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Aim: To delineate extent of horizontal cum vertical integrations and to analyze distribution of specializations based on the level of vertical diversification Study Design: ‘Ex-post-facto’ and ‘explorative’ research designs were used for conducting the present study. Place and Duration of Study: The study was conducted in Thruvanathapuram district of Kerala in India during 2018-2020. Methodology: Specialized homegardens identified under ‘ICAR Niche Area Excellence project’ were selected for the study. Sixty specialised homegardens with twelve homegardens each from each AEUs were selected for data enumeration. Results: Majority of specialized homegardens (60%) do have more than 4 tiers/levels of horizontal diversification whereas 40% have less than 4 tiers of diversification. Similarly, distribution of vertical diversification shows that 61.6% have less than 3 levels and 38.3 % have more than 3 levels of vertical diversification. Mean vertical diversification was found to be highest for livestock-cow (3.75) and least was for tubers and orchids (1) each. However 3 levels of vertical diversification was noticed for rubber, animal husbandry, coconut, goat, mushroom and aquaculture. Conclusion: Homegardens with specializations are the sustainable production system. If the competent authorities pay attention to the individual specializations of the system and give adequate thrust to the value addition of these specializations, then definitely homegardens can turn out to be better remunerative farming system fetching supplementary benefits.

This thesis had two major study components, Study I was concerned with an evaluation of educable mentally retarded children's responses and cognitive processes in hypothetical temptation to steal situations, whilst Study II examined the effectiveness of treatment programs in modifying the children's behaviour in such situations. Justification for this research emanated from the problems faced by the community in relationship to stealing. In Study I the sample, which consisted of 83 eleven to sixteen year old children, I.Q. 50-75, was randomly selected from a population of 108 children in special schools in Tasmania. The children were administered a series of tests including Jackson's Hypothetical Temptation to Steal Test (JHTST) and real life temptation measures. The major findings indicated that there was a significant discrepancy between the children's resistance responses on the behavioural ('did do') measure compared to the moral judgement ('should do') measure of the JHTST. The cognitive operations were analysed in terms of extrinsic, intrinsic and right/wrong cognitive processes. It was found that extrinsic yielding processes were used significantly more than intrinsic processes on both the 'did do' and 'should do' measures. A significantly greater number of children used right/wrong resistance processes on the 'did do' measure compared to extrinsic or intrinsic processes. There was no difference between right/wrong and extrinsic processes on the 'should do' measure. As a result of the findings from Study I a treatment program was designed. An evaluation of this treatment program constituted the basis for Study II. The treatment derived its main aspects and content from Jackson's (1968) model of cognitive processing in hypothetical temptation to steal situations. The content and format of the treatment owed much to a study done by Haines, Jackson and Davidson with normal children in 1980. Study II, which was based on the same population pool of 108 children Study I drew from, employed a four group design with one group receiving a direct instruction program (DIP). A second group, serving as an alternative treatment condition received a general instruction procedure (GIP), while a third group (no treatment control) experienced no specific intervention. The fourth group was a post-only control group employed to test for sensitization of testing effects. An analysis of the data from Study II indicated that the DIP group used both resistance responses and intrinsic resistance processes significantly more than the GIP and no treatment control groups on the behavioural measure of the JHTST. A three month follow-up probe indicated that the gains made by the DIP group were maintained. The implications of the study for the prevention of stealing were considered.

