Significant Deviations Research Articles

Comprehensive review of Transformer‐based models in neuroscience, neurology, and psychiatry

AbstractThis comprehensive review aims to clarify the growing impact of Transformer‐based models in the fields of neuroscience, neurology, and psychiatry. Originally developed as a solution for analyzing sequential data, the Transformer architecture has evolved to effectively capture complex spatiotemporal relationships and long‐range dependencies that are common in biomedical data. Its adaptability and effectiveness in deciphering intricate patterns within medical studies have established it as a key tool in advancing our understanding of neural functions and disorders, representing a significant departure from traditional computational methods. The review begins by introducing the structure and principles of Transformer architectures. It then explores their applicability, ranging from disease diagnosis and prognosis to the evaluation of cognitive processes and neural decoding. The specific design modifications tailored for these applications and their subsequent impact on performance are also discussed. We conclude by providing a comprehensive assessment of recent advancements, prevailing challenges, and future directions, highlighting the shift in neuroscientific research and clinical practice towards an artificial intelligence‐centric paradigm, particularly given the prominence of Transformer architecture in the most successful large pre‐trained models. This review serves as an informative reference for researchers, clinicians, and professionals who are interested in understanding and harnessing the transformative potential of Transformer‐based models in neuroscience, neurology, and psychiatry.

Improving Medication Management for Inpatients with a Secondary Diagnosis of Parkinson Disease.

Patients who have Parkinson disease require individualized medication regimens to optimize care. A review of the medication management of patients admitted to a tertiary care hospital with a secondary diagnosis of Parkinson disease found significant departures from the patients' home regimen. Medication regimens are often altered by health care teams unfamiliar with Parkinson disease-specific care in order to conform to standard hospital medication orders and administration times, potentially resulting in increased patient falls, delirium, and mortality.A nurse-led multidisciplinary team consisting of pharmacy, nursing, informatics, neurology, and quality personnel implemented a quality improvement (QI) project between July 2020 and July 2022 to identify patients with Parkinson disease, including those with a secondary diagnosis and those undergoing deep brain stimulation, and customize medication management in order to reduce length of stay, mortality, falls, falls with harm, and 30-day readmissions. The QI project team also evaluated patient satisfaction with medication management.Among patients with a secondary diagnosis of Parkinson disease, the proportion who had medication histories conducted by a pharmacy staff member increased from a baseline of 53% to more than 75% per month. For all patients with Parkinson disease, those whose medication history was taken by a pharmacy staff member had orders matching their home regimen 89% of the time, whereas those who did not had orders matching the home regimen only 40% of the time. Among patients with a secondary diagnosis of Parkinson disease, the length-of-stay index decreased from a baseline of 1 to 0.94 and observed-to-expected mortality decreased from 1.03 to 0.78. The proportion of patients experiencing a fall decreased from an average of 5% to 4.08% per quarter, while the proportion of patients experiencing a fall with harm decreased from an average of 1% to 0.75% per quarter. The rate of 30-day readmissions decreased from 10.81% to 4.53% per quarter. Patient satisfaction scores were 1.95 points higher for patients who had medication histories taken by pharmacy than for those who did not (5 versus 3.05).

Exploring Cloud Computing

Cloud computing has reached maturity since Amazon introduced the pioneering cloud services in 2006. Hong Kong, with its vast daily data processing needs across various sectors, is poised to embrace cloud services extensively, despite a gradual uptake initially. Cloud computing now dominates research agendas in computer science due to its profound implications across multiple computing sectors, particularly in managing big data, where it's indispensable. The establishment of a major cloud R&D centre in Hong Kong by Lenovo in January 2015 underscores this trend. Cloud computing, fulfilling the long-standing vision of computing as a utility, possesses the capability to revolutionize a significant portion of the IT industry. It enhances the attractiveness of software as a service and influences the design and procurement of IT hardware. For developers with innovative ideas for new Internet services, the need for substantial capital investment in hardware deployment or ongoing operational expenses is eliminated. Concerns about overprovisioning, which wastes resources, or under provisioning, which can lead to missed opportunities, are alleviated. Additionally, companies with extensive batch-oriented tasks can achieve results at unprecedented speeds, as the scalability of programs allows for efficient utilization of resources without incurring additional costs. This elasticity of resources, where utilizing 1,000 servers for one hour costs the same as using one server for 1,000 hours, represents a significant departure from traditional IT models.

