Precise Coordinates Research Articles

Subsistence strategies and climate change as key factors in the vicinity of the Great Wall in northern China from 500 BCE

Whether, and in what ways, the association between changes in climate and the shifting of imperial frontiers (i.e. the Great Wall) could be mediated by the social responses of major ethnic groups is a question that remains unanswered. Ulanqab is a region in Inner Mongolia associated with a long history of Great Wall construction from the Warring States to the Ming dynasties (~500 BCE–1644 CE) and the Great Wall is often regarded as the boundary of the Central Plains dynasty. This paper uses Ulanqab as a case study to investigate the historical inter-relationship between changes in climate and subsistence strategies on the one hand, and the positioning of the Great Wall on the other. We determined the precise geographical coordinates of the Great Wall in this region during each dynastic period of Chinese history, and compared its location to (1) the empire’s boundaries in each dynasty, (2) evidence that is available on climate change from high-resolution paleo-climate reconstructions and (3) subsistence strategies (whether agriculture, pastoralism, or nomadism) that people adopted as recorded in historical documents. Geo-referenced information from associated archaeological sites was also used to determine the extent of habitation in different areas. We found that during the period of agriculturalist empires (the Han and Ming dynasties), the Great Wall was a frontier between farming and animal husbandry, and was located to the south of the Daqing Mountains. In contrast, during the period of pastoralist empires (the Zhao Kingdom [of the Warring States], Northern Wei and Jurched Jin periods), the Great Wall was the frontier between animal husbandry and nomadism, and was located to the north of the Daqing Mountains. These mountains (42.5°N) seem to have been the northern limit of human settlements that were based on animal husbandry and farming. We observed that, when precipitation and temperature increased, this resulted in an augmentation of agricultural production that most likely facilitated the northward expansion of the agriculturalist peoples. A decrease in precipitation, on the other hand, resulted in the shrinkage of pastures and food, triggering a southward expansion that was characteristic of the pastoralist peoples. This study attempts to provide new insights into the interrelationships among changes in climate, human societies and geopolitics in Chinese history.

Assessment of geoid models for geopotential values determination in Mexico'S continuous monitoring network

The requirements of the reference systems related to the vertical part are aimed at adopting a global height system that allows taking advantage of the development of satellite technology that provides terrestrial data with higher quality. Due to this need, the International Association of Geodesy (IAG) has proposed the realization and implementation of the International Height Reference System (IHRS), based on geopotential numbers obtained from the gravity potential difference at a point on the Earth's surface (W(P)) referred to the geoid (W0), whose value adopted by convention corresponds to 62636853.4 m2s-2. Nowadays, efforts are underway worldwide to materialize this system. Considering this, the present work entails the analysis of information gathered from SIRGAS continuous monitoring stations in Mexico: MTY2, IDGO, INEG, MERI, and ICAM, to estimate its gravity potential from sufficiently precise geocentric coordinates and absolute gravity values, which when combined with heights derived from geoid models, allows obtaining a modern height system such as the IHRS. The analysis aims to evaluate the feasibility of estimating the gravity potential at the stations, by using precise geocentric coordinates and absolute gravity values which when combined with high-resolution geoid models such as EGM2008, GGM10, EIGEN6C4, XGM2019e_2159, SGGUGM2, and the xGEOID20B, to implement a modern height reference system in Mexico; as well as to contribute to the International Height Reference Framework (IHRF), which in its latest version only includes the MERI station, located in Mérida, Yucatán. Therefore, we consider that for their characteristics the stations analyzed can be included to densify the frame in the implementation of the system. The results show the differences in gravity potential in the different regions of the country, reinforcing the need to consider more reference stations. Additionally, benchmark points close to the selected stations were analyzed to determine the reference potential value of the GGM10, with which it was also possible to analyze the behavior between the observed geoid and the different geoid models, toward the modernization of the official height system, which is based on orthometric heights called NAVD88, which shows us the differences between the geoid observed with the official height system and the analyzed models. Finally, the study examines the consistency of the calculated orthometric heights with geopotential numbers derived from the different geoid models.

