Fibromyalgia, a chronic pain condition marked by abnormal pain processing, impacts a significant part of the population, leading to reduced quality of life and function. Hallmark symptoms include widespread persistent pain, sleep disturbances, fatigue, cognitive dysfunction, and mood changes. Through this updated review, we aim to contribute to the evolving understanding and management of fibromyalgia, offering insights into the diverse tools available to improve the lives of those affected by this challenging condition. Management begins with educating patients to ultimately relieve them of unnecessary testing and provide reassurance. Treatment emphasizes a comprehensive approach, combining nonpharmacological interventions such as aforementioned education, exercise, and psychotherapy, alongside pharmacologic management—namely duloxetine, milnacipran, pregabalin, and amitriptyline—which have consistent benefits for a range of symptoms across the spectrum of fibromyalgia. Notably, drugs like nonsteroidal anti-inflammatory drugs (NSAIDs) and acetaminophen are generally not recommended due to limited efficacy and associated risks. Lastly, a variety of other medications have shown promise, including NMDA-receptor antagonists, naltrexone, and cannabinoids; however, they should be used with caution due to a small amount of evidence and potential for adverse effects. Full article

This study aims to describe the prevalence of malocclusion and identify associated factors in preschool children. Completed in 2022–2023, this cross-sectional study included 523 children aged 26 to 80 months in municipal schools in Salvador. An oral examination was carried out on the children, and a questionnaire was self-administered by the parents. Descriptive analyses and multivariate logistic regression (the backward method, p-value ≤ 0.05, 95% CI) were conducted. The majority of children were female (51.82%), over 54 months old (52.2%), Black or mixed race (90.63%), and not affected by COVID-19 (92.35%). The prevalence of malocclusion was 43.21%, with open bite as the most common condition. There was a significant association between malocclusion and screen time (OR: 1.34; p: 0.116; CI: 1.0–1.94), physical/psychological aggression (OR: 2.55; p: 0.031; CI: 1.0–5.98), consumption of ultra-processed foods (OR: 1.77; p: 0.003; CI: 1.22–2.57), digital suction (OR: 3.1; p: 0.001; CI: 1.56–6.16), and the habit of biting objects (OR: 1.56; p: 0.121; CI: 1.0–2.73). The promotion of comprehensive health in early childhood and psychosocial interventions are recommended, aiming to reduce screen time, aggression, consumption of ultra-processed foods, thumb sucking, and the habit of biting objects to prevent malocclusion. Full article

The Women’s March on 21 January 2017, opened a new social and political landscape for Muslim women to engage in Islamic liberatory activism. I locate Muslim women’s participation in the marches following the 2017 ‘Muslim travel ban policy’ as a site for discovering the link between the politics of resistance and the utility of Islam as a source for liberation. I argue that Muslim women living in minority and post-secular contexts resort to faith as a source of agentival liberation to address the political rhetoric of anti-Islamic sentiments and policies. The outcome of this research demonstrates (1) how Muslim women activists challenge the Western narratives of being oppressed and explore the ways they want to represent themselves; (2) how Islam serves as a catalyst for theological resistance and how this enhances the role of Muslim women as moral and spiritual agents in transforming their political and social conditions; (3) how the Islamic liberation in the US context historically intersects with Black churches’ resistance toward White racism; and (4) how Muslim women’s agency as spiritual beings is linked to the promotion of justice in the Western liberatory movements. Overall, the article shows how Muslim women resort to their spiritual journey and use such narratives to confront unjust political rhetoric and policies. Full article

In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson’s disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F₁ score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson’s Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups. Full article

