Objective Approach Research Articles

Critical analysis of the emerging flood disaster resilience assessment indicators

Purpose The world is reeling from the effects of climate change with increased extreme precipitation. Flooding is amongst the most recurring and devastating natural hazards, impacting human lives and causing severe economic damage. This paper aims to conduct a systematic review to critically analyse the most reported and emerging flood disaster resilience indicators. Design/methodology/approach A total of 35 papers were selected through a systematic process using both Web of Science and Scopus databases. The selected literature was subjected to a thorough thematic content analysis. Findings From the review, 77 emerging flood disaster resilience assessment indicators were identified. Furthermore, based on the individual meanings and relationships of the derived indicators, they were further categorized into six groups, namely, physical, institutional, social, psychological, ecology and economic. More also, it was identified that most of the selected publications have used objective resilience measurement approaches as opposed to subjective resilience measurement approaches. Originality/value The generated list of flood disaster resilience indicators will provide insights into the capacities which can be improved to enhance the overall resilience to flood disasters in communities.

Development and Validation of a Driving Simulator for Comfort Assessment

AbstractThe passenger’s comfort is of vital importance in today’s car, even more so in the future autonomous car. Its thorough analysis requires the use of objective measures, but the subjectivity of the passenger must also be considered. The relationship between both aspects is an uncommon research avenue, and could greatly benefit from the use of a vehicle simulator. This study use a simulator to replicate a journey with diverse characteristics that numerous passengers may experience (including engaging in different activities). The appropriate variables for a proper comfort analysis are determined in this study, resulting in a comprehensive database for the same purpose. Additionally, a passenger comfort based questionnaire is developed and applied to obtain the subjective assessment of different passengers. The results of these questionnaires help not only in the identification and study of the self perception of comfort by the passengers but also in the use of the simulator for comfort experiments. The detailed comparison between both the objective approach of signal analysis and the subjective approach of questionnaires that is performed thanks to the simulator creates a foundation for future research and narrows the existing research gap.

Acute cholecystitis management at a tertiary care center: are we following current guidelines?

After the Tokyo 2018 guidelines (TG2018) were published, evidence from the 2018 CHOCOLATE RCT supported early cholecystectomy for acute cholecystitis (AC), even in high-risk patients. This study aims to investigate AC management at our tertiary care center in the years following these publications. A retrospective cohort study was performed on patients admitted from 2018 to 2023. AC severity was graded using TG2018 definitions. Comorbidities were summarized using Charlson Comorbidity Index (CCI) and frailty using the 5-item modified Frailty Index (5mFI). Compliance with TG2018 recommendations for management strategy was investigated. Outcomes were compared between patients who underwent surgery versus non-operative management (NOM). Subset analysis based on patients' age, frailty, and comorbidities was performed. Among 642 AC patients, 57% underwent cholecystectomy and 43% NOM (22% percutaneous cholecystostomy, 21% antibiotics only). NOM patients had greater length of stay (LOS), complications, deaths, readmissions, and discharge to nursing/rehab versus surgery patients. In 70% of patients managed non-operatively, TG2018 were not followed. Patients managed non-operatively despite TG2018 were more likely to undergo delayed cholecystectomy compared to those in whom guidelines were followed (17% vs. 4%). In subset analysis, healthy octogenarians were significantly less likely to be managed according to TG2018 (9.4%); patients undergoing surgery had a trend towards shorter LOS (3.1 vs. 4.8 days) than those managed non-operatively but no difference in other outcomes. Most patients undergoing NOM could potentially undergo cholecystectomy if guidelines are considered. A more objective approach to risk assessment may optimize patient selection and outcomes.