Identifying crystal structures beyond known prototypes from x-ray powder diffraction spectra

The large amount of powder diffraction data for which the corresponding crystal structures have not yet been identified suggests the existence of numerous undiscovered, physically relevant crystal structure prototypes. In this paper, we present a scheme to resolve powder diffraction data into crystal structures with precise atomic coordinates by screening the space of all possible atomic arrangements, i.e., structural prototypes, including those not previously observed, using a pre-trained machine learning (ML) model. This involves (i) enumerating all possible symmetry-confined ways in which a given composition can be accommodated in a given space group, (ii) ranking the element-assigned prototype representations using energies predicted using the Wyckoff representation regression ML model [Goodall , ], (iii) assigning and perturbing atoms along the degree of freedom allowed by the Wyckoff positions to match the experimental diffraction data, and (iv) validating the thermodynamic stability of the material using density-functional theory. An advantage of the presented method is that it does not rely on a database of previously observed prototypes and is, therefore capable of finding crystal structures with entirely new symmetric arrangements of atoms. We demonstrate the workflow on unidentified x-ray diffraction spectra from the ICDD database and identify a number of stable structures, where a majority turns out to be derivable from known prototypes. However, at least two are found not to be part of our prior structural data sets. Published by the American Physical Society 2024

BINDERS FROM SECONDARY RAW MATERIALS OF COAL ENRICHMENT WASTE FOR CONSTRUCTION OBJECT ERECTION USING THE 3D PRINTING METHOD

Formulation of the problem. The 3D printing construction technology is one of the modern methods of rapid construction, which allows for a significant automation of the building process while achieving high precision in the fabrication of complex configurations using equipment (3D printers) controlled by software to define precise coordinates of the printed structure model. The main materials for construction 3D printing technology are composite mixtures based on binders. Cement or composite binders can be used as binders [1]. The cement production technology involves the firing of its components at 1 450 °C, which requires significant energy resources and affects its final cost and the overall mixture. The utilization of coal enrichment waste, which contains clay minerals, quartz, pyrite, and a small amount of carbonates, impurities, and combustible substances, allows for reducing the consumption of technological fuel and the firing temperature of the binder by harnessing the heat-generating properties of coal inclusions. Thus, coal enrichment waste can be used as a fuel component in obtaining the binder. The purpose. Based on the conducted research, it can be concluded that the use of component binders derived from coal enrichment waste shows promise in the construction of buildings using 3D printing technology. By harnessing the heat-generating properties of these waste materials, it is possible to reduce the consumption of technological fuel and firing temperature required for binder production, consequently lowering CO2 emissions into the atmosphere and reducing the final cost of the construction mixture for 3D printing.

Development of Mg2TiO4:Mn4+ phosphors for enhanced red LED emission and forensic fingerprint analysis

The advancement in the development of inorganic phosphors marks a significant milestone in the fields of LED technology and forensic science. Herein, Mg2TiO4:Mn4+ (MTO:Mn4+) novel red-emitting phosphors are synthesized through a solvothermal method, which demonstrates a promising approach for enhancing latent fingerprint detection capabilities and improving the performance of red LEDs. The confirmation of the cubic structure post-annealing and the nano-nature as revealed by TEM analysis underpin the MTO:Mn4+ phosphors suitability for these applications. The broad excitation spectra and the sharp red emission at 659 nm, coupled with the optimal doping concentration, showcase the MTO:Mn4+ phosphor's efficient luminescence properties. Moreover, the calculated critical distance between Mn4+ ions (32.906 Å) elucidates the concentration quenching mechanism, which is pivotal for optimizing the performance. The high purity of the emitted red light (97.2 %) and the precise CIE coordinates (0.5969, 0.2926) of the MTO:Mn4+ phosphor suggest its potential for producing high-quality red LEDs. Additionally, the capability of MTO:Mn4+ phosphors to reveal detailed and high-resolution latent fingerprints offers a more reliable and efficient method for processing and analyzing crucial evidence. These findings not only contribute to the scientific understanding of MTO:Mn4+ phosphor materials but also pave the way for their practical application in cutting-edge technologies.

