Scientific Community Research Articles

Adaptation and feasibility of WHO PM+ for adolescents living with HIV in KwaZulu-Natal Province, South Africa: an implementation feasibility study protocol

IntroductionAdolescents living with HIV (ALHIV) are an extremely vulnerable population, with the burden of mental health problems carefully documented together with the constraints for receiving timely and adequate management of...

Il «Robotics Interest Questionnaire» (RIQ): uno strumento per rilevare i fattori sottesi all’insegnamento della robotica

THE «ROBOTICS INTEREST QUESTIONNAIRE» (RIQ): A TOOL TO DETECT FACTORS UNDERLYING ROBOTICS TEACHING Abstract The growing interest of the scientific and educational community in Educational Robotics (ER) is linked to the need to develop digital skills and computational thinking in the younger generation. Through ER it is possible to train multiple cognitive skills and not only programming skills. To this end, it is decisive to outline a new competence profile for teachers that can influence this aspect by activating ER training courses. The paper presents the «Robotics Interest Questionnaire» (RIQ), a validated questionnaire in Italian able to measure with some precision the factors involved in teaching robotics, and discusses the data collected on teachers of all levels (823 teachers). The results provide normative values useful, for example, to allow the diagnostic use of the RIQ in the startup phase of teacher training, in order to hypothesize the level possessed by the participants and orientate the didactic design and the content-methodological choices of the intervention, as well as the final evaluation of the training outcomes thanks to its ability to highlight, when used before and after, improvements in the dimensions investigated: knowledge and interest, sense of self-efficacy, problem solving and collaboration.

Advancing drug discovery through assay development: a survey of tool compounds within the human solute carrier superfamily

With over 450 genes, solute carriers (SLCs) constitute the largest transporter superfamily responsible for the uptake and efflux of nutrients, metabolites, and xenobiotics in human cells. SLCs are associated with a wide variety of human diseases, including cancer, diabetes, and metabolic and neurological disorders. They represent an important therapeutic target class that remains only partly exploited as therapeutics that target SLCs are scarce. Additionally, many small molecules reported in the literature to target SLCs are poorly characterized. Both features may be due to the difficulty of developing SLC transport assays that fulfill the quality criteria for high-throughput screening. Here, we report one of the main limitations hampering assay development within the RESOLUTE consortium: the lack of a resource providing high-quality information on SLC tool compounds. To address this, we provide a systematic annotation of tool compounds targeting SLCs. We first provide an overview on RESOLUTE assays. Next, we present a list of SLC-targeting compounds collected from the literature and public databases; we found that most data sources lacked specificity data. Finally, we report on experimental tests of 19 selected compounds against a panel of 13 SLCs from seven different families. Except for a few inhibitors, which were active on unrelated SLCs, the tested inhibitors demonstrated high selectivity for their reported targets. To make this knowledge easily accessible to the scientific community, we created an interactive dashboard displaying the collected data in the RESOLUTE web portal (https://re-solute.eu). We anticipate that our open-access resources on assays and compounds will support the development of future drug discovery campaigns for SLCs.

Leak and Burst Detection in Water Distribution Network Using Logic- and Machine Learning-Based Approaches

Urban water systems worldwide are confronted with the dual challenges of dwindling water resources and deteriorating infrastructure, emphasising the critical need to minimise water losses from leakage. Conventional methods for leak and burst detection often prove inadequate, leading to prolonged leak durations and heightened maintenance costs. This study investigates the efficacy of logic- and machine learning-based approaches in early leak detection and precise location identification within water distribution networks. By integrating hardware and software technologies, including sensor technology, data analysis, and study on the logic-based and machine learning algorithms, innovative solutions are proposed to optimise water distribution efficiency and minimise losses. In this research, we focus on a case study area in the Sunbury region of Victoria, Australia, evaluating a pumping main equipped with Supervisory Control and Data Acquisition (SCADA) sensor technology. We extract hydraulic characteristics from SCADA data and develop logic-based algorithms for leak and burst detection, alongside state-of-the-art machine learning techniques. These methodologies are applied to historical data initially and will be subsequently extended to live data, enabling the real-time detection of leaks and bursts. The findings underscore the complementary nature of logic-based and machine learning approaches. While logic-based algorithms excel in capturing straightforward anomalies based on predefined conditions, they may struggle with complex or evolving patterns. Machine learning algorithms enhance detection by learning from historical data, adapting to changing conditions, and capturing intricate patterns and outliers. The comparative analysis of machine learning models highlights the superiority of the local outlier factor (LOF) in anomaly detection, leading to its selection as the final model. Furthermore, a web-based platform has been developed for leak and burst detection using a selected machine learning model. The success of machine learning models over traditional logic-based approaches underscores the effectiveness of data-driven, probabilistic methods in handling complex data patterns and variations. Leveraging statistical and probabilistic techniques, machine learning models offer adaptability and superior performance in scenarios with intricate or dynamic relationships between variables. The findings demonstrate that the proposed methodology can significantly enhance the early detection of leaks and bursts, thereby minimising water loss and associated economic costs. The implications of this study are profound for the scientific community and stakeholders, as it provides a scalable and efficient solution for water pipeline monitoring. Implementing this approach can lead to more proactive maintenance strategies, ultimately contributing to the sustainability and resilience of urban water infrastructure systems.