Maintenance and Its Knowledges: Functional Exploration, Biographical Supervision, and Behavioural Examination

This introductory article to the thematic collection entitled “Maintenance and its Knowledges” makes a significant departure from breakdown-centred studies. It foregrounds the epistemic virtues of maintenance, a practice that cultivates continuity, by examining a still underestimated and unexplored dimension: the forms of knowledge associated with maintenance activities. A twofold aim guides such an exploration. First, repair and maintenance interventions are examined as particular sites and moments of knowledge generation. Second, building on the scholarship dedicated to improvisation and learning dynamics in the Global South that has structured numerous works on maintenance, this article considers how it can be extended towards the Global North. Recalling that maintenance has been mostly investigated from the question of how, it stresses out the various tools involved in knowledge generation, the organisation of tasks at play in different settings and their politics. It then goes beyond the how question to embrace a more ontological perspective, focussing on what people do properly maintain, and what kinds of knowledge emerge at this particular moment. Such a line of inquiry opens up three directions towards which the production of knowledge is oriented in maintenance: functional exploration, biographical supervision, and behavioural examination.

DRIVER DROWSINESS DETECTION SYSTEMS

Car accidents are frequently caused due to many reasons like drowsiness, drunkenness or tiredness, which has serious consequences for traffic safety. Numerous factors, such as fatigue, intoxication, or drowsiness, commonly contribute to car accidents, which have detrimental effects on traffic safety. Many fatal collisions might be avoided if sleepy drivers were warned beforehand. Many sleepiness detection technologies are available to identify and warn drivers of any indications of inattention while driving. Sensors in self-driving cars must be able to detect whether a driver is sleepy, agitated, or experiencing sharp emotional fluctuations. Thus far There have been numerous recommendations for enhanced warning systems to prevent sleepy driving. When these systems detect a significant lane departure, a change in the headway distance, or other indications of subpar driving, they sound an alarm. These systems rely heavily on machine learning algorithms, which allow them to learn and adjust to unique driving patterns and characteristics. In supervised learning, models are trained on labelled datasets to discriminate between awake and sleepy states. These approaches are widely used for classification tasks. Unsupervised learning techniques are also applied for anomaly detection, which finds departures from typical driving behaviour. The incorporation of cutting-edge sensors like facial recognition cameras and brainwave sensors, which offer more thorough insights into the driver's condition, has recently improved driver sleepiness detection systems. Moreover, these systems are more accurate and responsive when real-time data processing and cloud-based analytics are used. KEYWORDS Drowsiness-detection, Driver-Fatigue, Computer Vision, Real Time, survey, Eye Tracking Technology, Automotive Safety Systems, Driver Assistance Systems, Real-time Drowsiness Detection, Alertness Monitoring.

Finite Element Analysis of Stress Distribution in AL6061 Frame Structures Under Varying Applied Loads

The demand for lightweight and high-performance structures has driven the popularity of commercially available aluminum alloys such as AL6061, particularly in the transporterindustry. However, predicting stress distribution accurately within AL6061 frame structures under diverse loading conditions remains a significant challenge for clients due to the complex nature of their behavior. To address this challenge, a study has been conducted that utilizes the advanced 3D simulation software SolidWorks 2023 to model a custom dimension frame structure and evaluate their behavior under varied loads of up to 200% of the structure's weight, which is a significant departure from the standard loading conditions. The goal is to offer a thorough and accurate assessment of the frame structure's capabilities. This investigation analyzed the stress, strain, displacement, and safety factors of a specifically designed frame structure, ensuring that each value met or exceeded acceptable standards. This study also provides valuable insights into the structure's performance under various loading conditions, including the handling of heavy items and specimens that require the use of a transporter in industrial engineering applications. Additionally, a detailed assessment of the material properties of the aluminum alloy used in constructing the frame structure provides useful information for designing and optimizing high-performing structures in the industry. Based on the results, it appears that the critical von Mises stress, when exposed to maximum varying loads (76.54 MPa), remains below the structure's yield strength of 275 MPa. The equivalent strain reaches a maximum value of 0.5607E-03, and the structure's deformation at maximum loads is just 0.6705 mm. The safety factors of the structure, tested at three different varying loads, are above 3. These values conclusively demonstrate that the AL6061 frame structure is safe and reliable for industrial applications and can withstand loads up to 200% wt.