PURPOSE In this study, the noise reduction properties of the adaptive statistical iterative reconstruction (IR) on two different CT scanners of 64 and 256-slice were compared and their differences were assessed. METHODS AND MATERIALS The homogeneous module of the ACR CT phantom was scanned on the 64 and 256 slices CT scanners from the same vendor in the range of 15-40 mA. On each scanner, the data were reconstructed using filtered back projection (FBP) and at all strengths of IR with the STANDARD kernel. For each reconstruction, a 3D noise power spectrum (NPS) was calculated and the central frequency ratio in the xy plane (CFRxy ), CFR in the z-direction (CFRz ), and noise magnitude ratio (NMR) were derived. CFR is the central frequency ratio of NPS between the denoised image and the FBP image, and NMR is the ratio of the areas under the NPS curves. Ideally, both CFRxy and CFRz should be near 1, indicating minimal texture changes in both xy and z directions, while NMR should be as close to 0 as possible, indicating more noise reduction. RESULTS When comparing strengths with equivalent impact on noise texture, IR on the 64-slice reduced the noise magnitude in the xy plane more than that on the 256-slice. In the z-direction, the IR on the 256-slice produced a central frequency shift on the 256-slice but not on the 64-slice. In addition, the noise reduction effects of the IR on the 256-slice were affected when radiation exposure was below 2.0 mGy, but there was no observable dose-dependence on the 64-slice. CONCLUSIONS Our noise property analysis revealed that iterative reconstructions on different scanner platforms from the same vendor can be distinct, with unique effects on the noise texture and magnitude in CT images. The IR on a 64-slice scanner provides slightly enhanced noise reduction and maintains a noise reduction rate independent of dose, unlike the one on a 256-slice scanner. Notably, the IR on the 64-slice scanner was a 2D noise reduction technique (NRT), while the one on the 256-slice was a 3D NRT. These observations showcase the impact of different NRTs on clinical CT images, even when comparing the same NRT on different scanners.

The paper assessed the apipreneurs of Kerala based on their entrepreneurial potential. Ex post facto research design was followed for the study conducted during 2019-2020. The location of the study was South Kerala focusing on three districts viz., Pathanamthitta, Idukki and Kottayam. Seventy-five apipreneurs were randomly selected for the study. Based on the study it was found that about 69 per cent of the apipreneurs had medium level of entrepreneurial potential. The results obtained from principal component analysis on the dimensions of entrepreneurial potential, based on Eigen vectors revealed that the three dimensions viz., entrepreneurial motivation, management competencies and social competencies had influenced the entrepreneurial potential of the apipreneur.

The low exposures, unique x-ray beam geometry, and scanning design in dual-energy x-ray absorptiometry (DXA) make measurement and quality-control strategies different from traditional x-ray equipment. This study examines the dependence of measured entrance-air-kerma (EAK) on both dose sensor type and scan length. The feasibility of using EAK to compare scanner output between different scan modes, individual scanners, and scanner platforms was also established. Finally, the congruence between measured and vendor-reported EAK was analyzed.Four Hologic DXA scanners at two institutions and all four available scan modes were tested. EAK was measured directly by three types of Radcal dose sensors: 60-cc pancake ion-chamber (IC), 180-cc pancake IC, and solid-state detector. The coefficient of variation (COV) was used to assess the dependence of EAK on scan length. Variations in EAK between the types of dose sensors as well as measured versus vendor-reported values were evaluated using Bland-Altman analysis: mean ±95% prediction interval (PI): 1.96σ.Dose sensor variations in EAK were minimal, with a -3.5 ± 3.5% (mean ±95% PI) percent difference between the two sizes of IC's. The solid-state detector produced highly similar measurements to the 180-cc IC. These small differences were consistent across all scanners and all scan modes tested. Neither measured nor vendor-reported EAK values were found to show relevant dependence on scan length, with all COV values ≤4%. Differences between measured and reported EAK were higher at -6 ± 48%. Likely errors in vendor-reported EAK calculations were also identified.It is feasible to quantify DXA scanner stability using EAK as a quality-control metric with a variety of solid-state and IC dose sensors, and the scan length used is not critical. Although vendor-reported EAK was consistent among scanners of the same platform, measured EAK varied significantly from scanner to scanner. As a result, measured and reported EAK may not always be comparable.