Diesel engine combustion is dependent mainly on the fuel injection characteristics, particularly the injection pressure and rate, which directly affect the engine efficiency and emissions. Herein, an electrically controlled supercharger is added to a traditional high-pressure common rail system to form an ultrahigh-pressure common rail system. Then, the variations in the spray, combustion, and emission characteristics of a diesel engine with a variable fuel injection rate are analyzed. Moreover, a simulation model for a diesel engine combustion chamber is built and verified by experimental results for numerical analysis. The results reveal that the injection rate can be flexibly adjusted via regulation when the solenoid valves are opened on the electrically controlled supercharger. Specifically, (1) the boot-shaped injection rate has greater potential than the traditional rectangular injection rate in terms of combustion and emission; (2) the main injection advance angle at the boot-shaped injection rate can be properly increased to improve combustion; and (3) the pilot injection quantity and advance angle are strongly coupled with the boot-shaped injection rate, potentially enhancing the mixing efficiency of fuel and air in the cylinder to achieve favorable emission results. This paper provides good guidance for the reliable design and optimization of noble-metal-based diesel engines. Full article

Urgent action is required to mitigate the severe corrosion of carbon steel in low-latitude regions. The combination of high humidity, temperature, and salinity in these areas significantly accelerates steel corrosion, posing a substantial threat to the service safety of offshore engineering equipment. This study aims to elucidate the atmospheric corrosion mechanisms of 45# steel in low-latitude coastal areas. Samples of 45# steel were exposed to atmospheric conditions over various durations in the following three geographically distinct regions: Guangzhou, Wanning, and the South China Sea. The corrosion rates were calculated using weight loss tracking and potentiodynamic polarization measurements, while surface corrosion products were examined using X-ray diffraction (XRD) tests. The findings indicate a clear correlation between the corrosion rate of 45# steel and the latitude and specific location of the test area, with the highest to lowest rates observed in the South China Sea, Wanning, and Guangzhou, respectively. Similarly, the extent of corrosion rust penetration in defective coatings followed the same order. Moreover, the protection ability index (PAI) calculations revealed that none of the tested samples formed a protective corrosion film. Full article

Already used in the food, pharmaceutical, cosmetic, and agrochemical industries, encapsulation is a strategy used to protect active ingredients from external degradation factors and to control their release kinetics. Various encapsulation techniques have been studied, both to optimise the level of protection with respect to the nature of the aggressor and to favour a release mechanism between diffusion of the active compounds and degradation of the barrier material. Biopolymers are of particular interest as wall materials because of their biocompatibility, biodegradability, and non-toxicity. By forming a stable hydrogel around the drug, they provide a ‘smart’ barrier whose behaviour can change in response to environmental conditions. After a comprehensive description of the concept of encapsulation and the main technologies used to achieve encapsulation, including micro- and nano-gels, the mechanisms of controlled release of active compounds are presented. A panorama of natural polymers as wall materials is then presented, highlighting the main results associated with each polymer and attempting to identify the most cost-effective and suitable methods in terms of the encapsulated drug. Full article

Using green synthetic methods, a manganese-cobalt oxyhydroxide (MnCo-OOH) nanocomposite for electrocatalysis was prepared. Electrocatalysts were examined using powder X-ray diffraction analysis (XRD), Fourier transform infrared spectroscopy (FT-IR), and field-emission scanning electron microscopy (FESEM). In an alkaline medium, cyclic voltammetry and chronoamperometric analysis were applied to assess the electrocatalytic features of the MnCo-OOH nanocomposite. A strong correlation existed between MnCo-OOH’s morphology, crystallinity, and electrochemical activity. Upon examining the electrochemical characteristics, the as-deposited MnCo-OOH catalyst demonstrated a significantly lower overpotential, achieving 75 mA·cm⁻² OER current density at 370 mV, four times larger than 19.7 mA·cm⁻² for CoOOH catalysts, signifying that the MnCo-OOH catalyst exhibits a higher electrocatalytic OER features. In addition, the MnCo-OOH nanocomposite demonstrated a high current density of 30 and 65 mA·cm⁻² at 1.55 and 1.60 V_RHE for 12 h in 1.0 M KOH aqueous electrolyte. As a result of this study, it was determined that the fabricated MnCo-OOH nanocomposite would be an appropriate electrocatalyst in water electrolysis. Full article