Using ZDI maps to determine magnetic forces and torques at the photospheres of early-type stars

ABSTRACT We use the magnetic field components measured by Zeeman Doppler imaging (ZDI) to calculate the stellar surface force and torque due to magnetic stresses for the fast rotators σ Ori E, 36 Lyn, and CU Vir, and the slow rotator τ Sco. If we assume the stars have spherical photospheres, the estimated torques give spin-down time-scales no larger than 7 × 105 yr. For σ Ori E, the predicted spin-down time-scale, ≃ 6000 yr, is much less than the observationally measured time-scale of ≃ 106 yr. However, for CU Vir, we find that the spin-down time-scale from its ZDI map is 7 × 105 yr in good agreement with its average rate of spin-down from 1960 to 2010. With the exception of τ Sco, the net force due to magnetic stresses at the stellar surface are large compared to the surface-integrated pressure. We discuss possible reasons for the large values of the forces (and torques), and suggest that the likely explanation is that rotation and the magnetic stresses create significant departures from spherical symmetry.

The impact of digital health technologies on pharmacy services and patient care

This comprehensive review delves into the profound impact of digital health technologies on the landscape of pharmacy services and patient care. The integration of electronic prescribing medication adherence apps, and tele-pharmacy services has revolutionized medication management in pharmacies. This amalgamation has not only streamlined processes but has also significantly minimized errors, leading to enhanced operational efficiency. Pharmacists, equipped with wearable devices, now engage in remote patient monitoring, allowing for personalized interventions and proactive healthcare strategies. The incorporation of genomic data, data analytics, and clinical decision support systems marks a transformative shift towards precision medicine within pharmacies. These technologies empower pharmacists to design tailored treatment plans based on individual genetic profiles, thereby optimizing therapeutic outcomes. As pharmacies increasingly embrace these digital tools, they metamorphose into dynamic healthcare hubs. Beyond traditional roles, pharmacies now offer virtual consultations and actively participate in patient education initiatives. This paradigm shift underscores a commitment to patient-centered care, exemplified by improved medication safety and a more interconnected healthcare ecosystem. The ongoing integration of digital health technologies holds promise for continued advancements in pharmacy services. This trajectory signifies a significant departure towards a more proactive, personalized, and technologically driven approach to patient care. The review underscores the transformative potential of digital health technologies in redefining the role of pharmacies and elevating the standards of patient-centered healthcare delivery.

Optimizing Parameter Efficiency in Machine Learning Models: A Focus on Reducing Memory Overhead with L-BFGS-Optimized Algorithms

This research addresses the critical challenge of detecting cotton leaf diseases through an advanced image processing and machine learning approach. The study primarily focuses on the extraction of significant features from cotton leaf images to facilitate accurate disease identification. The novelty of this work lies in the application of the Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm for optimizing Support Vector Machines (SVM), enhancing the precision of disease detection in cotton leaves. This research method involves a comprehensive process of acquiring high-resolution leaf images, followed by an effective feature extraction technique that isolates crucial characteristics indicative of various plant diseases. Subsequently, these features are employed to train an SVM model optimized by the L-BFGS algorithm, a decision process that marks a significant departure from traditional gradient descent methods in SVM training. A pivotal aspect of this study is the comparative analysis conducted between the proposed L-BFGS-optimized SVM model and prevalent machine learning models including Random Forest, Decision Tree, Regression Models, and K-Nearest Neighbors (KNN). This comparison is grounded on various performance metrics such as accuracy, precision, recall, and F1-score, offering a comprehensive evaluation of each model's effectiveness in disease detection. The results of this research are expected to demonstrate the superiority of the L-BFGS-optimized SVM in terms of accuracy and efficiency in detecting cotton leaf diseases. This advancement holds substantial promise for agricultural technology, potentially leading to more informed and timely decision-making in crop management and disease prevention. The findings of this study aim to contribute significantly to the field of agricultural informatics, particularly in the domain of plant disease detection and management, by providing a more robust, accurate, and efficient tool for farmers and agriculturalists.

Ring Resonator Measurement: Characterizing High Dielectric Constant Materials for the Fourth Industrial Revolution

This study introduces a ring resonator measurement technique targeting high dielectric constant materials. Operating within a frequency range of 0.1 GHz to 5 GHz, the resonator utilizes FR4 substrate and copper conductor. A significant departure from existing methods involves employing a silver-coated ring directly on the sample, mitigating Wheeler's gap issue induced by airgaps across material layers. Furthermore, the research reveals that as dielectric constants increase, microstrip line impedance drastically falls below 25 ohms, potentially leading to inaccurate loss tangent determinations. This paper focuses solely on HFSS simulations to demonstrate the method's feasibility. The proposed technique offers promising prospects for accurately characterizing high dielectric constant materials, thereby facilitating advancements in various applications, particularly within the Fourth Industrial Revolution framework- 5G and 6G technologies.