Stronger together: different community science platforms all contribute to wildlife research

Context Engaging the general public can increase spatio-temporal coverage of wildlife monitoring. Given the potentially substantial costs, we need to evaluate the contribution of known and planned initiatives and confirm whether multiple platforms increase the efficiency of data collection. As observer behaviour affects data quantity and quality, users of specialised and generalist platforms are expected to behave differently, resulting in more connected networks for specialised and higher nestedness for generalist platforms. Specialist observers are expected to contribute a balanced ratio of rare and common species, whereas non-specialist contribution will depend more on species detectability. Aims We aim to evaluate whether the combined contribution of observers from different platforms can improve the quality of occurrence and distribution data of 218 endemic Atlantic Forest bird species in Brazil. We also describe and compare observer-bird species interaction networks to illustrate observer behaviour on different platforms. Methods On the basis of data from five community science platforms in Brazil, namely, eBird, WikiAves, Biofaces, iNaturalist and Táxeus, we compared the spatial distribution of bird observations, the number of observers, the presence of the same observers on various platforms, bird species coverage, and the proportion of duplicate observations within and among platforms. Key results Although species coverage of the joint dataset increased by up to 100%, spatial completeness among the five platforms was low. The network of individual platforms had low values of clustering, and the network of the joint dataset had low connectance and high nestedness. Conclusions Each platform had a somewhat unique contribution. Pooling these datasets and integrating them with standardised data can inform our knowledge on bird distributions and trends in this fragile biome. Nevertheless, we encourage observers to provide precise coordinates, dates and other data (and platforms to accommodate such data) and recommend submitting data from all platforms into the Global Biodiversity Information Facility to support wildlife research and conservation. Implications If new platforms engage more and different people, new initiatives can cover poorly represented areas and successfully expand monitoring effort for Atlantic Forest endemic bird species.

Improving visual grounding with multi-modal interaction and auto-regressive vertex generation

We propose a concise and consistent network focusing on multi-task learning of Referring Expression Comprehension (REC) and Segmentation (RES) within Visual grounding (VG). To simplify the model architecture and achieve parameter sharing, we reconstruct the Visual grounding task as a floating-point coordinate generation problem based on both image and text inputs. Consequently, rather than separately predicting bounding boxes and pixel-level segmentation masks, we represent them uniformly as a sequence of coordinate tokens and output two corner points of bounding boxes and polygon vertices autoregressively. To improve the accuracy of point generation, we introduce a regression-based decoder. Inspired by bilinear interpolation, this decoder can directly predict precise floating-point coordinates, thus avoiding quantization errors. Additionally, we devise a Multi-Modal Interaction Fusion (M2IF) to address the imbalance between visual and language features in the model. This module focuses visual information on regions relevant to textual descriptions while suppressing the influence of irrelevant areas. Based on our model, Visual grounding is realized through a unified network structure. Experiments conducted on five benchmark datasets (RefCOCO, RefCOCO+, RefCOCOg, ReferItGame and Flickr30K Entities) demonstrate that the proposed unified network outperforms or is on par with many existing task-customized models. Codes are available at https://github.com/LFUSST/MMI-VG.

