Postharvest handling of the potato is an important factor not only in preventing postharvest losses but also in maintaining its safety and nutritional quality. Exposure of the potato to unfavorable conditions such as light, extreme temperatures, and bruising can result in accumulation of glycoalkaloids, which are toxic substances. This study was a cross-sectional survey which aimed to investigate the postharvest handling practices of potatoes and perception of potato safety among open air market traders in Nairobi County, Kenya. Information was collected from 100 potato traders using a semistructured questionnaire that assessed postharvest handling practices such as potato transportation, exposure to sunlight, and storage. Results indicated that most of the potatoes (88%) took one day to be transported to the market, with the storage period at the market ranging from 2 to 3 days for most traders (42%). Forty-seven percent (47%) of the vehicles and hand-pulled carts used to transport potatoes had open backs, while 53% had closed backs. Over half (69%) of the potatoes in the markets were directly exposed to sunlight, with 75% of the traders leaving their potatoes in the open covered with a polythene bag after the day's activities. Greening, sprouting, or bruised potatoes were mostly sold as seed, sold to restaurants and French fries vendors, or sold to consumers at a lower price. More than half of the traders did not think that consumption of greened potatoes is harmful to health. The results clearly show that there is poor handling of the potatoes by the traders which increases the risk of consumer exposure to glycoalkaloids. There is, therefore, a need to create awareness among traders on appropriate postharvest handling of potatoes to protect consumer health and reduce economic losses as well. Solanum tuberosum) is an important staple food crop globally. The crop is ranked third after rice and wheat in terms of global human consumption with more than 1 billion people eating it regularly [The potato , a family of steroidal toxic secondary metabolites that occur in all parts of the potato as natural toxins, synthesized as a form of defense against parasites and diseases due to their antimicrobial, insecticidal, and fungicidal properties . Of inteIn Kenya, one of the major constraints facing the potato value chain is poor postharvest handling especially during marketing and distribution, and significant losses have been reported to occur at this stage . It has The study was carried out in Nairobi County, Kenya. The county hosts the capital city of Kenya and has nine subcounties: Makadara, Embakasi, Starehe, Langata, Kasarani, Westlands, Kamukunji, Dagorreti, and Njiru . The couThe study was of a cross-sectional study design employing qualitative data collection methods through interviews and observation. The study population consisted of open air market traders who sell potato tubers. A randomly chosen sample of 100 traders, obtained using the Fischer's formula , particiData obtained were coded and entered into SPSS for Windows software (IBM version 21) and analyzed. Descriptive statistics, namely, percentages and frequencies, were used to express the results of sociodemographic characteristics of the study population and the different postharvest handling practices. Chi-square test of significance was used to test for any existing significant associations between the various variables under study with a p-value = 0.05 being set as the level of significance.The sociodemographic characteristics of the potato traders are represented in The age of the respondents ranged from 20 years to 80 years with the majority falling in the 30-39-year bracket (35%). With regard to education level, most of the traders had attained primary (44%) and secondary (44%) levels with only 2% of them having studied up to tertiary level.Shangi variety was the most common variety sold in Nairobi (94%) as shown in As shown in Ninety-three (93) traders used a vehicle (lorry or pick-up truck) or hand-pulled cart to transport their potatoes from the point of supply to the point of sale while 7 traders used motorcycles. Further assessment of the design of the back of the vehicles and hand-pulled carts used for transporting potatoes revealed that 47% had open backs while 53% had closed backs. This shows that over half of the potatoes are not exposed to sunlight during transportation since the closed backs protect the potatoes from direct sunrays. However, a sizeable proportion of potatoes (47%) are exposed to direct sunlight during transportation in vehicles with open backs. This mode of transport can be a risk factor for the development of glycoalkaloids in the tubers during transportation and subsequent storage. The mode of transport has a huge influence on development of GAs in potatoes since an open back allows for direct exposure of the tubers to unfavorable conditions such as direct sunlight and high temperatures which accelerate the development of GAs [Three quarters (75%) of the potatoes on the market were left out overnight while covered with a polythene bag after the day's activities until the next day when the traders opened their businesses. The covering of potatoes with a polythene bag means that there is poor aeration and the temperatures inside the cover might rise, which is a risk factor for the development of glycoalkaloids due to exposure to high temperatures. Furthermore, since the potatoes are stored outside and sold in the open, they are exposed to sunlight, further increasing the chance of accumulation of glycoalkaloids in the tubers.Generally, most of the potatoes take 1 to 3 days on the market, meaning that they are high moving commodities due to the high demand for potato products among Nairobi residents. However, a few of the traders who stocked large quantities of potatoes stored them for longer periods of 1 to 4 weeks. This may affect the quality of potatoes and hence a food safety risk given that potatoes are semiperishable products and longer storage periods increases the chances of greening, sprouting, and development of glycoalkaloids in the tubers, especially if stored under poor storage conditions .The location, gender, and variety of potato were found to influence the postharvest handling practices of potato . Duratio et al. [The