Cyclins, commonly referred to as co-enzymes, are a pivotal family of proteins that modulate cellular growth by activating cell-cycle mediators, proving essential for the cell cycle. Due to the marked dissimilarity in their sequences, effective differentiation among cyclins remains a challenging endeavour. In this study, an innovative methodology was proposed, wherein the amino acid composition was utilized to inform an SVM-based classification approach. SVMs, being supervised machine learning algorithms, are typically employed for classification and regression tasks. From the data analyzed, eighteen (18) feature labels were extracted, culminating in an extensive set of thirteen thousand one hundred and fifty-one (13,151) discernible features. Employing the jackknife cross-validation technique revealed that this SVM-informed approach facilitated the identification of cyclins with an accuracy rate of 91.9\%, a notable improvement from prior studies. Such advancements underscore the potential for more accurate and efficient differentiation of cyclins in future endeavours.

In this investigation, the robust H$\infty$ control of nonlinear electric vehicles (EVs), powered by permanent magnet synchronous motors (PMSM), was examined. Emphasis was placed on enhancing the accuracy and robustness of the vehicle speed regulation by incorporating a meticulous H$\infty$ method, supplemented by the proficient integration of Linear Matrix Inequality (LMI). A solution predicated on the LMI approach was devised, encompassing two distinct H$\infty$ controllers for both current and speed control. Subsequent to an extensive analysis of the mathematical and control model of the EV, weighting functions were judiciously selected to optimize stability and performance. The proposed methodology offers significant advancements in the domain of EV control strategies and proffers insights into the application of robust control methods. Through comprehensive simulations, the effectiveness of the outlined method was validated, revealing impeccable speed control and ensuring steadfast performance when applied to the dynamic model of an EV equipped with a PMSM motor. This research elucidates the progressive strides made in the realm of EV control tactics and offers profound understandings of robust control methodologies.

To assess sustainable attitudes towards environmental issues, understanding the most impactful variables amongst sub-dimensions of attitudes proves critical. In this research, the subdimensions of attitudes of students towards environmental challenges were modelled. An online Likert-scale questionnaire, spanning from 1 'Strongly Disagree' to 5 'Strongly Agree', was administered to 380 high school and associate degree students in Afyonkarahisar city center between 15 September and 15 November 2022. The questionnaire aimed to gauge the students' attitudes using the Affective, Cognitive, and Behavioural sub-dimensions. Results revealed a statistically significant effect coefficient of 0.557 between the cognitive and affective attitudes. In a similar vein, the cognitive attitude's impact on behavioural attitude was found to be statistically significant with an effect coefficient of 0.534. However, a coefficient of 0.017 between affective and behavioural attitudes demonstrated no statistically significant mediator effect. Contrary to the initial hypotheses surrounding the mediator effect of affective attitude on behavioural attitude, the findings indicate that cognitive and affective attitudes independently influence behavioural attitude. Within the cognitive dimension, the awareness of the escalating environmental problems emerged as a paramount item. It is implied that for fostering sustainable environmental behaviour, the cognitive dimension plays a pivotal role.

Innovative lightweight smart structures incorporating piezoelectric material-based active elements, both as sensors and actuators, have been identified to present manifold advantages over traditional passive systems. Such structures have become intrinsically integrated into smart mechatronic systems, necessitating advanced design, testing, and control techniques. Real-time simulation of shell-type deformable objects, especially when employing the finite element method for non-linear analysis and control, has been challenging due to the extensive computational demand. Presented herein is an efficacious implementation leveraging machine learning with the isogeometric finite element formulation. This implementation focuses on shell-like smart mechatronic structures crafted from composite laminates comprising piezoelectric layers, which are characterised by electro-mechanical coupling. The foundation for the shell kinematics is derived from the Mindlin-Reissner assumptions, effectively incorporating transverse shear effects. While the inclusion of machine learning facilitates real-time efficient operations, the isogeometric finite element analysis (FEA) introduces pronounced advantages over conventional finite element method (FEM), also serving as a valuable source of offline data crucial for the training phases of machine learning algorithms. A piezo-laminated semicircular arch has been analysed to exemplify the effectiveness and performance of the presented methodology. Explorations into further machine learning techniques and intelligent control schemes are also contemplated.

