Linear Discriminant Function Analysis Research Articles

Metric analysis of the patella for sex estimation in a Portuguese sample.

The biological profile estimation is the first step toward positive identification. However, it is not always possible to access a complete and well-preserved skeleton due to postmortem damage and taphonomic changes. As such, there is a need to develop new alternatives to analyze different bones of the human skeleton. The present study aims to analyze the patellar osteometry, with attention to its degree of sexual dimorphism, to establish a simple method for estimating sex in Portuguese adults. Six measurements were taken from 222 patella pairs, including 117 females and 105 males from the XXI Century Identified Skeleton Collection of the University of Coimbra. Subsequently, this method was validated in a different sample of 50 individuals equally representing both sexes. Maximum height stands out with a 77.0% of correct sex estimation, reaching 98.0% when applied to the new sample. The linear discriminant function analysis containing all the six variables showed the best results, with 80.2% of correct classification after cross-validation and 96.0% when applied to the independent sample.

Flight calls and trills of Evening Grosbeaks can be used to map movements and ranges of call types 1 and 2

Evening Grosbeak (Coccothraustes vespertinus) is a species of North American Fringillid finch thought to be one of the fastest declining songbirds across North America. It has been divided into five groups, potentially distinct lineages, based in part on structure of their flight calls. The primary flight calls of each type exhibit structural variation that has not been described and the degree to which that variation might lead to identification errors has not been quantified. We describe the variation in call structure of type 1 Evening Grosbeaks recorded at a spring migratory stopover site (Corvallis, Oregon) and nearby areas in the Pacific Northwest, USA. We recorded grosbeaks weekly from April through early June 2023. We reviewed more than 10,000 recorded call notes to characterize the variety of calls and their configurations. We found a high diversity of call notes including at least 11 recurring readily identifiable variants of the primary flight calls, all of which were attributed to individuals thought to be type 1 birds. Geographically, the nearest neighbors of type 1 Evening Grosbeaks are type 2s, which have uncommonly been recorded in our study area but whose breeding range appears to overlap that of type 1 in southern Oregon. We quantified recordings of type 2 flight calls and compared them with type 1 flight calls, finding that linear discriminant function analyses correctly identified >95% of recordings to type. Inclusion of a metric of asymmetry in call shape improved correct classification to 98.5%. We also found that the other dominant calls given by both types, buzzy trills, could be identified correctly to type with a high level of confidence. The sufficiently different characteristics of flight calls and trills indicate that types 1 and 2 are identifiable spectrographically in most cases, providing confidence that the geographic distribution and migratory movements of call-type populations, despite being essentially identical in plumage, can be documented effectively by characteristics of call notes.