Application of intuitionistic fuzzy set in social life cycle impact assessment

Abstract Purpose Intuitionistic fuzzy set (IFS) has been found promising for handling ambiguity forms of uncertainty in social data. This paper undertakes an application of an intuitionistic fuzzy set in the social life cycle impact assessment (S-LCIA) of a public building project. This work proposes to combine an intuitionistic fuzzy set (IFS) with multi-attribute decision-making in converting qualitative data into quantitative social impacts of the building project. This research utilises IFS to accommodate and handle experts’ imprecise cognitions in S-LCIA to facilitate the identification and prioritisation of the most pressing indicators accompanying social impacts in a project. Methods Data were collected using questionnaire(s), structured interviews, and through targeted focus group sessions. Intuitionistic fuzzy set (IFS) approach was used to model the obtained data from structured interviews. Using IFS, this research also accounts for missing or ambiguous data that emerged during the data elicitation process. In combining IFS with multi-attribute decision-making techniques, the social impacts of selected stakeholders were evaluated using fuzzy set and IFS approaches. Sensitivity analysis was then used to test the robustness of the results and ranking was conducted based on each social stakeholder subcategory. Results and discussion Results revealed that approximately 23% of missing datapoints in the public case study building were incorporated in the S-LCIA using IFS. The issues of highest priority in each of the considered subcategory in the public case study building project according to IFS are (i) consumer privacy by adopting more functional planning (0.27), (ii) public commitment to sustainability (0.33), and (iii) education provided in the local community by having an indigenous botanical element for children (0.39), respectively. Conversely, the FS technique inferred that the issue of highest priority in the consumer stakeholder is the feedback mechanism (0.26). The overall degree of correlation between the IFS and FS is only 0.234, revealing that IFS provides a different perspective from conventional FS when used in modelling social data. Conclusion The IFS method provides an objective and systematic approach for dealing with a heterogeneous scope of imprecise and inexact social data in achieving holistic social life cycle assessment results. Furthermore, expansive stakeholder involvement would rely on a robust approach for improving social well-being in public buildings, thereby leaving no one behind in accomplishing a sustainable world.

Mapping landforms of a hilly landscape using machine learning and high-resolution LiDAR topographic data

Landform maps are important tools in assessment of soil- and eco-hydrogeomorphic processes and hazards, hydrological modeling, and natural resources and land management. Traditional techniques of mapping landforms based on field surveys or from aerial photographs can be time and labor intensive, highlighting the importance of remote sensing products based automatic or semi-automatic approaches. In addition, the time-intensive manual labeling can also be subjective rather than an objective identification of the landform. Here we implemented such an objective approach applying a random forest machine learning algorithm to a set of observed landform data and 1m horizontal resolution bare-earth digital elevation model (DEM) developed from airborne light detection and ranging (LiDAR) data to rapidly map various landforms of a hilly landscape. The landform classification includes upland plateaus, ridges, convex slopes, planar slopes, concave slopes, stream channels, and valley bottoms, across a 400 km2 hilly landscape of the Ozark Mountains in northeastern Oklahoma. We used 4200 landform observations (600 per landform) and eight topographic indices derived from 2 m, 5 m and 10 m resolution LiDAR DEM in random forest algorithm to develop 2 m, 5 m and 10 m resolution landform models. We test the effectiveness of DEM resolution in mapping landforms via comparison of observed landforms with modeled landforms. Results showed that the approach mapped ∼84% of observed landforms when covariates were at 2 m resolution to ∼89% when they were at 10 m resolution. However, predicted maps showed that the 2 m resolution covariates performed better at capturing accurate landform boundaries and details of small-sized landforms such as stream channels and ridges. The approach presented here significantly reduces the time required for mapping landforms compared to traditional techniques using aerial imagery and field observations and allows incorporation of a wide variety of covariates. The landform map developed using this approach has several potential applications. It could be utilized in hydrological modeling, natural resource management, and characterizing soil-geomorphic processes and hazards in a hilly landscape.

