Accurate and reliable analyses of high-alpine landslide displacement magnitudes and rates are key requirements for current and future alpine early warnings. It has been proved that high spatiotemporal-resolution remote sensing data combined with digital image correlation (DIC) algorithms can accurately monitor ground displacements. DIC algorithms still rely on significant amounts of expert input; there is neither a general mathematical description of type and spatiotemporal resolution of input data nor DIC parameters required for successful landslide detection, accurate characterisation of displacement magnitude and rate, and overall error estimation. This work provides generic formulas estimating appropriate DIC input parameters, drastically reducing the time required for manual input parameter optimisation. We employed the open-source code DIC-FFT using optical remote sensing data acquired between 2014 and 2020 for two landslides in Switzerland to qualitatively and quantitatively show which spatial resolution is required to recognise slope displacements, from satellite images to aerial orthophotos, and how the spatial resolution affects the accuracy of the calculated displacement magnitude and rate. We verified our results by manually tracing geomorphic markers in orthophotos. Here, we show a first generic approach for designing and optimising future remote sensing-based landslide monitoring campaigns to support time-critical applications like early warning systems. Full article

The use of solar energy has shown the fastest global growth of all renewable energy sources. Efforts towards careful evaluation are required to select optimal locations for the installation of photovoltaics (PV) because their effectiveness is strongly reliant on exposure to solar irradiation. Assessing the shadows cast by nearby buildings and vegetation is essential, especially at the city scale. Due to urban complexity, conventional methods using Digital Surface Models (DSM) overestimate solar irradiation in dense urban environments. To provide further insights into this dilemma, a new modeling technique was developed for integrated 3D city modeling and solar potential assessment on building roofs using light detection and ranging (LiDAR) data. The methodology used hotspot analysis to validate the workflow in both site and without-site contexts (e.g., trees that shield small buildings). Field testing was conducted, covering a total area of 4975 square miles and 10,489 existing buildings. The results demonstrate a considerable impact of large, dense trees on the solar irradiation received by smaller buildings. Considering the site’s context, a mean annual solar estimate of 99.97 kWh/m²/year was determined. Without considering the site context, this value increased by 9.3% (as a percentage of total rooftops) to 109.17 kWh/m²/year, with a peak in July and troughs in December and January. The study suggests that both factors have a substantial impact on solar potential estimations, emphasizing the importance of carefully considering the shadowing effect during PV panel installation. The research findings reveal that 1517 buildings in the downtown area of Austin have high estimated radiation ranging from 4.7 to 6.9 kWh/m²/day, providing valuable insights for the identification of optimal locations highly suitable for PV installation. Additionally, this methodology can be generalized to other cities, addressing the broader demand for renewable energy solutions. Full article

The rapid expansion of the world’s population has resulted in an increased demand for agricultural products which necessitates the need to improve crop yields. To enhance crop yields, it is imperative to control weeds. Traditionally, weed control predominantly relied on the use of herbicides; however, the indiscriminate application of herbicides presents potential hazards to both crop health and productivity. Fortunately, the advent of cutting-edge technologies such as unmanned vehicle technology (UAVs) and computer vision has provided automated and efficient solutions for weed control. These approaches leverage drone images to detect and identify weeds with a certain level of accuracy. Nevertheless, the identification of weeds in drone images poses significant challenges attributed to factors like occlusion, variations in color and texture, and disparities in scale. The utilization of traditional image processing techniques and deep learning approaches, which are commonly employed in existing methods, presents difficulties in extracting features and addressing scale variations. In order to address these challenges, an innovative deep learning framework is introduced which is designed to classify every pixel in a drone image into categories such as weed, crop, and others. In general, our proposed network adopts an encoder–decoder structure. The encoder component of the network effectively combines the Dense-inception network with the Atrous spatial pyramid pooling module, enabling the extraction of multi-scale features and capturing local and global contextual information seamlessly. The decoder component of the network incorporates deconvolution layers and attention units, namely, channel and spatial attention units (CnSAUs), which contribute to the restoration of spatial information and enhance the precise localization of weeds and crops in the images. The performance of the proposed framework is assessed using a publicly available benchmark dataset known for its complexity. The effectiveness of the proposed framework is demonstrated via comprehensive experiments, showcasing its superiority by achieving a 0.81 mean Intersection over Union (mIoU) on the challenging dataset. Full article