Komagataella phaffii (formerly Pichia pastoris) is a methylotrophic yeast widely used in laboratories around the world to produce recombinant proteins. Given its advantageous features, it has also gained much interest in the context of modern biotechnology. In this review, we present the utilization of K. phaffii as a platform to produce several products of economic interest such as biopharmaceuticals, renewable chemicals, fuels, biomaterials, and food/feed products. Finally, we present synthetic biology approaches currently used for strain engineering, aiming at the production of new bioproducts. Full article

Fusarium head blight (FHB) can cause serious yield loss and significant mycotoxin contamination, which seriously threaten global food security and safety. Breeding stable and durable cultivars that are resistant to FHB is one of the most effective approaches to controlling this disease. Fhb1 is a well-known genetic locus for FHB resistance, but its resistance is not always effective across diverse wheat genetic backgrounds. To achieve a high and durable level of resistance, the discovery and use of additional quantitative trait loci (QTL) for FHB resistance are essentially needed in breeding programs. In this study, two independent wheat natural populations of different origins were used for mining resistance QTL with a major and stable effect. Using genome-wide association analysis (GWAS), a total of 58 marker–trait associations (MTAs) on chromosomes 1A, 2B, 3A, 3B, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A and 7B were found to be significant for type II resistance to FHB. These 58 MTAs represent 24 putative QTL. Among these QTL, Qfhb.yzu.3B.1 and Qfhb.yzu.6B.3 were stably detected in the two natural populations across three consecutive experimental years. The favorable haplotypes at the two QTL could significantly reduce the disease severity, either individually or in combination. These two QTL are also additive to Fhb1 in cultivars with different genetic backgrounds. Breeder-friendly markers were designed to differentiate the contrasting alleles at these two loci, thus proving very useful for improving FHB resistance in wheat by marker-assisted selection. Full article

Precipitation is an essential component of the hydrological cycle that directly affects human lives. An accurate and early detection of a future rainfall event can help prevent social, environmental, and economic losses. Traditional methods for accurate rainfall prediction have faltered due to their weakness in quantifying nonlinear climatic conditions as they involve numerical weather prediction using radar to solve complex mathematical equations based on contemporary meteorological data. This study aims to develop a precise rainfall forecast model using machine learning (ML), and this model focuses on long short-term memory (LSTM) to enhance rainfall prediction accuracy. In recent years, machine learning (ML) algorithms have emerged as powerful tools for predicting extreme weather phenomena worldwide. For instance, long short-term memory (LSTM) is a forecast model that effectively estimates the amount of precipitation based on historical data. We analyzed 85,470 pieces of daily rainfall data from 1983 to 2021 collected from each of four synoptic stations in Rwanda (Kigali Aero, Ruhengeri Aero, Kamembe Aero, and Gisenyi Aero). Advanced ML algorithms, including convolutional neural networks (CNNs), gated recurrent units (GRUs), and LSTM, were applied to predict extreme rainfall events. LSTM outperforms the CNN and GRU with 99.7%, 99.8%, and 99.7% accuracy. LSTM’s ability to filter out noise showed important patterns by handling irregularities in rainfall data to improve forecast results. Our outcomes have significant implications for disaster preparedness and risk mitigation efforts in Rwanda, where frequent natural disasters, including floods, pose a challenge. Our research also demonstrates the superiority of LSTM-based ML algorithms in predicting extreme rainfall events, highlighting their potential to enhance disaster risk resilience and preparedness strategies in Rwanda. Full article