Functional excitation-inhibition ratio for social anxiety analysis and severity assessment

IntroductionSocial anxiety disorder (SAD) is a prevalent psychiatric condition characterized by an intense fear of and avoidance of social situations. Traditional assessment methods for SAD primarily rely on subjective self-report questionnaires and clinical interviews, which can be prone to biases and inaccuracies. This study aims to explore the functional excitation-inhibition (fEI) ratio derived from EEG data as a potential objective biomarker for assessing SAD severity. MethodsResting-state EEG data were collected from 20 control subjects and 60 individuals with varying degrees of SAD severity (mild, moderate, and severe). The fEI ratio was estimated across different EEG bands and analyzed, focusing on differences between control subjects and SAD groups. ResultsSignificantly higher fEI ratios were observed in the alpha and low beta EEG bands in individuals with SAD compared to controls, especially within the prefrontal cortex. Additionally, a positive correlation was found between the fEI ratio and the severity of social anxiety symptoms across SAD severity levels. DiscussionThe findings indicate that the fEI ratio in the alpha and low beta bands may serve as a promising biomarker for assessing SAD severity. These results contribute to a deeper understanding of the neural mechanisms underlying social anxiety, offering a potentially more objective approach to SAD assessment compared to traditional methods.

Comprehensive atlas of mitochondrial distribution and dynamics during oocyte maturation in mouse models

BackgroundOocytes, the largest cells in mammals, harbor numerous mitochondria within their cytoplasm. These highly dynamic organelles are crucial for providing energy resources and serving as central regulators during oogenesis. Mitochondrial dynamics ensure proper energy distribution for various cellular processes involved in oocyte maturation. Previous studies have used alterations in mitochondrial distribution as a biomarker to assess the oocyte health. However, there are discrepancies between studies regarding mitochondrial distribution profiles in healthy oocytes. Consequently, a comprehensive mitochondrial distribution profile in oocytes during maturation has not been fully characterized. Additionally, there is a lack of objective, quantitative methods to evaluate alterations in mitochondrial distribution profiles in oocytes.MethodsThis study aims to provide an in-depth overview of mitochondrial distribution profiles in mouse oocytes at different maturation stages: germinal vesicle (GV) stage, metaphase I (MI), and mature metaphase II (MII). Freshly collected mouse GV, MI and MII oocytes were stained with MitoTracker Red. Confocal microscopy was used to obtain images of mitochondrial distribution profiles in these oocytes. Using the Imaris software, we reconstructed three-dimensional (3D) surface renderings of each oocyte and quantitatively illustrated the mitochondrial distribution profiles.ResultsAt the GV stage, mitochondria in oocytes were evenly distributed throughout the ooplasm. As oocytes progressed to MI and MII stages, mitochondria aggregated and formed clusters, the mean size of mitochondrial clusters and the proportions of clustered mitochondria increased along with the maturation of oocytes.ConclusionsOur findings reveal that mitochondria in mouse oocytes are highly dynamic, undergoing significant reorganizations during oocyte maturation. We for the first time provided comprehensive mitochondrial distribution profiles in mouse oocytes at the GV, MI and MII stages. These mitochondrial distribution profiles were further quantitatively evaluated. Our methods provide an objective and standardized approach for evaluating alterations in mitochondrial dynamics, which can be used as biomarkers to monitor oocyte conditions during maturation.

X-Tracker: Automated Analysis of Xenopus Tadpole Visual Avoidance Behavior.

Xenopus laevis tadpoles exhibit an avoidance behavior when they encounter a moving visual stimulus. A visual avoidance event occurs when a moving object approaches the eye of a free-swimming animal at an approximately 90-degree angle and the animal turns in response to the encounter. Analysis of this behavior requires tracking both the free-swimming animal and the moving visual stimulus both prior to and after the encounter. Previous automated tracking software does not discriminate the moving animal from the moving stimulus, requiring time-consuming manual analysis. Here we present X-Tracker, an automated behavior tracking code that can detect and discriminate moving visual stimuli and free-swimming animals and score encounters and avoidance events. X-Tracker is as accurate as human analysis without the human time commitment. We also present software improvements to our previous visual stimulus presentation and image capture that optimize videos for automated analysis, and hardware improvements that increase the number of animal-stimulus encounters. X-Tracker is a high throughput, unbiased, and significant time-saving analysis system that will greatly facilitate visual avoidance behavior analysis of Xenopus laevis tadpoles, and potentially other free-swimming organisms. The tool is available at https://github.com/ClineLab/Tadpole-Behavior-Automation.