China is one of the countries with the largest citrus cultivation areas, and its citrus industry has received significant attention due to its substantial economic benefits. Traditional manual forestry surveys and remote sensing image classification tasks are labor-intensive and time-consuming, resulting in low efficiency. Remote sensing technology holds great potential for obtaining spatial information on citrus orchards on a large scale. This study proposes a lightweight model for citrus plantation extraction that combines the DeepLabV3+ model with the convolutional block attention module (CBAM) attention mechanism, with a focus on the phenological growth characteristics of citrus in the Guangxi region. The objective is to address issues such as inaccurate extraction of citrus edges in high-resolution images, misclassification and omissions caused by intra-class differences, as well as the large number of network parameters and long training time found in classical semantic segmentation models. To reduce parameter count and improve training speed, the MobileNetV2 lightweight network is used as a replacement for the Xception backbone network in DeepLabV3+. Additionally, the CBAM is introduced to extract citrus features more accurately and efficiently. Moreover, in consideration of the growth characteristics of citrus, this study augments the feature input with additional channels to better capture and utilize key phenological features of citrus, thereby enhancing the accuracy of citrus recognition. The results demonstrate that the improved DeepLabV3+ model exhibits high reliability in citrus recognition and extraction, achieving an overall accuracy (OA) of 96.23%, a mean pixel accuracy (mPA) of 83.79%, and a mean intersection over union (mIoU) of 85.40%. These metrics represent an improvement of 11.16%, 14.88%, and 14.98%, respectively, compared to the original DeepLabV3+ model. Furthermore, when compared to classical semantic segmentation models, such as UNet and PSPNet, the proposed model achieves higher recognition accuracy. Additionally, the improved DeepLabV3+ model demonstrates a significant reduction in both parameters and training time. Generalization experiments conducted in Nanning, Guangxi Province, further validate the model’s strong generalization capabilities. Overall, this study emphasizes extraction accuracy, reduction in parameter count, adherence to timeliness requirements, and facilitation of rapid and accurate extraction of citrus plantation areas, presenting promising application prospects. Full article

Layered double oxides are widely employed in catalyzing the aldol condensation for producing biofuels, but its selectivity and stability need to be further improved. Herein, a novel MCM-41-supported Mg–Al-layered double oxide (LDO/MCM-41) was prepared via the in situ integration of a sol–gel process and coprecipitation, followed by calcination. This composite was first employed to catalyze the self-condensation of cyclopentanone for producing high-density cycloalkane precursors. LDO/MCM-41 possessed large specific surface area, uniform pore size distribution, abundant medium basic sites and Bronsted acid sites. Compared with the bulk LDO, LDO/MCM-41 exhibited a higher selectivity for C10 and C15 oxygenates at 150 °C (93.4% vs. 84.6%). The selectivity for C15 was especially enhanced on LDO/MCM-41, which was three times greater than that on LDO. The stability test showed that naked LDO with stronger basic strength had a rapid initial activity, while it suffered an obvious deactivation due to its poor carbon balance. LDO/MCM-41 with lower basic strength had an enhanced stability even with a lower initial activity. Under the optimum conditions (50% LDO loading, 170 °C, 7 h), the cyclopentanone conversion on LDO/MCM-41 reached 77.8%, with a 60% yield of C10 and 15.2% yield of C15. Full article

Urban heat vulnerability varies within and across cities, necessitating detailed studies to understand diverse populations’ specific vulnerabilities. This research assessed urban heat vulnerability at block group level in three Alabama cities: Birmingham, Montgomery, and Auburn-Opelika. The vulnerability index combines exposure, sensitivity, and adaptive capacity subindices, incorporating Landsat 8 satellite-derived Land Surface Temperature (LST), demographic, and socioeconomic data using factor analysis and geospatial techniques. Results showed strong positive correlations between LST and impervious surfaces in Auburn-Opelika and Montgomery, with a moderate correlation in Birmingham. An inverse correlation between LST and Normalized Difference Vegetation Index was observed in all cities. High LST correlated with high population density, varying across cities. Birmingham and Montgomery’s central areas exhibited the highest heat exposure, influenced by imperviousness, population density, and socioeconomic factors. Auburn-Opelika had limited high heat exposure block groups, and high sensitivity did not always align with exposure. Correlations and cluster analysis were used to dissect the heat vulnerability index, revealing variations in contributing factors within and across cities. This study underscores the complex interplay of physical, social, and economic factors in urban heat vulnerability and emphasizes the need for location-specific research. Local governance, community engagement, and tailored interventions are crucial for addressing unique vulnerabilities in each urban context. Full article

