The 2023 Bible Society New Zealand’s translation of sample biblical passages into the Māori language, He Tīmatanga, caused controversy by incorporating names of Māori gods. Those who objected typically assumed inconsistency with the Bible’s purported monotheism. But ‘monotheism’, in the sense that only one god exists, is not present in the Bible. Moreover, missionary adherence to monotheism in the mid-nineteenth century widely assumed a ‘degeneration model’ that also promoted European religious, moral, and cultural superiority. This article adopts a hermeneutical strategy to counter monotheistic misreadings of the Bible, and their racist effects, by reading Māori stories of the ancient divine hero Tāwhaki alongside the ancient divine heroes who feature in Gen 6:1–4’s account of the Nephilim. First, the comparison provides resources for the translation of Gen 6:1–4 into the Māori language and worldview. Second, the Tāwhaki narratives stimulate a reappraisal of longstanding problems in the interpretation of Gen 6:1–4, especially the meaning of the phrase “the sons of the gods”. Supported by analysis also of the Sumerian King List, this article argues that all three major interpretations of “the sons of the gods” are fundamentally consistent: they are gods, elite human rulers, and also Sethites. Full article

Hybrid bonded–bolted composite material interference connections significantly enhance the collaborative load-bearing capabilities of the adhesive layer and bolts, thus improving structural load-carrying capacity and fatigue life. So, these connections offer significant developmental potential and application prospects in aircraft structural assembly. However, interference causes damage to the adhesive layer and composite laminate around the holes, leading to issues with interface damage. In this study, we employed experimental and finite element methods. Initially, different interference-fit sizes were selected for bolt insertion to analyze the damage mechanism of the adhesive layer during interference-fit bolt installation. Subsequently, a finite element tensile model considering damage to the adhesive layer and composite laminate around the holes post-insertion was established. This study aimed to investigate damage in composite bonded–bolted hybrid joints, explore load-carrying rules and failure modes, and reveal the mechanisms of interference effects on structural damage and failure. The research results indicate that the finite element prediction model considering initial damage around the holes is more effective. As the interference-fit size increases, damage to the adhesive layer transitions from surface debonding to local cracking, while damage to the composite matrix shifts from slight compression failure to severe delamination and fiber-bending fracturing. The structural strength shows a trend of initially increasing and then decreasing, with the maximum strength observed at an interference-fit size of 1.1%. Full article

Currently, a clear interest has been given to berries due to their richness in active metabolites, including anthocyanins and non-coloured phenolics. Therefore, the main aim of the present work is to investigate the phenolic profile, antioxidant abilities, and antiproliferative effects on normal human dermal fibroblasts (NHDF) and human colon carcinoma cell line (Caco-2) cells of phenolic-rich extracts from three red fruits highly appreciated by consumers: two species of blackberries (Rubus fruticosus and Rubus ulmifolius) and one species of mulberry (Morus nigra). A total of 19 different phenolics were identified and quantified by HPLC-DAD-ESI/MSⁿ and HPLC-DAD, respectively. Focusing on the biological potential of the phenolic-rich extracts, all of them revealed notable scavenging abilities. Concerning the antiproliferative properties, R. fruticosus presented a cytotoxic selectivity for Caco-2 cells compared to NHDF cells. To deeper explore the biological potential, combinations with positive controls (ascorbic acid and 5-fluorouracil) were also conducted. Finally, the obtained data are another piece of evidence that the combination of phenolic-rich extracts from natural plants with positive controls may reduce clinical therapy costs and the possible toxicity of chemical drugs. Full article