Association between empty nose syndrome and laryngopharyngeal reflux disease: a preliminary cohort study.

To investigate the association between laryngopharyngeal reflux disease (LPRD) and Empty Nose Syndrome (ENS). Nasal and laryngopharyngeal reflux symptoms were investigated in patients with ENS. Symptoms were evaluated with reflux symptom score-12 (RSS-12), nasal obstruction symptom evaluation (NOSE), empty nose syndrome 6-item questionnaire (ENS6Q), empty nose syndrome index (ENSI), and sinonasal outcome tool-22 (SNOT-22). The anxiety and depression were assessed with the general anxiety disorder-7 (GAD-7), and patient health questionnaire-9 (PHQ-9). A study of association was conducted between demographics and patient-reported outcome questionnaires. Forty-one ENS patients were included (20 females (48.8%)). The control groups included 27 patients with rhinitis/rhinosinusitis and 36 asymptomatic individuals. The ENSI and ENS6Q detected ENS in 97.6% and 90.2% of cases, respectively. The mean scores of ENSI, ENS6Q, RSS-12, NOSE, and SNOT-22 were significantly higher in the ENS group compared to controls. The prevalence of suspected LPRD was 90.2% in the ENS group, which was significantly higher compared to controls. The prevalence of mild, moderate, moderately severe, and severe depression in ENS patients was 7.3% (n = 3), 4.9% (n = 2), 39.0% (n = 16), and 46.3% (n = 19), respectively. RSS-12 reported significant and high associations with the ENS6Q (rs=0.939; p = .001) and ENSI (rs=0.699; p = .001). LPRD symptoms and prevalence were significantly higher in ENS patients compared to controls. Future controlled studies are needed to investigate the prevalence of LPRD in ENS patients through objective approaches (impedance-pH monitoring, nasal digestive enzyme measurements).

Understanding COVID-19 Media Framing: Comparative Insights from Germany, the US, and the UK During Omicron

ABSTRACT This study examines the media framing of COVID-19 reporting in Germany, the United States, and the United Kingdom during the period of the highly contagious Omicron variant from December 2021 to March 2022. By identifying issue-specific media frames using hierarchical cluster analysis, the study gains insights into the distinctive characteristics of journalistic roles within each country and sheds light on the similarities and differences in media framing. Findings reveal that the German Süddeutsche Zeitung prioritizes the economic and Omicron impact on society and reports rather emotionally through evaluations and blame on social issues but frames otherwise quite neutral, reflecting its role in opinion formation and information dissemination. Conversely, the American New York Times takes an objective and informative approach, except similar to the Süddeutsche Zeitung when dealing with social impacts of the pandemic, addressing Omicron, economic impacts, and vaccination challenges while emphasizing shared burdens of the pandemic. The British Guardian gives equal weight to economic and social consequences and the Omicron impact and also raises the issues concerning the healthcare sector. Blame and negative evaluations cross all frames and newspapers, although (emotionally charged) evaluations and accusations are most prevalent in the Guardian, often directed at unvaccinated people and politics.

A Composite Approach for Evaluating Operational Cloud Seeding Effect in Stratus Clouds

Robust water management is in intense demand in many water scarcity areas, such as arid and semi-arid regions in the world. As part of the regional water management strategy, rain enhancement is vital to replenish groundwater reservoirs, and the key challenge is how to assess its effectiveness. Some recent weather modification experiments attained cloud seeding effect through advanced in situ measurement coupled with accurate numerical simulation. However, there is still a lack of an objective and scientific approach to quantitatively evaluate the rain enhancement effect, especially for many non-randomized operational cloud seeding activities in China. In this study, we proposed a composite evaluation approach by analyzing two operational aircraft cloud seeding cases in stratus clouds in Shaanxi, China. By calculating the aircraft cloud seeding agent plumes, the target areas (as well as the control areas) of cloud seeding were dynamically and roughly determined. Physical properties, such as radar reflectivity and precipitation, were individually quantified in these areas. The cloud seeding effect was then evaluated by calculating the difference in parameter variation between target and control areas. This approach can be applied to qualitative analysis in a single aircraft cloud seeding operation and can also provide quantitative statistical results from multiple cloud seeding cases. We found that the average precipitation enhancement percentage of 18 operational aircraft cloud seeding cases is ~4.84%. Note that the homogeneity hypothesis of the seeding cloud, the error in the calculation of the target area, and the selection of control areas are the major uncertainties likely in the evaluation of the cloud seeding effect by this approach.