This study aims to identify an equation for predicting the calorific value for heat-treated biomass using structural analysis. Different models were constructed using 129 samples of cellulose, hemicellulose, and lignin, and calorific values obtained from previous studies. These models were validated using 41 additional datasets, and an optimal model was identified using its results and following performance metrics: the coefficient of determination (R²), mean absolute error (MAE), root-mean-squared error (RMSE), average absolute error (AAE), and average bias error (ABE). Finally, the model was verified using 25 additional data points. For the overall dataset, R² was ~0.52, and the RMSE range was 1.46–1.77. For woody biomass, the R² range was 0.78–0.83, and the RMSE range was 0.9626–1.2810. For herbaceous biomass, the R² range was 0.5251–0.6001, and the RMSE range was 1.1822–1.3957. The validation results showed similar or slightly poorer performances. The optimal model was then tested using the test data. For overall biomass and woody biomass, the performance metrics of the obtained model were superior to those in previous studies, whereas for herbaceous biomass, lower performance metrics were observed. The identified model demonstrated equal or superior performance compared to linear models. Further improvements are required based on a wider range of structural biomass data. Full article

Inappropriate soil management accelerates soil erosion and thus poses a serious threat to food security and biodiversity. Due to poor data availability and fragmented terrain, the landscape pattern of garden erosion in tropical Xishuangbanna is not clear. In this study, by integrating multi-source satellite imagery, field investigation and visual interpretation, we realized high-resolution mapping of gardens and soil conservation measures at the landscape scale. The Chinese Soil Loss Equation (CSLE) model was then performed to estimate the garden erosion rates and to identify critical erosion-prone areas; the landscape pattern of soil erosion was further discussed. Results showed the following: (1) For the three major plantations, teas have the largest degree of fragmentation and orchards suffer the highest soil erosion rate, while rubbers show the largest patch area, aggregation degree and soil erosion ratio. (2) The average garden erosion rate is 1595.08 t·km⁻²a⁻¹, resulting in an annual soil loss of 9.73 × 10⁶ t. Soil erosion is more susceptible to elevation and vegetation cover rather than the slope gradient. Meanwhile, irreversible erosion rates only occur in gardens with fraction vegetation coverage (FVC) lower than 30%, and they contribute 68.19% of total soil loss with the smallest land portion, indicating that new plantations are suffering serious erosion problems. (3) Garden patches with high erosion intensity grades and aggregation indexes should be recognized as priorities for centralized treatment. For elevations near 1900 m and lowlands (<950 m), the decrease in the fractal dimension index of erosion-prone areas indicates that patches are more regular and aggregated, suggesting a more optimistic conservation situation. Full article

The high salt content in saline–alkaline land leads to insufficient nutrients, thereby reducing agricultural productivity. This has sparked widespread interest in improving saline–alkaline soil. In this investigation, 16S rRNA gene high-throughput sequencing was employed to examine the impacts of three cropping systems (monoculture, rotation, and mixture) on soil bacterial communities. It was found that cropping rotations and mixtures significantly increased soil bacterial α-diversity. Random forest analysis showed a significant linear relationship between AK and EC and bacterial α-diversity. In addition, principal coordinates analysis (PCoA) further confirmed the significant differences in β-diversity between different soil layers. Through co-occurrence network analysis, it was found that cropping rotations and mixtures increased the stability and complexity of co-occurrence networks. By calculating NST to analyze the assembly process of soil bacterial communities in different cropping systems, it was found that the assembly process of soil bacterial communities was dominated by a stochastic process. Functional prediction results showed that a large number of C, N, and S cycling microbes appeared in soil bacterial communities. Our study aims to establish a fresh perspective on the improvement and recovery of saline–alkaline soil. Full article

To address the problem that traditional spectral clustering algorithms cannot obtain the complete structural information of networks, this paper proposes a spectral clustering community detection algorithm, PMIK-SC, based on the point-wise mutual information (PMI) graph kernel. The kernel is constructed according to the point-wise mutual information between nodes, which is then used as a proximity matrix to reconstruct the network and obtain the symmetric normalized Laplacian matrix. Finally, the network is partitioned by the eigendecomposition and eigenvector clustering of the Laplacian matrix. In addition, to determine the number of clusters during spectral clustering, this paper proposes a fast algorithm, BI-CNE, for estimating the number of communities. For a specific network, the algorithm first reconstructs the original network and then runs Monte Carlo sampling to estimate the number of communities by Bayesian inference. Experimental results show that the detection speed and accuracy of the algorithm are superior to other existing algorithms for estimating the number of communities. On this basis, the spectral clustering community detection algorithm PMIK-SC also has high accuracy and stability compared with other community detection algorithms and spectral clustering algorithms. Full article