Leak and Burst Detection in Water Distribution Network Using Logic- and Machine Learning-Based Approaches

Urban water systems worldwide are confronted with the dual challenges of dwindling water resources and deteriorating infrastructure, emphasising the critical need to minimise water losses from leakage. Conventional methods for leak and burst detection often prove inadequate, leading to prolonged leak durations and heightened maintenance costs. This study investigates the efficacy of logic- and machine learning-based approaches in early leak detection and precise location identification within water distribution networks. By integrating hardware and software technologies, including sensor technology, data analysis, and study on the logic-based and machine learning algorithms, innovative solutions are proposed to optimise water distribution efficiency and minimise losses. In this research, we focus on a case study area in the Sunbury region of Victoria, Australia, evaluating a pumping main equipped with Supervisory Control and Data Acquisition (SCADA) sensor technology. We extract hydraulic characteristics from SCADA data and develop logic-based algorithms for leak and burst detection, alongside state-of-the-art machine learning techniques. These methodologies are applied to historical data initially and will be subsequently extended to live data, enabling the real-time detection of leaks and bursts. The findings underscore the complementary nature of logic-based and machine learning approaches. While logic-based algorithms excel in capturing straightforward anomalies based on predefined conditions, they may struggle with complex or evolving patterns. Machine learning algorithms enhance detection by learning from historical data, adapting to changing conditions, and capturing intricate patterns and outliers. The comparative analysis of machine learning models highlights the superiority of the local outlier factor (LOF) in anomaly detection, leading to its selection as the final model. Furthermore, a web-based platform has been developed for leak and burst detection using a selected machine learning model. The success of machine learning models over traditional logic-based approaches underscores the effectiveness of data-driven, probabilistic methods in handling complex data patterns and variations. Leveraging statistical and probabilistic techniques, machine learning models offer adaptability and superior performance in scenarios with intricate or dynamic relationships between variables. The findings demonstrate that the proposed methodology can significantly enhance the early detection of leaks and bursts, thereby minimising water loss and associated economic costs. The implications of this study are profound for the scientific community and stakeholders, as it provides a scalable and efficient solution for water pipeline monitoring. Implementing this approach can lead to more proactive maintenance strategies, ultimately contributing to the sustainability and resilience of urban water infrastructure systems.

Electronic governance and its role in promoting entrepreneurship - a survey study of a sample of colleges at Sulaimani Polytechnic University

This research aims to explain the role of electronic governance in its various dimensions in promoting entrepreneurship, by applying it in the colleges of Sulaimani Polytechnic University. To achieve the research objectives, the researcher collected data from various sources. The descriptive analytical approach was used to conduct the study, and the questionnaire was a main tool for collecting information. The research community consisted of college deans, assistant deans, department heads, department rapporteurs, division and unit officials, and professors. Their number reached 250 individuals, where 200 questionnaires were distributed and 49 questionnaires were returned. The total percentage of questionnaires received was 24.5%. SPSS program was used to analyze the data. The study reached a number of results, the most important of which is that the colleges of Sulaimani Polytechnic University give great importance to the variable of electronic governance in its various dimensions (Standards and applications, electronic participation, monitoring and evaluation, capabilities and human resources), and entrepreneurship in its various dimensions (creativity and innovation, initiative, take advantage of the opportunities, risk). The study found that there is a strong and direct correlation between electronic governance in its various dimensions and entrepreneurship. The research concluded that there is a need to activate the university’s great capabilities in implementing e-governance, through the presence of effective strategies to solve problems.

