Miracle-dz/UniFAR-FTUs huggingface dataset:
Industrial Environmental Pollution Monitoring and Prediction Analysis Using IoT and Machine Learning

Monitoring and predicting air quality become the most significant operations in many urban and industrial regions. Various types of pollution have a substantial impact on air quality. Effective air quality monitoring models are needed in light of the increased air pollution; these models gather information on air pollutant concentrations. This study describes a comprehensive air quality monitoring architecture built around the (IoT model and fully integrated into existing industrial architectures. It involves the creation of two small, high-precision devices for real-time monitoring of polluting gases and particles in the environment. These gadgets can also capture helpful information such as the humidity and temperature of the area. Machine learning (ML) techniques were also used for the datasets retrieved by the system. The results revealed that the long short-term memory (LSTM) technique was the most accurate in the device's air quality dataset. For example, this gives the proposed solution intelligence to forecast when security standards may be exceeded.
Thermal Design of Stacked Power Modules for Electric Drive Applications

This paper reports the thermal design of a stacked arrangement inserting four IGBT modules between five heat sinks to achieve good cooling performance while enabling them to be installed into the limited space. Such stacked four IGBT modules can be used for a variable voltage converter (VVC), a three-phase motor inverter (M-INV) and a three-phase generator inverter (G-INV) of one electric control system in one electric vehicle or hybrid electric vehicle. Work presented in this paper are the thermally related structure design and thermal-electric and computational fluid dynamics coupled simulation, while the prototyping and thermal test of the stacked arrangement are still ongoing. The simulation results indicate that the stacked arrangement can be designed into a compact 121 mm × 94.7 mm × 90.8 mm structure while achieving the maximum virtual junction temperature below 150 °C under the coolant feed temperature of 70 °C, flow rate of 10 L/min and pressure drop of 0.16 bar for the VVC delivering 150 kW, the M-INV delivering 100 kW and the G-INV delivering 50 kW. If validated with the ongoing work, the stacked arrangement can be employed to increase power density and improve electrical and thermal performance of the power modules in the electric control systems with multiple converters/inverters.
Optical FBMC Signals using Frame Repetition for Indoor Wireless Communication System

Light emitting diode (LED) nonlinear distortions have a significant influence on the performance of various optical multicarrier models for visible light communication systems. For the first time, Flip-FBMC based frame repetition model is proposed to minimize the impact of intrinsic imaginary interference (IMI) and, metric of clipping distortion for high power with interference approximation method (IAM) preamble. Besides, due to the optical multicarrier signals are forcibly clipped at the LED maximum permissible voltage, the first theoretical analysis is presented to mitigate the induced IMI outside the first order neighbor zone that cannot be covered using IAM. Moreover, PHYDYAS profile through the proposed design represents as a viable candidate to cope with the restricted dynamic range of the LED. Thereby, based on the realistic-experimental parameters, the proposed model offers a superior performance gain to Flip-CP-OFDM signal.
Evaluation of a Finger Dummy with a Built-in Sensor System for Safety Assessment

Labor shortages and low productivity demand more collaborative operation systems using industrial robots in the current global manufacturing industry. Injuries will possibly occur due to the closer interaction between the robot system and humans. In the field of skin contact safety, the techniques and methods of quantitative assessment are expected. A human dummy consisting of built-in sensors is a solution to obtain the internal physical field. In this study, a dummy finger equipped with a built-in film sensor for contact safety assessment is proposed and evaluated by experiment. The strain measurement model and strain energy sensing principle, which are the most general assessment indexes, are explained. Two kinds of contact conditions on the finger dummy, press and press with slip, are conducted for evaluation. Shear strain and vibration are both successfully measured and reflected in the signal. Measurement of the strain energy transformed from the strain is validated by comparing it with the work result obtained from the force sensor. This sensor system combined with the human body model is evaluated as potentially feasible for assessing physical contact safety.
Temporal Landmarks and Nostalgic Consumption: The Role of the Need to Belong

This study investigates the influence of temporal landmarks on nostalgic consumption through the mediating role of the need to belong. In particular, the study identifies end landmarks as one of the triggers of landmarks, a phenomenon that has not been studied in the existing nostalgic consumption literature. The research is composed of one pilot study and three experiments to test our research hypotheses. The results show that end temporal landmarks trigger feelings of nostalgia, which leads to nostalgic consumption through the need to belong. This study underscores the mediating role of the need to belong, which plays an important role in leading to nostalgic consumption. Building upon theoretical perspectives on the need to belong, our study enriches the research literature by linking extreme consumer emotional statuses, such as social anxiety, to the consumer need to belong, showing that consumer nostalgic consumption can become a coping strategy that counteracts these negative feelings and helps in regaining connection and supporting social relationship networks. Marketers may use the signs of end temporal landmarks to increase consumers’ nostalgia, which, in turn, will enhance consumers’ need to belong and thus lead to the purchasing and consumption of nostalgic products.
Zero shear viscosity from oscillation tests on aged and non-aged bitumens