Sodium-ion batteries (SIBs) have received considerable attention in recent years. Anode material is one of the key factors that determine SIBs’ electrochemical performance. Current commercial hard carbon anode shows poor rate performance, which greatly limits applications of SIBs. In this study, a novel vanadium-based material, SrV₄O₉, was proposed as an anode for SIBs, and its Na⁺ storage properties were studied for the first time. To enhance the electrical conductivity of SrV₄O₉ material, a microflower structure was designed and reduced graphene oxide (rGO) was introduced as a host to support SrV₄O₉ microflowers. The microflower structure effectively reduced electron diffusion distance, thus enhancing the electrical conductivity of the SrV₄O₉ material. The rGO showed excellent flexibility and electrical conductivity, which effectively improved the cycling life and rate performance of the SrV₄O₉ composite material. As a result, the SrV₄O₉@rGO composite showed excellent electrochemical performance (a stable capacity of 273.4 mAh g⁻¹ after 200 cycles at 0.2 A g⁻¹ and a high capacity of 120.4 mAh g⁻¹ at 10.0 A g⁻¹), indicating that SrV₄O₉@rGO composite can be an ideal anode material for SIBs. Full article

As global water scarcity becomes increasingly acute, water demand forecasting has emerged as a critical component in water resource management and planning. This review aims to comprehensively survey and analyze the current state of research, existing issues, and development trends in the field of water demand forecasting. Presently, there are numerous studies on water demand forecasting; however, most of the forecasting results tend to be overestimated. On the mechanistic level, research has gradually shifted from considering single factors to accounting for the complex influences of multiple factors. This paper summarizes the mechanism of water demand from the three levels of agriculture, industry, and residential life. In terms of forecasting methods, various techniques have been explored and applied, particularly new methods based on artificial intelligence and machine learning, which have demonstrated significant advantages in improving forecasting accuracy and handling nonlinear relationships. Despite the notable progress and practical achievements in water demand forecasting, several challenges and issues remain. Future research should focus on diversifying methodologies, comprehensively considering multiple influencing factors, further refining forecasting models and technical systems, strengthening uncertainty and risk management, and emphasizing practical applications and policy guidance. Full article

The increased prevalence of obesity worldwide has been implicated in the alarming rise of the incidence of gestational diabetes and preeclampsia, which are both considered threatening conditions for both mother and fetus. We studied gene polymorphisms of the proinflammatory cytokine Interleukin 6 (IL-6) and the gene expression levels of VEGF (vascular endothelial growth factor) and VEGF-R (endothelial growth factor receptor), all known to be involved in pregnancy complications, aiming to identify possible predisposing risk factors in pregnancies with obesity. The G allele of IL-6 was found to correspond with an increased risk for gestational diabetes and preeclampsia occurrence. Furthermore, in obese pregnant mothers with either gestational diabetes or pre-existing type 2 diabetes and those who developed preeclampsia, it was confirmed that gene expression levels of VEGF were reduced while they were increased for VEGF receptors. We conclude that the genetic profile of an obese pregnant woman shares a common background with that of a patient with pre-existing type 2 diabetes mellitus, and therefore predisposes them to complications in pregnancy. Full article

In the early 2000s, matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) emerged as a performant and relevant tool for identifying micro-organisms. Since then, it has become practically essential for identifying bacteria in microbiological diagnostic laboratories. In the last decade, it was successfully applied for arthropod identification, allowing researchers to distinguish vectors from non-vectors of infectious diseases. However, identification failures are not rare, hampering its wide use. Failure is generally attributed either to the absence of respective counter species MS spectra in the database or to the insufficient quality of query MS spectra (i.e., lower intensity and diversity of MS peaks detected). To avoid matching errors due to non-compliant spectra, the development of a strategy for detecting and excluding outlier MS profiles became compulsory. To this end, we created MSProfileR, an R package leading to a bioinformatics tool through a simple installation, integrating a control quality system of MS spectra and an analysis pipeline including peak detection and MS spectra comparisons. MSProfileR can also add metadata concerning the sample that the spectra are derived from. MSProfileR has been developed in the R environment and offers a user-friendly web interface using the R Shiny framework. It is available on Microsoft Windows as a web browser application by simple navigation using the link of the package on Github v.3.10.0. MSProfileR is therefore accessible to non-computer specialists and is freely available to the scientific community. We evaluated MSProfileR using two datasets including exclusively MS spectra from arthropods. In addition to coherent sample classification, outlier MS spectra were detected in each dataset confirming the value of MSProfileR. Full article