Electronic governance and its role in promoting entrepreneurship - a survey study of a sample of colleges at Sulaimani Polytechnic University

This research aims to explain the role of electronic governance in its various dimensions in promoting entrepreneurship, by applying it in the colleges of Sulaimani Polytechnic University. To achieve the research objectives, the researcher collected data from various sources. The descriptive analytical approach was used to conduct the study, and the questionnaire was a main tool for collecting information. The research community consisted of college deans, assistant deans, department heads, department rapporteurs, division and unit officials, and professors. Their number reached 250 individuals, where 200 questionnaires were distributed and 49 questionnaires were returned. The total percentage of questionnaires received was 24.5%. SPSS program was used to analyze the data. The study reached a number of results, the most important of which is that the colleges of Sulaimani Polytechnic University give great importance to the variable of electronic governance in its various dimensions (Standards and applications, electronic participation, monitoring and evaluation, capabilities and human resources), and entrepreneurship in its various dimensions (creativity and innovation, initiative, take advantage of the opportunities, risk). The study found that there is a strong and direct correlation between electronic governance in its various dimensions and entrepreneurship. The research concluded that there is a need to activate the university’s great capabilities in implementing e-governance, through the presence of effective strategies to solve problems.

Enzyme-Immobilized Porous Crystals for Environmental Applications.

Developing efficient technologies to eliminate or degrade contaminants is paramount for environmental protection. Biocatalytic decontamination offers distinct advantages in terms of selectivity and efficiency; however, it still remains challenging when applied in complex environmental matrices. The main challenge originates from the instability and difficult-to-separate attributes of fragile enzymes, which also results in issues of compromised activity, poor reusability, low cost-effectiveness, etc. One viable solution to harness biocatalysis in complex environments is known as enzyme immobilization, where a flexible enzyme is tightly fixed in a solid carrier. In the case where a reticular crystal is utilized as the support, it is feasible to engineer next-generation biohybrid catalysts functional in complicated environmental media. This can be interpreted by three aspects: (1) the highly crystalline skeleton can shield the immobilized enzyme against external stressors. (2) The porous network ensures the high accessibility of the interior enzyme for catalytic decontamination. And (3) the adjustable and unambiguous structure of the reticular framework favors in-depth understanding of the interfacial interaction between the framework and enzyme, which can in turn guide us in designing highly active biocomposites. This Review aims to introduce this emerging biocatalysis technology for environmental decontamination involving pollutant degradation and greenhouse gas (carbon dioxide) conversion, with emphasis on the enzyme immobilization protocols and diverse catalysis principles including single enzyme catalysis, catalysis involving enzyme cascades, and photoenzyme-coupled catalysis. Additionally, the remaining challenges and forward-looking directions in this field are discussed. We believe that this Review may offer a useful biocatalytic technology to contribute to environmental decontamination in a green and sustainable manner and will inspire more researchers at the intersection of the environment science, biochemistry, and materials science communities to co-solve environmental problems.

Ordering the alphabet soup: Strategies to improve consistency and develop a framework of tools for fisheries science vocabulary

Quantitative methods for assessing the status of marine fish populations began in the 19th century with simple catch-curve analysis and have evolved through the decades to use more advanced integrated statistical modeling. The corresponding assessment documents that communicate model estimates to other scientists and fisheries managers, generally, have grown in length and complexity, often describing hundreds of parameter estimates and population dynamics. These documents use a wide range of synonyms or similar terms that can vary either within a single assessment document or among other documents within or across fisheries management regions. The lack of standardization of terms used in assessment documents can lead to challenges in scientific reviews, communicating scientific results to fisheries managers, and for understanding assessments from other management regions. We analyzed the text of 134 assessment documents used to communicate the results of assessment software to managers from the varying management regions in the United States, Australia, and European International Council for the Exploration of the Sea (ICES) to quantify the presence of synonyms or similar terms that are often used interchangeably and highlight ways in which the output language could be harmonized without modifying meaning. Next, we developed an open source web-based dictionary and schema for fisheries science where the community can propose and adopt terms to create an agreed upon terminology. Finally, looking forward to future development, we propose approaches to leverage pre-existing frameworks such as Google coding standards to guide the development of tools to merge long-term ecological data sets, name objects in code bases, and report output. While this guidance is tailored to fisheries practitioners working within the U.S. management system, we aim to align with international standards to increase consistency across the international fisheries science community.