Leishmaniasis and Human African trypanosomiasis pose significant public health threats in resource-limited regions, accentuated by the drawbacks of the current antiprotozoal treatments and the lack of approved vaccines. Considering the demand for novel therapeutic drugs, a series of BODIPY derivatives with several functionalizations at the meso, 2 and/or 6 positions of the core were synthesized and characterized. The in vitro activity against Trypanosoma brucei and Leishmania major parasites was carried out alongside a human healthy cell line (MRC-5) to establish selectivity indices (SIs). Notably, the meso-substituted BODIPY, with 1-dimethylaminonaphthalene (1b) and anthracene moiety (1c), were the most active against L. major, displaying IC₅₀ = 4.84 and 5.41 μM, with a 16 and 18-fold selectivity over MRC-5 cells, respectively. In contrast, the mono-formylated analogues 2b and 2c exhibited the highest toxicity (IC₅₀ = 2.84 and 6.17 μM, respectively) and selectivity (SI = 24 and 11, respectively) against T. brucei. Further insights on the activity of these compounds were gathered from molecular docking studies. The results suggest that these BODIPYs act as competitive inhibitors targeting the NADPH/NADP⁺ linkage site of the pteridine reductase (PR) enzyme. Additionally, these findings unveil a range of quasi-degenerate binding complexes formed between the PRs and the investigated BODIPY derivatives. These results suggest a potential correlation between the anti-parasitic activity and the presence of multiple configurations that block the same site of the enzyme. Full article

This paper presents an in-depth analysis of the oscillation phenomenon occurring in multi-chip parallel automotive-grade power modules under short-circuit conditions and investigates three suppression methods. We tested and analyzed two commercial automotive-grade power modules, one containing two chips and the other containing a single chip, and found that short-circuit gate oscillations were more likely to occur in multi-chip parallel packaged modules than in single-chip packaged modules. Through experimental and simulation analyses, we observed that gate oscillations were mainly caused by the interaction between internal parasitic parameters of the module and the external drive circuit, and we found that high drive resistance and low common emitter inductance between parallel chips could effectively suppress gate voltage oscillations. We also analyzed the two mainstream suppression schemes, increasing the drive gate resistance and placing the drive capacitors in parallel. Unfortunately, we found that these suppression schemes were not ideal solutions because both schemes changed the switching characteristics of the power module. As an alternative, we propose a simple and effective solution that involves adding parallel connections between the parallel chips. Simulation calculations showed that this optimized method reduced the emitter inductance between parallel chips in the upper bridge arm by about 30% and in the lower bridge arm by 35%. Through short-circuit experiments conducted at different DC bus voltages, it has been verified that the new optimized solution effectively resolves gate oscillation issues without affecting the switching characteristics of the power module. Full article

During the transition from liquid to solid, the thermal conductivity coefficient λ of concrete decreases. Although λ of hardened concrete is well investigated, there is limited research on the transition from liquid to solid and how it depends on hydration. Currently, only simplified qualitative approaches exist for the liquid state and the transient process. An experimental method is not available. For this purpose, a test rig is designed to experimentally capture the evolution of λ for fine-grain concretes during transition. The performance of the test setup is evaluated on a characteristic high-performance concrete (HPC). The results are compared to theoretical predictions from the literature. The developed test rig is mapped in a digital twin to investigate extended boundary conditions, such as different heat sources and temperatures of the experimental setup. It allows the experiment to be repeated and optimized for different setups with little effort. The test principle is as follows: A liquid concrete sample is heated through a controlled external source, while the transient temperature distribution over the height is measured with a fiber optic sensor. The thermal conductivity is derived from the heat flux induced and the temperature distribution over an evaluation length. Experiments show that λ in the liquid state is approximately 1.4 times greater than in the solid state and exponentially decreases for the transient process. Numerical results on the digital twin indicate that the robustness of the results increases with the temperature of the heat source. Moreover, the derivation in λ turns out to be strongly dependent on the evaluation length. A length of three times the maximum grain diameter is recommended. Full article

Addressing the constraints inherent in traditional primary Air Quality Index (AQI) forecasting models and the shortcomings in the exploitation of meteorological data, this research introduces a novel air quality prediction methodology leveraging machine learning and the enhanced modeling of secondary data. The dataset employed encompasses forecast data on primary pollutant concentrations and primary meteorological conditions, alongside actual meteorological observations and pollutant concentration measurements, spanning from 23 July 2020 to 13 July 2021, sourced from long-term air quality projections at various monitoring stations within Jinan, China. Initially, through a rigorous correlation analysis, ten meteorological factors were selected, comprising both measured and forecasted data across five categories each. Subsequently, the significance of these ten factors was assessed and ranked based on their impact on different pollutant concentrations, utilizing a combination of univariate and multivariate significance analyses alongside a random forest approach. Seasonal characteristic analysis highlighted the distinct seasonal impacts of temperature, humidity, air pressure, and general atmospheric conditions on the concentrations of six key air pollutants. The performance evaluation of various machine learning-based classification prediction models revealed the Light Gradient Boosting Machine (LightGBM) classifier as the most effective, achieving an accuracy rate of 97.5% and an F₁ score of 93.3%. Furthermore, experimental results for AQI prediction indicated the Long Short-Term Memory (LSTM) model as superior, demonstrating a goodness-of-fit of 91.37% for AQI predictions, 90.46% for O₃ predictions, and a perfect fit for the primary pollutant test set. Collectively, these findings affirm the reliability and efficacy of the employed machine learning models in air quality forecasting. Full article