Deep Learning: A Climate Smart Agriculture Tool for Groundnut Farmers

Groundnut is an oil seed crop, which is grown widely in the country, and approximately 80% of groundnut is produced in the rainfed condition. Unlike weather factors even the prices of agriculture commodities are volatile in nature and the groundnut prices also behaves in an unusual pattern. A traditional farmer faces recurrent challenges. Deep learning methods and the accessibility of satellite imagery have, however, created new opportunities for more accurate and effective agricultural yield estimates. Large-scale yield estimation and understanding the impact of the variability of agricultural growing circumstances are critical due to the increased frequency of extreme climate occurrences. Crop growth condition models can be utilized with time series of spatially explicit information from satellite remote sensing (RS). For efficient agricultural management, guaranteeing food security, and making wise decisions about resource allocation and market forecasting, accurate and timely estimation of crop yields is essential. Crop yield estimation has historically depended on time-consuming field surveys and statistical models. Machine Learning (ML) is an exciting application of Artificial Intelligence. It provides the ability to learn by experiences without any explicit. The proposed model is based on simple\and cost-effective hardware that can be used by agriculture officers and farmers to get good productivity of crops. SCS model is trained by classifying dataset and tested subsequently. The accuracy and performance of an ML classifier depend only on the type and size of the dataset. Crop selection by real-time sensing data and soil analysis attributes is a big contribution in research of smart agriculture. A model was proposed basing on three modules: crop selection, crop management, and crop maturity. It used parameters soil moisture, temperature, humidity, air pressure, and air quality with weather conditions for better crop selection and health monitoring. A real-time sensory data was used for analysis on Thing Speak application with KNN algorithm. Some data mining techniques are applied for data preprocessing and comparing real-time data with trained data for crop prediction. It also considered crop prices for crop prediction, listed on National Commodity and Derivative Exchange. The KNN classifier is applied for data analysis has focused on IoT-oriented agricultural methods for weather monitoring. The prediction methods are investigated for commercial and scientific perspectives, cost of IoT components, Think Speak application is used for data analysis. An Android application is also designed to intimate the farmers about required water level of fields. IoT framework best use of land to improve farming methods and increase crop production with profit maximization. A wireless sensor network was deployed in the field to sense data for different parameters and for proper monitoring of field. It proposed a crop prediction method for crop yield maximization and quality of crops by considering real-time data of metrological factors using ML algorithms: precipitation, temperature, humidity, and solar light. Soft computing techniques can be employed to estimate the yield of various crops. As a result of rapid advancements in technology, crop models and decision tools have emerged as vital components of precision agriculture worldwide. These models and tools utilize linear regression techniques, nonlinear simulations, expert systems, Adaptive Neuro-Fuzzy Interference Systems, Support Vector Machines, Data Mining, Genetic Programming, and Artificial Neural Network (ANN) to predict harvest outcomes particularly under the influence of climate change. These prediction methods play a significant role in improving the accuracy and reliability of yield estimation in agricultural systems. ANNs successfully address identification, classification, and regression challenges in crop disease identification, harvest mechanization and product quality sorting. Multiple linear regression and discriminant function analysis were employed to construct a groundnut yield forecasting model, utilizing weather indices including maximum temperature, minimum temperature, total rainfall, morning relative humidity, and evening relative humidity. Employing techniques such as stepwise multiple linear regression, principal component analysis was combined with stepwise multiple linear regression, ANN, and penalized regressions like least absolute shrinkage and selection operator and elastic net. The models, particularly least absolute shrinkage and selection operator and elastic net, demonstrated remarkable accuracy, boasting a normalized root mean square error of under 10% across most test locations. Farmers using traditional methods in agriculture face problems such as low crop yield due to unpredicted weather, wrong amount of water and nutrients, and wrong selection of crop. In previous research work, limited parameters were used that are insufficient for high yield of crops. Our research is aimed at maximizing the crop yield by selecting suitable crop. We tackle this issue by applying technology methodically and evidencebased analysis. For instance, adding required amount of nutrients gives improved yields. Our work is based on selection of the influential parameters. Deep learning techniques used to give improved accuracy with less computational cost as compared to previous research.

Quantifying Intermittent Flow Regimes in Ungauged Basins: Optimization of Remote Sensing Techniques for Ephemeral Channels Using a Flexible Statistical Classification

Intermittent and ephemeral channels are a critical component of the global hydrologic network. The dominant feature in dryland environments, ephemeral channel transmission loss facilitates aquifer recharge. Characterizing flow intermittency improves groundwater storage estimates; however, limited gauging of intermittent systems impedes this understanding. This research develops an improved classification for surface flow, optimized for ephemeral systems using linear discriminant function analysis and remotely sensed imagery. It further applies this methodology to assess temporal and spatial flow patterns across the Souss channel, an ungauged, ephemeral system in central Morocco. Linear discriminant function analysis demonstrates high predictive accuracy for Landsat imagery, with significantly improved classification success as compared to the Modified Normalized Difference Water Index. Application to the Souss channel from 1984 to 2022 points to a decreasing trend in flow frequency. Despite this change, flow events remain concentrated within the wet season, critical for regional aquifer recharge. Spatial flow characteristics further support sustained infiltration, with the majority of events focused within the upstream channel section during both dry and wet seasons. Decreased occurrence moving downstream highlights the likely impact of additional factors such as transmission loss, evapotranspiration, and agricultural abstraction contributing to channel intermittency.

One nation, under war: Did the language of Fox News and MSNBC converge during the invasion of Ukraine?

AbstractEvidence suggests that political differences have increased markedly in the United States in recent decades. Differences may also emerge in the way that partisans express themselves through language, and it is possible that language differences vary in times of crisis and war. In the current work we examined over a decade's worth of transcripts from a liberal (MSNBC) and conservative (Fox) news network. More specifically, we examined evidence for two competing perspectives on language differences during the Russian invasion of Ukraine in 2022. One perspective, the depolarization perspective, suggests that we should see decreased differences, or parity in language styles between the two networks leading up to and during the invasion. Another perspective, the polarization perspective, suggests we should see increased differences in language styles between networks leading up to and during the early stages of the invasion. We examined an index of personalizing and formalizing language as well as 77 Linguistic Inquiry and Word Count (LIWC) dictionaries plus noun frequency using smoothed curves and linear discriminant function analyses (LDA) to examine the pattern of results in our data. Our results provide more support for the depolarization perspective, showing that both Fox News and MSNBC became more similar than different leading up to and during the invasion. Implications are discussed.