findings reveal different preferences on the mode of transport for different markets, locations, gender, and different potato varieties. Vehicle transportation was most preferred by significant proportions of the various segments of the population. Potato production in Kenya is mainly done in the highlands and, due to their high perishability, a faster means of transport is most preferred; however, this attracts additional costs in terms of transportation . A study et al.  showed cClosed back vehicles were used for transportation mostly in Kamukunji and Starehe Subcounties at 51.3% and 65.3%, respectively, this being significantly (p<0.05) higher than other subcounties. Different vehicle designs were preferred in different markets as Kawangware and Kangemi at 55.7% and 59%, respectively, which preferred closed back modes of transport (p<0.05). The golf variety of potatoes was majorly (100%) transported using closed back vehicle designs (p<0.05).The study findings reveal that a higher proportion of the respondents in the majority of the subcounties and markets would prefer using closed back design of vehicles to transport their potatoes. Regardless of the gender or potato variety, most of the traders would still prefer using closed back modes of transport to ferry their products. This is an effort to reduce spoilage of the potato during transportation. Further, the use of vehicles with closed backs reduces the exposure of the tubers to direct sunlight which has food safety implications as this reduces the risk of accumulation of glycoalkaloids in the potatoes.From observations, it was noted that more than half (69%) of the potatoes in the surveyed markets were exposed to sunlight. Potato variety and gender were found to be factors that predicted likelihood of exposure of potato to sunlight as shown in y = Ax + Bz, where y is exposure of potato to sunlight, x and z are the independent variables, gender and potato variety, and A and B are constants. A and B accounted for 30% change in y.The findings show that exposure of potatoes to sunlight is a common practice among the traders regardless of education level, age, and all locations with no difference in handling to curtail the same. Individuals who were males were five times more likely to have their potatoes exposed to light than the females. Purchase of potatoes from the male traders poses greater risk of exposure to consumption of glycoalkaloids. Interestingly, the education level of the traders did not influence how they handled the potatoes with respect to exposure to sunlight. et al. [More than half (56%) of potato traders in open air markets in Nairobi sell greened potatoes or potatoes that show signs of greening to farmers as seed . This sh et al.  found th et al. . This prThe present study sought to assess the knowledge of traders regarding the harmful effects of consuming greening or sprouting potatoes. More than half of the traders (53%) said that consuming greening or sprouting potatoes does not have any harmful effects on health; 28% said that greening or sprouting potatoes are harmful to health, while 19% did not know whether greened or sprouting potatoes were harmful to health or not. This means that a large number of them do not know the harmful effects of consuming such potatoes which may explain why some of them sold such potatoes to unsuspecting customers, sold them at a lower price, used them for their own consumption, or added them to their customers as an incentive.Sociodemographic characteristics of the traders significantly (p<0.05) influenced their perception of the safety of potatoes . MajoritThe findings show gender, location, and age as the possible factors that may influence trader perception of the safety of potatoes. Best practice of avoiding consumption of greening potatoes was mainly among traders in Kawangware and Kangemi markets, Dagoretti and Westlands Subcounties, or those of older ages. However, poor food safety practices of selling damaged or bruised potatoes were common among male traders and in Dagoretti Subcounty and Kawangware market. Stress such as bruising and damage results in increased glycoalkaloid levels in potatoes . InasmucThe variety of ware potato was significantly (p<0.05) associated with the traders' understanding of greening in potatoes. Majority of traders that traded in Shangi variety perceived greening in potatoes as a sign of the potato becoming a seed, whereas those trading in Golf variety perceived it as a sign of spoilage. Shangi variety is the most utilized variety in Kenya; thus most traders would prefer not to suffer any losses by discarding greening potatoes and would rather sell them off as seed.Postharvest handling practices among the potato traders in open air markets in Nairobi may impact negatively the quality of the potatoes especially in terms of safety. There is a low level of knowledge on the health effects of consuming greened, bruised, or sprouting potatoes leading to the poor perception of potato safety among the traders. More than half of the potatoes on the market are exposed to unfavorable temperature and light conditions as seen through the direct exposure of the tubers to sunlight. This means potato consumers in Nairobi may be experiencing long term exposure to these toxins due to buying of greening, bruised, or sprouting potatoes for home consumption or consuming of potato products such as French fries from restaurants or roadside vendors who use greening, bruised, or sprouting potatoes sold to them by the traders to make these potato products.Therefore, it is important that all potato traders be continuously sensitized and educated on the health effects of glycoalkaloids and proper postharvest handling of potatoes to prevent continued consumer exposure to these toxins. Farmers and transporters of the tubers to the market should also be included in the sensitization activities. In addition, the Nairobi County Government should channel resources towards building permanent stalls that have proper storage places with proper protection against the sun to prevent against direct exposure of potatoes to the sun. The traders should also be facilitated to acquire sacks and bags made from other materials such as sisal or net bags which allow for proper aeration and less build-up of heat to store their potatoes.