Effectiveness of newborn infant parasympathetic evaluation (NIPE) index for guiding intraoperative fentanyl administration in children under 2 years: a randomized controlled trial.

Assessing pain in infants and neonates is challenging due to their inability to communicate verbally. While validated subjective tools exist, they rely on interpreting the child's behavior, leading to potential inconsistencies and underestimation of pain. Based on heart rate variability, the newborn infant parasympathetic evaluation (NIPE) index offers a more objective approach to pain assessment in children under 2 years. Although promising, research on its effectiveness during surgery under general anesthesia remains limited and inconclusive. This study compared the effectiveness of NIPE-guided fentanyl administration to traditional vital signs (heart rate and mean arterial pressure) in managing pain during surgery in children under 2 years. Seventy children undergoing head, neck, or upper extremity surgery were randomized into group N (NIPE) or group C (Control) with 35 participants in each group. Both groups received standardized anesthesia and monitoring, including NIPE. Group N received fentanyl when NIPE scores fell below 50, while group C received fentanyl upon a 20% increase in heart rate or mean arterial pressure (MAP). Postoperative pain was assessed using the Face, Legs, Activity, Cry, and Consolability (FLACC) scores, alongside fentanyl consumption, sedation levels, and potential side effects. Both groups exhibited similar outcomes in terms of postoperative pain scores, fentanyl consumption, sedation levels, and absence of side effects (postoperative respiratory depression or nausea and vomiting). Additionally, intraoperative NIPE scores, heart rate, and MAP were comparable between the groups. There are strong correlations between both groups in terms of NIPE scores (r = 0.735, p < 0.0001), heart rate (r = 0.630, p < 0.0001), and MAP (r = 0.846, p < 0.0001). In both group C and group N, the NIPE scores reveal strong negative correlations with heart rate (r = -0.610, p < 0.0001, and r = -0.674, p < 0.0001) and MAP (r = -0.860, p < 0.0001, and r = -0.756, p < 0.0001). NIPE-guided intraoperative fentanyl administration was not superior to heart rate/MAP-guided administration, as both achieved similar pain management outcomes in this study. However, NIPE may offer a more practical and precise approach, as it is an objective tool with a defined threshold. These findings suggest NIPE's promise as a valuable tool for managing pain in children under 2 years undergoing surgery. However, confirmation of its widespread effectiveness requires further research with larger, multicenter studies encompassing procedures with a broader spectrum of pain severity.

AI-Enhanced GraphoText Analysis for Tracking Counselling Therapy Progress: Integrating Multimodal Graphology and Machine Learning

Despite the robustness of existing instruments, routine manual observation in psychotherapy faces significant challenges, including lack of time, labour, high implementation costs, and potential biases such as response bias and social desirability. This study proposes a dynamic assessment approach using machine learning and multimodal features extracted from handwriting, incorporating graphology-based and content-based features. The integration of content-based and graphology-based features involves combining text and handwriting features with the Support Vector Machine (SVM) using the Radial Basis Function Kernel (RBF). The results show that this approach achieves an impressive accuracy rate of 86.25%. The proposed framework not only improves psychotherapeutic practise, but also offers new insights into human cognition and emotional dynamics by revealing intricate patterns in handwriting. This advance facilitates data-driven decision-making, improves the quality of patient care, and overcomes challenges associated with manual monitoring, including social desirability bias and response set bias. This research paves the way for innovative methods at the interface of mental health and technology, and promises a more objective and efficient approach to monitoring the progress of psychotherapy.