Advancements in technology have revolutionized communication, socialization, and work paradigms. The surges in globalization, the permeation of digital culture, and the expansion of online communication tools have prompted organizations globally to adopt virtual teams. These virtual environments, while beneficial, present a myriad of challenges that necessitate the application of system dynamics to optimize performance. A systematic review was conducted to analyze previous studies focusing on the leadership of virtual teams within the context of systems thinking. Seven databases, including Sage Online, Springer, JSTOR, Taylor and Wiley Online Library, Francis Online, Google Scholar, and Semantic Scholar, were utilized. From an initial pool of 5,070 studies, 30 were meticulously screened, summarized, and synthesized based on pre-established inclusion and exclusion criteria. The review highlighted the recurrent emphasis on factors such as communication technology, trust, intra-team relationships, and leadership strategies as pivotal for enhancing virtual team performance. This synthesis aims to present a comprehensive overview of current research trajectories in the field, delineating existing research gaps, limitations, and challenges.

The role of transport infrastructure, especially railways, in shaping a nation's socio-economic and cultural dynamics is of paramount importance. The present research delves into the profound influence of the railway network on Iran's regional transformation, from its inception to present times. An in-depth historical evaluation uncovers the genesis and expansion of the Iranian railway system, linking it intricately with pivotal junctures in the nation's trajectory. Emphasis is placed on regions undergoing substantial developmental shifts, attributable to enhanced rail connectivity, offering distinct examples of varied growth paradigms. Economic repercussions manifest as interregional trade augmentation, resurgence of industries, and alterations in employment landscapes, thereby positing railways as an integral component of Iran's economic blueprint. Concurrently, an exhaustive scrutiny of socio-cultural realms underscores railways' pivotal role in fostering intercultural exchanges and expediting urbanisation trends. From an environmental perspective, the sustainability merits of rail transport are illuminated, accentuating the increasing pertinence of ecological considerations in railway's prospective expansion. Through meticulous case studies, a comparative narrative emerges between areas endowed with rail connectivity and those situated in relative isolation. The objective is to elucidate railways as instigators of transformative shifts. This study culminates with projections grounded in potential technological advancements poised to reshape Iran's railway infrastructure and the ensuing regional implications. Findings underscore railways' monumental impact on Iran's socio-economic fabric, illuminating their potential as change agents and offering invaluable insights for global infrastructure strategising.

In 2020, the world witnessed an unprecedented event: the outbreak of the COVID-19 pandemic, leading to significantly curtailed human activities. This study sought to elucidate the potential spatial ramifications of this on land surface temperatures (LSTs) in the renowned tourist locale of Kuta, Bali, Indonesia. Landsat 8 satellite imagery from 2019-2021, complemented by spatial data from local agencies, was employed for this analysis. LST processing was achieved through the calculation of Spectral Radiance/Top of Atmosphere, Brightness Temperature, and the conversion of Brightness Temperature to actual LST. In 2019, observed LSTs in Kuta District varied from 20.1℃ to over 32℃, with the predominant temperature range being 28.1℃ - 31.99℃, covering an expansive 1487.03 ha or 70.26% of the entire area. By 2020, a notable decline was discerned with temperatures peaking at 27.99 ℃ and the most prevalent temperature range being 24.1℃ - 27.99℃, encompassing an area of 1105.46 ha (52.23%). Contrarily, 2021 experienced an upswing, with the apex temperature touching 31.99℃, and the dominant temperature bracket being 28.1℃ - 31.99℃, spanning 974.90 ha (46.06%). A discernable correlation was identified between tourism activities and LST fluctuations, with temperature reductions conspicuous in zones endowed with tourism amenities.