Communication of laboratory critical risk results is essential for patient safety, as it allows early decision making. Our aims were: 1) to retrospectively evaluate the current protocol for telephone notification of critical risk results in terms of rates, efficiency and recipient satisfaction, 2) to assess their use in clinical decision making and 3) to suggest alternative tools for a better assessment of notification protocols.The biochemical critical risk result notifications reported during 12 months by routine and STAT laboratories in a tertiary care hospital were reviewed. Total number of reports, time for the notification and main magnitudes with critical risk results were calculated. The use of notifications in clinical decision making was assessed by reviewing medical records. Satisfaction with the notification protocol was assessed through an online questionnaire to requesting physicians and nurses.Critical result was yielded by 0.1% of total laboratory tests. Median time for notification was 3.2 min (STAT) and 16.9 min (routine). The magnitudes with a greater number of critical results were glucose and potassium for routine analyses, and troponin, sodium for STAT. Most notifications were not reflected in the medical records. Overall mean satisfaction with the protocol was 4.2/5.The results obtained indicate that the current protocol is appropriate. Nevertheless, there are some limitations that hamper the evaluation of the impact on clinical decision making. Alternatives were proposed for a proper and precise evaluation. A significant percentage of clinical decisions rely on laboratory test results , the Australasian Association of Clinical Biochemists and the Royal College of Pathologists  (The aims of our study were: 1) to retrospectively evaluate the current protocol for telephone notification of critical risk results at a tertiary care hospital in terms of rates, efficiency and recipient satisfaction, 2) to assess the use of these notifications in clinical decision making by the attending physicians and 3) based on these, to suggest alternative tools for a better assessment of notification protocols.etc.) and is accredited in accordance with ISO 15189:2012.This is a retrospective observational study performed at the Hospital Universitari Son Espases , providing service to a population of 325,285 inhabitants. The biochemistry core laboratory gives support to the specialization units of the hospital, the primary care centres, as well as to other institutions , result notification is performed telephonically by the laboratory staff  to the attending professional (physician or nurse), and immediately the LIS notification request is filled with the univocal identification of the professional receiving the notification. Likewise, the personal code of the professional who makes the notification is also recorded on the LIS. The notified test is highlighted on the laboratory report. Therefore, a total traceability throughout the whole notification process is assured. Results are subsequently validated and a laboratory report is generated. The lack of timeframes for reporting is consistent with all hospital departments or with primary care centres and critical result notification is carried out by telephone only. As escalation procedure, if the critical result applies to an outpatient and the requesting physician could not be notified, the emergency healthcare service is contacted through a 061-call (emergency number in Spain).The protocol for critical risk result is as follows: when a critical risk result is obtained in the laboratory, and confirmed by repetition, the laboratory information system  identifies it and a notification request is automatically created . After ruling out preanalytical interferences , and percentage of unsuccessful notifications .e.g. a single patient sample showed high glucose concentration together with high potassium).The percentage of critical risk result notifications by magnitude was quantified and related with the most prevalent pathologies attended at our hospital. A single notification may imply several critical results . One of the questions referred to the professional’s satisfaction with the current critical result notification protocol in our hospital. The survey was prepared on SurveyMonkey.com and internally circulated by email to all attending professionals in our healthcare area between June to December 2018. Participation was voluntary and anonymous. Satisfaction scoring established between 0 (very bad) and 5 (excellent).An online survey was prepared in order to find out to what extent attending physicians and nurses were satisfied with different aspects of the laboratory, including accessibility, kindness, incidence communication, quality of laboratory reports, Surveyed universe was all attending physicians (1014) and nurses (1496) in our hospital, as well as in primary care centres. Data were downloaded on Microsoft Excel 2010 .In accordance with the communication channels in our clinical laboratory, assessment of notification strategy usefulness was achieved by randomly reviewing 100 medical records from patients at the emergency room (ER), along with the medical records of all patients from the outpatient consultation offices in our hospital who had have at least one critical result from the laboratory within the evaluated period. The following were quantified as effectiveness indicators:Percentage of notifications made which appeared in the medical records of the patient by the total number of critical risk result notifications reviewed by department.Percentage of cases where physicians carried out corrective actions: requested inter-specialty consulting, requested complementary testing, initiated or changed treatments, or (if outpatients) derived them to the hospital after laboratory notification with respect to the total number of medical record reviewed by department.The Kolmogorov-Smirnov test was used to assess normality of all variables. Differences between groups were assessed using the Student’s t-test if normally distributed, and the Mann-Whitney’s U-test if not. Statistical significance was set at 0.05. SPSS v.24 software  was used for all statistical analyses.e.g. creatinine from normality to 600 µmol/L, or ALT from normality to 3000 U/L).Percentages of critical risk result notifications are outlined in Regarding the online questionnaire circulated to physicians and nurses in our healthcare area, a response rate of 7.9% was achieved for hospital professionals. Results are highlighted in With regard to the use of notifications in clinical decision making, results are shown in etc.), and their critical results do not appear in the LIS. Our alert list also differs significantly from those reported elsewhere, including the low threshold for sodium ion or the high threshold for potassium ion  .In the ER, a large number of troponin (high-sensitivity troponin I) results are notified successfully, although only the first altered value is notified.The results of the questionnaire circulated to all physicians and nurses of our healthcare reveals high satisfaction with the existing protocol in our hospital. Although greater participation would increase the robustness of our conclusions, the 298 answers from professionals represent the total of users of our laboratory services.Satisfaction was higher in physicians than nurses, maybe due to the fact that this group is responsible for medical actions and has full consciousness of the importance of timely notifications. In addition, primary care professionals gave higher scores, as notifications give to the physician the possibility to anticipate the medical actions which otherwise would occur later at regular medical visit.e.g. resin given to a patient with a potassium concentration of 6.8 mmol/L, or