Mini–unmanned aerial vehicles (mini-UAVs) are emerging as a promising platform for through-wall radar to sense the enclosed space in cities, especially high-rise buildings, due to their excellent maneuverability. However, due to unavoidable environmental interference such as airflow, mini-UAVs are prone to trajectory deviation thus degrading their sensing accuracy. Most of the existing approaches model the impact of trajectory deviation into a polynomial phase error on the received signal, which cannot fit the space-variant motion error well. Moreover, the large trajectory deviations of UAVs introduce the unavoidable envelope error. This article proposes an autofocusing algorithm based on the back projection (BP) image, which directly estimates the trajectory deviations between the actual and measured track. Thus, the problem of the 2D space variability of the motion error can be circumvented. The proposed method mainly consists of two steps. First, we estimate the trajectory deviation in the line-of-sight (LOS) direction by exploring the underlying linear property of the wall embedded in the BP imaging result. Then, the estimated trajectory deviation in the LOS direction is compensated for to obtain an updated BP image, followed by a Particle Swarm Optimization (PSO) approach to estimate the trajectory deviation along the track through focusing targets behind the wall. Simulations and practical experiments show that the proposed algorithm can accurately estimate the serious trajectory deviations larger than the range resolution, improving the sensing robustness of UAV-borne through-wall radar greatly. Full article

Energy transition and the creation of sustainable jobs are major concerns towards achieving Sustainable Development Goals (SDGs) 7 and 13, particularly in emerging petroleum-producing economies such as Ghana. Our study examines Ghana’s sectorial employment vulnerability to the dynamics of energy transition. Employing a dynamic ARDL simulation model, we use quarterly data from 2011 to 2021 from Ghana’s Energy Commission, the Bank of Ghana, and the Public Interest and Accounting Committee. We find that transition scenarios increase industrial sector employment. Also, industrial sector employment changes more favorably under the 5% scenario than under the 1% scenario. Agriculture industry employment is positively impacted by the 1% energy transition scenarios but negatively impacted by the 5% scenarios. Up to the sixth year, both transition scenarios increase employment in the services sector; however, employment opportunities are more affected by the 1% scenario than by the 5% scenario. Therefore, developing a policy architecture that aids Ghana’s transition to renewable energy is essential. Full article

This paper contextualizes Okudzhava’s song “And We to the Doorman” (AWD), initially marginal in the Soviet poetic mainstream. It explores its shifts in tone, irregular rhythms, colloquial language, and semi-criminal undertones. AWD’s structure, with uneven stanzas and no clear refrain, reveals underlying symmetry and recurring themes. The meter is predominantly iambic but varies. Unconventional verse endings and various rhyme schemes, including distant chains, characterize its prosody. The narrative touches on social cohesion and class conflict. The style reflects a challenging attitude toward privilege, employing rhetorical devices and indirect threats. The melody aligns with thematic elements, featuring repetitive patterns and a spoken quality. Semantically, AWD presents an ambiguous message on class struggle and moral issues. In sum, this analysis uncovers Okudzhava’s song’s formal complexities, thematic nuances, and stylistic innovations. Full article