This paper analyses the possibility of damaging and destroying an identification chip of the Mifare type in a frequently used contactless identification card of size ID-1, following the standard ISO/IEC 7810 (i.e., with dimensions 85.60 × 53.98 × 0.76 mm), using the magnetic field of an adjacent conductor in which a current pulse of a defined shape and amplitude is flowing. For analysis purposes, the nonlinear current–voltage characteristic of the Mifare chip voltage limiter was measured and approximated, and the mutual inductance of the straight conductor and the rectangle coil antenna in the card was calculated. Next, a mathematical analysis was conducted based on the description of the equivalent electrical circuit by the differential equations. The results of the mathematical analysis were verified by a simulation in the free simulation software Micro-Cap 12. The peak value of the current pulse that can damage the Mifare chip was measured by a combination wave generator. Based on these measurements and the chip characteristics, the energy capable of destroying the chip was calculated. The characteristics of chip damage were determined using a comparison of the resonant characteristics of undamaged and damaged RFID cards with Mifare chips. Full article

(1) Background: The aim of our research was to systematically review papers specifically focused on the hepatocellular carcinoma (HCC) diagnostic performance of DL methods based on medical images. (2) Materials: To identify related studies, a comprehensive search was conducted in prominent databases, including Embase, IEEE, PubMed, Web of Science, and the Cochrane Library. The search was limited to studies published before 3 July 2023. The inclusion criteria consisted of studies that either developed or utilized DL methods to diagnose HCC using medical images. To extract data, binary information on diagnostic accuracy was collected to determine the outcomes of interest, namely, the sensitivity, specificity, and area under the curve (AUC). (3) Results: Among the forty-eight initially identified eligible studies, thirty studies were included in the meta-analysis. The pooled sensitivity was 89% (95% CI: 87–91), the specificity was 90% (95% CI: 87–92), and the AUC was 0.95 (95% CI: 0.93–0.97). Analyses of subgroups based on medical image methods (contrast-enhanced and non-contrast-enhanced images), imaging modalities (ultrasound, magnetic resonance imaging, and computed tomography), and comparisons between DL methods and clinicians consistently showed the acceptable diagnostic performance of DL models. The publication bias and high heterogeneity observed between studies and subgroups can potentially result in an overestimation of the diagnostic accuracy of DL methods in medical imaging. (4) Conclusions: To improve future studies, it would be advantageous to establish more rigorous reporting standards that specifically address the challenges associated with DL research in this particular field. Full article

The use of a transformer backbone in LiDAR point-cloud-based models for 3D object detection has recently gained significant interest. The larger receptive field of the transformer backbone improves its representation capability but also results in excessive attention being given to background regions. To solve this problem, we propose a novel approach called deformable voxel set attention, which we utilized to create a deformable voxel set transformer (DVST) backbone for 3D object detection from point clouds. The DVST aims to efficaciously integrate the flexible receptive field of the deformable mechanism and the powerful context modeling capability of the transformer. Specifically, we introduce the deformable mechanism into voxel-based set attention to selectively transfer candidate keys and values of foreground queries to important regions. An offset generation module was designed to learn the offsets of the foreground queries. Furthermore, a globally responsive convolutional feed-forward network with residual connection is presented to capture global feature interactions in hidden space. We verified the validity of the DVST on the KITTI and Waymo open datasets by constructing single-stage and two-stage models. The findings indicated that the DVST enhanced the average precision of the baseline model while preserving computational efficiency, achieving a performance comparable to state-of-the-art methods. Full article

This paper presents an advanced method that combines coupling-of-modes (COM) theory and the finite element method (FEM), which enables the quick extraction of COM parameters and the accurate prediction of the electroacoustic and temperature behavior of surface acoustic wave (SAW) devices. For validation, firstly, the proposed method is performed for a normal SAW resonator. Then, the validated method is applied to analysis of an I.H.P. SAW resonator based on a 29°YX−LT/SiO₂/SiC structure. Via optimization, the electromechanical coupling coefficient (K²) is increased up to 13.92% and a high quality (Q) value of 1265 is obtained; meanwhile, the corresponding temperature coefficient of frequency (TCF) is −10.67 ppm/°C. Furthermore, a double-mode SAW (DMS) filter with low insertion loss and excellent temperature stability is also produced. It is demonstrated that the proposed method is effective even for SAW devices with complex structures, providing a useful tool for the design of SAW devices with improved performance. Full article