insulin given to a patient with a glucose concentration of 33.5 mmol/L). In contrast, laboratory notifications in the outpatient consultation offices lead to corrective actions in only 54/86 cases . Results not leading to a corrective action were mainly from endocrinology (8/32) and nephrology (18/32) outpatients. This observation may be due to the fact that most patients have chronic diseases under treatment, especially individuals with CKD, with a recent kidney transplantation, or unbalanced diabetes. It might be interesting to re-evaluate and redesign the alert list with these medical areas based on our results.With regard to the indicators for the clinical use of the notification protocol in the ER, as we can state by the revision of medical records, all reviewed critical risk results lead to a corrective action aiming at eradication of the life-threatening condition (etc.). The lack of statements on the medical records about laboratory notifications might be due to a lack of time, staff, or the prioritization of clinical actions over the completeness of the medical records.According to the medical records reviewed, all corrective actions were related to the critical result reported and were performed after the notification. However, most of the medical records do not state the laboratory phone call. This fact highlights the challenge of assessing the real clinical impact of the notifications because it is not possible to know specifically whether the notification triggered a corrective action itself, as physicians could have adopted them afterwards, once they had received the electronic laboratory report, or simply based on clinical characteristics , the acknowledgement in the medical records of the notification made by the laboratory, or the calculation of TAT between telephone notification and the diagnostic/therapeutic action taken by the physician. This places emphasis on the need for healthcare professionals to be more actively involved in the protocol.In our study, a basic approach for the evaluation of the real clinical use of critical result notification by telephone was presented. Despite the inherent difficulties of a proper evaluation of the clinical impact, other possible strategies are possible, including the performance of specific questionnaires, which enable to evaluate the notification protocol to the full extent (quickness of laboratory action, comprehension of the given information and its utility, courtesy, communication style, physician opportunity for timely or early decision making, This study has several limitations, mainly related to its retrospective nature and trust in the records from the laboratory and hospital information systems. It may be possible that actions and records might not have been performed in a timely manner, and our current system does not allow the true time needed to carry out the notifications to be confirmed. Besides, the influence of haemolysis on the analysis of potassium ion in heparinized whole blood was out of the scope of our study. Regarding the online questionnaire, more detailed and useful data would be obtained if more specific questions were included. In addition to the reduced number of medical records reviewed, the current study design makes it challenging to assess the efficacy of the protocol in terms of clinical impact, so different evaluation approaches need to be adopted.This study aimed to bring to light the results from our hospital in order to enable further protocols for phone notification of critical laboratory values, hence contributing in the establishment of international harmonized postanalytical phase-related criteria and indicators. According to the results obtained in this study we believe that our protocol of critical risk results is appropriate, since all the indicators measured are in accordance with previous publications, and the satisfaction of physicians and nurses with the current protocol is high. Nevertheless, there are some limitations that hamper the evaluation of the impact on clinical decision making. Alternatives were proposed for a proper and precise evaluation.


Food supply to poor populations is a big challenge, particularly in periods of economic stress and in an era of chronic diseases epidemics. In Israel more than 4000 poor families are currently receiving food support. Many of the food support products given to the family have low nutrient values, therefore not appropriately ensuring food security of the population.The aim of the current study was to examine for the first time the demographic, nutritional and chronic diseases profiles of food support beneficiaries, so as to aid in planning future food support components in Israel. In addition, the study examined associations between levels of food insecurity status and selected morbidities among food support recipients.In 2016, 3000 families (classified as very poor) in 24 municipalities received food support in Israel from the “National Food Security Project” (NFSP), under the guidance of the National Food Security Council. The 400 new families who joined the program in 2016 were requested to complete a questionnaire regarding the demographic and health characteristics of their families. Three hundred sixty-two of them completed the questionnaire for a response rate of 90%. The current study includes these families only.. Of the 362 responding families, about 82% of them were food insecure, with more than half severely food-insecure; this, despite receiving food support.The disposable income per capita of the surveyed families was very low – less than NIS 1100 a month ($280). About half the families were working families and 40% of them were in debtAbout one-third of the families had at least one member with anemia, and a quarter of the families had a member with hyperlipidemia. Hypertension is present in about 22% of the families, diabetes in 17%, and there is a 12% incidence of at least one family member with heart disease. These rates are markedly higher than those in the general population. Higher levels of food insecurity were associated with higher levels of hyperlipidemia, heart disease and hypertension.The nutrition and medical status of the population receiving food support is much worse than in the general population. There is a need to improve the nutritional value of food support; this could include greater emphasis on whole grains, fruits and vegetables. There is also a need for a nationwide education program to focus on healthy nutrition and to subsidize healthy foods. Many health and nutrition promotion models show that in order to effect changes in dietary habits and behaviors related to improving nutrition, there is a need for nutrition education . Food support plays a major role in the food aid sector by distributing donated and purchased groceries directly to food insecure families. The public health implications of food insecurity are significant, and food insecurity is more prevalent among certain population groups .Lack of appropriate nutrition education, coupled with food insecurity' may lead to poor food choices, which in turn, impacts upon health status , 2. HungStudies have shown the connections between food insecurity and morbidity.Food insecurity was associated with higher HbA1c levels which did not improve over time , 5. DeprThe link between economic and health status is well known and documented in many countries. More income and better education are linked to longer life expectancy and better health outcomes   had similar socio-demographic characteristics  to those not receiving support with rates of smoking, having at least one chronic illness, estimating their own health status as moderate to poor and low consumption of fruits and vegetables similar in both groups In the low SES population the prevalence of