Bitumens are complex mixtures of hydrocarbons that show a wide range of molecular weights. This makes bitumens to behave as viscoelastic materials, whose mechanical response depends on the magnitude of the loads that are applied on them, as well as on the time and temperature the loads are applied. Quality specifications for bitumens have been developed in order to provide rational parameters for the bitumen resistance to rutting, cracking and ageing under service conditions. Some parameters based on the complex shear modulus that are already in use, have shown limitations when evaluating and comparing the bitumens resistance to rutting. On the other side, the zero shear viscosity has shown satisfactory correlation for the rutting resistance of bitumens in pavements. This work presents the determination of zero shear viscosity for two Colombian bitumens, compared to Boscan bitumen. Each bitumen was tested in three different ageing level (initial, rolling thin film ageing and pressure vessel ageing). Master curves for complex viscosity versus frequency were obtained by oscillation tests, and master curves data were fitted to three rheological models (Carreau, Cross-Sibilsky and Cross-Williamson), by using the least squares error method. Other parameters from the models were also estimated. It was found that zero shear viscosity depends on the ageing level of the bitumen, while the dependence on the type of bitumen is not significant. Besides, the three models can describe properly the relationship between complex viscosity and frequency. The results show that zero shear viscosity can be used as a parameter in quality specification for the three bitumens studied.
Sol–Gel Synthesis of Nanostructured Mesoporous Silica Powder and Thin Films

Mesoporous materials are special nanoporous materials containing well-defined mesochannels with a pore diameter between 2 and 50 nm. The high surface area, ordered structure, tunable pore size, and easiness of functionalization have made mesoporous silica powder and thin films interesting materials for a wide range of applications including drug delivery, absorption, separation, catalysis, energy conversion, and storage. The sol–gel process has emerged as a promising technique for the synthesis of nanostructured mesoporous silica materials as it provides the advantages of low-temperature processing and easy control of the synthesis parameters. Although it offers several advantages over other synthesis techniques, it also has the drawbacks of high sensitivity to processing conditions. Hence, this review paper aims to give critical insights into the sol–gel process, the chemistry of sol–gel silica, the formation mechanism of mesoporosity, and the effects of the reaction parameters. A good understanding of these phenomena is essential to better control and optimize the properties of the final material for specific needs and applications. Additionally, this review paper discusses the different methods applied to the synthesis of nanostructured ordered mesoporous thin film silica, including the Electrochemically Assisted Self-Assembly method of synthesis. The EASA method is a novel and promising technique for the synthesis of well-ordered and vertically aligned pore channels of mesoporous thin films as it is required for mass transport applications. Moreover, the effects of sol composition, pH, applied potential, and deposition time on the final thickness of the thin film are elaborated on in detail. Furthermore, this comprehensive review highlights the potential and opportunities for future research and development in the area to further advance and use its full potential advantages.
An interceptive child-discipline progression model (ICdP) for child protection developed from parents’ conceptualization of discipline

Child protection is a crucial aspect of social work practice worldwide. Despite efforts to ensure a safe environment for children's development, child abuse in various forms continues to increase. One factor contributing to this issue is the way parents discipline their children. To better understand parents' disciplinary choices, the study was conducted using a qualitative approach with an Afrikology design. The study involved 24 participants, including 20 parents and four key informants, who were interviewed using an interview guide. The results showed that parents view discipline as a relational, punishment, modeling, mannerism, process, and skills-set. These perceptions influence the disciplinary methods used by parents, and understanding this thinking process can help inform child protection policies and interventions. To promote positive disciplinary practices, the researchers proposed an Interceptive Child-Discipline Progressive Model (ICdP), which used the ‘dare’ concept through the Delphi panel method for model development. This model encourages self-reflection during disciplinary interventions to identify and intercept negative ideas and notions perceived as a discipline before enactment to protect children from abuse under the pretext of discipline. How to reference using ASWNet style: Simango, T. G & Mafa, I. H (2023). An interceptive child-discipline progression model (ICdP) for child protection developed from parents’ conceptualization of discipline. African Journal of Social Work, 13(5), 244-253. https://dx.doi. org/10.4314/ajsw.v13i5. 3 Visit journal website: https://ajsw.africasocialwork. net
Construction of a corresponding empirical model to bridge thermal properties and synthesis of thermoresponsive poloxamines