A new method for the synthesis and deposition of tungsten oxide nanopowders directly on the surface of a carbon-fiber-reinforced polymer composite (CFRP) is presented. The CFRP was chosen because this material has very good thermal and mechanical properties and chemical resistance. Also, CFRPs have low melting points and are transparent under ionized radiation. The synthesis is based on the direct interaction between high-power-density microwaves and metallic wires to generate a high-temperature plasma in an oxygen-containing atmosphere, which afterward condenses as metallic oxide nanoparticles on the CFRP. During microwave discharge, the value of the electronic temperature of the plasma, estimated from Boltzmann plots, reached up to 4 eV, and tungsten oxide crystals with a size between 5 nm and 100 nm were obtained. Transmission electron microscopy (TEM) analysis of the tungsten oxide nanoparticles showed they were single crystals without any extended defects. Scanning electron microscopy (SEM) analysis showed that the surface of the CFRP sample does not degrade during microwave plasma deposition. The X-ray attenuation of CFRP samples covered with tungsten oxide nanopowder layers of 2 µm and 21 µm thickness was measured. The X-ray attenuation analysis indicated that the thin film with 2 µm thickness attenuated 10% of the photon flux with 20 to 29 KeV of energy, while the sample with 21 µm thickness attenuated 60% of the photon flux. Full article

The Celestial Object Rendering TOol (CORTO) offers a powerful solution for generating synthetic images of celestial bodies, catering to the needs of space mission design, algorithm development, and validation. Through rendering, noise modeling, hardware-in-the-loop testing, and post-processing functionalities, CORTO creates realistic scenarios. It offers a versatile and comprehensive solution for generating synthetic images of celestial bodies, aiding the development and validation of image processing and navigation algorithms for space missions. This work illustrates its functionalities in detail for the first time. The importance of a robust validation pipeline to test the tool’s accuracy against real mission images using metrics like normalized cross-correlation and structural similarity is also illustrated. CORTO is a valuable asset for advancing space exploration and navigation algorithm development and has already proven effective in various projects, including CubeSat design, lunar missions, and deep learning applications. While the tool currently covers a range of celestial body simulations, mainly focused on minor bodies and the Moon, future enhancements could broaden its capabilities to encompass additional planetary phenomena and environments. Full article

Background: Children’s gait is traditionally understood to mature as young as three years old through pre-adolescence. Studies looking at gait biomechanics suggest that gait matures around three years old, while studies investigating gait variability propose a much later maturation. The studies that have examined children’s gait variability did so while the children walked around a track or down hallways that created a discontinuous gait, potentially affecting the measures of variability and the efficacy of the results. Purpose: Therefore, the purpose of our study was to investigate the development of gait dynamics and gait variability in children in a more continuous fashion, in this case, by walking on a treadmill. Methods: To accomplish this, we included four age groups of children, ranging 2–10 years old, walking on a treadmill for at least three minutes while stride time and stride length were collected. Stride time and stride length’s variability was then analyzed using linear (mean, standard deviation, coefficient of variation) and nonlinear (sample entropy, detrended fluctuation analysis) measures across the varying ages of our participants. Results: Interestingly, both the linear and nonlinear variabilities of the stride time and stride length measures decreased as the groups of children got older. Specifically, CV ST (2–3 (9.3 ± 4%), 8–10 (3.6 ± 0.7%), p < 0.05) and CV SL (2–3 (11.4 ± 3%), 8–10 (4.6 ± 1%), p < 0.05) were our strongest linear measures, and DFA ST (2–3 (0.97 ± 0.12), 8–10 (0.82 ± 0.10), p < 0.05) and DFA SL (2–3 (0.91 ± 0.04), 8–10 (0.81 ± 0.03), p < 0.05) were our strongest nonlinear measures, particularly between the youngest and oldest groups. This trend of variability decreasing with age suggests that as children’s gait matures, their gait becomes more stable and reliable. Significance: Our study rejects the notion that children’s gait is mature by the age of three, as some would suggest. By analyzing the variability of stride time and stride length, we can see that even later into childhood, children’s gait continues to change and evolve. Full article