The use of the shape and chemistry of fish otoliths as a subpopulational discrimination tool for Eugerres brasilianus in lagoon systems in the Southwest Atlantic Ocean

The Brazilian mojarra, Eugerres brasilianus, is an economically important species for the artisanal fisheries usually found in the estuarine waters along the southwest Atlantic Ocean. Despite this, knowledge about its population structure is scarce, and no management strategies have been applied to ensure the fisheries sustainability in Brazil. Thus, the present study intended to understand the population dynamics of E. brasilianus in three costal lagoons located in Rio de Janeiro, Brasil. A total of 90 individuals were collected in the lagoon systems of Itaipu, Saquarema and Araruama, between December 2019 and March 2020. A pre-selection of 30 individuals per location from the same age group (2 years old), following age estimation by counting the annual growth increments, were used. The contour of the sagittal otoliths was evaluated using elliptical Fourier descriptors (EFD), and the multi-elemental signatures (MES) of the whole otoliths were obtained using solution-based inductively coupled plasma mass spectrometry. Data were analyzed using univariate and multivariate statistics to assess the degree of separation between individuals from different lagoons. EFD revealed significant differences among individuals from the different sampling regions. MES exhibited distinct regional patterns, mainly driven by differences in Cu/Ca, Li/Ca, Mg/Ca, Mn/Ca, and Sr/Ca ratios. Reclassification accuracy rates obtained from linear discriminant function analyses using both EFD and MES of otoliths were 100% (Itaipu), 97% (Araruama) and 90% (Saquarema). Therefore, a clear distinction was observed among these groups, which was related to the inherent characteristics of each lagoon system, their semi-restricted connectivity with the adjacent coastal zone, as well as the estuarine-opportunistic behavior of the species. Thus, the results suggest that these fisheries should be managed as different subpopulation-units. However, more studies should be carried out about the fish movements and life history events of this species in southeastern Brazil.

Otolith shape and elemental signatures provide insights into the connectivity of euryhaline Chelon labrosus inhabiting two close estuaries with different burdens of xenoestrogens in the Southern Bay of Biscay

Intersex gonads have been observed in thicklip grey mullet Chelon labrosus, inhabiting estuaries with high burdens of xenoestrogens in the Southeast Bay of Biscay, but knowledge of population connectivity among estuaries is lacking for this euryhaline fish species. This study investigates the population structure of C. labrosus using otolith shape and elemental signatures of 60 adults (overall length ∼ 38 cm) from two estuaries 21 nautic miles apart, one with a high incidence of intersex condition (Gernika), and the other one pristine (Plentzia). Otolith shape analyses were performed using elliptical Fourier descriptors, while elemental signatures of whole sagittae were obtained by inductively coupled plasma mass spectrophotometry. Univariate and multivariate statistics were applied to determine if otolith signatures show patterns of homogeneity between estuaries. The data indicated significant differences in the otolith shape and elemental composition between mullets of Gernika and Plentzia. Elemental differences were mainly driven by Sr, Li (both higher in Plentzia) and Ba (higher in Gernika). The 98% re-classification success rate obtained from stepwise linear discriminant function analysis suggests that Gernika and Plentzia individuals form separated population units. The limited connectivity between these two close estuaries would indicate a different life history of exposure to chemicals, which might explain the high prevalence of intersex condition in Gernika and its absence in Plenztia.

Climate, not Quaternary biogeography, explains skull morphology of the long-tailed macaque on the Sunda Shelf