A STUDY OF THE STANDARDIZED PRECIPITATION INDEX (SPI) AND MACHINE LEARNING TECHNIQUES FOR DROUGHT PREDICTION IN THE STATE OF PARAÍBA, BRAZIL

This study aimed to identify and analyze droughts in Paraíba, using the Standardized Precipitation Index (SPI) and machine learning algorithms for predicting SPI for the subsequent years (2020-2021) at six rainfall stations distributed across the mesoregions of Paraíba. The Precipitation data were downloaded from the Global Precipitation Climatology Centre (GPCC) and the National Oceanic and Atmospheric Administration (NOAA) database, covering the period from 1991 to 2019. Three machine learning algorithms were selected based on their ability to fit historical SPI data: Extra Trees Regressor, Gradient Boosting Regressor, and Random Forest Regressor. The applied machine learning models yielded satisfactory results, with the Extra Trees Regressor consistently producing the highest R² value across all stations, indicating high data explainability. The predictions were analyzed to determine their accuracy and reliability, providing valuable insights into precipitation variability and drought occurrence in different mesoregions of Paraíba. In conclusion, this study contributed to understanding climate variability and its implications in Paraíba, offering valuable insights into drought occurrence and the importance of adaptive approaches to mitigate adverse impacts. The application of SPI and machine learning techniques proved effective in analyzing and predicting precipitation, providing an objective approach to characterizing drought and rainfall intensity in specific regions.

High‐Latitude Eddy Covariance Temporal Network Design and Optimization

AbstractEcosystems at high latitudes are changing rapidly in response to climate change. To understand changes in carbon fluxes across seasonal to multi‐decadal timescales, long‐term in situ measurements from eddy covariance networks are needed. However, there are large spatiotemporal gaps in the high‐latitude eddy covariance network. Here we used the relative extrapolation error index in machine learning‐based upscaled gross primary production as a measure of network representativeness and as the basis for a network optimization. We show that the relative extrapolation error index has steadily decreased from 2001 to 2020, suggesting diminishing upscaling errors. In experiments where we limit site activity by either setting a maximum duration or by ending measurements at a fixed time those errors increase significantly, in some cases setting the network status back more than a decade. Our experiments also show that with equal site activity across different theoretical network setups, a more spread out design with shorter‐term measurements functions better in terms of larger‐scale representativeness than a network with fewer long‐term towers. We developed a method to select optimized site additions for a network extension, which blends an objective modeling approach with expert knowledge. This method greatly outperforms an unguided network extension and can compensate for suboptimal human choices. For the Canadian Arctic we show several optimization scenarios and find that especially the Canadian high Arctic and north east tundra benefit greatly from addition sites. Overall, it is important to keep sites active and where possible make the extra investment to survey new strategic locations.

Third-octave analyses describing two perceptual dimensions of sound reproduction and the resulting overall perceived dissimilarity between loudspeakers or headphones.

Many objective measurements have been proposed to evaluate sound reproduction, but it is often difficult to link measured differences with the differences perceived by listeners. In the literature, the best correlations with perception were obtained for measures involving an auditory model. The present study investigated simpler measurements to highlight the signal processing steps required to make the link with perception. It is based on dissimilarity evaluations from two previous studies: 1 study comparing 12 single loudspeakers using 3 musical excerpts, 1 study comparing 21 headphones using 2 musical excerpts, and both studies highlighting 2 perceptual dimensions associated with the relative strengths of bass and midrange. The objective approach compared several signal analyses computing the dissimilarity between the spectra of the recorded sound reproductions. The results show that a third-octave analysis can accurately describe the overall dissimilarity between the loudspeakers or headphones and the two underlying perceptual dimensions.

A Criticism of the Kyoto Protocol with an Objective Approach

The Kyoto Protocol, established to reduce greenhouse gas emissions, one of the human-induced causes of climate change, imposes obligations on developed countries. In this study: 1) what obligations the articles of the Kyoto Protocol impose on the countries that are party to the protocol, 2) whether the articles of the protocol are impartial, equal, and fair in their obligations to the contracting countries and their binding, 3) how the economic support of developed countries to reduce emissions to developing countries can be evaluated in the context of the principle of equality/justice, 4) it has been discussed whether the non-bindingness of the countries those are not party to the protocol. Considering all protocol items, the discussion was made from a critical but constructive point of view. It can be said that the obligations arising from the Kyoto Protocol bring disadvantages for some countries compared to others.

