifier. In more detail, a bag-of-birds approach is employed to randomly create balanced subsets of sounds from different species for repeated classification runs. The number of species present in each subset is varied between 10 and 300, randomly drawing sounds of species from a dataset of 659 bird species taken from Xeno-Canto database. We observe that the shallow artificial neural network trained on pre-computed sound features is able to classify the bird sounds relatively well. The classification performance is evaluated using several common measures such as precision, recall, accuracy, mean average precision and area under the receiver operator characteristics curve. All of these measures indicate a decrease in classification success as the number of species present in the subsets is increased. We analyze this dependence in detail and compare the computed results to an analytic explanation assuming dependencies for an idealized perfect classifier. Moreover, we observe that the classification performance depends on the individual composition of the subset and varies across 20 randomly drawn subsets.

The purpose of the present study was to investigate the effect of post-activation performance enhancement (PAPE) induced by eccentric half-squat exercise on vertical jump performance in male jumpers. The jumping height, peak power, and work were measured and evaluated in twenty male jumpers. Participants performed five eccentric half-squats at 85% of their one-repetition maximum (1 RM), with a knee angle below 90°, followed by immediate and 2min delayed jump assessments using Squat Jump (SJ) and Countermovement Jump (CMJ) tests. Results showed that this specific PAPE protocol did not significantly improve jump performance for both SJ and CMJ tests. Factors such as stimulus specificity, rest intervals, muscle length, and the balance between potentiation and fatigue may explain these results. The study highlights the complexity of PAPE responses and suggests that a single set of eccentric squats with a short rest may not improve jump performance in male jumpers. Further research is required to optimize the interplay between conditioning stimulus and rest periods to maximize PAPE effects in athletic performance enhancement.