diabetes was twice as high among food support clients and they also reported worse health status. Considerably lower fruit consumption and lower hypertension prevalence among females was noted, and lower overweight prevalence among male food bank clients were found [In the USA, in the 2007–2008 National Health and Nutrition Examination Survey, food insecurity was measured using the United States Department of Agriculture (USDA) food security module. A sub sample included youth ages 4–17, living in families whose income is below 200% of the federal poverty line. It was found that (Body Mass Index) BMI z-scores indicating obesity were higher among participants in the food support programs than among the equivalent youth not receiving food support . Similare found .Israel is similar to other countries in that those living in poverty and receiving food support over many years received food support which was intended to supplement caloric intake but not necessarily foods of high nutritional value. Partially as a result of these actions, the prevalence of chronic nutrition-related diseases in this population group has increased. The USA governmental programs described  have recently changed the types of foods offered, so as to improve health outcomes.According to the National Households Survey carried out in 2012 by the National Insurance Institute in Israel (NII), 8.3% of households were classified as being moderately food insecure and 10.6% were classified as being severely food insecure . In the In the USA, three governmental nutrition assistance programs are in place, namely the Supplemental Nutrition Assistance Program (SNAP), the Special Nutrition Program for Women, Infants and Children (WIC), and the National School Lunch Program (NSLP) and these three programs serve there as the backbone of the nutrition safety net. These programs have been successful in achieving many of their initial goals of improving food purchases, food intake, and/or nutritional status of low-income, nutritionally vulnerable Americans. The emphasis in these programs has now broadened to focus also on obesity prevention with recent changes in program components reflecting these revised objectives. In a new study, Harnack et al. (2016) show that food benefit programs that pair incentives for purchasing more fruits and vegetables with restrictions on less nutritious foods, may reduce energy intake and improve the nutritional quality of the diet . These chttps://brookdale.jdc.org.il/en/publication/food-security-israel-2003-implications-patterns-nutrition/).Several studies about food insecurity in Israel have been published, though none of them relate to the chronic disease situation of the food insecure population. One study  has related to the technical aspects of food rescue in Israel . AnotherIn 2012 the Welfare Ministry launched the “National Food Security Project” (NFSP), under the guidance of the National Food Security Council. This project is operated by Eshel Jerusalem, which allocates monthly food baskets, valued at NIS (New Israeli Shekel) 300–600In Israel about 3000 families are supplied with dry foods once a month (see below).Participating families are accompanied by the project coordinators and in some cities families are required to attend workshops and training groups on nutrition and home finances.This project operates on the basis of the eligibility tests determined by Endeweld and Shmueli  (2013). Due to the limited scope of the project at this stage, most of the participating families are amongst the poorest in Israel – that is, their net income per standard person falls by tens of percentage points below the official poverty lineIn the framework of the research and assessment accompanying the project, participants were asked to fill out a questionnaire which examined the periodic changes in their food security level, their degree of satisfaction with the service provided, (both the food aspect and the workshops in which some of them participate), recommended improvements, etc.The aim of the current study was to examine for the first time the demographic, nutritional and chronic diseases profiles of food support beneficiaries so as to aid in planning future food support components in Israel, so as to improve chronic diseases profiles as needed. In addition, the research aimed to examine whether the level of food insecurity of families living in severe poverty enrolled in the food support program are associated with differences in selected morbidities.In 2016 all of the 400 new families joining the National Food Security Program (NFSP) were requested to complete the questionnaire which included a health component- their completion of the questionnaire being a prerequisite for entry into the program. three hundred sixty-two agreed, for a response rate of 90%. The socio-demographic profiles of these “new” families is not different from those previously enrolled, thus validating the representativeness of this study. This is based on a comparison, done to ensure homogeneity, of the socio-demographic profiles of the newly-enrolled participants (as determined from eligibility tests as part of enrolment) with the socio-demographic profiles of existing participants - done to ensure homogeneity.Beginning in 2016 a new questionnaire was distributed to participants. Included in this questionnaire, besides socio-demographic questions, questions related to health and questions related to the level of satisfaction with the services provided to them by the NFSP, was a new section - the Six-Item Short Form of the 18-item U.S. Household Food Security Survey Module [The Six-Item Short Form questionnaire, to which 362 families responded  is the data source of the current research.In addition, the new questionnaire included five questions on family morbidity, developed by researchers at the National Center for Health Statistics in collaboration with Abt Associates Inc , 20. TheIt should be noted that besides the limitation that these questions did not differentiate between adults or children, in addition they provide no information on the prevalence of the condition/s within the family  existence of food insecurity; (2) scores for the family (as described above).Additionally included in the vector were the independent variables –demographic characteristics  and economic variables , gathered both from the questionnaire above and other information received from families participating in the project and other tools. Thus, for example, we used financial data from the eligibility test, which includes information about income, expenses and family debt.The analyses of associations between food security and chronic disease were not carried out to test whether food security is a cause of chronic illness; this would not have been possible given the cross-sectional, single point in time, nature of the study. Rather, these associations were analyzed undertaken primarily to assess the extent of significant health challenges among persons experiencing food insecurity, so that these challenges can be taken into account in the design of food assistance programs.Respondents’ population segmentation by demographic, socio-economic, morbidity and food security variables are shown in Table It can be seen that only 12% of the respondents are Arab families, a slightly lower rate than in the general population (14.6% ). Among The disposable income per capita is very low – less than NIS 1100 a month. About half the families are working families and 40% of them are in debt to various entities, such as banks, municipalities (city taxes), charity organizations, etc. About two-thirds of respondents are female and in the study sample there is a greater concentration of single-parent families  than in the general population (5.7%).Of the 362 families, about half