Investigating Feed-Forward Back-Propagation Neural Network with Different Hyperparameters for Inverse Kinematics of a 2-DoF Robotic Manipulator: A Comparative Study

Inverse kinematics is a significant challenge in robotic manipulators, and finding practical solutions plays a crucial role in achieving precise control. This paper presents a study on solving inverse kinematics problems using the Feed-Forward Back-Propagation Neural Network (FFBP-NN) and examines its performance with different hyperparameters. By utilizing the FFBP-NN, our primary objective is to ascertain the joint angles required to attain precise Cartesian coordinates for the end-effector of the manipulator. To accomplish this, we first formed three input-output datasets (a fixed-step-size dataset, a random-step-size dataset, and a sinusoidal-signal-based dataset) of joint positions and their respective Cartesian coordinates using direct geometrical formulations of a two-degree-of-freedom (2-DoF) manipulator. Thereafter, we train the FFBP-NN with the generated datasets using the MATLAB Neural Network Toolbox and investigate its potential by altering the hyperparameters (e.g., number of hidden neurons, number of hidden layers, and training optimizer). Three different training optimizers are considered, namely the Levenberg-Marquardt (LM) algorithm, the Bayesian Regularization (BR) algorithm, and the Scaled Conjugate Gradient (SCG) algorithm. The Mean Squared Error is used as the main performance metric to evaluate the training accuracy of the FFBP-NN. The comparative outcomes offer valuable insights into the capabilities of various network architectures in addressing inverse kinematics challenges. Therefore, this study explores the application of the FFBP-NNs in tackling the inverse kinematics, and facilitating the choice of the most appropriate network design by achieving a portfolio of various experimental results by considering and varying different hyperparameters of the FFBP-NN.

Automatic house location identification using location service based VANET

SummaryThe vehicular ad hoc network (VANET) provides a variety of applications and is gaining popularity due to the reuse of network resources. Exact location identification with optimum delay is the demand of all vehicle users. Currently, individuals are utilizing the Global Positioning System (GPS) to ascertain the precise geographical coordinates of a given location. The drawback of GPS technology is its inability to accurately determine location and use GPS in some remote areas. Searching for the location of a particular house randomly incurs a loss of fuel as well as increases delay. This motivates us to propose one automated method through VANET using location service‐based routing for the location identification of a house. The proposed work involves searching for the location of a house using the open‐source MongoDB database, and the operations on the database are performed using the tool Node‐Red. By simulation using SUMO and Network Simulator 2.35, the proposed work is evaluated and compared with existing location service‐based routing like geographic location service (GLS) and hierarchical location service (HLS). The proposed work performs better in terms of routing efficiency (end‐to‐end latency, packet delivery rate), location efficiency (request sends, query success rate, and request travel time), and routing and location overhead (MAC bandwidth consumption). Also, the performance of the proposed work is presented as stable by increasing the number of vehicles. The statistical analysis of the packet delivery ratio and CBR end‐to‐end latency is carried out using T score.

Development of custom amplicon-based test systems for detecting KRAS, NRAS, and BRAF gene mutations in colorectal cancer using next-generation sequencing (NGS).

e15061 Background: Polymerase chain reaction (PCR)-based mutation diagnostics in colorectal cancer is widely available. However, PCR has limitations in identifying rare mutations and precise mutation coordinates. Targeted sequencing, though promising, often incurs high costs and longer research durations, demanding higher DNA sample quality. Our study demonstrates the benefits of using a customized amplicon test system for diagnosing somatic mutations in tumor tissue, including detecting rare mutations not captured by PCR, requiring less input biomaterial, and similar research costs. Methods: A targeted NGS assay system was developed using region-specific primers for KRAS (exons 2, 3, and 4), NRAS (exons 2, 3, and 4), and BRAF (exon 15). Library preparation involved a three-step PCR process: Target PCR incorporating Illumina overhang adapter sequences, Index PCR introducing multiplexing indices and sequencing adapters, and PCR cleanup using magnetic beads. Sequencing utilized MiSeq v2 reagents with paired 300‐bp reads, generating high-quality, full-length reads. Validation included re-testing known samples. Tumor cell percentages in FFPE specimens were determined, and DNA was extracted using the QIAamp DNA FFPE Tissue Kit. Data analysis utilized proprietary software. Results: Out of 1234 samples, 1133 were tested, with mutations detected in 685 samples (60.45%). KRAS mutations were found in 548 (48.36%), NRAS mutations in 40 (3.5%), and BRAF mutations in 88 (7.7%) samples. Double mutations were observed in 9 samples (0.79%). Data on rare and double mutations are presented in the table. Conclusions: The developed test system accurately identifies essential mutations in colorectal cancer, including rare and potentially clinically significant variants. The required DNA amount ranged from 10 to 30 ng in 5 μl, nearly ten times less than well-known commercial PCR test systems like COBAS (Roche) and therascreen (QIAGEN). The cost of the study is comparable to PCR with a minimum study duration of 4 days. [Table: see text]