Sundaland, comprising the low-lying Sunda Shelf, the major islands of Borneo, Sumatra, and Java, as well as many smaller surrounding islands, formed a contiguous landmass through much of the Pliocene and Pleistocene. Subsequent late-Pleistocene rising sea levels have often been invoked as major zoogeographic barriers to gene flow and associated phenotypic and taxonomic divergence of Sundaic mammals. However, previous examinations of mammalian biogeography have painted a complex picture, suggesting that mammalian diversification in the region cannot easily be explained by a single overarching biogeographical phenomenon. Instead, it may be that the significant climatic changes and their concomitant impacts on local environments were the more significant control. In this context, we study adult skull morphology of a widely distributed primate in insular and continental Southeast Asia, the long-tailed macaque (Macaca fascicularis), using a 3D geometric morphometric (GM) approach. We used principal component analysis and linear discriminant function analysis to analyze the associations between morphological divergence and island biogeography, taxonomy, and phylogeny. Reduced rank regressions (RRR) were employed to investigate associations with climate of the present and last interglacials and the last glacial maximum (LGM). There was very poor morphological divergence and discrimination among specimens from different biogeographical zones. There was also large overlap between M. fascicularis subspecies in skull size and shape, with M. f. fascicularis occupying the largest range of size and shape variation, consistent with its wide geographical distribution. Nonetheless, sundaic M. f. fascicularis and M. f. philippinensis, restricted to the Philippines, were successfully discriminated on size and shape, supporting the latter as a valid subspecies. There was moderate morphological differentiation associated with the deepest split in mitochondrial DNA lineages (M. f. aureus versus the rest) in skull size and shape. Contrary to biogeography, we detected strong associations between skull morphology and climate. Skull form (size and shape combined) was better explained by climate closer to present conditions and correlated most strongly with the current interglacial, even though past climatic conditions do show correlations with cranial morphology. We found no strong evidence that cranial and mandibular variation in M. fascicularis consistently tracks insularity caused by Quaternary sea level change. Instead, the morphological overlap evident in our sample indicates connections between subspecies and is consistent with the history of Sundaland as a single landmass for large portions of the Quaternary.

Adaptation in motor strategies for postural control associated with sensory reweighting

The purpose of this study was to identify and differentiate the motor strategies associated with sensory reweighting adapted during specific sensory integration tasks by healthy young adults. Thirty-six subjects (age range: 21–33 years) performed standing computerized dynamic posturography balance tasks across progressively increasing amplitudes of visual (VIS), somatosensory (SOM) and both (VIS+SOM) systems perturbation conditions. Adaptation in the motor strategy was measured as changes in electromyographic (EMG) activities and joint angles. The contribution of the perturbed sensory input in maintaining postural stability was calculated to determine the sensory reweighting. A multivariate design was used to model a linear combination of motor adaptation variables that discriminates specific sensory integration tasks. Results showed a significant progressive decrease in postural sway per unit amplitude of sensory perturbation in each condition, indicating dynamic sensory reweighting. Linear discriminant function analysis indicated that the adaptation in motor strategy during the VIS condition was associated with increased activity of EMG and joint angles in the upper body compared to the lower body. Conversely, during the SOM and VIS+SOM conditions, the adaptation in motor strategy was associated with decreased activity of EMG and joint angles in the lower body compared to the upper body. Therefore, the adaptation in motor strategies associated with sensory reweighting were different for different sensory integration tasks.

Ecogeographic patterns of macrophyte metacommunities in the Hengduan Mountain region

ABSTRACT Biological communities exhibit multiple distribution patterns at the metacommunity scale, and assessing the major drivers of these patterns is a key issue in community ecology. Here we investigated how the environmental and geographic gradients shape the distribution patterns of macrophytes at the metacommunity level. We applied the framework of the elements of the metacommunity structure (EMS) to identify the distinct types of 48 macrophyte metacommunities in the Hengduan Mountain region (HDMR) of China. We then used a generalized linear model and model selection approaches to determine which variables contributed to the variations of EMS and linear discriminant function analysis to evaluate how well the tested variables predicted metacommunity patterns. We found wide variations in the 3 EMS (i.e., coherence, range turnover, and range boundary clumping): latitude and alpha diversity were most important in determining coherence; nestedness was mostly related to turnover; and sampling depth was significantly associated with boundary clumping. Seven metacommunity types were identified in HDMR, and most metacommunities best fitted the Gleasonian and Clementsian patterns as well as their quasi-structures. Notably, Gleasonian and Q-Gleasonian patterns as well as the other 3 patterns (i.e., Q-nested, evenly spaced, and nested) were for the first time detected for macrophytes. These metacommunity types were best discriminated by nestedness, altitude, and latitude. Our results provide strong evidence of the impact of geographic patterns on macrophyte metacommunities, with the Gleasonian patterns dominant at both ends of the latitude/altitude gradient and Clementsian patterns common near the central part of the gradient.