ABSTRACT The thermoresponsive properties of poloxamine (tetra-branch PEO-PPO block copolymer) hydrogels are related to several variables. Of particular interest to this study were the molecular weight of the polymer, the molar ratio between PEO and PPO blocks, and the concentration of the aqueous solution. Accurately controlling the thermoresponsive behaviors of the polymer is critical to the application of such materials; therefore, the structure–property relationship of tetra-branch PEO-PPO block copolymer was studied by synthesis via anionic ring-opening polymerization (AROP). The structure–property relationships were studied by measuring the thermoresponsive behavior via differential scanning calorimetry (DSC) and developing an empirical model which statistically fit the collected data. This empirical model was then used for designing poloxamines that have critical micellization temperatures (CMT) between room temperature and physiological temperature. The model was validated with three polymers that targeted a CMT of 308 K (35°C). The empirical model showed great success in guiding the synthesis of poloxamines showing a temperature difference of less than 3 K between the predicted and the observed CMTs. This study showed a great potential of using an empirical model to set synthesis parameters to control the properties of the polymer products.
PaCO2 Association with Traumatic Brain Injury Patients Outcomes at High Altitude: A Prospective Single-Center Cohort Study

Background partial pressure of carbon dioxide (PaCO2) is generally known to influence outcome in patients with traumatic brain injury (TBI) at normal altitudes. Less is known about specific relationships of PaCO2 levels and clinical outcomes at high altitudes. Methods This is a prospective single-center cohort of consecutive TBI patients admitted to a trauma center located at 2600 meter above sea level. An unfavorable outcome was defined as the Glasgow Outcome Scale-Extended (GOSE) < 4 at 6-month follow-up. Results 81 patients with complete data, 80% (65/81) were men, and median (IQR) age was 36 (25–50) years). Median Glasgow Coma Scale (GCS) on admission was 9 (6–14), 49% (40/81) were severe (GCS: 3–8), 32% (26/81) moderate (GCS 12 − 9), and 18% (15/81) mild (GCS 13–15) TBI. The median (IQR) Abbreviated Injury Score of the Head (AISh) was 3 (2–4). Frequency of an unfavorable outcome (GOSE < 4) was 30% (25/81), median GOSE was 4 (2–5), and 6-month mortality was 24% (20/81). Comparison between patients with favorable and unfavorable outcomes revealed that those with unfavorable outcome were older, median [49 (30–72) vs. 29 (22–41), P < 0.01], had lower admission GCS [6 (4–8) vs. 13 (8–15), P < 0.01], higher AIS head [4 (4–4) vs. 3(2–4), p < 0.01], higher APACHE II score [17(15–23) vs 10 (6–14), < 0.01), higher Charlson score [0(0–2) vs. 0 (0–0), P < 0.01] and higher PaCO2 (mmHg), mean ± SD, 39 ± 9 vs. 32 ± 6, P < 0.01. In a multivariate analysis, age (OR 1.14 95% CI 1.1–1.30, P < 0.01), AISh (OR 4.7 95% CI 1.55–21.0, P < 0.05), and PaCO2 (OR 1.23 95% CI: 1.10–1.53, P < 0.05) were significantly associated with the unfavorable outcomes. When applying the same analysis to the subgroup on mechanical ventilation, AISh (OR 5.4 95% CI: 1.61–28.5, P = 0.017) and PaCO2 (OR 1.36 95% CI: 1.13–1.78, P = 0.015) remained significantly associated with the unfavorable outcome. Conclusion Higher PaCO2 levels are associated with an unfavorable outcome in ventilated TBI patients. These results underscore the importance of PaCO2 level in TBI patients and whether it should be adjusted for populations living at higher altitudes.
A targeted amplicon next-generation sequencing assay for tryptase genotyping to support personalized therapy in mast cell-related disorders

Tryptase, the most abundant mast cell granule protein, is elevated in severe asthma patients independent of type 2 inflammation status. Higher active β tryptase allele counts are associated with higher levels of peripheral tryptase and lower clinical benefit from anti-IgE therapies. Tryptase is a therapeutic target of interest in severe asthma and chronic spontaneous urticaria. Active and inactive allele counts may enable stratification to assess response to therapies in asthmatic patient subpopulations. Tryptase gene loci TPSAB1 and TPSB2 have high levels of sequence identity, which makes genotyping a challenging task. Here, we report a targeted next-generation sequencing (NGS) assay and downstream bioinformatics analysis for determining polymorphisms at tryptase TPSAB1 and TPSB2 loci. Machine learning modeling using multiple polymorphisms in the tryptase loci was used to improve the accuracy of genotyping calls. The assay was tested and qualified on DNA extracted from whole blood of healthy donors and asthma patients, achieving accuracy of 96%, 96% and 94% for estimation of inactive α and βΙΙΙFS tryptase alleles and α duplication on TPSAB1, respectively. The reported NGS assay is a cost-effective method that is more efficient than Sanger sequencing and provides coverage to evaluate known as well as unreported tryptase polymorphisms.
Identification of abdominal aortic aneurysm subtypes based on mechanosensitive genes