From selfish silo to collaborative culture – embracing data-enabled cancer research

From selfish silo to collaborative culture – embracing data-enabled cancer research Aedin Culhane and Mark Lawler, Co-Leads of the eHealth Hub for Cancer, reflect on their data-enabled cancer research journeys, how their collaborative team science approach has reaped significant dividends in cancer research and policy and how the hub is inducing a paradigm shift in how health data are deployed on the island of Ireland. An article in The Wall Street Journal (not the normal reading material for scientists) in 2011 highlighted a new approach to performing scientific research that was gaining significant credence at the time. Entitled ‘The New Einsteins Will Be Scientists Who Share’ (with the strapline ‘From cancer to cosmology, researchers could race ahead by working together – online and in the open’), the article presaged an unprecedented change in how scientists interact with each other, ushering in a culture of collaboration and cross-disciplinary research. Nowhere was this change more obvious than in the genomics and data science community, where a bottom-up movement led to the creation of the Global Alliance for Genomics and Health (GA4GH), bringing together researchers from around the world to work together to address some of human health’s greatest challenges through the deployment of data and data tools.

The emergent community of esports fans in Japan: an analysis of motivations and preferences

ABSTRACT Purpose/Rationale This study examines esports players’ and viewers’ motivations in Japan to inform market segmentation and strategy for industry stakeholders. Design/methodology/approach Employing purposive sampling, data from 500 Japanese esports participants were collected through an online survey including behavioural, motivational, and demographic factors. Cluster analysis was applied to identify distinct groups. Findings Results revealed three unique clusters each for players (all-rounder players, immersive fun seekers, and skill-based players) and viewers (casual viewers, entertainment-excitement seekers and skill appreciation seekers), with each exhibiting distinct motivations and preferences. Socio-economic factors did not significantly differentiate clusters, but fan behavioural patterns were useful in characterising them. Practical Implications Our study provides insights into Japan’s market preferences for console gaming and skill development and guides stakeholders in targeted strategy development for market engagement and growth. Research Contribution By applying the Uses and Gratifications theory within the Japanese context, this study enhances esports online community research by revealing distinct segment characteristics in a culturally unique setting and enriches esports interaction understanding. Originality/value This study sheds light on the motivations and preferences of esports players and viewers in Japan’s emerging market, offering valuable insights for stakeholders to better understand and engage their audience.

The emergent community of esports fans in Japan: an analysis of motivations and preferences

ABSTRACT Purpose/Rationale This study examines esports players’ and viewers’ motivations in Japan to inform market segmentation and strategy for industry stakeholders. Design/methodology/approach Employing purposive sampling, data from 500 Japanese esports participants were collected through an online survey including behavioural, motivational, and demographic factors. Cluster analysis was applied to identify distinct groups. Findings Results revealed three unique clusters each for players (all-rounder players, immersive fun seekers, and skill-based players) and viewers (casual viewers, entertainment-excitement seekers and skill appreciation seekers), with each exhibiting distinct motivations and preferences. Socio-economic factors did not significantly differentiate clusters, but fan behavioural patterns were useful in characterising them. Practical Implications Our study provides insights into Japan’s market preferences for console gaming and skill development and guides stakeholders in targeted strategy development for market engagement and growth. Research Contribution By applying the Uses and Gratifications theory within the Japanese context, this study enhances esports online community research by revealing distinct segment characteristics in a culturally unique setting and enriches esports interaction understanding. Originality/value This study sheds light on the motivations and preferences of esports players and viewers in Japan’s emerging market, offering valuable insights for stakeholders to better understand and engage their audience.