Alzheimer’s disease (AD), a progressive neurological disorder, predominantly impacts cognitive functions, manifesting as memory loss and deteriorating thinking abilities. Recognized as the primary form of dementia, this affliction subtly commences within brain cells and gradually aggravates over time. In 2023, dementia's financial burden for elderly adults aged 65 and older was projected to reach \$345 billion, encompassing health care, long-term care, and hospice services. Alarmingly, Alzheimer's disease claims one in three seniors, outnumbering combined fatalities from breast and prostate cancer. Currently, the diagnostic landscape for Alzheimer's lacks definitive tests, and diagnoses based purely on biological definitions have been observed to possess low predictive accuracy. In the presented study, a diagnostic methodology has been proposed using machine learning models that harness image features derived from brain MRI scans. Specifically, nine salient image features, grounded in color, texture, shape, and orientation, were extracted for the study. Four classifiers — Naïve-Bayes, Logistic regression, XGBoost, and AdaBoost — were employed, as the challenge presented a binary classification scenario. A grid search parameter optimization technique was employed to fine-tune model configurations, ensuring optimal predictive outcomes. Conducted experiments utilizing the Kaggle dataset, and for each model, parameters were rigorously optimized. The XGBoost classifier demonstrated superior performance, achieving a test accuracy of 92%, while Naïve Bayes, Logistic Regression, and AdaBoost registered accuracies of 63%, 70%, and 72%, respectively. Relative to contemporary methods, the proposed diagnostic approach exhibits commendable accuracy in predicting AD. If AI-based predictive diagnostics for AD are realized using the strategies delineated in this study, significant benefits may be anticipated for healthcare practitioners.

Using panel data from 30 Chinese provinces spanning 2003-2019, the relationship between trade openness and haze pollution, moderated by environmental regulation, was investigated through spatial econometric models. It was observed that the effect of trade openness on haze pollution was negative, albeit insignificant, suggesting that trade openness alone did not markedly influence haze reduction in China. Contrarily, environmental regulation, while intensifying haze pollution, displayed a significant moderating role. When combined with environmental regulations, trade openness showed potential in mitigating haze pollution, thus enhancing environmental quality. Although trade openness did not display significant regional variance in its impact on haze pollution, considerable regional disparities were found in the effects of environmental regulation on haze pollution and its moderating influence on the trade openness-haze pollution relationship.

In the quest to achieve Sustainable Development Goals (SDGs), health economics and the financing of health expenditures emerge as pivotal elements. This literature-based exploration delves into the intricate nexus between health financing and sustainable development. Interpretations of pertinent data tables suggest that the financing level of health services at the household level is typically below the global average, indicating a prominent gap in health financing development. Specifically, Turkey's stance on health financing is evaluated against global benchmarks, highlighting its unique challenges and opportunities. This research underscores the intrinsic relationship between health financing and sustainable development, emphasizing the imperative for ongoing evaluation and enhancement in this domain to foster sustainable progression. Notably, the study refrains from employing statistical methodologies, relying solely on literature assessments and data table interpretations. Health financing, pivotal to sustainable development, invariably demands continual advancements and research.

In addressing the challenge of obstacle scattering inversion amidst intricate noise conditions, a model predicated on convolutional neural networks (CNN) has been proposed, demonstrating high precision. Five distinct noise scenarios, encompassing Gaussian white noise, uniform distribution noise, Poisson distribution noise, Laplace noise, and impulse noise, were evaluated. Far-field data paired with the Fourier coefficients of obstacle boundary curves were employed as network input and output, respectively. Through the convolutional processes inherent to the CNN, salient features within the far-field data related to obstacles were adeptly identified. Concurrently, the statistical characteristics of the noise were assimilated, and its perturbing effects were diminished, thus facilitating the inversion of obstacle shape parameters. The intrinsic capacity of CNNs to intuitively learn and differentiate salient features from data eradicates the necessity for external intervention or manually designed feature extractors. This adaptability confers upon CNNs a significant edge in tackling obstacle scattering inversion challenges, particularly in light of fluctuating data distributions and feature variability. Numerical experiments have substantiated that the aforementioned CNN model excels in addressing scattering inversion complications within multifaceted noise conditions, consistently delivering solutions with remarkable precision.