Background Rupture of abdominal aortic aneurysm (rAAA) is a fatal event in the elderly. Elevated blood pressure and weakening of vessel wall strength are major risk factors for this devastating event. This present study examined whether the expression profile of mechanosensitive genes correlates with the phenotype and outcome, thus, serving as a biomarker for AAA development. Methods In this study, we identified mechanosensitive genes involved in AAA development using general bioinformatics methods and machine learning with six human datasets publicly available from the GEO database. Differentially expressed mechanosensitive genes (DEMGs) in AAAs were identified by differential expression analysis. Molecular biological functions of genes were explored using functional clustering, Protein–protein interaction (PPI), and weighted gene co-expression network analysis (WGCNA). According to the datasets (GSE98278, GSE205071 and GSE165470), the changes of diameter and aortic wall strength of AAA induced by DEMGs were verified by consensus clustering analysis, machine learning models, and statistical analysis. In addition, a model for identifying AAA subtypes was built using machine learning methods. Results 38 DEMGs clustered in pathways regulating ‘Smooth muscle cell biology’ and ‘Cell or Tissue connectivity’. By analyzing the GSE205071 and GSE165470 datasets, DEMGs were found to respond to differences in aneurysm diameter and vessel wall strength. Thus, in the merged datasets, we formally created subgroups of AAAs and found differences in immune characteristics between the subgroups. Finally, a model that accurately predicts the AAA subtype that is more likely to rupture was successfully developed. Conclusion We identified 38 DEMGs that may be involved in AAA. This gene cluster is involved in regulating the maximum vessel diameter, degree of immunoinflammatory infiltration, and strength of the local vessel wall in AAA. The prognostic model we developed can accurately identify the AAA subtypes that tend to rupture.
When the antidote is the poison: Investigating the relationship between people’s social media usage and loneliness when face-to-face communication is restricted

When governments mandated lockdowns to limit the spread of the coronavirus, the resulting reduction of face-to-face communication threatened many people’s psychological well-being by fostering feelings of loneliness. Given social media’s eponymous social nature, we study the relationship between people’s social media usage and their loneliness during these times of physical social restrictions. We contrast literature highlighting the social value of social media with a competing logic based on the “internet paradox,” according to which increased social media usage may paradoxically be associated with increasing, not decreasing, levels of loneliness. As the extant literature provides opposing correlational insights into the general relationship of social media usage and loneliness, we offer competing hypotheses and offer novel longitudinal insights into the phenomenon of interest. In the empirical context of Germany’s initial lockdown, our research uses survey panel data from February 2020 (before the lockdown) and April 2020 (during the lockdown) to contribute longitudinal evidence to the matter. We find that more usage of social media in the studied lockdown setting is indeed associated with more, not less loneliness. Thus, our results suggest a “social media paradox” when physical social restrictions are mandated and caution social media users and policy makers to not consider social media as a valuable alternative for social interaction. A post-hoc analysis suggests that more communication via richer digital media which are available during physical lockdowns (e.g., video chats) softens the “social media paradox”. Conclusively, this research provides deeper insights into the social value of social interactions via digital media during lockdowns and contributes novel insights into the relationship between social media and loneliness during such times when physical social interaction is heavily restricted.
Machine Learning-Based Classification of Transcriptome Signatures of Non-Ulcerative Bladder Pain Syndrome

Lower urinary tract dysfunction (LUTD) presents a global health challenge with symptoms impacting a substantial percentage of the population. The absence of reliable biomarkers complicates the accurate classification of LUTD subtypes with shared symptoms such as non-ulcerative Bladder Pain Syndrome (BPS) and overactive bladder caused by bladder outlet obstruction with Detrusor Overactivity (DO). This study introduces a machine learning (ML)-based approach for the identification of mRNA signatures specific to non-ulcerative BPS. Using next-generation sequencing (NGS) transcriptome data from bladder biopsies of patients with BPS, benign prostatic obstruction with DO, and controls, our statistical approach successfully identified 13 candidate genes capable of discerning BPS from control and DO patients. This set was validated using Quantitative Polymerase Chain Reaction (QPCR) in a larger patient cohort. To confirm our findings, we applied both supervised and unsupervised ML approaches to the QPCR dataset. A three-mRNA signature TPPP3, FAT1, and NCALD, emerged as a robust classifier for non-ulcerative BPS. The ML-based framework used to define BPS classifiers establishes a solid foundation for comprehending the gene expression changes in the bladder during BPS and serves as a valuable resource and methodology for advancing signature identification in other fields. The proposed ML pipeline demonstrates its efficacy in handling challenges associated with limited sample sizes, offering a promising avenue for applications in similar domains.