received someThe food security situation of the respondent families is very difficult, as to be expected: about 82% of them are food insecure, with more than half severely food-insecure [as defined by the score on the HFSS 6 item questionnaire. It should be noted that, according to one interpretation, even a score of 1 (out of 6) indicates moderate food insecurity. Taking into account the families who received this score , it turns out that all had some level of some food insecurity (none received a score of zero). The average score of the Food Insecurity Rasch Scale is 6.4  among respondents’ families.The following table shows the scores distribution.It should be noted that these levels of food insecurity existed while these families were participating in the NFSP. According to all indices developed during the research, the project had a significant positive impact on the food security status of the participating families. That is, the situation would have been much worse if these families had not participated in the project.Examination of morbidity data shows a relatively high incidence of illness/conditions in the families. About one-third of the families have at least one member with anemia, and a quarter of the families have a member with hyperlipidemia. Hypertension is present in about 22% of the families, diabetes in 17%, and there is a 12% incidence of at least one family member with heart disease. About 55% of the families have at least one of the five diseases/conditions indicated in the questionnaire. These are well above the rates in the general population, in particular for heart disease, at 4.6% and diabetes., at 7.7% [Table In order to examine the factors associated with the likelihood of morbidity from the five diseases/conditions investigated, we estimated the logistic regression model in two forms, with the dependent variable in both forms being the presence or absence of selected diseases/conditions in the family. As noted, food security variables were of two types (1) the existence of food insecurity (2) scores for the family. Independent variables include demographic and socio-economic variables as seen in Tables The results show that in the two models estimated  relationship between morbidity and receipt of disability benefits from the National Insurance Institute (but no significant correlation with income support). Among Jews in general there are greater chances of higher morbidity than among Arabs and there is lower morbidity among ultra-Orthodox than among other Jewish population groups. Each additional year in the age of respondents increases the chances of morbidity from 5 to 9%, and the marital status “married” significantly increases the chances of diabetes in the family.The aim of this study was to examine for the first time the connection between the levels of food insecurity and selected morbidities by describing the profiles of 362 households of the 4000 participating in the “National Food Security Project” (NFSP). These 362 families responded to a questionnaire specifically designed to determine the level of satisfaction of the households, the level of food insecurity (according to the six-item module of the Household Food Security Survey) and information about selected morbidities in these households. In particular, the respondents  answered whether someone in the family (regardless of whether adult or child) had one or more of the following conditions: anemia, hyperlipidemia, diabetes, heart disease or hypertension.The results show that for every additional point in the food insecurity scale score the risk of hyperlipidemia, diabetes, heart disease and hypertension increases by 20, 16, 31 and 21%, respectively within the families who receive food support. These compare to much lower prevalence of these diseases/conditions in the general population in Israel. Among Jews in the study there is greater prevalence of morbidity than among Arabs. The biggest difference between Jews and Arabs was found in the probability of anemia, which is 3.6 times higher among Jews as compared with Arabs. This may be attributed to meat consumption habits of Arab families. There is also lower morbidity among the ultra-Orthodox as compared to other Jewish population groups. This may be explained by different lifestyle habits not related to nutrition habits, such as lower levels of stress and anxiety, and a more supportive community. It is also possible that the range of ages is lower in Ultra Orthodox households, and the respondent’s age component (which is part of the vector of explanatory variables in the regression) did not completely control for the full effect of age.An additional significant finding is the difference in prevalence of diabetes which is much higher in families where heads of households were married as compared to single head of households. The finding may be partly explained by the nature of food purchasing for larger families, in that the necessity to purchase large food quantities on a limited budget often results in selection of cheaper foods of lower nutritional value, thus resulting in increased prevalence of diseases/conditions such as diabetes and hyperlipidemia, which are partly attributable to poor dietary habits. In addition, purchasing in bulk may reduce the degree to which the quality of the food.is checked.These results can be seen, with the limitations of the comparisons, in other countries such as the USA.There is no doubt regarding the importance of the programs but could they be improved, in order to give that population the right food with the right educational programs?In the USA, programs for nutritional assistance -Supplemental Nutrition Assistance Program (SNAP), Special Nutrition Program for Women, Infants and Children (WIC), and National School Lunch Program (NSLP)-serve as the backbone of the nutrition safety net in the USA.These programs have been successful in achieving many of their initial goals of improving food purchases, food intake, and/or nutritional status of low-income, vulnerable Americans. The only goal that was not achieved yet was the health benefit and obesity prevention, so as to prevent the resultant increased risk of chronic disease development .A few studies done on the SNAP program found that the program did not address the problem of obesity and may even contribute to the problem .This mayThere have been many papers published investigating the correlation between food assistance programs and obesity and chronic disease. It appears that the correlation depends on age, gender and the specific food assistance program. For example, it was found that participants in the Supplemental Nutrition Assistance Program (SNAP) were more likely than nonparticipants  to be obese . HoweverA current study published by a Portuguese group show that when examined the impact of food insecurity on health and health-related issues they found that subjects with food insecurity reported worse quality of life score HRQoL and more physical disability when compared to subjects without food insecurity . A higheThe possible correlation between food assistance programs and obesity and chronic diseases may have to do with the diet quality of the participants.Among adults with obesity and diabetes, food insecurity is associated with lower overall dietary quality, increased consumption of unhealthy foods and lower consumption of healthy foods. These individuals are limited to cheaper, more calorie-dense and high-sugar foods that promote high blood sugar .https://brookdale.jdc.org.il/en/publication/food-security-israel-2003-implications-patterns-nutrition/). It has been suggested that limiting the choices of foods that SNAP participants receive would be beneficial, but the implementation is complicated . Overall, it is understood that nutrition education must be