Forecasting of groundnut yield using meteorological variables

Agriculture is one of the most significant economic sectors in India, which is strongly dependent on climatic conditions. Groundnut is one of the major oilseed crops of Gujarat and Junagadh has highest production of groundnut in Gujarat. For effective, forward-looking, and current planning, especially in agriculture, which is fraught with uncertainty, reliable and timely forecasts are essential. Therefore, effective yield forecast of such important oilseed crop is necessary for future planning and policy making. In the present investigation, the time series data of groundnut yield and weather parameters of 29 years Junagadh (1991-92 to 2019-20) were used. The week wise weather indices were generated using correlation between de-trend yield and weekly weather variables. Multiple Linear Regression (MLR) and Discriminant Function Analysis were used to develop yield forecasting model using weather indices before three week and one week before harvest, respectively. These models were compared using Coefficient of multiple determination (Adj. R2), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The study found that model-1A, developed using MLR technique have high value of adj. R2(78.8%) and low RMSE value (671.72). Multiple linear regression (MLR) was found to be more accurate than the discriminant function analysis approach.

Predicting Scalar Implicature Interpretations From Lexical Knowledge.

We measure typically developing monolingual child Spanish speakers' lexical development with a range of standard expressive and receptive tests. We also measure their comprehension of sentences with the existential quantifier algunos "some" to determine their abilities to generate "some, but not all" scalar implicatures or pragmatically enriched quantifier interpretations. We then determine the degree to which lexical development predicts implicature interpretations. We fit regression models with lexical measures as predictor variables and implicature interpretations as the outcome variable. We then divide the child sample into implicature generators (50/61) and implicature nongenerators (11/61) and test the usefulness of the four lexical measures in a linear discriminant function analysis to separate children into these two categories. Results show significant correlations between each lexical measure and the outcome variable and, in a regression, that three of four lexical measures account for unique variance. Furthermore, the linear discriminant function analysis separates children into implicature nongenerators with 100% accuracy (11/11) and implicature generators with 88% accuracy (44/50). The Quantity Scale, or set of quantity-expressing determiners, proposed by Horn and Grice, develops as a function of the links among its quantifiers. We speculate that children's lexicons refract approximate number system representations in language- and morpheme-specific ways. These quantified noun phrases (NPs) are then merged into sentences interpreted pragmatically with conversationally computed implicatures, using higher order reasoning.

Grain size analysis and characterization of sedimentary environment of the surface sediments along the Syrian Coast, Umm al-Tuyour (Latakia)

Detailed sedimentary study of a section of the Syrian coast (Umm al-Tuyour, Latakia) was carried out in order to determine the grain size analysis and characterization of sedimentary environment. Twenty-two samples of surface sediments were collected along the coastal area during the summer period of 2022. This research used an integrated methodology of analysis and software to determine the dynamic beach environment in the study area. The observation from this study shows that the sediments are coarse to very coarse grain size, moderately sorted, near symmetrical, and finally mesokurtic. These data confirm that the deposition occurred under high-energy conditions with continuous mixing of sediments by waves and ocean currents, where erosion processes are active. The results of the linear discriminant function analysis performed on the samples indicate beach, shallow marine deposition environment and the influence of fluvial processes. Analysis of sediments using CM diagram suggests that sediment deposition is mainly by rolling under tractive current conditions.

Strontium isotopes in otoliths reveal a diversity of natal origins for Tarek (Alburnus tarichi) in Lake Van, Turkey

Understanding the relative contributions of different spawning habitats to adult fish populations is central to effective fisheries management and species conservation. The Tarek (Alburnus tarichi) is an adfluvial cyprinid that is endemic to the alkaline-saline waters of Lake Van, Turkey. Tarek are culturally and economically important to the region, and also threatened by anthropogenic impacts, including poaching, dams, water diversions, pollution, and habitat degradation. Here we analyzed otoliths from 120 adult fish caught in Lake Van in 2016–2017 to reconstruct the age structure and natal origins of this Tarek population. Ages ranged from 2 to 10 years, with most fish belonging to the 2011–2014 cohorts (age 3–5). We analyzed strontium isotope ratios from water samples collected in 2016 and 2018 to build a baseline map and then used linear discriminant function analysis to classify Tarek to their likely natal origins. We found that adult Tarek originated from at least 7 different major tributaries of Lake Van, with a majority of fish originating from the Gevas and Engil tributaries in the south. Furthermore, the relative contributions of fish from each tributary varied among years, suggesting that a mosaic of natal habitats may be important for population stability. These results suggest that protection of all Lake Van watersheds from anthropogenic disturbance could be valuable for maintaining the stability of the Lake Van Tarek population and fishery.