Рецензия на монографию «Боров А.Х. Концептуальные итоги и проблемы изучения опыта революции и гражданской войны в Кабардино-Балкарии (1917–1918). – Нальчик: редакци-онно-издательский отдел ИГИ КБНЦ РАН, 2024. –152 с.»

The study of the problems associated with the spread of the ideas of the Russian revolution in the Northern Caucasus, with the history of the civil war in the region, is marked by discord in Cauca-sian historiography. This led to our interest in the work of A.H. Borov "Conceptual results and problems of studying the experience of the revolution and the Civil war in Kabardino-Balkaria." The very formulation of the question promises an objective approach to the analysis of published scientific literature on the topic. An unbiased but clear analysis of the previous scientific heritage, compared with the events and actors, allowed the author to consider various interpretations of the problem of civil confrontation in the revolutionary years on the materials of the Ter region, which included the Nalchik district. The author of the monograph summarizes the results of the study of this historical period, arranging the facts in a strict chronology and rethinking them. A deep histo-riographical analysis, verification of various concepts, and powerful scientific erudition led the author to conclusions about the peculiarities of the modernization processes of North Caucasian society under the influence of "external" challenges and "internal" conditions. The work of A.H. Borov is an example of honest scientific work and will certainly contribute to the creation of a generalized history of Kabardino-Balkaria.

Noncontact Perception of Thin-Film Transistors by the Synergy of Both Capacitive and Electrostatic Induction Mechanisms.

In recent years, the rapid expansion of research and application of the Internet of Things and wearable electronics has prompted the development of a variety of sensors to perceive physical or chemical information from both the human body and the environment, among which the proximity sensor is a kind of noncontact sensor used to detect the approach of a target and thus exhibits promising applications in human-machine interactions. Thin-film transistors are one type of key components in modern electronics and have been further developed as electrostatic-induction-type proximity sensors to perceive the approach of electrically charged objects. However, they are immune to the approach of a zero-potential object. Capacitive-induction-type proximity sensors are capable of detecting the approach of conductive targets while being less sensitive to insulated ones. Integration of both electrostatic and capacitive induction mechanisms into one proximity sensor is highly expected to broaden its perception to a variety of targets. Here, an interdigital electrode was introduced as an extended gate into an amorphous metal oxide thin-film transistor to construct proximity sensors that combine both electrostatic and capacitive induction mechanisms and therefore can sensitively perceive the approach of a variety of objects that were electrically charged, grounded, or floated. Besides proximity sensing, remote velocity measurement and positioning of an invasive object were also realized, which further extended its functions as a kind of interdigital-electrode gate transistor.

A machine learning and linear programming aided approach to wine ranking and selection

PurposeThis paper aims to mitigate the subjective nature of wine rating by introducing statistical and optimization tools for analysis, providing a unique approach not found in existing literature.Design/methodology/approachThe research uses an unsupervised machine learning algorithm, k-means, to cluster wines based on their chemical characteristics, followed by the application of the PROMETHEE II multicriteria decision-making model to rank the wines based on their sensorial characteristics and selling price. Lastly, a linear programming model is used to optimize the selection of wines under different scenarios and constraints.FindingsThe study presents a method to rank wines based on both chemical and sensorial characteristics, providing a more comprehensive assessment than traditional subjective ratings. Clustering wines based on their characteristics and ranking them according to sensorial characteristics provides the user/consumer with meaningful information to be used in an optimization model for wine selection.Practical implicationsThe proposed framework has practical implications for wine enthusiasts, makers, tasters and retailers, offering a systematic approach to ranking and selecting/recommending wines based on both objective and subjective criteria. This approach can influence pricing, consumption and marketing strategies within the wine industry, leading to more informed and precise decision-making.Originality/valueThe research introduces a novel framework that combines machine learning, decision-making models and linear programming for wine ranking and selection, addressing the limitations of subjective ratings and providing a more objective approach.