Sensitivity analysis of space-based water vapor differential absorption lidar at 823 nm

Measurements of water vapor are important for understanding the hydrological cycle, the thermodynamic structure of the lower troposphere, and broader atmospheric circulation. Subsequently, many scientific communities have emphasized a need for high-accuracy and spatial resolution profiles of water vapor within and above the planetary boundary layer (PBL). Advancements in lidar technologies at the NASA Langley Research Center are ongoing to enable the first space-based water vapor differential absorption lidar (DIAL) that can provide high-accuracy and vertical resolution retrievals of moisture in the PBL and through the mid-troposphere. The performance of this space-based DIAL is assessed here for sensitivity throughout the troposphere and globally with representative canonical cases of water vapor and aerosol loading. The specific humidity retrieval sensitivity to systematic and random errors is assessed, and measurement resolutions and capabilities are provided. We show that tunable operation along the side of the 823-nm absorption line allows for the optimization of the lower-tropospheric water vapor retrievals across different meteorological regimes and latitudes and provides the operational flexibility needed to dynamically optimize random errors for different scientific applications. The analysis presented here suggests that baseline and threshold systematic error requirements of <1.5% and <2.5%, respectively, are achievable. Random error is shown to dominate the retrieval, with errors on the order of 5% within the PBL being achievable with 300-m vertical 50-km horizontal resolutions over open ocean and on the order of 10%–15% over high-albedo surfaces. The flexibility of the DIAL method to trade retrieval precision for spatial resolution is shown, highlighting its strengths over passive techniques to tailor retrievals to different scientific applications. Combined, the total error budget demonstrated here indicates a high impact for space-based DIAL, with technologies being advanced for space missions within the next 5–10 years.

Nano-Fortifying the Future Plate: A Critical Review of Nanotechnology Applications in Food Enrichment

Proportion of children globally experience dietary deficiencies, which have a negative imp A considerable act on their physical and cognitive growth and heighten their vulnerability to illnesses. In addition, undernutrition worsens the intensity of several infections, such as measles and malaria. Food fortification is an important nutritional strategy that helps to combat micronutrient deficiencies and decrease their occurrence in many low- and middle-income nations. Although there has been a significant rise in recent years, food fortification schemes have also encountered considerable criticism from the scientific community. The present review aimed to clarify the actual influence of food fortification measures on the mitigation of malnutrition. A detailed review of the current state of the art in nanotechnology applications for food enrichment is provided in this manuscript. A comprehensive literature search was conducted utilizing databases such as Web of Science/ Knowledge, Google Scholar, Elsevier ScienceDirect, and Springer Online Journals. The search query included phrases such as food fortification, micronutrient shortages, fortified food, the impact of food fortification, and food-to-food fortification. The paper discusses the nanostructures, such as nanoparticles, nanocapsules, and nanoemulsions, in encapsulating, delivering, and safeguarding bioactive compounds within food matrices, with a particular emphasis on nanofortification. Recent advancements in nano-enabled food fortification are summarised in the manuscript, which also identifies significant challenges and opportunities for future commercialization and research. Food fortification is essential for both developed and developing nations to ensure that processed foods contain the necessary nutrients, hence improving their nutritional value for human consumption. Nanotechnology can be applied to enrich food by improving bioavailability and providing a delivery system for phytochemicals, vitamins, and minerals. This review enhances our knowledge regarding the profound consequences it may have on the future of nutrition and food technology.

Risk factors for PPCs in laparoscopic non–robotic vs. laparoscopic robotic abdominal surgery (LapRas): rationale and protocol for a patient–level analysis of LAS VEGAS and AVATaR

IntroductionPostoperative pulmonary complications (PPCs) vary amongst different surgical techniques. We aim to compare the incidence of PPCs after laparoscopic non–robotic versus laparoscopic robotic abdominal surgery. Methods and analysisLapRas (Risk Factors for PPCs in Laparoscopic Non–robotic vs Laparoscopic robotic abdominal surgery) incorporates harmonized data from 2 observational studies on abdominal surgery patients and PPCs: ‘Local ASsessment of VEntilatory management during General Anaesthesia for Surgery’ (LAS VEGAS), and ‘Assessment of Ventilation during general AnesThesia for Robotic surgery' (AVATaR). The primary endpoint is the occurrence of one or more PPCs in the first five postoperative days. Secondary endpoints include the occurrence of each individual PPC, hospital length of stay and in–hospital mortality. Logistic regression models will be used to identify risk factors for PPCs in laparoscopic non–robotic versus laparoscopic robotic abdominal surgery. We will investigate whether differences in the occurrence of PPCs between the two groups are driven by differences in duration of anesthesia and/or the intensity of mechanical ventilation. Ethics and disseminationThis analysis will address a clinically relevant research question comparing laparoscopic and robotic assisted surgery. No additional ethical committee approval is required for this metanalysis. Data will be shared with the scientific community by abstracts and original articles submitted to peer-reviewed journals. RegistrationThe registration of this post-hoc analysis is pending; individual studies that were merged into the used database were registered at clinicaltrials.gov: LAS VEGAS with identifier NCT01601223, AVATaR with identifier NCT02989415.