Polycotyly: How Little Do We Know?

Polycotyly, an interesting characteristic of seed-bearing dicotyledonous plants with more than two cotyledons, represents one of the least explored plant characters for utilization, even though cotyledon number was used to classify flowering plants in 1682. Gymnosperm and angiosperm species are generally known to have one or two cotyledons, but scattered reports exist on irregular cotyledon numbers in many plant species, and little is known about the extent of polycotyly in plant taxa. Here, we attempt to update the documentation of reports on polycotyly in plant species and highlight some lines of research for a better understanding of polycotyly. This effort revealed 342 angiosperm species of 237 genera in 80 (out of 416) families and 160 gymnosperm species of 26 genera in 6 (out of 12) families with reported or cited polycotyly. The most advanced research included the molecular-based inference of the phylogeny of flowering plants, showing a significant departure from the cotyledon-based classification of angiosperm plants, and the application of genetic cotyledon mutants as tools to clone and characterize the genes regulating cotyledon development. However, there were no reports on breeding lines with a 100% frequency of polycotyly. Research is needed to discover plant species with polycotyly and to explore the nature, development, genetics, evolution, and potential use of polycotyly.

Leveraging CSR To Promote Inclusive and Sustainable Development: Evidence from CSR Reporting of NSE Companies

The Companies Act (2013) in India has brought about manifold changes to the regulation of companies. The provisions of new law require giving emphasis on corporate social causes marking a significant departure from previous mere voluntary guidelines. As a result of these provisions, India will now be recognized as world’s CSR “frontrunner” nation. Section 135 of the Act read with Schedule VII likely to better promote the process of the link between CSR and broad-based inclusive and sustainable development goals. Thus, a total of 34 out of top 100 companies listed in NSE were chosen as samples for analyzing their CSR practices and reporting initiatives. Content Analysis Method is used to extract data from annual reports of companies. Descriptive and inferential statistics such as, t-test, ANOVA, correlation and regression were used to examine data. A CSR practices and reporting grid and a model were proposed in study respectively for developing relevant score and performing regression analysis. The findings show that Indian companies are making significant contributions toward achieving the goals of inclusive and sustainable development through their efforts to implement CSR as evidenced form their CSR reporting.

Detection of the Actinide Th in an r-process-enhanced Star with Accretion Origin

The thorium and six second-peak r-process element (56 ≤ Z ≤ 72) abundances are determined for the α-poor star LAMOST J1124+4535 based on a high-resolution spectrum obtained with the High Dispersion Spectrograph on the Subaru Telescope. The age of J1124+4535 is 11.3 ± 4.4 Gyr using thorium and other r-process element abundances. J1124+4535 is confirmed to be a Galactic halo metal-poor ([Fe/H] = −1.27 ± 0.1) star with extreme r-process element overabundance ([Eu/Fe] = 1.13 ± 0.08) and α element deficiency ([Mg/Fe] = −0.31 ± 0.09) by the LAMOST-Subaru project. Along with the subsolar α-to-iron ratios (e.g., [Mg/Fe], [Si/Fe], [Ca/Fe]), the relatively low abundances of Na, Cr, Ni, and Zn in J1124+4535 show a significant departure from the general trends of the Galactic halo but are in good agreement with those of dwarf galaxies. The chemical abundances and kinematics of J1124+4535 suggest it was formed in the late stage of star formation in a dwarf galaxy that has been disrupted by the Milky Way. The star formation of its progenitor dwarf galaxy lasted more than 2 Gyr and has been affected by a rare r-process event before the occurrence of accretion event.