Purpose Assessment of image quality directly in clinical image data is an important quality control objective as phantom-based testing does not fully represent image quality across patient variation. Computer algorithms for automatically measuring noise in clinical computed tomography (CT) images have been introduced, but the accuracy of these algorithms is unclear. This work benchmarks the accuracy of the global noise (GN) algorithm for automatic noise measurement in contrast-enhanced abdomen CT exams in comparison to precise reference noise measurements. The GN algorithm was further optimized compared to the previous report in the literature. Methods Reference values of noise were established in a public image dataset of 82 contrast-enhanced abdomen CT exams. The reference noise values were obtained by manual regions-of-interest measurements of pixel standard deviation in the liver parenchyma according to an instruction protocol. Noise measurements taken by six observers were averaged together to improve reference noise statistical precision. The GN algorithm was used to automatically measure noise in each image set. The accuracy of the GN algorithm was determined in terms of RMS error compared to reference noise. The GN algorithm was optimized by conducting 1000 trials with random algorithm parameter values. The trial with the lowest RMS error was used to select optimum algorithm parameters. Results The range of noise across CT image sets was 8.8-28.8 HU. Reference noise measurements were made with a precision of ±0.78 HU (95% confidence interval). The RMS error of automatic noise measurement was 0.93 HU (0.77-1.19 HU 95% confidence interval). The automatic noise measurements were equally accurate across image sets of varying noise magnitude. Optimum GN algorithm parameter values were: a kernel size of 7 pixels, and soft tissue lower and upper thresholds of 0 and 170 HU, respectively. Conclusions The performance of automatic noise measurement was benchmarked in a large clinical CT dataset. The study provides a framework for thorough validation of automatic clinical image quality measurement methods. The GN algorithm was optimized and validated for automatic measurement of soft-tissue noise in abdomen CT exams.

Highlights • ANN prediction models directly compare online, offline, 60Co-, and linac-MRgRT plans. • Roughly one third of 60Co adapted plans deemed inferior to comparable offline plans. • Linac adapted plans essentially equivalent to comparable offline plans. • Linac plans clearly superior to 60Co in terms of plan quality in majority of cases. • Prediction models enable improved assessment of MRgRT plans prior to treatment., Comprehensive analysis of daily, online adaptive plan quality and safety in magnetic resonance imaging (MRI) guided radiation therapy is critical to its widespread use. Artificial neural network models developed with offline plans created after simulation were used to analyze and compare online plans that were adapted and reoptimized in real time prior to treatment. Roughly one third of 60Co adapted plans were of inferior quality relative to fully optimized, offline plans, but MRI-linac adapted plans were essentially equivalent to offline plans. The models also enabled clear justification that MRI-linac plans are superior to 60Co in an overwhelming majority of cases.

PURPOSE 99m Tc-MAA-SPECT/CT may be used in 90 Y-glass microsphere radioembolization treatment planning to assess perfused liver volumes and absorbed dose distributions. The partition model (PM) offers a more detailed planning dosimetry option beyond the single-compartment model more traditionally used in 90 Y radioembolization. As 90 Y radioembolization treatments shift toward activities and doses that aim to achieve tumor control, accurate and reliable treatment planning dosimetry for both tumors and normal liver (NL) becomes more critical. In this work, we explore the accuracy and precision of 90 Y dosimetry predictions from pretherapy 99m Tc-MAA and PM. METHODS Both PM and voxel dosimetry models were used to calculate tumor and NL mean doses using both planning 99m Tc-MAA and verification 90 Y-SPECT/CT in this retrospective analysis of hepatocellular carcinoma cases treated with glass microspheres (NCT01900002, n = 32). Linear regression models were developed at first access, and then later correct, the estimates by (a) 99m Tc-MAA for 90 Y voxel dosimetry and (b) 99m Tc-MAA PM for voxel dosimetry, separately for both tumors and NL. Bland-Altman analysis was then used to evaluate the accuracy and precision of the regression model predictions with the mean bias and 95% prediction intervals (PI, ±1.96σ). Two categories of cases were stratified (catheter matched vs catheter unmatched) by establishing the level of 99m Tc-MAA and 90 Y catheter position alignment. Only catheter-matched cases were included in the 99m Tc-MAA vs 90 Y voxel dosimetry comparison, while all cases were used to compare dosimetry models (PM vs voxel). RESULTS Half (16/32) of cases were deemed catheter matched. 99m Tc-MAA could reliably predict NL doses in catheter-matched cases after application of the linear model, with mean bias (PI) of -1% (±31%). PM was equivalent to voxel dosimetry for NL doses with mean bias (PI) of 0% (±1%). Even among catheter-matched cases, 99m Tc-MAA planning for 90 Y tumor voxel doses was poor, overestimating dose by an average of nearly 40%. Upon application of the linear model, 99m Tc-MAA predictions for 90 Y tumor voxel dose were only minimally biased (-4%) but possessed very large PI (±104%). PM predictions for tumor voxel dose using the linear model also showed small bias (-6%) but maintained similarly high PI of ±90%. Cases with tumors representing a large majority (>80%) of the total tumor volume demonstrated the best scenarios for 99m Tc-MAA and PM tumor dose predictions, with mean biases (PI) of -3% (±53%) and -4% (±21%), respectively. CONCLUSION The unconditional use of 99m Tc-MAA to predict 90 Y dosimetry across all cases is not recommended due to: (a) demonstrated the risk of unmatched catheter positions between procedures, and (b) large bias and uncertainty in 99m Tc-MAA predictions in cases with matched catheter locations. However, NL voxel dose predictions with 99m Tc-MAA are clinically viable and either PM or voxel dosimetry can be used to produce equivalent predictions. Both 99m Tc-MAA and PM can provide tumor dose predictions with potential clinical utility, but only in catheter-matched cases and with tumors comprising a clear majority (>80%) of the total tumor volume. These findings stratify the predictive fidelity of 99m Tc-MAA- and PM-based treatment planning for 90 Y dosimetry in improving treatment outcomes.