Hydrogen Evolving Mono‐Nuclear Manganese Complexes with Carefully Positioned Pyridine Base Ligands

AbstractEnormous efforts have been made by the scientific community in the development of bio‐mimetic/bio‐inspired hydrogen‐evolving complexes as catalysts for an alternative hydrogen energy carrier and a renewable energy sources. In this regard, new bio‐inspired mono‐nuclear Mn(I) carbonyl model complexes fac‐[(Mn(CO)3(κ2‐SN2C7H5)(κ1‐PPh2Py))] 1 and fac‐[(Mn(CO)3(κ2‐S2NC7H4)(κ1‐PPh2Py))] 2 with 2‐mercaptobenzimidazole (N,N) and 2‐mercaptobenzothiazole (S,N) ligands and a N‐based phosphine ligand (PPh2Py) have been synthesized, spectroscopically characterized and evaluated as electro‐catalysts for hydrogen generation. In contrast to related mono‐nuclear (N,N/S,N)−Mn−PPh3 complexes,, the newly introduced pyridine nitrogen atom of the PPh2Py ligand placed in an axial position in the second coordination sphere of the Mn(I) complexes, provides a first site for protonation and enables a fast intra‐molecular proton transfer to the central metal atom with a weaker acid. Theoretical DFT calculations enable a detailed picture of catalytic pathways, assign redox transitions and structural changes. Both the molecular complexes were tested as electro‐catalysts for hydrogen generation in CH3CN. They effectively catalyzed the electrochemical proton reduction with the weaker acetic acid as the proton source and displayed a turnover frequency (TOF/s−1; O.P., η/V) of 610; 1.02 and 615; 1.12, respectively. The computer‐aided design of dedicated proton transfer pathways from second coordination sphere ligands is thus able to allow a hydrogen evolution reactivity with a weaker acid (acetic acid).

Strategija za ocjenjivanje učinka formaldehida u anatomskom patološkom laboratoriju treći dio

To date, formaldehyde (FA) is one of the more common chemicals and its use is widespread around the world in different sectors, especially in chemical facilities and health care. FA is widely used in working activities owing to its chemical and physical properties. However, it also represents a concerning hazard for the workers’ health due to its toxicity and recognized carcinogenicity. The FA exposure evaluation in occupational setting has arisen interest in the scientific community that leads to the development of several analytical instruments in order to assess both long term and short-term exposure. The paper presents and discusses an equivalence tests procedure via the 2.4-dinitrophenylhydrazine (DNPH)-active air sampling formaldehyde (FA) reference method and two non-reference instruments based on continuous, direct reading monitoring, namely ProCeas® (AP2E) and NEMo XT (Ethera). The FA standard atmosphere calibration system was used to check the reference method by Pearson’s test. Subsequently, the Passing-Bablok test was carried out between the non-reference methods and the DNPH method for potential systematic or proportional errors, and finally the Bland-Altman plot was applied to determine the mean bias and the variances of the recorded values by the reference and non-reference methods in on-field sampling. The results showed a good correlation between the non-references method and the DNPH ones, suggesting their possible applications in heterogeneous occupational scenarios.

Computational Design of Bidentate Hypervalent Iodine Catalysts in Halogen Bond-Mediated Organocatalysis.