Motion object detection (MOD) with freely moving cameras is a challenging task in computer vision. To extract moving objects, most studies have focused on the difference in motion features between foreground and background, which works well for dynamic scenes with relatively regular movements and variations. However, abrupt illumination changes and occlusions often occur in real-world scenes, and the camera may also pan, tilt, rotate, and jitter, etc., resulting in local irregular variations and global discontinuities in motion features. Such complex and changing scenes bring great difficulty in detecting moving objects. To solve this problem, this paper proposes a new MOD method that effectively leverages local and global visual information for foreground/background segmentation. Specifically, on the global side, to support a wider range of camera motion, the relative inter-frame transformations are optimized to absolute transformations referenced to intermediate frames in a global form after enriching the inter-frame matching pairs. The global transformation is fine-tuned using the spatial transformer network (STN). On the local side, to address the problem of dynamic background scenes, foreground object detection is optimized by utilizing the pixel differences between the current frame and the local background model, as well as the consistency of local spatial variations. Then, the spatial information is combined using optical flow segmentation methods, enhancing the precision of the object information. The experimental results show that our method achieves a detection accuracy improvement of over 1.5% compared with the state-of-the-art methods on the datasets of CDNET2014, FBMS-59, and CBD. It demonstrates significant effectiveness in challenging scenarios such as shadows, abrupt changes in illumination, camera jitter, occlusion, and moving backgrounds. Full article

The surfactant cetyltrimethylammonium bromide (CTAB) induces the aggregation of gold nanoclusters (GNCs), leading to the development of a proposed fluorometric technique for detecting thiocyanate (SCN⁻) ions based on an anti-aggregation mechanism. This approach is straightforward to execute, highly sensitive, and selective. A significant quenching effect occurs in fluorescence upon using the aggregation agent CTAB in GNCs synthesis, resulting in a transition from intense red fluorescence to dim red. The decrease in fluorescence intensity of GNCs in the presence of CTAB is caused by the mechanism of fluorescence quenching mediated by aggregation. As the levels of SCN⁻ rise, the fluorescence of CTAB-GNCs increases; this may be detected using spectrofluorometry or by visually inspecting under UV irradiation. The recovery of red fluorescence of CTAB-GNCs in the presence of SCN⁻ enables the precise and discerning identification of SCN⁻ within the concentration range of 2.86–140 nM. The minimum detectable concentration of the SCN⁻ ions was 1 nM. The selectivity of CTAB-GNCs towards SCN⁻ ions was investigated compared to other ions, and it was demonstrated that CTAB-GNCs exhibit exceptional selectivity. Furthermore, we believe that CTAB-GNCs have novel possibilities as favorable sensor candidates for various industrial applications. Our detection technique was validated by analyzing SCN⁻ ions in milk samples, which yielded promising results. Full article

In non-Markovian tandem queueing networks the output process of one site, which is the input process to the next site, is not renewal. Consequently, the correlation analysis of that output processes is essential when studying such networks. A correlation analysis in the M/G/1 queue has been studied in the literature via derivation of the joint Laplace-Stieltjes transform (LST) of the sum of two consecutive inter-departure times. That LST is obtained by considering all possible cases at departure epochs. However, those epochs are expressed via dependent variables. In this paper, we first extend the analysis to the more general PH/G/1 queue, and investigate various queues, such as E₂/G/1 and C₂/C₂/1. Then, we consider the lag-n correlation, which requires derivation of the joint LST of sum of n + 1 consecutive inter-departure times. Yet, deriving this LST by the common approach becomes impractical for n + 1 ≥ 3, as the number of all possible cases at departure epochs increases significantly. To overcome this obstacle, we derive a corresponding single-parameter LST, which expresses the sum of n+1 consecutive inter-departure times via the (n + 1)-st departure epoch only. Consequently, the latter LST is expressed via a much fewer number of possible cases, and not less important, as a function of independent variables only, eliminating the need to derive the corresponding joint density. Considering the M/G/1 and the E₂/G/1 queues, we demonstrate that the joint LST can be reconstructed directly via the corresponding single-parameter LST when n + 1 = 2. We further conjecture that the multi-parameter joint LST can be reconstructed from the corresponding single-parameter LST in more general queues and for values of n + 1 > 2. The conjecture is validated for various PH/G/1 queues and proved for n + 1 = 3 in the M/G/1 case. The new approach facilitates the calculation of lag-n correlation of the departure process from PH/G/1 queue for n + 1 ≥ 3. Our analysis illuminates the cases when using renewal approximation of the output process provides a proper approximation when studying non-Markovian stochastic networks. Full article