implemented to improve diet quality of SNAP participants as is done in the Israeli programs.Data show that SNAP participants received a larger percentage of their calories from empty calories  when compared to nonparticipants . OverallIn a study that was conducted among 279 lower income participants not enrolled in SNAP program, Harnack et al. (2016) wanted to evaluate whether prohibiting the purchase of less nutritious foods with SNAP benefits improves the nutritional quality of foods purchased and consumed. It was found that a food benefit program that pairs incentives for purchasing more fruits and vegetables with restrictions on the purchase of less nutritious foods may reduce energy intake and improve the nutritional quality of the diet of participants compared with a program that does not include incentives or restrictions.The WIC program, with estimated expenditures of $6.8 billion annually and its authorized vendors, serves approximately 8.9 million infants, children, and pregnant/postpartum women each month. This population also faces the problem of an increased prevalence of obesity. In 2009 fruits and vegetables were added to the program to try to contain the obesity epidemic which is common among lower socioeconomic status women in the United States. This action resulted in lower prices of fruit and vegetables in small stores where those women tend to buy their food products . Improvihttps://brookdale.jdc.org.il/en/publication/food-security-israel-2003-implications-patterns-nutrition/).Finally, new regulations for the NSLP aim to improve nutrient intake . Though Because of the results of these previous studies, this year these programs are aiming to address the obesity and resulting chronic disease problem and to change the quality of the food products and the environment and education to allow for better nutrition habits and status.The methodology and the questionnaire did not provide morbidity data on each household member, and it is possible that less morbidity was correlated to a younger household composition (family with many young children) rather than other demographic characteristics.The obesity information is lacking because the questionnaire did not directly examine this. Unlike questions on the other disease states which did not specifically relate to the respondent but rather to the incidence within the family, questions on weight and height are of necessity person-specific and may have met with an antagonistic response, because of perceived stigmatization. Therefore, questions on weight/height and obesity were not included. It is recognized that this issue is very important and future research of this nature will attempt to explore obesity prevalence in a non-intrusive manner.There is adequate representation of Arab families in the sample (12%) as compared to 13% in the general population. However, about 30% of Arab families suffer from food insecurity, thus were in fact underrepresented in the sample. As the initial stages of the proposed new initiative were in the central areas of the country, and many Arab villages and towns are in the periphery, they were underrepresented. However, the number of Arab families included is large enough to allow for statistical comparisons.In addition, there may be municipalities which, for various reasons, do not participate in the National Food Security Project and their health and socio-demographic characteristics may be different.The low socioeconomic population in Israel suffers more from obesity and diabetes and heart diseases: the incidence of diabetes in the general population is 9.7% but in the low socioeconomic population it has reached 25%  (like in


Machine learning (ML) has become a vital part of medical imaging research. ML methods have evolved over the years from manual seeded inputs to automatic initializations. The advancements in the field of ML have led to more intelligent and self-reliant computer-aided diagnosis (CAD) systems, as the learning ability of ML methods has been constantly improving. More and more automated methods are emerging with deep feature learning and representations. Recent advancements of ML with deeper and extensive representation approaches, commonly known as deep learning (DL) approaches, have made a very significant impact on improving the diagnostics capabilities of the CAD systems.This review aimed to survey both traditional ML and DL literature with particular application for breast cancer diagnosis. The review also provided a brief insight into some well-known DL networks.In this paper, we present an overview of ML and DL techniques with particular application for breast cancer. Specifically, we search the PubMed, Google Scholar, MEDLINE, ScienceDirect, Springer, and Web of Science databases and retrieve the studies in DL for the past 5 years that have used multiview mammogram datasets.The analysis of traditional ML reveals the limited usage of the methods, whereas the DL methods have great potential for implementation in clinical analysis and improve the diagnostic capability of existing CAD systems.From the literature, it can be found that heterogeneous breast densities make masses more challenging to detect and classify compared with calcifications. The traditional ML methods present confined approaches limited to either particular density type or datasets. Although the DL methods show promising improvements in breast cancer diagnosis, there are still issues of data scarcity and computational cost, which have been overcome to a significant extent by applying data augmentation and improved computational power of DL algorithms. Cancer is one of the leading causes of female deaths worldwide. It has caused more deaths than any other diseases such as tuberculosis or malaria. The World Health Organization (WHO) agencies for cancer research  and American Cancer Society) report that 17.1 million new cancer cases are recorded in 2018 worldwide [Breast cancer is among the 4 leading cancers in women worldwide . The IARC statistics show that breast cancer accounts for 25% of all cancer cases diagnosed in women worldwide. Around 53% of these cases come from developing countries, which represent 82% of the world population . It is rIn breast, the cancer cells may spread to lymph nodes or even cause damage to other parts of the body such as lungs. Breast cancer more often starts from the malfunctioning of milk-producing ducts . However, it may also begin in the glandular tissues called lobules or other cells or tissues within the breast . ResearcTo visualize the internal breast structures, a low-dose x-ray of the breasts is performed; this procedure is known as mammography in medical terms. It is one of the most suitable techniques to detect breast cancer. Mammograms expose the breast to much lower doses of radiation compared with devices used in the past . In receIn this review, we present the recent work in breast cancer detection using conventional machine learning (ML) and deep learning (DL) techniques. The aim of this work was to provide the reader with an introduction to breast cancer literature and recent advancements in breast cancer diagnosis using multiview digital mammograms (DMs). The survey aimed to highlight the challenges in the application of DL for early detection of breast cancer using the multiview digital mammographic data. We present the recent studies that have addressed these challenges and finally provide some insights and discussions on the current open problems. This review is divided into 2 major parts. The first part presents a brief introduction of different steps of a conventional ML method , whereas the second part focuses on DL techniques, with an emphasis on multiview  mammographic data. The present DL literature can be characterized for breast density discrimination, detection, and