BackgroundThe tribal inhabitants of the Skardu valley (Pakistan) live in an area of great endemic botanic diversity. This paper presents the first quantitative ethnomedicinal spectrum of the valley and information on the uses of medicinal plant. This paper aims to analyze and catalogue such knowledge based on Relative Frequency Citation (RFC) and Use Value (UV) of medicinal plants in addition to the configuration of the Pearson correlation coefficient.MethodsThe field study was carried out over a period of approximately 2 years (2011–2013) using semi-structured interviews with 71 informants (most of the informants belonged to an age between 50 and 70 years) in six remote locations in the valley. Ethnomedicinal data was analyzed using frequency citation (FC), relative frequency of citation (RFC) and use value (UV) along with a Pearson correlation coefficient (PCC). Demographic characteristics of participants, ethnobotanical inventory of plants and data on medicinal application and administration were recorded.ResultsA total of 50 medicinal plants belonging to 25 families were reported to be used against 33 different ailments in the valley. The maximum reported medicinal plant families were Asteraceae (7 report species), Lamiaceae (6) , Polygonaceae (4) and Rosaceae (4), the most dominant life form of the species includes herbs (38) followed by shrubs and subshrubs (12), the most frequent used part was leaves (41%) followed by root (26%), flower (14%), fruit (9%), seeds (8%), bulb (1%) and bark (1%), the most common preparation and administration methods were infusion (32%), decoction (26%), paste (18%), herbal juice (17%) and powder drug (7%). The Pearson correlation coefficient between RFC and UV was 0.732 showing highly positive significant association.ConclusionsIn this study, we have documented considerable indigenous knowledge about the native medicinal plants in Skardu valley for treating common ailments which are ready to be further investigated phytochemically and pharmacologically which leads to natural drug discovery development. The study has various socioeconomic dimensions which are associated with the local communities.

Purpose The purpose of this paper is to investigate new emerging organizational parameters and their roles in successful change implementation. These organizational parameters are rarely investigated especially in the context of organizational change (OC) in private and public sector organizations. Design/methodology/approach In cumulative, 403 valid responses have been obtained randomly from public sector workers by using self-administered questionnaires. Findings The results reveal that knowledge sharing regarding incremental and radical changes can helpful for effective OC implementation. Findings highlight the significant role of emotional and social intelligence in managing resistance and bringing openness to change in these organizations. It is also found that social media has become an important emerging organizational parameter to foster effective communication and knowledge sharing during OC implementation. Apart from the direct effects, readiness to change has multiple effects coupled with emerging organizational parameters to implement change successfully. Research limitations/implications The results of the current study offer diversified implications for theory, practice and global society. The theoretical base is taken from the well-known theories of management (i.e. Lewin’s three-step model, field theory, intelligence theory, cost-effective theory, social exchange theory, social network theory and social penetration theory). Emerging organizational parameters that have a potential impact on effective change implementation are identified. The findings suggest that global organizations should have to initiate effective networking structure using social media applications and social intelligence skills to remain connected and get positive responses about change formulation and implementation decision. Originality/value A majority of studies have presented the research model on OC implementation in the context of developed countries, which form 30 percent of the world’s population, mostly the Americas and Europe. It is observed that a developing country, such as Pakistan, has a culture that is based on power distance, collectivism and more political influence as compared to developed countries. Triandis et al. (1980) argued that any theoretical contribution without considering the cultural aspect can lead to bias findings. There is limited research available in the world that is conducted to examine the interactive effects of readiness to change on the relationship between effective change implementation, knowledge sharing, intelligence and social media. These findings are useful to plan and execute OC using new emerging organizational parameters.

BackgroundThis paper constitutes an important ethnobiological survey in the context of utilizing biological resources by residents of Kala Chitta hills of Pothwar region, Pakistan. The fundamental aim of this research endeavour was to catalogue and analyse the indigenous knowledge of native community about plants and animals. The study is distinctive in the sense to explore both ethnobotanical and ethnozoological aspects of indigenous culture, and exhibits novelty, being based on empirical approach of Multinomial Logit Specifications (MLS) for examining ethnobotanical and ethnozoological uses of specific plants and animals.MethodsTo document the ethnobiological knowledge, the survey was conducted during 2011–12 by employing a semi-structured questionnaire and thus 54 informants were interviewed. Plant and animal specimens were collected, photographed and properly identified. Distribution of plants and animals were explored by descriptive and graphical examination. MLS were further incorporated to identify the probability of occurrence of diversified utilization of plants and animals in multipurpose domains.ResultsTraditional uses of 91 plant and 65 animal species were reported. Data analysis revealed more medicinal use of plants and animals than all other use categories. MLS findings are also in line with these proportional configurations. They reveal that medicinal and food consumption of underground and perennial plants was more as compared to aerial and annual categories of plants. Likewise, medicinal utilization of wild animals and domestic animals were more commonly observed as food items. However, invertebrates are more in the domain of medicinal and food utilization. Also carnivores are fairly common in the use of medicine while herbivores are in the category of food consumption.ConclusionThis study empirically scans a good chunk of ethnobiological knowledge and depicts its strong connection with indigenous traditions. It is important to make local residents beware of conservation status of species and authentication of this knowledge needs to be done in near future. Moreover, Statistically significant findings impart novelty in the existing literature in the field of ethnobiology. Future conservation, phytochemical and pharmacological studies are recommended on these identified plants and animals in order to use them in a more sustainable and effective way.

Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.

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