Structure Design and Strength Analysis of the Glass Reinforcement Plastic (GRP) Malay Traditional “Perahu Bedar”

The Perahu Bedar, a traditional fishing boat in the Terengganu region of Malaysia, has been historically crafted from Cengal wood. In response to challenges posed by wood scarcity and limitations in skilled craftsmen, this study pioneers the exploration of Glass Reinforced Plastic (GRP) as a novel alternative material for constructing the traditional Perahu Bedar, measuring 4.32 meters in length. Through a rigorous analysis, the research delves into the structural design intricacies and strength attributes of the GRP Perahu Bedar, marking a significant departure from conventional wood-based construction methods. The study conducts a comprehensive analysis of the structure design and strength characteristics of the GRP Perahu Bedar. The weight, buoyancy force, and load distribution along the boat are analyzed. The data shows varying weight and buoyancy forces along the stations, with positive load values indicating upward forces contributing to buoyancy and negative load values representing downward forces. This analysis provides insights into the boat’s stability and distribution of forces. The sheer force and bending moment along the Perahu Bedar are evaluated. The sheer force values gradually increase or decrease along the boat, while the bending moment is highest at the midsection and decreases towards the ends. These results indicate the distribution of forces and stress on the boat’s structure, aiding in understanding its integrity and stability. The Factor of Safety (FoS) analysis demonstrates a FoS value of 2.2368, indicating a safety margin greater than 1. This suggests that the GRP Perahu Bedar design meets safety requirements and can withstand applied 
 
 stresses without exceeding yield strength. The FoS value provides assurance of structural integrity and safety during normal operating conditions. In summary, this study underscores the groundbreaking potential of Glass Reinforced Plastic (GRP) as a viable alternative to traditional wooden Perahu Bedar due to very high cost of Cengal Wood (wood that been used to build Peahu Bedar). By meticulously analyzing critical factors such as weight, buoyancy, load, shear force, bending moment, and Factor of Safety (FoS), the research offers invaluable insights into the structural integrity and safety aspects of GRP Perahu Bedar. These revelations not only herald a new era of sustainable and cost-effective boatbuilding practices but also serve as a crucial step towards safeguarding and perpetuating the rich maritime heritage of the region.

Assessment of the impact of the implemented learning spaces on science achievement: Building Resilience at General Tomas Mascardo National High School

General Tomas Mascardo National High School is currently facing a significant challenge of classroom shortages, posing a threat to the fundamental principle of "education for all." This study aims to evaluate the impact of makeshift classrooms on the academic performance of science subject to students during the school year 2023–2024, adopting a mixed-methods approach. The analysis reveals variations in mean percentage scores across sections and shifts, emphasizing the need to investigate specific factors influencing performance outcomes. Despite small attendance fluctuations, the overall trend suggests a high level of students’ presence, indicating a generally conducive learning environment. However, the study identifies significant disparities in academic performance, particularly in the Quarterly Assessment (Q.A) variable among makeshift classes. The negative impact on academic performance is evident, highlighted by a statistically significant deviation in Q.A scores, emphasizing a specific area of concern. Irregular attendance patterns during September and October further contribute to the negative effect on academic performance, emphasizing the need for targeted interventions to address attendance issues systematically. Themes emerging from student responses encompass environmental factors, comfort, focus, motivation, adaptation, preference for regular classrooms, teacher impact, external factors, and physical environment. Morning shift students highlight noise and discomfort as significant barriers, while afternoon shift students express mixed feelings, acknowledging spaciousness but preferring regular classrooms for comfort. Both shifts discuss motivation, with morning shift students suggesting adaptive measures and crediting supportive teachers. In contrast, afternoon shift students express various emotions, including decreased motivation due to negative thoughts and noise pollution affecting focus. External factors, distractions, and concerns related to the physical environment are prevalent in both shifts, underlining the complexity of challenges students face in makeshift classrooms. Recommendations include a comprehensive investigation into academic disparities, addressing the quality of answers, targeted interventions for attendance improvement, and a holistic approach to creating a conducive learning environment.

Labor market peer firms: understanding firms’ labor market linkages through employees’ internet “also viewed” firms

AbstractThis paper studies the grouping of firms based on their labor-market connections, a significant departure from the traditional approach of grouping based on product-market connections. It also proposes a measure of labor market peers by analyzing the “also viewed” companies on two major online labor market platforms, LinkedIn and Glassdoor. Using the labor market peer measure, I examine whether firms that hire employees with similar skills and that are presumably exposed to the same labor-related risks and shocks exhibit a strong comovement of stock returns and accounting-based performance variables. I find that labor market peers overlap but differ from traditional product-market-based industry groupings, have significant incremental power to explain stock return and accounting-based performance measure comovements, and outperform traditional industry groupings in explaining return and wage comovements when a base firm shares more labor skills with its peers. Overall, the study highlights that labor market peers capture fundamental linkages between firms that are challenging to identify using traditional industry measures.

Evaluation of a Proportional Response Addition Approach to Mixture Risk Assessment and Predictive Toxicology Using Data on Four Trihalomethanes from the U.S. EPA's Multiple-Purpose Design Study.