Sphractal: Estimating the Fractal Dimension of Surfaces Computed from Precise Atomic Coordinates via Box‐Counting Algorithm (Adv. Theory Simul. 6/2024)

Sphractal: Estimating the Fractal Dimension of Surfaces Computed from Precise Atomic Coordinates via Box‐Counting Algorithm (Adv. Theory Simul. 6/2024)

Development of optical coherence tomography incorporated image processing algorithms for the multidimensional assessment of concealed enamel micro-crack

Enamel microcracks (EMCs) are tiny fractures that occur in the outer layer of teeth, allowing some extrinsic stains and leading to an unaesthetic tooth. Additionally, the EMCs enhance the accumulation of plaque and thus EMCs could be highly suspectable zones for caries development. Therefore, the assessment of EMCs is crucial for early diagnosis and to prevent unwanted damage to teeth. In this study, a concealed EMCs detection and length-measuring algorithm, and an EMCs width-measuring algorithm were applied for optical coherence tomography (OCT) image analysis to assess the EMCs progression in both length and width. Additionally, a surface noise-removing algorithm was developed to obtain a better visualization of OCT cross-sectional and volumetric images. The developed algorithms were successfully used to detect concealed EMCs and track their progression in both length and width directions before and after the orthodontic treatment. Obtained results revealed a micro-scale progression of EMCs in both length and width directions after the orthodontic treatment, where the averaged progression of EMCs in length and width were 712.5 and 92.75 µm, respectively. Therefore, high-resolution, non-invasive OCT imaging with developed image processing algorithms can be a fruitful solution to use as a diagnostic tool to monitor the precise coordinates of concealed EMCs throughout the orthodontic treatment.

Sonic Weapon for Women Safety

This paper introduces an IoT-integrated teaser gun designed specifically for enhancing women's safety. The device, incorporating an ESP32 module, integrates GPS tracking and IoT functionalities to provide real-time location monitoring and emergency response capabilities. Upon activation via a panic button, precise GPS coordinates are transmitted to a centralized database accessible through web and mobile applications, enabling rapid assistance in critical situations. Furthermore, the device employs preset location monitoring to identify and alert users and designated receivers of potential safety threats in high-risk areas. Audible alert mechanisms are integrated to promptly notify users of impending dangers, empowering women to prioritize their safety effectively. This innovative solution addresses the urgent need for proactive measures to combat gender-based violence, offering a comprehensive approach to ensuring women's safety in vulnerable situations.

