Geometric construction and reconfiguration analysis of multi-mode two-loop spatial mechanisms and their multi-loop extensions

Multi-mode multi-loop spatial mechanisms (MMSMs) are an important class of reconfigurable mechanisms, yet their diversity remains highly limited. This paper focuses on the geometric construction and reconfiguration analysis of multi-mode two-loop spatial mechanisms (MTSMs) and their extensions to MMSMs. Using the construction method, three types of MTSMs with two motion modes are synthesized by combining two classical Bricard mechanisms while constraining their undesired motion modes. Reconfiguration analysis of the proposed MTSMs is conducted using dual quaternions and the natural exponential function substitution to prove their motion characteristics. Subsequently, the construction method is extended to synthesize novel MMSMs with two motion modes. Various MMSMs are formed and further adopted to construct double-layer MMSMs for multi-mode morphing wings. Finally, the mobility properties of the double-layer MMSMs in both the contraction-expansion and parallelogram modes are substantiated through dual quaternions. This work provides a novel idea for constructing MMSMs from MTSMs without altering their motion characteristics

A Lightweight Model LGCSPNet for Sitting Posture Risk Management Applications

Current methods for sitting posture recognition typically follow a pipeline involving keypoint extraction and skeleton graph construction, followed by pose classification using Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs). However, CNNs struggle to model long-range dependencies among keypoints, whereas ViTs suffer from high computational costs. Moreover, both approaches tend to introduce redundancy during feature modeling. To improve efficiency, some studies have explored direct classification using keypoint coordinates, but these methods often fail to balance high accuracy with computational efficiency. To this end, this paper proposes a new model LGCSPNet with lightweight graph convolution modules (LGC) and a contrastive learning module. Firstly, LGC enables efficient full-keypoint communication by shifting features across keypoint channels, allowing each keypoint to access global context at minimal computational cost. Building on this, LGC enhances sitting posture detection by computing 3D attention weights via a parameter-free energy function with a closed-form solution, enhancing feature learning for posturally significant keypoints. The contrastive learning module enhances differentiation between similar postures in different categories by strategically selecting feature samples. Experiments on public human posture datasets and our custom sitting posture dataset show that LGCSPNet has only 0.097M parameters while achieving a 99% recognition rate. It surpasses existing models in terms of parameter quantity and accuracy. Guided by ergonomic metrics, our model enables posture correction and mitigates long-term sitting-related injuries

Strategies for Success? Market entry strategies of new craft beer producers

Fewer than half of UK start-up businesses survive beyond five years (ONS, 2020). The Scottish Small Business Survey of 2019 found competition in the market and uncertainty as to how to face it were considered the most significant barrier to success by almost half of SMEs (Scottish Government, 2020). This chapter considers how four Scottish breweries have formulated start-up strategies to respond to competition in an everincreasingly crowded marketplace in order to maximise their likelihood of survival. The findings from each of these case studies are presented in an accessible format, and indicate that a variety of approaches to the development of the businesses can be adopted, albeit planned approaches dominate. Drawing on real life experiences of four successful businesses, the practical choices they took provide guidance and inspiration for other aspiring craft beer entrepreneurs in selecting an appropriate approach to and content of their founding strategy

Associations between neighborhood compactness, perceived accessibility to urban amenities, and mental distress of older adults in a high-density city

Neighborhood compactness has been identified as a sustainable approach for fostering social relationships. However, existing studies have not clearly demonstrated whether neighborhood compactness in high-density cities directly influences the mental distress of community-dwelling older adults, or whether it indirectly influences mental distress through perceived accessibility to urban amenities (PA). Using a composite score of three dimensions (depressive symptoms, loneliness, and social isolation) to measure mental distress, and applying a mediation model within a cross-sectional design involving 947 older adults (aged ≥ 60) with at least mild symptoms in Hong Kong, our findings suggest an indirect pathway between neighborhood compactness, PA, and mental distress. Neighborhood compactness was positively associated with PA (B = 7.791, p < 0.001), and PA was negatively associated with mental distress (B = −0.12, p = 0.017). However, no direct impact of neighborhood compactness on mental distress was found. Moderated mediation analysis further indicated that neighborhood compactness and PA supported older females but not males. These results align with our hypothesis that (1) neighborhood compactness may not always be beneficial for older adults with mental distress in high-density cities unless compactness itself enhances PA and facility usage to support community-dwelling individuals, and (2) gender differences may result in varying interactions and perceptions of urban amenities and the built environment. To support the concept of “aging in place” in the future, urban plans aimed at enhancing neighborhood compactness and its social impacts should focus on addressing social inequality, including strategies to improve urban design, social participation, and gender-specific protocols, so that older adults can achieve better community awareness, environmental satisfaction, and facility usage, ultimately reducing mental distress