Home gardens are a time tested food production system which can be projected as a promise to the future to mitigate the issues related to the global food security crisis. The home gardens in Kerala are an integral part of the rich tradition which impart a dominant role in livelihood security of the people. The diversity profile of traditional homegardens of high range area and its spatial patterns were investigated in the present study revealed that a mean Shannon and Wiener diversity index (H) of 2.185 was recorded which denoted a dwindling trend in species diversity in banana based high range home gardens of Idukki. An appraisal of the diversity of crops revealed that the highest diversity was recorded in spices (H=1.425) and was on par with fruit crops (H=1.339) whereas lowest was noted on fodder crops (H= 0.054) followed by medicinal plants (H=0.197). Furthermore, the mean total diversity index exhibited a positive significant correlation with the total area of the home garden with a correlation coefficient of 0.621 which signified the importance of spatial dimensions in determining the diversity profile of the home gardens.

Purpose Daily online adaptive plan quality in magnetic resonance imaging guided radiation therapy (MRgRT) is difficult to assess in relation to the fully optimized, high quality plans traditionally established offline. Machine learning prediction models developed in this work are capable of predicting 3D dose distributions, enabling the evaluation of online adaptive plan quality to better inform adaptive decision‐making in MRgRT. Methods Artificial neural networks predicted 3D dose distributions from input variables related to patient anatomy, geometry, and target/organ‐at‐risk relationships in over 300 treatment plans from 53 patients receiving adaptive, linac‐based MRgRT for abdominal cancers. The models do not include any beam related variables such as beam angles or fluence and were optimized to balance errors related to raw dose and specific plan quality metrics used to guide daily online adaptive decisions. Results Averaged over all plans, the dose prediction error and the absolute error were 0.1 ± 3.4 Gy (0.1 ± 6.2%) and 3.5 ± 2.4 Gy (6.4 ± 4.3%) respectively. Plan metric prediction errors were −0.1 ± 1.5%, −0.5 ± 2.1%, −0.9 ± 2.2 Gy, and 0.1 ± 2.7 Gy for V95, V100, D95, and Dmean respectively. Plan metric prediction absolute errors were 1.1 ± 1.1%, 1.5 ± 1.5%, 1.9 ± 1.4 Gy, and 2.2 ± 1.6 Gy. Approximately 10% (25) of the plans studied were clearly identified by the prediction models as inferior quality plans needing further optimization and refinement. Conclusion Machine learning prediction models for treatment plan 3D dose distributions in online adaptive MRgRT were developed and tested. Clinical integration of the models requires minimal effort, producing 3D dose predictions for a new patient’s plan using only target and OAR structures as inputs. These models can enable improved workflows for MRgRT through more informed plan optimization and plan quality assessment in real time.