Evaluating statistical models for establishing morphometric taxonomic identifications and a new approach using Random Forest

Faunal analyses depend on accurate taxonomic identifications, but distinguishing between morphologically similar fauna is not always possible through visual comparisons with comparative reference skeletons. Previous research has addressed this limitation through morphometric modeling using Linear Discriminant Function Analysis (LDA) or Principal Component Analysis (PCA). However, both approaches are limited by their assumptions and their ability to estimate error, constraining their empirical use for identifying faunal specimens. Random Forest (RF), a machine learning method, can resolve these limitations. Here, we evaluate the predictive power of LDA, PCA, and RF for taxonomic identification using morphometric modeling to determine which approach is best suited for faunal analyses. We use cranial specimens of modern Dipodomys spp. (kangaroo rat) and Leporidae (rabbit and hare) species to simulate complete datasets and datasets with missing measurement variables. We use these datasets to estimate species identification error rates and assess how well each statistical approach establishes species-level identifications under different conditions. Results indicate that RF outperforms LDA and PCA. RF more accurately predicts species identification with a complete dataset and when missing measurement data are interpolated. Next, using faunal material from Abrigo de los Escorpiones, a trans-Holocene site in Baja California, we demonstrate the use of RF for species identification and highlight that LDA, PCA, and RF all produce significantly different species identifications of the faunal material, emphasizing the need to validate statistical models used for taxonomic identification. Ultimately, this study highlights RF's predictive power and utility for faunal analysis, making it an important tool for zooarchaeological and paleoecological research.

Sex classification using the human sacrum: Geometric morphometrics versus conventional approaches.

The human pelvis shows marked sexual dimorphism that stems from the conflicting selective pressures of bipedal locomotion and parturition. The sacrum is thought to reflect this dimorphism as it makes up a significant portion of the pelvic girdle. However, reported sexual classification accuracies vary considerably depending on the method and reference sample (54%-98%). We aim to explore this inconsistency by quantifying sexual dimorphism and sex classification accuracies in a geographically heterogeneous sample by comparing 3D geometric morphometrics with the more commonly employed linear metric and qualitative assessments. Our sample included 164 modern humans from Africa, Europe, Asia, and America. The geometric morphometric analysis was based on 44 landmarks and 56 semilandmarks. Linear dimensions included sacral width, corpus depth and width, and the corresponding indices. The qualitative inspection relied on traditional macroscopic features such as proportions between the corpus of the first sacral vertebrae and the alae, and sagittal and coronal curvature of the sacrum. Classification accuracy was determined using linear discriminant function analysis for the entire sample and for the largest subsamples (i.e., Europeans and Africans). Male and female sacral shapes extensively overlapped in the geometric morphometric investigation, leading to a classification accuracy of 72%. Anteroposterior corpus depth was the most powerful discriminating linear parameter (83%), followed by the corpus-area index (78%). Qualitative inspection yielded lower accuracies (64–76%). Classification accuracy was higher for the Central European subsample and diminished with increasing geographical heterogeneity of the subgroups. Although the sacrum forms an integral part of the birth canal, our results suggest that its sex-related variation is surprisingly low. Morphological variation thus seems to be driven also by other factors, including body size, and sacrum shape is therefore likely under stronger biomechanical rather than obstetric selection.

Forewarning of insect incidence based on weather variables for management of cropping practices in Sugarcane

The early shoot borer, top borer, root borer, internode borer are major insect occurs in most of the sugarcane growing areas of the Gujarat and cause extensive damage to the sugarcane crop, which leads to losses in the crop yield. The weather discrepancies acting an important role in development of sugarcane insect and pest. The proper management of cropping practices may leads to overcome on it. Therefore it need to develop weather based approaches for forewarning the insect incidence which helps to farmers takes timely control measures to reduce the damage and yield losses due to this borer complex. Current study, relationship between insects incidence with weekly average weather parameters has been workout by using Karl-Pearson’s correlation approach on the 18 years of the data (2000-01 to 2017-18) in the Navsari district. The some of the weather variables were found significantly correlated with insect incidence. The multiple linear regression (MLR) and discriminant function analysis approach were adopted for statistical forewarning of the insect incidence. It was observed that MLR technique found better than discriminant function analysis for forecasting of insect incidence for forewarning of early shoot borer at 90 DAP and top shoot borer incidence at 5th month of crop season respectively.