In recent years, halogen bond-based organocatalysis has garnered significant attention as an alternative to hydrogen-based catalysis, capturing considerable interest within the scientific community. This transition has witnessed the evolution of catalytic scaffolds from monodentate to bidentate architectures, and from monovalent to hypervalent species. In this DFT-based study, we explored a bidentate hypervalent iodine(III)-based system that has already undergone experimental validation. Additionally, we explore various functionalisations (-CF$_3$, -CH$_3$, -tBu, -OH, -OMe, -NO$_2$, -CN) and scaffold modifications, such as sulfur oxidation, theoretically proposed for an indole-based Michael addition. The investigated systems favour bidentate O-type binding, underlining the importance of ligand coordination in catalytic activity. Electron-deficient scaffolds exhibited stronger binding and lower activation energies, indicating the pivotal role of electronic properties for $\sigma$-hole-based catalysis. Of these groups, Lewis-base-like moieties formed stabilising intramolecular interactions with hypervalent iodines when in the ortho-position. Furthermore, inductive electron withdrawal was deemed more effective than mesomeric withdrawal in enhancing catalytic efficacy for these systems. Lastly, increasing sulfur oxidation was theoretically proven to improve catalytic activity significantly.

Sensitivity analysis of space-based water vapor differential absorption lidar at 823 nm

Measurements of water vapor are important for understanding the hydrological cycle, the thermodynamic structure of the lower troposphere, and broader atmospheric circulation. Subsequently, many scientific communities have emphasized a need for high-accuracy and spatial resolution profiles of water vapor within and above the planetary boundary layer (PBL). Advancements in lidar technologies at the NASA Langley Research Center are ongoing to enable the first space-based water vapor differential absorption lidar (DIAL) that can provide high-accuracy and vertical resolution retrievals of moisture in the PBL and through the mid-troposphere. The performance of this space-based DIAL is assessed here for sensitivity throughout the troposphere and globally with representative canonical cases of water vapor and aerosol loading. The specific humidity retrieval sensitivity to systematic and random errors is assessed, and measurement resolutions and capabilities are provided. We show that tunable operation along the side of the 823-nm absorption line allows for the optimization of the lower-tropospheric water vapor retrievals across different meteorological regimes and latitudes and provides the operational flexibility needed to dynamically optimize random errors for different scientific applications. The analysis presented here suggests that baseline and threshold systematic error requirements of <1.5% and <2.5%, respectively, are achievable. Random error is shown to dominate the retrieval, with errors on the order of 5% within the PBL being achievable with 300-m vertical 50-km horizontal resolutions over open ocean and on the order of 10%–15% over high-albedo surfaces. The flexibility of the DIAL method to trade retrieval precision for spatial resolution is shown, highlighting its strengths over passive techniques to tailor retrievals to different scientific applications. Combined, the total error budget demonstrated here indicates a high impact for space-based DIAL, with technologies being advanced for space missions within the next 5–10 years.

SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification

A common and natural physiological response of the human body is cough, which tries to push air and other wastage thoroughly from the airways. Due to environmental factors, allergic responses, pollution or some diseases, cough occurs. A cough can be either dry or wet depending on the amount of mucus produced. A characteristic feature of the cough is the sound, which is a quacking sound mostly. Human cough sounds can be monitored continuously, and so, cough sound classification has attracted a lot of interest in the research community in the last decade. In this research, three systematic conglomerated models (SCMs) are proposed for audio cough signal classification. The first conglomerated technique utilizes the concept of robust models like the Cross-Correlation Function (CCF) and Partial Cross-Correlation Function (PCCF) model, Least Absolute Shrinkage and Selection Operator (LASSO) model, elastic net regularization model with Gabor dictionary analysis and efficient ensemble machine learning techniques, the second technique utilizes the concept of stacked conditional autoencoders (SAEs) and the third technique utilizes the concept of using some efficient feature extraction schemes like Tunable Q Wavelet Transform (TQWT), sparse TQWT, Maximal Information Coefficient (MIC), Distance Correlation Coefficient (DCC) and some feature selection techniques like the Binary Tunicate Swarm Algorithm (BTSA), aggregation functions (AFs), factor analysis (FA), explanatory factor analysis (EFA) classified with machine learning classifiers, kernel extreme learning machine (KELM), arc-cosine ELM, Rat Swarm Optimization (RSO)-based KELM, etc. The techniques are utilized on publicly available datasets, and the results show that the highest classification accuracy of 98.99% was obtained when sparse TQWT with AF was implemented with an arc-cosine ELM classifier.