The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation

The vast potential of high-resolution satellite images, including Kompsat-3A, has been demonstrated across diverse applications, such as mapping and disaster monitoring. However, these images can only be utilized as reliable GIS (geographic information system) data when they possess precise geographical information. To achieve this, sensor model information, represented by RPCs (rational polynomial coefficients), requires bias compensation through GCPs (ground control points). Though having a substantial number of well-distributed GCPs across satellite images is ideal, the acquisition process is often restricted due to cost and inaccessibility. The uniform distribution of GCP chips is not guaranteed, necessitating an investigation into the impact of GCP distribution on the bias compensation process, which is the focus of this study. Experiments were meticulously conducted using Kompsat-3A data using dense GCP information. The dense GCP information was automatically generated from aerial orthoimages through a three-step process. Firstly, the GCP chips were extracted from the aerial images, focusing on feature points. Secondly, these chips were projected onto the target Kompsat-3A data to align them accurately. Lastly, precise satellite image coordinates of the chips were obtained through image matching between the chips and the target Kompsat-3A image. The dense GCPs enabled detailed bias analysis that exhibited skewness in most Kompsat-3A data. This necessitates the implementation of an affine model for proper bias compensation over the entire image space. Next, the study delved into the influence of GCP distribution on RPC bias compensation. To this end, each target satellite image space was divided into nine zones, with the dense GCPs assigned accordingly. The accuracy of bias compensation was analyzed across nine experimental cases, ranging from GCPs occupying only one zone to GCPs covering all nine zones. It was observed that GCPs covering at least four or five zones should be utilized for reliable RPC bias compensation of Kompsat-3A, especially when aiming for a high level of accuracy with an RMSE of one pixel. Finally, it was concluded that GCPs covering three zones yielded satisfactory results as a minimum GCP requirement, but this was contingent upon their distribution not following a straight zone pattern.

Detection Method for Rice Seedling Planting Conditions Based on Image Processing and an Improved YOLOv8n Model

In response to the need for precision and intelligence in the assessment of transplanting machine operation quality, this study addresses challenges such as low accuracy and efficiency associated with manual observation and random field sampling for the evaluation of rice seedling planting conditions. Therefore, in order to build a seedling insertion condition detection system, this study proposes an approach based on the combination of image processing and deep learning. The image processing stage is primarily applied to seedling absence detection, utilizing the centroid detection method to obtain precise coordinates of missing seedlings with an accuracy of 93.7%. In the target recognition stage, an improved YOLOv8 Nano network model is introduced, leveraging deep learning algorithms to detect qualified and misplaced seedlings. This model incorporates ASPP (atrous spatial pyramid pooling) to enhance the network’s multiscale feature extraction capabilities, integrates SimAM (Simple, Parameter-free Attention Module) to improve the model’s ability to extract detailed seedling features, and introduces AFPN (Asymptotic Feature Pyramid Network) to facilitate direct interaction between non-adjacent hierarchical levels, thereby enhancing feature fusion efficiency. Experimental results demonstrate that the enhanced YOLOv8n model achieves precision (P), recall (R), and mean average precision (mAP) of 95.5%, 92.7%, and 95.2%, respectively. Compared to the original YOLOv8n model, the enhanced model shows improvements of 3.6%, 0.9%, and 1.7% in P, R, and mAP, respectively. This research provides data support for the efficiency and quality of transplanting machine operations, contributing to the further development and application of unmanned field management in subsequent rice seedling cultivation.

Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

As smart cities expand rapidly, the demand for strong fire protection has become even more essential. A significant challenge lies in ensuring that fire detection systems integrate seamlessly into our modern infrastructures. Leveraging multisensor systems can yield reliable data on potential fires. Particularly in smart buildings, the effectiveness of multisensor fire detection algorithms becomes paramount. This research introduces a fuzzy logic-driven method that harnesses the power of smoke, flame, and temperature sensors. While smoke, flames, and elevated temperatures are primary fire indicators, they can manifest concurrently or sequentially. We have amalgamated various fuzzy logic rules (IF-THEN structures) to gauge the intensity of fires. When a fire ignites, the sensors spring into action, identifying its source and promptly notifying users via the Internet and GSM modems. Moreover, they relay the fire’s precise geographic coordinates to fire departments. The fire’s status will be consistently updated on a dedicated online portal. It was observed from our results that the system proficiently sends fire alerts to residents, and the fire status undergoes regular updates at 45-second intervals. This refreshing is initiated by the identification of a designated percentage of smoke and flame. These outcomes validate the system’s effectiveness in enhancing the precision and responsiveness of fire detection capabilities.