This paper explores the influence and significance of the activities of the monks’ militia during the Japanese invasion of Chosŏn, from the perspective of the religious efficacy of Buddhism and the spread of the Buddhist concept of wonder. After examining the concept that the monks’ militia played an important part in the war, fighting against enemies in major battles and constructing and defending fortresses, this paper proposes that the religious efficacy of Buddhism was revealed through the performance of burial and guiding ceremonies. Restoring the religious wonder of Buddhism, which had been criticized by the Confucian literati, Buddhist rituals for consoling the bereaved and praying for the welfare of the dead came to thrive. A dilemma existed between the principle of keeping the Buddhist precepts and the reality of fulfilling the demands of loyalty since the activities of the monks’ militia greatly damaged the Buddhist community. While killing was a direct infringement of the values of the sangha, the monks violated this precept in the cause of protecting the state and practicing loyalty. In this situation, where there was such a dilemma between the Buddhist and secular worlds, these monks’ prioritization of loyalty not only indicated the desperate national situation of the time but also reflected the social, cultural, and political context of the Confucian society of Chosŏn. This paper also explores how renowned generals of the monks’ militia, including Samyŏng Yujŏng, emerged as heroes among the people, and memories of their deeds were transmitted through wonder stories. Yujŏng was highly praised as a symbol of Buddhist loyalty, and his heroic story was expanded and reproduced among the population through folk tales and novels. While the intellectuals of Chosŏn who followed Confucian values did not believe those wonder stories, the trauma that the war left behind demanded the appearance of wondrous heroes who helped people overcome that trauma, and this demand enabled Yujŏng to emerge as one of these heroic figures. The activities of the monks’ militia, the religious efficacy of Buddhism, and the creation of the heroic narratives of the monks’ militia generals prove that Buddhism had a firm foundation in late Chosŏn society. Full article

Changing weather patterns have already put pressure on cropping systems around the globe. Projected increases in mean temperatures and variance in precipitation will likely affect the profitability of current cropping patterns, leading to shifts in which crops are grown in a given location. The pressure on water resources in a location, in terms of both water quantity and water quality, will also change with the types of crops grown. While the southeastern United States is projected to become warmer under each of the representative concentration pathways, it is also projected to become somewhat wetter. California’s Central Valley, where much of the fresh produce in the US is grown, will likely continue to suffer significant and extended droughts. The southeastern US is a prime candidate for expanding fresh produce production in response to reduced yields in the west. This paper explores the consequences on water withdrawals and water quality of shifting from row crop to vegetable production in the southeastern US. The water quality consequences are based on changes in pesticide products and application rates. The water quantity consequences are based on crop water needs. The methodology used here can be applied to other production systems around the world. Identifying the water quality and quantity implications of shifting cropping patterns is critical to the long-term sustainability of water resources. Full article

The metal–organic framework (MOF) materials with significant steadiness and a large specific surface area have been popular with supercapacitor material in recent years. However, its application in supercapacitors is restricted due to the low specific capacitance and poor conductivity. Herein, sulfur compounds with a high theoretical specific capacitance and highly conductive titanium nitride (TiN) were introduced into Co-based metal–organic frameworks/nickel foam (Co-MOF/NF) through a two-step hydrothermal technique (nickel sulfide/titanium nitride@ Co-based metal–organic frameworks/nickel foam). In detail, the fabricated nickel sulfide/titanium nitride@Co-based metal–organic frameworks/nickel foam (Ni₃S₂/TiN@Co-MOF/NF) electrode material exhibits a markedly high specific capacitance (2648.8 F g⁻¹) at 1 A g⁻¹, compared with that (770 F g⁻¹) of the precursor Co-MOF/NF. And its mass specific capacitance is retained 88.3% (8 A g⁻¹) after 5000 cycles. Furthermore, a non-symmetrical supercapacitor (ASC) composed of Ni₃S₂/TiN@Co-MOF/NF and AC exhibits excellent power density (801.8 W kg⁻¹) and energy density (97.8 W h kg⁻¹). Therefore, Ni₃S₂/TiN@Co-MOF/NF with excellent electrochemical properties and stability provides new ideas for the development of excellent supercapacitor electrode materials. Full article