Cyclic air braking is a key element for ensuring safe train operation when running on a long and steep downhill railway section. In reality, the cyclic braking performance of a train is affected by its operating environment, speed and air-refilling time. Existing optimization algorithms have the problem of low learning efficiency. To solve this problem, an intelligent control method based on the deep Q-network (DQN) algorithm for heavy-haul trains running on long and steep downhill railway sections is proposed. Firstly, the environment of heavy-haul train operation is designed by considering the line characteristics, speed limits and constraints of the train pipe’s air-refilling time. Secondly, the control process of heavy-haul trains running on long and steep downhill sections is described as the reinforcement learning (RL) of a Markov decision process. By designing the critical elements of RL, a cyclic braking strategy for heavy-haul trains is established based on the reinforcement learning algorithm. Thirdly, the deep neural network and Q-learning are combined to design a neural network for approximating the action value function so that the algorithm can achieve the optimal action value function faster. Finally, simulation experiments are conducted on the actual track data pertaining to the Shuozhou–Huanghua line in China to compare the performance of the Q-learning algorithm against the DQN algorithm. Our findings revealed that the DQN-based intelligent control strategy decreased the air braking distance by 2.1% and enhanced the overall average speed by more than 7%. These experiments unequivocally demonstrate the efficacy and superiority of the DQN-based intelligent control strategy. Full article

Bending fatigue failures are commonly related to the wear behavior in an active system. The surface wear and plastic deformation of the tribolayer play crucial roles in the wear–bending fatigue behaviors of steels. In particular, the lamellar structure of martensitic steel leads to its unique wear–bending fatigue behavior. In this work, the wear–bending fatigue testing method and device were introduced to explore the wear–bending fatigue behavior of the martensitic steel. The effect of wear on the alternating bending fatigue life of 20CrNi2Mo martensitic steel was studied under low and high fatigue stress. The influence of wear debris on the fatigue life at two different sliding speeds was also analyzed. The results show that the fatigue life decreased with the wear load increased under high bending stress. Moreover, for systems with nanoscale wear debris on the steel surface, the wear–bending fatigue lifetimes are significantly enhanced compared with large wear debris. Full article

Enrofloxacin is a broad-spectrum synthetic antimicrobial drug widely used in veterinary medicine. The present study aimed to determine the effective enrofloxacin dose for treating Pseudomonas aeruginosa and Enterococcus faecalis infection in Oreochromis niloticus. P. aeruginosa and E. faecalis isolates were verified using selective differential media and biochemically using the Vitek 2 test. Bacterial isolates were virulent for O. niloticus with LD₅₀ equal to 2.03 × 10⁶ and 2.22 × 10⁷ CFU fish⁻¹ for P. aeruginosa and E. faecalis, respectively. Infected fish suffered from decreased feed intake followed by off-food, tail erosion, darkening of the external body surface, exophthalmia, ascites, and loss of escape reflex. Internally, congested hemorrhagic hepatopancreas with engorged distended gall bladder were dominant. The posterior kidney was congested with enlarged spleen, and empty elementary tract. Pathologically, severe degenerative changes were dominant in the hepatopancreas, posterior kidney, spleen, stomach, and gills of infected fish. Antimicrobial sensitivity test indicated the high susceptibility of P. aeruginosa and E. faecalis to enrofloxacin with MIC estimated at 1 and 0.0625 µg/mL, respectively. Enrofloxacin effectively protected O. niloticus against E. faecalis and P. aeruginosa infection when used with medicated feed at doses of 10 and 20 mg kg⁻¹ body weight. Full article

The use of 3D laparoscopic partial nephrectomy has emerged as a cornerstone in the surgical arsenal for addressing renal tumors, particularly in managing challenging cases characterized by deeply seated tumors embedded within the renal parenchyma. In these intricate scenarios, the utilization of intraoperative ultrasound (IOUS) acquires paramount importance, serving as an indispensable tool for guiding and meticulously monitoring the surgical process in real time. To further explore the efficacy of IOUS-guided techniques, we conducted a retrospective study comparing outcomes in patients who underwent partial nephrectomy with IOUS guidance (n = 60) between 2020 and 2022 with a cohort from 2018 to 2019 without IOUS guidance (n = 25). Our comprehensive analysis encompassed various post-operative parameters, including the duration until food resumption, analgesia requirements, and length of the hospital stay. While these parameters exhibited comparable outcomes between the two groups, notable distinctions emerged in the intraoperative metrics. The IOUS-guided cohort demonstrated significantly reduced blood loss, a shorter median operative duration, and diminished ischemia time (p = 0.001). These compelling findings underscore the undeniable benefits of IOUS-guided techniques in not only facilitating the attainment of negative surgical margins but also in enhancing procedural safety and precision, thereby contributing to improved patient outcomes in the management of renal tumors. Full article