In this study, proportional response addition (Prop-RA), a model for predicting response from chemical mixture exposure, is demonstrated and evaluated by statistically analyzing data on all possible binary combinations of the four regulated trihalomethanes (THMs). These THMs were the subject of a multipurpose toxicology study specifically designed to evaluate Prop-RA. The experimental design used a set of doses common to all components and mixtures, providing hepatotoxicity data on the four single THMs and the binary combinations. In Prop-RA, the contribution of each component to mixture toxicity is proportional to its fraction in the mixture based on its response at the total mixture dose. The primary analysis consisted of 160 evaluations. Statistically significant departures from the Prop-RA prediction were found for seven evaluations, with three predications that were greater than and four that were less than the predicted response; interaction magnitudes (n-fold difference in response vs. prediction) ranged from 1.3 to 1.4 for the former and 2.6 to 3.8 for the latter. These predictions support the idea that Prop-RA works best with chemicals where the effective dose ranges overlap. Prop-RA does not assume the similarity of toxic action or independence, but it can be applied to a mixture of components that affect the same organ/system, with perhaps unknown toxic modes of action.

Exploring the potential of inherent chiral single-walled carbon nanotubes in enantioselective electrochemical sensing: A novel microsensor for discriminating DOPA enantiomers

A range of electrochemical sensors capable of enantiomer discrimination have been developed, but the creation of their electrochemical interfaces remains complex. This often entails combining externally introduced chiral centers with conductive materials. This study marks a significant departure from this approach by exploiting the inherent chirality of single-walled carbon nanotubes (SWCNTs) for enantiomer discrimination. We have crafted a miniaturized, enantioselective electrochemical microsensor by seamlessly integrating high-purity SWCNTs, possessing inherent chirality, with an ultra-sensitive carbon fiber microelectrode. Using differential pulse voltammetry, we observed a notable difference in peak current, highlighting the sensor's precise ability to distinguish between enantiomers of 3,4-dihydroxyphenylalanine (DOPA). Furthermore, we investigated the intricacies of the interplay between enantioselective interfaces and the molecules under discrimination, offering deeper insights into the mechanisms governing this phenomenon. This study provides substantial momentum into the realm of enantioselective electrochemical sensing and sheds light on the unique attributes of inherently chiral carbon nanotubes in such applications. This paves the way for a new breed of chiral sensors that are simpler, more efficient, and cost-effective.

Mixed Traffic Behaviour in Heavy Flow: Two-Lane Road Insights

The current paper outlines a comprehensive methodology for modeling speed data on two-lane roads during periods of heavy traffic characterized by heterogeneous vehicle types. In such scenarios, the presence of diverse vehicle types causes a significant departure from the normal speed distribution model. This deviation becomes particularly pronounced during heavy traffic flows due to the frequent interactions among vehicles within the traffic stream. Consequently, there arises a necessity to develop a modeling approach specifically tailored to such flow conditions. Drawing upon empirical data collected from a major intercity road in India, this study uncovers a notable skewness in speed data under heavy traffic conditions. This skewness primarily stems from the formation of vehicle platoons within the traffic stream, exerting a substantial influence on their speed characteristics. By scrutinizing the distribution of this data, the study concludes that a logarithmic transformation effectively aligns with the assumption of normality. This assertion is supported by various goodness-of-fit metrics, affirming the suitability of the proposed modeling approach for capturing the intricacies of speed behavior in heterogeneous traffic environments.

Decoding the Shift: An Inquiry into the Evolution of Dressing Patterns among Female Christians: A Study of the Universal Reformed Christian (NKST) Church (1911-2022)

The study examined the historical evolution of human attire, tracing its progression from primal nakedness to the development of clothing for decency. Societies have refined dressing norms, reflecting cultural values and moral standards. However, the 20th to 21st centuries saw a significant departure from traditional dressing, particularly among women, transcending religious affiliations. Indecent dressing and exposing body contours became prevalent even in the Orthodox Church, like the NKST. The study aimed to understand the causes of this shift and its societal consequences. While decent dressing is a cultural cornerstone for older generations, some conservative newer generations still uphold this tradition. The analysis emphasizes the negative repercussions of indecent dressing and advocates for a return to decent dressing, especially among female Christians in the Universal Reformed Christian Church and other religious communities. The study contributes to the broader discourse on cultural values, highlighting the importance of reinstating modesty in attire for societal well-being.