Crime prediction and monitoring in Porto, Portugal, using machine learning, spatial and text analytics

Crimes are a common societal concern impacting quality of life and economic growth. Despite the global decrease in crime statistics, specific types of crime and feelings of insecurity, have often increased, leading safety and security agencies with the need to apply novel approaches and advanced systems to better predict and prevent occurrences. The use of geospatial technologies, combined with data mining and machine learning techniques allows for significant advances in the criminology of place. In this study, official police data from Porto, in Portugal, between 2016 and 2018, was georeferenced and treated using spatial analysis methods, which allowed the identification of spatial patterns and relevant hotspots. Then, machine learning processes were applied for space-time pattern mining. Using lasso regression analysis, significance for crime variables were found, with random forest and decision tree supporting the important variable selection. Lastly, tweets related to insecurity were collected and topic modeling and sentiment analysis was performed. Together, these methods assist interpretation of patterns, prediction and ultimately, performance of both police and planning professionals

A Systematic Review and Comparative Meta-analysis of Non-destructive Fruit Maturity Detection Techniques

The global fruit industry is growing rapidly due to increased awareness of the health benefits associated with fruit consumption. Fruit maturity detection plays a crucial role in fruit logistics and maintenance, enabling farmers and fruit industries to grade fruits and develop sustainable policies for enhanced profitability and service quality. Non-destructive fruit maturity detection methods have gained significant attention, especially with advancements in machine vision and spectroscopic techniques. This systematic review provides a concise overview of the techniques and algorithms used in fruit quality grading by farmers and industries. The study reviewed 63 full-text articles published between 2012 and 2023 along with their bibliometric analysis. Qualitative analysis revealed that researchers from various disciplines contributed to this field, with techniques falling into 3 categories: machine vision (mathematical modelling or deep learning), spectroscopy and other miscellaneous approaches. There was a high level of diversity among these categories, as indicated by an I-square value of 88.37% in the heterogeneity analysis. Meta-analysis, using odds ratios as the effect measure, established the relationship between techniques and their accuracy. Machine vision showed a positive correlation with accuracy across different categories. Additionally, Egger's and Begg's tests were used to assess publication bias and no strong evidence of its occurrence was found. This study offers valuable insights into the advantages and limitations of various fruit maturity detection techniques. For employing statistical and meta-analytical methods, key factors such as accuracy and sample size have been considered. These findings will aid in the development of effective strategies for fruit quality assessment

Divided We Rule: Influencer Polarization on Twitter during Political Crises in India

Influencers are key to the nature and networks of information propagation on social media. Influencers are particularly important in political discourse through their engagement with issues, and may derive their legitimacy either solely or in large part through online operation, or have an offline sphere of expertise such as entertainers, journalists etc. To quantify influencers' political engagement and polarity, we use Google's Universal Sentence Encoder (USE) to encode the tweets of 6k influencers and 26k Indian politicians during political crises in India. We then obtain aggregate vector representations of the influencers based on their tweet embeddings, which alongside retweet graphs help compute their stance and polarity with respect to these political issues. We find that influencers engage with the topics in a partisan manner, with polarized influencers being rewarded with increased retweeting and following. Moreover, we observe that specific groups of influencers are consistently polarized across all events. We conclude by discussing how our study provides insights into the political schisms of present-day India, but also offers a means to study the role of influencers in exacerbating political polarization in other contexts

Early Stage Detection of Parkinson Disease

Parkinson’s disease is a chronic neurodegenerative condition that demonstrate the progressive loss of the ability to correlate movements mainly occurs in the elderly. For the purpose of monitoring tremors in Parkinson’s disease, a system has to be designed and developed. For coordination of movements, people with Parkinson’s, deprive of a chemical called dopamine which behaves as the messenger between the brain parts and the nervous system .Detecting Parkinson’s disease is a very arduous task as there is no evidence currently present to do this. Therefore, the main intention of our work is the designing of a system for recognizing Parkinson’s disease at an initial stage. An Android application is being designed that allows the status of PD patients to be assessed based on the tests found on the Unified Parkinson’s Disease Rating Scale approved by the Movement Disorders Society (MDS-UPDRS)

Primary Pleural Synovial Sarcoma: A Rare Cause of Hemorrhagic Pleural Effusion

Primary pleural synovial sarcoma (PPSS) is a rare malignant pleural tumor comprising < 1% of all primary lung malignancies. Primary pleural mesothelioma (PPM) has many similar features that may cause a diagnostic dilemma due to overlapping clinical and histopathological features. We present the case of a young male with recurrent hemorrhagic pleural effusion without any obvious lung mass who was diagnosed with PPSS. This rare entity must be considered with a high index of suspicion while evaluating pleural tumors

