Variation in the sacroiliac joint in Felidae

Felidae species show a great diversity in their diet, foraging and hunting strategies, from small to large prey. Whether they belong to solitary or group hunters, the behavior of cats to subdue resisting small or large prey presents crucial differences. It is assumed that pack hunting reduces the per capita risk of each individual. We hypothesize that the sacroiliac articulation plays a key role in stabilizing the predator while subduing and killing prey. Using CT-scan from 59 felid coxal bones, we calculated the angle between both iliac articular surfaces. Correlation of this inter-iliac angle with body size was calculated and ecological stressors were evaluated on inter-iliac angle. Body size significantly influences inter-iliac angle with small cats having a wider angle than big cats. Arboreal species have a significantly larger angle compared to cursorial felids with the smallest value, and to scansorial and terrestrial species with intermediate angles. Felids hunting large prey have a smaller angle than felids hunting small and mixed prey. Within the Panthera lineage, pack hunters (lions) have a larger angle than all other species using solitary hunting strategy. According to the inter-iliac angle, two main groups of felids are determined: (i) predators with an angle of around 40° include small cats (i.e., Felis silvestris, Leopardus wiedii, Leptailurus serval, Lynx Canadensis, L. rufus; median = 43.45°), the only pack-hunting species (i.e., Panthera leo; median = 37.90°), and arboreal cats (i.e., L. wiedii, Neofelis nebulosa; median = 49.05°), (ii) predators with an angle of around 30° include solitary-hunting big cats (i.e., Acinonyx jubatus, P. onca, P. pardus, P. tigris, P. uncia; median = 31.80°). We suggest different pressures of selection to interpret these results. The tightening of the iliac wings around the sacrum probably enhances big cats’ ability for high speed and large prey control. In contrast, pack hunting in lions reduced the selective pressure for large prey.

Effects of the Community Cats Program on Population Control, Migration and Welfare Status of Free-Roaming Cats in Tokyo, Japan

The community cats program (CCP), which includes trap–neuter–return activities, has been promoted in Japan to reduce the population of free-roaming cats without harmful effects on their welfare. To ascertain the effects of the CCP, a two-year route census of free-roaming cats was conducted in an area with CCP and the other area without CCP in urban Tokyo, Japan. The estimated number of cats was lower in the CCP area than the non-CCP area, but there was no difference in the rate of decline in cat populations between areas. More cats emigrated or disappeared rather than immigrated in both areas in the second year and more males tended to immigrate into the CCP area. There was no difference in the behavior of cats between areas and among seasons. The proportion of cats with poor health was lower in the CCP area than the non-CCP area. These results suggest that the CCP may improve the welfare of free-roaming cats. As the effect of CCP was restrictive in reducing the population of free-roaming cats, the further promotion of neutering of cats may be necessary to reduce the population density of cats.

Solving the Manufacturing Cell Design Problem through Binary Cat Swarm Optimization with Dynamic Mixture Ratios

In this research, we present a Binary Cat Swarm Optimization for solving the Manufacturing Cell Design Problem (MCDP). This problem divides an industrial production plant into a certain number of cells. Each cell contains machines with similar types of processes or part families. The goal is to identify a cell organization in such a way that the transportation of the different parts between cells is minimized. The organization of these cells is performed through Cat Swarm Optimization, which is a recent swarm metaheuristic technique based on the behavior of cats. In that technique, cats have two modes of behavior: seeking mode and tracing mode, selected from a mixture ratio. For experimental purposes, a version of the Autonomous Search algorithm was developed with dynamic mixture ratios. The experimental results for both normal Binary Cat Swarm Optimization (BCSO) and Autonomous Search BCSO reach all global optimums, both for a set of 90 instances with known optima, and for a set of 35 new instances with 13 known optima.

Epidemiology of domestic cat behavioral problems in the city of Porto Alegre/Brazil: a survey of small animal veterinary practitioners

ABSTRACT: The popularity of cats as a companion animal has been growing. Additionally, there is also an increase in the number of complaints regarding behavioral problems associated with the species. Behavioral problems interfere with the animal welfare and quality of people’s lives that are living with them, and are one of the main reasons for abandonment and euthanasia in some countries. This study aimed to investigate the prevalence of behavioral problems among cats with the help of veterinary practitioners in the city of Porto Alegre/Brazil. This evaluation was performed through a questionnaire in veterinary clinics and hospitals of small animals, which was answered by veterinarians responsible for clinical care. The study showed that most veterinarians (92.0%) have been sought to assist with behavioral problems and that dogs were the most consulted species (72.0%). Regarding cats, scratching was the most common owners’ complaints (40.0%), followed by aggressiveness (39.0%), and inappropriate elimination of urine (38.0%). Aggression (73.8%) and scratching (56.9%) were the most frequent reasons that may lead to the abandonment or euthanasia of cats. Results of this research highlighted the importance of having a better understanding about the normal behavior of cats, and the fundamental role of the veterinarian in preventing behavioral problems and abandonment of these animals.

Improved Cat Swarm Optimization for Simultaneous Allocation of DSTATCOM and DGs in Distribution Systems

This paper addresses a new methodology for the simultaneous optimal allocation of DSTATCOM and DG in radial distribution systems to maximize power loss reduction while maintaining better node voltage profiles under multilevel load profile. Cat Swarm Optimization (CSO) is one of the recently developed powerful swarm intelligence-based optimization techniques that mimics the natural behavior of cats but usually suffers from poor convergence and accuracy while subjected to large dimension problem. Therefore, an Improved CSO (ICSO) technique is proposed to efficiently solve the problem where the seeking mode of CSO is modified to enhance its exploitation potential. In addition, the problem search space is virtually squeezed by suggesting an intelligent search approach which smartly scans the problem search space. Further, the effect of network reconfiguration has also been investigated after optimally placing DSTATCOMs and DGs in the distribution network. The suggested measures enhance the convergence and accuracy of the algorithm without loss of diversity. The proposed method is investigated on 69-bus test distribution system and the application results are very promising for the operation of smart distribution systems.

Chaotic Cat Swarm Algorithms for Global Numerical Optimization

A novel Chaotic Improved Cat Swarm Algorithm (CCSA) is presented for global optimization. The CSA is a new meta-heuristic optimization developed based on imitating the natural behavior of cats and composed of two sub-models: tracing mode and seeking mode, which model upon the behaviors of cats. Here different chaotic maps are utilized to improve the seeking mode step of the algorithm. Seven different chaotic maps are investigated and the Logistic and Sinusoidal maps are found as the best choices. Comparing the new algorithm with the CSA method demonstrates the superiority of the CCSA for the benchmark functions.

Cat Swarm Optimization-Based Schemes for Resource-Constrained Project Scheduling

This paper presents a cat swarm optimization (CSO)-based method for resource constrained project scheduling problem (RCPSP). The CSO simulates the behavior of cats in two sub-models and potential solution to the RCPSP is presented by the multidimensional positions of cats. CSO-based scheme for the RCPSP has three main stages: first randomly initialize the parameters of cats, then update the position in iteration and calculate the fitness through serial SGS method, finally terminate the process if the condition is satisfied. Compared to the other widely used heuristic methods, CSO is easy to understand and to implement. The adoption of CSO in solving RCPSP indicates the universality of CSO in solving operational problems. When solving RCPSP, some refinement of original CSO are made. The performance of the proposed algorithm is compared against a set of heuristic and meta-heuristic methods, and it is tested on standard problem sets called PSPLIB which is freely available on the Internet. The empirical results show that CSO has an average good performance among the other compared methods.