We have described the influence of selected factors that increase the toxicity of nanoplastics (NPs) and microplastics (MPs) with regard to cell viability, various types of cell death, reactive oxygen species (ROS) induction, and genotoxicity. These factors include plastic particle size (NPs/MPs), zeta potential, exposure time, concentration, functionalization, and the influence of environmental factors and cell type. Studies have unequivocally shown that smaller plastic particles are more cytotoxic, penetrate cells more easily, increase ROS formation, and induce oxidative damage to proteins, lipids, and DNA. The toxic effects also increase with concentration and incubation time. NPs with positive zeta potential are also more toxic than those with a negative zeta potential because the cells are negatively charged, inducing stronger interactions. The deleterious effects of NPs and MPs are increased by functionalization with anionic or carboxyl groups, due to greater interaction with cell membrane components. Cationic NPs/MPs are particularly toxic due to their greater cellular uptake and/or their effects on cells and lysosomal membranes. The effects of polystyrene (PS) vary from one cell type to another, and normal cells are more sensitive to NPs than cancerous ones. The toxicity of NPs/MPs can be enhanced by environmental factors, including UV radiation, as they cause the particles to shrink and change their shape, which is a particularly important consideration when working with environmentally-changed NPs/MPs. In summary, the cytotoxicity, oxidative properties, and genotoxicity of plastic particles depends on their concentration, duration of action, and cell type. Also, NPs/MPs with a smaller diameter and positive zeta potential, and those exposed to UV and functionalized with amino groups, demonstrate higher toxicity than larger, non-functionalized and environmentally-unchanged particles with a negative zeta potential. Full article

This study reports five types of metal-doped (Co, Cu, Sn, V, and Zr) NASICON-type Li_1.3Al_0.3Ti_1.7(PO₄)₃ (LATP)/polymer composite solid electrolytes (CSEs) enabling Li₄Ti₅O₁₂ (LTO) anodes to have high rate capability and excellent cycling performance. The high Li⁺-conductivity LATP samples are successfully synthesized through a modified sol–gel method followed by thermal calcination. We find that the cation dopants clearly influence the substitution of Al for Ti, with the type of dopant serving as a crucial factor in determining the ionic conductivity and interfacial resistance of the solid electrolyte. The CSE containing poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP), lithium bis(trifluoromethanesulfonyl)imide (LiTFSI), and Sn-LATP shows an ionic conductivity of 1.88 × 10⁻⁴ S cm⁻¹ at ambient temperature. The optimum conductivity can be attributed to alterations in the lattice parameters and Li⁺ transport pathways owing to Sn doping. The solid-state cell equipped with the LTO-supported CSE containing Sn-LATP fillers demonstrates both excellent high rate capability at 5 C (with a capacity retention of 86% compared to the value measured at 0.2 C) and superior cycling stability, maintaining high Coulombic efficiency (>99.0%) over 510 cycles. These findings indicate that the proposed CSE is highly promising for use in solid-state lithium batteries with desirable charge–discharge properties and high durability. Full article