Primary pleural synovial sarcoma: a rare cause of hemorrhagic pleural effusion

Primary pleural synovial sarcoma (PPSS) is a rare malignant pleural tumor comprising < 1% of all primary lung malignancies. Primary pleural mesothelioma (PPM) has many similar features that may cause a diagnostic dilemma due to overlapping clinical and histopathological features. We present the case of a young male with recurrent hemorrhagic pleural effusion without any obvious lung mass who was diagnosed with PPSS. This rare entity must be considered with a high index of suspicion while evaluating pleural tumors

Data from: Multiscale factors affecting human attitudes toward snow leopards and wolves

The threat posed by large carnivores to livestock and humans makes peaceful coexistence between them difficult. Effective implementation of conservation laws and policies depends on the attitudes of local residents toward the target species. There are many known correlates of human attitudes toward carnivores, but they have only been assessed at the scale of the individual. Because human societies are organized hierarchically, attitudes are presumably influenced by different factors at different scales of social organization, but this scale dependence has not been examined. We used structured interview surveys to quantitatively assess the attitudes of a Buddhist pastoral community toward snow leopards (Panthera uncia) and wolves (Canis lupus). We interviewed 381 individuals from 24 villages within 6 study sites across the high-elevation Spiti Valley in the Indian Trans-Himalaya. We gathered information on key explanatory variables that together captured variation in individual and village-level socioeconomic factors. We used hierarchical linear models to examine how the effect of these factors on human attitudes changed with the scale of analysis from the individual to the community. Factors significant at the individual level were gender, education, and age of the respondent (for wolves and snow leopards), number of income sources in the family (wolves), agricultural production, and large-bodied livestock holdings (snow leopards). At the community level, the significant factors included the number of smaller-bodied herded livestock killed by wolves and mean agricultural production (wolves) and village size and large livestock holdings (snow leopards). Our results show that scaling up from the individual to higher levels of social organization can highlight important factors that influence attitudes of people toward wildlife and toward formal conservation efforts in general. Such scale-specific information can help managers apply conservation measures at appropriate scales. Our results reiterate the need for conflict management programs to be multipronged

Multiscale factors affecting human attitudes toward snow leopards and wolves-Dataset

Sheet 1: Contains the meta data describing all the column heads of sheet 2. Sheet 2 contains the raw data. Column heads describe the values contained within them. Rows are individual observations (interviews)

Development of immobilized novel fungal consortium for the efficient remediation of cyanide-contaminated wastewaters

Free cyanide is a hazardous pollutant released from steel industries. Environmentally-safe remediation of cyanide-contaminated wastewater is required. In this work, Pseudomonas stutzeri (ASNBRI_B12), Trichoderma longibrachiatum (ASNBRI_F9), Trichoderma saturnisporum (ASNBRI_F10) and Trichoderma citrinoviride (ASN-BRI_F14) were isolated from blast-furnace wastewater and activated-sludge by enrichment culture. Elevated microbial growth, rhodanese activity (82 %) and GSSG (128 %) were observed with 20 mg-CN L-1. Cyanide degradation > 99 % on 3rd d as evaluated through ion chromatography, followed by first-order kinetics (r2 = 0.94-0.99). Cyanide degradation in wastewater (20 mg-CN L-1, pH 6.5) was studied in ASNBRI_F10 and ASN-BRI_F14 which displayed increased biomass to 49.7 % and 21.6 % respectively. Maximum cyanide degradation of 99.9 % in 48 h was shown by an immobilized consortium of ASNBRI_F10 and ASNBRI_F14. FTIR analysis revealed that cyanide treatment alters functional groups on microbial cell walls. The novel consortium of T. saturnisporum-T. citrinoviride in the form of immobilized culture can be employed to treat cyanide -contaminated wastewater

Major Biological Control Strategies for Plant Pathogens

Food security has become a major concern worldwide in recent years due to ever increasing population. Providing food for the growing billions without disturbing environmental balance is incessantly required in the current scenario. In view of this, sustainable modes of agricultural practices offer better promise and hence are gaining prominence recently. Moreover, these methods have taken precedence currently over chemical-based methods of pest restriction and pathogen control. Adoption of Biological Control is one such crucial technique that is currently in the forefront. Over a period of time, various biocontrol strategies have been experimented with and some have exhibited great success and promise. This review highlights the different methods of plant-pathogen control, types of plant pathogens, their modus operandi and various biocontrol approaches employing a range of microorganisms and their byproducts. The study lays emphasis on the use of upcoming methodologies like microbiome management and engineering, phage cocktails, genetically modified biocontrol agents and microbial volatilome as available strategies to sustainable agricultural practices. More importantly, a critical analysis of the various methods enumerated in the paper indicates the need to amalgamate these techniques in order to improve the degree of biocontrol offered by them