(1) Self-regulation of driving is a means of maintaining one’s driving identity. The purpose of this study was to investigate the extent to which older drivers with Mild Cognitive Impairment (MCI) are metacognitively aware of the requirements of specific demanding driving conditions and whether this awareness is linked to subjective assessments of cognition. (2) One hundred seventeen (117) older MCI drivers participated in a telephone survey in which they reported their metacognitive experiences in nine driving conditions, listed as an aim of self-regulation. The analyses included the participants’ subjective cognitive assessments, both in terms of their cognitive state and their perceived driving self-efficacy. (3) The analyses pointed out a direct and negative effect of age on the formation of the metacognitive feeling of certainty. Furthermore, an indirect effect of sex through driving self-efficacy was established. This effect was negative in the case of the metacognitive feeling of difficulty and the estimation of effort and positive in the case of the metacognitive feeling of certainty. (4) This position points out the need to establish appropriate levels of the perceived self-efficacy of older drivers with MCI, and it raises issues when it moves to fictitious levels. Full article

The Dinaric karst in the north differs from the rest of the karst in Croatia in terms of karstification depth. The infiltrating precipitation drains in cascades from deeply karstified mountainous areas to the shallow or fluviokarst, forming the tributaries of the Kupa River. Time series analyses were conducted on a 5-year dataset to elucidate the hydrogeological conceptual model of the area and clarify disparate findings from tracer tests under varying hydrological conditions. The flow duration curve, autocorrelation functions, and recession curves were used to evaluate the spring discharge variability, the karstification degree, and the karst aquifer’s size. The crosscorrelation function and temperature dynamics were employed to assess the spring’s response to recharge and the hydrogeological system behavior. Comparative analysis with previous studies was conducted to contextualize the obtained results. The research outcomes delineated several key findings: (i) the deep karst zone is less developed than the shallow karst zone; (ii) groundwater exchange is significantly faster in shallow karst; (iii) groundwater divides in the Kapela Mountain are zonal; (iv) the homogenization of groundwater occurs during periods of high water levels; (v) fast water exchange transpires without concurrent groundwater temperature homogenization; and (vi) a definition of the boundary between deep and fluviokarst in Croatia. Full article

Many scholars have understood conspiracy theories as sense-making mechanisms. Among them, a particular strand further inspected them in parallel with religion and magic. This comparison bears the risk of framing conspiracy theories as irrational interpretations and anachronisms with respect to contemporary ways of explaining reality. This contribution takes off from the reflections of those who have highlighted this risk. It tries to confront the possibility of using the concept of enchantment without implying a normative judgment on the irrationality of conspiracy theories. This paper carries out this effort by closely inspecting Max Weber’s texts. I argue that Weber’s notion of enchantment and disenchantment allows for a punctual use of both, devoid of normative implications. After setting out this non-normative notion of enchantment, this paper examines the characteristics of the enchanted worldview and its usefulness in reading conspiracy theories. Finally, this paper supports this effort using the identified characteristics to systematise a reading of conspiracy-based accounts of the COVID-19 pandemic. Full article

Analyzing crowdfunding data has been the focus of many research efforts, where analysts typically explore this data to identify the main factors and characteristics of the lending process as well as to discover unique patterns and anomalies in loan distributions. However, the manual exploration and visualization of such data is clearly an ad hoc, time-consuming, and labor-intensive process. Hence, in this work, we propose LoanVis, which is an automated solution for discovering and recommending those valuable and insightful visualizations. LoanVis is a data-driven system that utilizes objective metrics to quantify the “interestingness” of a visualization and employs such metrics in the recommendation process. We demonstrate the effectiveness of LoanVis in analyzing and exploring different aspects of the Kiva crowdfunding dataset. Full article