Exercise physiology is the science that studies the processes of physical activity and their impact on the body. It usually requires a multidisciplinary approach with a team of experts because of the multiple physiological systems involved. At the cellular level, exercise can trigger a cascade of events that involve metabolic pathways, muscle recruitment and conditioning, neuromuscular adaptations, and optimization of energy production. On a systematic level, this has involved the cardiovascular system, the respiratory system, the musculoskeletal system, the peripheral and central nervous systems, and even the endocrine system to improve adaptation to meet the demands of exercise, improving strength and endurance with the goal of maximizing performance. This field, which began largely with a focus on the kinetics of oxygen consumption and the relationship between aerobic and anaerobic metabolism, has expanded with technological advancements such as wearable devices, advanced imaging techniques, and genomics, which has allowed for a greater appreciation of the physiologic responses of exercise at the level of molecular and cellular interactions. Recently, this science has evolved into the study of overall health promotion and disease prevention to meet individual fitness needs and goals. This philosophy, which is captured by the motto “Exercise is Medicine”, has been adopted as a Global Health Initiative by the World Health Organization (WHO). Despite the advancements in technology and impactful, large-scale studies, a variety of questions, such as how physical activity interacts with aging to affect physical and cognitive function, how inactivity influences acute and chronic disease states, and how we maximize human performance in extreme conditions like microgravity environments, remain. The purpose of this review is to present and discuss established concepts, controversial topics, and unanswered questions within exercise physiology that have allowed this field to become a science that embraces the overall well-being of individuals. Full article

Fairness Artificial Intelligence (AI) aims to identify and mitigate bias throughout the AI development process, spanning data collection, modeling, assessment, and deployment—a critical facet of establishing trustworthy AI systems. Tackling data bias through techniques like reweighting samples proves effective for promoting fairness. This paper undertakes a systematic exploration of reweighting samples for conventional Machine-Learning (ML) models, utilizing five models for binary classification on datasets such as Adult Income and COMPAS, incorporating various protected attributes. In particular, AI Fairness 360 (AIF360) from IBM, a versatile open-source library aimed at identifying and mitigating bias in machine-learning models throughout the entire AI application lifecycle, is employed as the foundation for conducting this systematic exploration. The evaluation of prediction outcomes employs five fairness metrics from AIF360, elucidating the nuanced and model-specific efficacy of reweighting samples in fostering fairness within traditional ML frameworks. Experimental results illustrate that reweighting samples effectively reduces bias in traditional ML methods for classification tasks. For instance, after reweighting samples, the balanced accuracy of Decision Tree (DT) improves to 100%, and its bias, as measured by fairness metrics such as Average Odds Difference (AOD), Equal Opportunity Difference (EOD), and Theil Index (TI), is mitigated to 0. However, reweighting samples does not effectively enhance the fairness performance of K Nearest Neighbor (KNN). This sheds light on the intricate dynamics of bias, underscoring the complexity involved in achieving fairness across different models and scenarios. Full article

Background: A high incidence rate of nasopharyngeal carcinoma (NPC) has been observed in Southeast Asia compared to other parts of the world. Radiomics is a computational tool to predict outcomes and may be used as a prognostic biomarker for advanced NPC treated with concurrent chemoradiotherapy. Recently, radiomic analysis of the peripheral tumor microenvironment (TME), which is the region surrounding the gross tumor volume (GTV), has shown prognostic usefulness. In this study, not only was gross tumor volume (GTVt) analyzed but also tumor peripheral regions (GTVp) were explored in terms of the TME concept. Both radiomic features and delta radiomic features were analyzed using CT images acquired in a routine radiotherapy process. Methods: A total of 50 patients with NPC stages III, IVA, and IVB were enrolled between September 2004 and February 2014. Survival models were built using Cox regression with clinical factors (i.e., gender, age, overall stage, T stage, N stage, and treatment dose) and radiomic features. Radiomic features were extracted from GTVt and GTVp. GTVp was created surrounding GTVt for TME consideration. Furthermore, delta radiomics, which is the longitudinal change in quantitative radiomic features, was utilized for analysis. Finally, C-index values were computed using leave-one-out cross-validation (LOOCV) to evaluate the performances of all prognosis models. Results: Models were built for three different clinical outcomes, including overall survival (OS), local recurrence-free survival (LRFS), and progression-free survival (PFS). The range of the C-index in clinical factor models was (0.622, 0.729). All radiomics models, including delta radiomics models, were in the range of (0.718, 0.872). Among delta radiomics models, GTVt and GTVp were in the range of (0.833, 0.872) and (0.799, 0.834), respectively. Conclusions: Radiomic analysis on the proximal region surrounding the gross tumor volume of advanced NPC patients for survival outcome evaluation was investigated, and preliminary positive results were obtained. Radiomic models and delta radiomic models demonstrated performance that was either superior to or comparable with that of conventional clinical models. Full article