Sphractal: Estimating the Fractal Dimension of Surfaces Computed from Precise Atomic Coordinates via Box‐Counting Algorithm

AbstractThe fractal dimension of a surface allows its degree of roughness to be characterized quantitatively. However, limited effort is attempted to calculate the fractal dimension of surfaces computed from precisely known atomic coordinates from computational biomolecular and nanomaterial studies. This work proposes methods to estimate the fractal dimension of the surface of any 3D object composed of spheres, by representing the surface as either a voxelized point cloud or a mathematically exact surface, and computing its box‐counting dimension. Sphractal is published as a Python package that provides these functionalities, and its utility is demonstrated on a set of simulated palladium nanoparticle data.

Experimental verification and rapid estimation of uncalibrated cable force via video-based and vibration-based measurements.

The stayed-cable is an important component of cable-stayed bridges, with cable force being a focal point during construction and bridge operation. The advancement of camera and image processing technology has facilitated the integration of computer vision technology in structural inspection and monitoring. This paper focuses on enhancing cable force measurement methods and addressing the limitations of traditional testing techniques by conducting experimental research on cable force estimation using video recording. The proposed approach involves capturing video footage of the target on the cable with a smartphone. Subsequently, a combination of techniques such as the background subtraction method, image morphology processing, and Hough transform image processing technology are employed to detect the precise center coordinates and ultimately obtain the accurate displacement-time curve of the cable's vibration. In addition, the graphic Circularity Coefficient (CC) has been introduced to assess its effectiveness in post-motion-blur image processing for circular targets. The fundamental frequency of the cable is determined by the fast Fourier transformation, and the relationship between the cable force and the fundamental frequency is used to estimate the cable force. The experimental results are compared with data from accelerometers and force gauges, demonstrating that the frequency measurement error is below 1.2% and the cable force test error is less than 3%. In the process of acquiring the cable's fundamental frequency, the test directly employs the pixel as the displacement unit, eliminating the need for image calibration. The innovative use of the CC in processing motion-blurred targets ensured accurate recognition of target coordinates. The experimental findings highlight the method's simplicity, speed, and accuracy.

Analysis of the Influence of Envelope Heat Gain on Hygrothermal Comfort in Tropical Housing: The Case of Benin

The current construction of residential buildings in Benin neglects the importance of thermal insulation of their envelopes. Instead, it relies on active systems as a solution to mitigate over-consumption of electrical energy and guarantee a certain level of thermal comfort inside these buildings. It should be noted that Beninese architects are generally unfamiliar with these thermal aspects.
 The main aim of this study is to assess the effects of thermal insulation on walls and roof, determine the optimum thickness of insulating materials, and integrate a controlled mechanical ventilation system to improve thermal comfort in a humid tropical climate. The investigations were carried out on an example of a typical residential building located in Cotonou, Benin, with precise geographical coordinates (latitude 6°38' North, longitude 2°34' East). To analyze the thermal behavior of this building, a series of dynamic thermal simulations were carried out using TRNSYS software, which was used both to model the building structure and to run the various simulations.
 The results of this study revealed that thermal insulation of the walls and roof, using polystyrene, significantly reduced the need for air conditioning. What's more, the integration of a controlled mechanical ventilation system not only ensured indoor air quality, but also helped regulate indoor humidity, reducing it from 67% to 76%.

Revisiting the Dynamics of Two-Body Problem in the Framework of the Continued Fraction Potential

In this analytical study, a novel solving method for determining the precise coordinates of a mass point in orbit around a significantly more massive primary body, operating within the confines of the restricted two-body problem (R2BP), has been introduced. Such an approach entails the utilization of a continued fraction potential diverging from the conventional potential function used in Kepler’s formulation of the R2BP. Furthermore, a system of equations of motion has been successfully explored to identify an analytical means of representing the solution in polar coordinates. An analytical approach for obtaining the function t = t(r), incorporating an elliptic integral, is developed. Additionally, by establishing the inverse function r = r(t), further solutions can be extrapolated through quasi-periodic cycles. Consequently, the previously elusive restricted two-body problem (R2BP) with a continued fraction potential stands fully and analytically solved.