Strategies for Success? Market entry strategies of new craft beer producers

Fewer than half of UK start-up businesses survive beyond five years (ONS, 2020). The Scottish Small Business Survey of 2019 found competition in the market and uncertainty as to how to face it were considered the most significant barrier to success by almost half of SMEs (Scottish Government, 2020). This chapter considers how four Scottish breweries have formulated start-up strategies to respond to competition in an everincreasingly crowded marketplace in order to maximise their likelihood of survival. The findings from each of these case studies are presented in an accessible format, and indicate that a variety of approaches to the development of the businesses can be adopted, albeit planned approaches dominate. Drawing on real life experiences of four successful businesses, the practical choices they took provide guidance and inspiration for other aspiring craft beer entrepreneurs in selecting an appropriate approach to and content of their founding strategy

Investigating the effect of climate-related hazards on claim frequency prediction in motor insurance with incomplete data

A climate-related dataset provided by a Greek insurance company is analysed to quantify the risks that weather-related hazards, driven by climate change, pose to motor insurance. However, accurately modelling the relationship between these hazards and claim frequencies is challenging, largely because the available records are incomplete. Specifically, they capture only those storm events that result in at least one claim while omitting unreported events. To address this limitation, we introduce a novel class of compound frequency models for the joint analysis of storm occurrences and the corresponding claim frequencies with accurate predictive power. These models are specifically designed to recover the joint distribution of actual storm events and underlying claim processes even when faced with incomplete data. Additionally, we incorporate geospatial covariates to evaluate their influence on both storm occurrences and claim frequencies. Given Greece’s vulnerability to extreme weather due to its geographical position, understanding the influence of climate change on insurance risks is critical. Notably, our findings reveal a negative intrinsic dependence between actual storm counts and per-storm claim frequencies, suggesting potential diversification benefits for insurers as climate change leads to more frequent weather-related hazards

Assessing public pensions using risk measures: pay-as-you-go versus mixed schemes

Pay-as-you-go (PAYG) pension systems are heavily affected by demographic risks. To mitigate the financial burden, mixed pension schemes that combine elements of funding and PAYG have been proposed. In this paper, we introduce a mixed scheme framework designed for a shrinking working-age population under a defined benefit scheme. We evaluate its performance using non-life risk measures such as the one-year ruin probability and the Value at Risk of the accumulated deficits over time. We also examine the implications of guaranteeing a return of zero on the investments within the funding component. Furthermore, we explore the creation of a buffer fund that invests part of the capital in the financial markets, thereby alleviating the financial pressures of the PAYG part. Our findings indicate that, while the proposed mixed framework does not hedge against demographic risk, it enhances the financial health of the system and delays the need for pension reforms as a result

A Dynamics-Based Method for Determining the Local Finite Mobility of Single-loop Spatial Mechanisms

This paper proposes a method for calculating the local finite mobility of single-loop spatial mechanisms based on modal analysis. Using spanning tree-based multibody dynamics, the single-loop spatial mechanism is modeled as a tree-like kinematic chain with serial chains closed by the constraints represented by a spring force model. The dynamic model is linearized using Taylor expansion. The stiffness matrix is then yield. The correspondence between vibrating/non-vibrating generalized coordinates and the nonzero/zero eigenvalues in the stiffness matrix is clarified. The mobility of the single loop spatial mechanism is then determined by the number of zero eigenvalues in the stiffness matrix. The method is then validated and analyzed by calculating the DOF of Sarrus mechanism, Bennett mechanism, 3-mode 7R mechanism, a mechanism with special parameters and a variable-DOF 8R mechanism. One contribution is that this work enhances spanning-tree-based dynamic modeling by analyzing joint selection strategies and introducing spring forces to replace kinematic constraints. The Other contribution is that based on the linearized model, a modal analysis framework is established to determine the mobility of single-loop spatial mechanisms.</p

A Comparative Analysis of Transformer Models in Social Bot Detection

Social media has become a key medium of communication in today's society. This realisation has led to many parties employing artificial users (or bots) to mislead others into believing untruths or acting in a beneficial manner to such parties. Sophisticated text generation tools, such as large language models, have further exacerbated this issue. This paper aims to compare the effectiveness of bot detection models based on encoder and decoder transformers. Pipelines are developed to evaluate the performance of these classifiers, revealing that encoder-based classifiers demonstrate greater accuracy and robustness. However, decoder-based models showed greater adaptability through task-specific alignment, suggesting more potential for generalisation across different use cases in addition to superior observa. These findings contribute to the ongoing effort to prevent digital environments being manipulated while protecting the integrity of online discussion

MIDGARD: A Robot Navigation Simulator for Outdoor Unstructured Environments

We present MIDGARD, a simulation platform based on Unreal Engine for training autonomous robots in complex outdoor unstructured environments. It offers photorealistic 3D scenes, procedural scene generation, and integration with ROS and OpenAI Gym. The focus of MIDGARD is on navigation, where an autonomous agent travels from random initial positions to designated target locations avoiding obstacles, enabling researchers to develop and evaluate novel algorithms and navigation methods. We evaluate MIDGARD’s suitability as a research tool by training navigation algorithms based on reinforcement learning; we also assess sim-to-real transfer capabilities in a traversable horizon prediction task, using deep learning models on RGB images only. MIDGARD builds and docs are available at www.midgardsim.org