Depositional mode and Provenance of Kopili sandstones from parts of Dima Hasao District, Assam, NE India: Evidence from Grain size distribution and Heavy mineral assemblage

This study presents a record of the depositional mode and provenance of the Kopili sandstones outcropped in and around Umrangso, the Dima Hasao district of Assam, India using proxies such as grain size and heavy mineral study. The Grain size study reveals that the sandstones of the Kopili formation consist predominantly of fine-grained sands mixed with medium sands. The grain size statistical parameters divulge that the sandstones are characterized mostly by moderately sorted sands, coarse skewed to near symmetrical, leptokurtic to extremely leptokurtic nature. The frequency distribution curves reflect mostly bimodal as well as polymodal distribution of sediments. The Linear Discriminant Function analysis indicates shallow marine beach sub environment in agitated water depositional condition for the Kopili sediments. The CM pattern discloses that saltation and suspension are the dominant transporting modes. The Heavy mineral analysis discerns that opaques dominate over transparent heavies. Transparent heavies in decreasing order of abundance are zircon, tourmaline, rutile, clinopyroxene, staurolite, hornblende, garnet, chlorite and epidote. The zircontourmaline- rutile (ZTR) maturity index (average 53.15%) discloses sub-mature to mature nature of the Kopili sandstones. The heavy mineral assemblage corroborates their mixed provenance (silicic igneous and metamorphic) for the Kopili sandstones.

Grain Size Analysis, Textural Characterisation and Depositional Environment of Turonian Amasiri Sandstone, Southern Benue Trough, Nigeria

Grain size analysis of Turonian Amasiri Sandstone in southern Benue Trough has been undertaken to determine the controversial depositional environment of the formation. The formation was first studied on outcrops and 26 representative samples were collected and subjected to particle size analysis in line with standard procedures for dry sieving. Various methods of environmental interpretation of grain size distribution data were applied to constrain the depositional of the sandstones. The result indicates that the sands are medium and coarse-grained with mean size ranging from 0.15 to 1.87φ and averaging 0.96 φ. The sandstones are moderately to poorly sorted with standard deviation values ranging from 0.72 to 1.38 φ and averaging 1.07 φ. They exhibit a wide range of distribution from strongly coarse skewed to strongly fine skewed with skewness values ranging from -2.31 to 1.52 φ and averaging -0.04 φ but indicate a narrow range of kurtosis from mesokurtic to leptokurtic distribution with values ranging from 0.99 to 3.49 φ and an average of 2.06 φ. The sediments have bimodal with minor polymodal and unimodal distribution with primary modal size of 1.2 φ. The bivariate plots of size statistical parameters indicate fluvial environment of deposition. However, linear discriminant function analysis and the interpretations of log-probability plots indicate deposition in a fluvial, beach, and shallow marine settings, and thus suggesting a possible deposition in high-energy transitional environment. The C-M pattern of the samples indicates that sediments were transported mainly by rolling and suspension with subordinate fractions moved by rolling as well as suspension. Thus, it is deduced that Amasiri Sandstone was deposited in fluvial, beach, and agitated shallow marine environments.

Sex Estimation Using Geometric Morphometrics

Creating and testing efficient techniques for the sex estimation of modern human skeletal remains has been a significant focus in biological anthropology. It is well established that the innominate, particularly the pubic bone, is a sexually dimorphic part of the human skeleton, but prone to fragmentation. Using modern pubic bones of known age and sex, this study aims to capture shape differences using geometric morphometrics (GMM) to test classification accuracy of segments of the pubic bone. The sample consists of 70 left adult pubic bones from the William M. Bass Donated Skeletal Collection, with 35 males and 35 females of mixed age and population affinity. Landmarks were placed on the dorsal surface of the pubic body and ischiopubic ramus to capture their overall shape in two dimensions, so the study is easily replicable and applicable. The scans were separately run through a generalized Procrustes, principal components (PCA), and canonical linear discriminant function analysis (DFA). The DFA results show high classification accuracy for the pubic body (94% males, 100% females) and the ischiopubic ramus (100% females, 97% males), with the PCA DFA allowing a researcher to explore specific shape changes driving the differentiation between groups. GMM was able to quantify and successfully discriminant the shape changes between males and females for small elements of the pubis, which can be applied to fragmentary remains and future morphological methods.