A novel approach was developed that combined LP-based row generation with optimization-based sorting to tackle computational challenges posed by budget allocation problems with combinatorial constraints. The proposed approach dynamically generated constraints using row generation and prioritized them using optimization-based sorting to ensure a high-quality solution. Computational experiments and case studies revealed that as the problem size increased, the proposed approach outperformed simplex solutions in terms of solution search time. Specifically, for a problem with 50 projects (N = 50) and 2,251,799,813,685,250 constraints, the proposed approach found a solution in just 1.4 s, while LP failed due to the problem size. The proposed approach demonstrated enhanced computational efficiency and solution quality compared to traditional LP methods. Full article

Treatment of bladder cancer remains a critical unmet need and requires advanced approaches, particularly the development of local drug delivery systems. The physiology of the urinary bladder causes the main difficulties in the local treatment of bladder cancer: regular voiding prevents the maintenance of optimal concentration of the instilled drugs, while poor permeability of the urothelium limits the penetration of the drugs into the bladder wall. Therefore, great research efforts have been spent to overcome these hurdles, thereby improving the efficacy of available therapies. The explosive development of nanotechnology, polymer science, and related fields has contributed to the emergence of a number of nanostructured vehicles (nano- and micro-scale) applicable for intravesical drug delivery. Moreover, the engineering approach has facilitated the design of several macro-sized depot systems (centimeter scale) capable of remaining in the bladder for weeks and months. In this article, the main rationales and strategies for improved intravesical delivery are reviewed. Here, we focused on analysis of colloidal nano- and micro-sized drug carriers and indwelling macro-scale devices, which were evaluated for applicability in local therapy for bladder cancer in vivo. Full article

This article considers the deformation behavior of Panda optical fiber using different models of material behavior for the tasks of predicting residual stresses after drawing when cooling from 2000 °C to room temperature (23 °C) and indenting the fiber into an aluminum half-space at different parameters. These studies were conducted for single- and double-layer protective coatings (PCs), at different values of external load and thickness of single-layer PC. This paper determined the fields of residual stresses in the fiber formed during the drawing process. They are taken into account in modeling the fiber performance in the further process of this research. This article investigated two variants of PC behavior. The influence of behavior models and the number of covering layers on the deformation of the “fiber-half-space” system was analyzed. This paper establishes qualitative and quantitative regularities of the influence of the external load magnitude and relaxation properties of PCs on the deformation and optical characteristics of Panda optical fiber. Full article

This paper proposes a multiport energy management system (EMS) and its rule-based expert control strategy for a 150 kW range-extended towing vessel (RETV). The system integrates a diesel generator system, a permanent magnet synchronous motor, a lithium battery, and supercapacitors. To verify its feasibility and effectiveness, the proposed multiport EMS was modelled and tested through MATLAB/Simulink. Simulation results demonstrate that the designed multiport EMS works efficiently under the five typical operating conditions of the 150 kW RETV. In addition, two case studies were conducted and compared to investigate the impact of the battery’s initial state of charge (SoC) on the system’s energy efficiency. It was found that an overall 85% energy efficiency can be achieved for the RETV when the initial SoC is either 75% or 15%. The battery consistently operates within the optimal SoC range of 20% to 80%, and the supercapacitors effectively meet the instantaneous high-power demand. Full article

The dynamic performance of a 2-DOF hip joint rehabilitation robot configuration for patients with hip joint dyskinesia was analyzed. There were eight revolute pairs on one side of the hip joint rehabilitation robot configuration. The dynamics of the robot configuration were analyzed with the Newton–Euler method, and a dynamic model was developed. On the basis of the solved dynamic model, the dynamic performance index of the hip joint rehabilitation robot configuration is given, and the performance atlas under different parameters is drawn. The performance of the hip joint rehabilitation robot is theoretically verified. This study provides a theoretical basis for the research and development of exoskeleton rehabilitation robots. Full article

Huntington’s Disease (HD) is a devastating neurodegenerative disorder characterized by progressive motor dysfunction, cognitive impairment, and psychiatric symptoms. The early and accurate diagnosis of HD is crucial for effective intervention and patient care. This comprehensive review provides a comprehensive overview of the utilization of Artificial Intelligence (AI) powered algorithms in the diagnosis of HD. This review systematically analyses the existing literature to identify key trends, methodologies, and challenges in this emerging field. It also highlights the potential of ML and DL approaches in automating HD diagnosis through the analysis of clinical, genetic, and neuroimaging data. This review also discusses the limitations and ethical considerations associated with these models and suggests future research directions aimed at improving the early detection and management of Huntington’s disease. It also serves as a valuable resource for researchers, clinicians, and healthcare professionals interested in the intersection of machine learning and neurodegenerative disease diagnosis. Full article