classification of the lesion in breast cancer in the multiview digital mammographic data. The rest of this review is organized as follows.In this section, we present various steps involved in a computer-aided diagnosis (CAD) system using the conventional workflow. The steps involved are outlined in Mammogram preprocessing is one of the primary steps in a CAD system. In the preprocessing step, the unwanted objects are removed from the mammograms, which include annotations, labels, and background noises as can be seen in Image enhancement techniques are used to improve the mammogram’s quality in terms of improving the contrast and enhancing its readability. It helps the system to detect the mammographic lesions with poor visibility and contrast by improving it. The major goal of mammogram enhancement is to improve the image quality on the mammograms with low contrast. The low-contrast regions with small abnormalities are often concealed in surrounding tissues, leading to a misdiagnosis. The image enhancements improve the overall quality of the images, thus making it relatively easier for the reader and CAD systems to detect these subtle abnormalities. The enhancements may add distortions to the anatomical characteristics of an image or amplify the noises. Thus, only those methods would be acceptable that maintain a similar appearance to the original image. Recently, with the introduction of direct digital technology in mammography, with dynamic range, improved contrast, and signal to noise ratio, there is a limited scope of mammogram enhancement.The enhancement techniques are generally divided into 3 categories: spatial domain, frequency domain, and a combination of spatial and frequency domain techniques . HoweverThe segmented region is vital for feature extraction and detection of abnormal tissues in the breast, and it needs to be well focused and precise. Therefore, the segmentation is important to extract an ROI that provides a precise measurement of breast regions with abnormalities and normal regions. Segmentation involves the fundamental step of separating the breast region from the background and aims to separate the breast regions from the other objects. It is an important step to preserve the margin characteristics of mammograms before any further processing.The segmentation aims to extract ROIs with possible masses, and it may involve partitioning of the mammogram into several nonoverlapping regions with candidate mass lesions. At the detection stages, higher sensitivity rate and more FPs are expected. In ML methods, learning the significant or most informative features from the medical images plays a vital role, as these features are used as discriminators in later stages for segmentation or classification. Most of these features are manually designed (handcrafted) based on clinicians’ experience and prior knowledge about the target domain. Thus, the ML methods can be more problem oriented and often make it difficult for a nonexpert to exploit the full potential of the method. The feature extraction is the step that characterizes the features of a specific region. The significant features are retained for the classification step. To measure features from the ROIs, its properties such as mass size, regular or irregular shapes, homogeneity of boundaries, and density of tissues are utilized . It is wClassification is the last step to determine the lesion under observation is normal or cancerous regions. If it is classified as a cancerous region, further classification is done to determine the pathology of cancer, ie, benign or malignant. The classification step itself is heavily dependent on other intermediate steps, especially segmentation and feature extraction. In breast cancer classification, some of the commonly used classifiers include support vector machine (SVM) -34, artiA substantial amount of research on breast mass, microcalcification detection, and classification can be found in literature ,34,41-45DL algorithms have made significant improvements in performance compared with other traditional ML and artificial intelligence . The appThe DL architectures can be characterized into 3 categories: unsupervised DL networks, also known as generative networks; supervised networks or discriminative networks; and hybrid or ensemble networks.Convolutional neural network (CNN) is a state-of-the-art DL technique that is comprising many stacked convolutional layers . The mosThe AlexNet  architecLater, VGG 16 architecture was proposed by visual geometry group (VGG) , Oxford The GoogleNet  proposedWith increasing network depth, the accuracy of the network is saturated and thus degrades rapidly. This degradation is not caused by overfitting problem, but with the addition of more layers, the training error also increases that leads to degradation problem. The degradation problem was solved by introducing the residual network (ResNet) by He et al . The resMany researchers have used DL approaches in medical image analysis. The success of DL is largely depending on the availability of large number of training samples to learn the descriptive feature mappings of the images, which give very accurate results in classification. For example, the image classification task, the network is trained over more than 1 million images with more than 1000 class data. However, in the case of medical images, the amount of available training data is not that big in size. Moreover, it is also difficult to acquire a large number of labeled images, as the annotation itself is an expensive task and for some diseases  are scarce in the datasets . In addiTo select the relevant recent studies on breast cancer diagnosis, we consider the studies in the past 5 years from well-known publishing platforms such as PubMed, Google Scholar, MEDLINE, Science Direct, Springer, and Web of Science databases. The search terms convolutional neural networks, deep learning, breast cancer, mass detection, transfer learning, and multiview are combined.In this section, we first present the methods that used breast density estimation methods as a tool for early diagnosis. Second, the methods used transfer learning and image features classification of suspected lesions into mass and normal class. Finally, we present the segmentation methods using semantic features for localization of masses and classifying the pathology.Mammographic density is an important indicator of early breast cancer detection. In the United States, more than 30 states have agreed to use breast density as an earlier risk marker for cancer screening programmes . The quaOne such attempt has been made by Mohamed et al  using CNIn another study, Ahn et al  presente, CC and MLO). Their study aimed to use the residual CNN to discriminate the BI-RADS densities into 4 categories. The residual CNN consisted of 70 layers with 7 residual learning blocks. In addition, 2 other networks with 36 and 48 weighted layers but less residual blocks were also trained to compare the performance. The ResNets could minimize the cross-entropy loss to maximize classification accuracy. Their results showed that with increased residual layer, the classification accuracies improved. However, the computational cost was increased.In a similar study, Xu et al  classifiKallenberg et al  proposedIonescu et al  proposedGeras et al  in theirSummary of the aforementioned methods is presented in The automatic detection of masses at an early stage in DMs is still a hot topic of research. DL has significantly overcome the shortcomings of conventional methods by learning the object features. The learning curves of the DL methods have enabled to highlight the most relevant ROIs in DMs. In this section, we present the recent CNN-based methods for the detection of masses in mammograms using transfer learning techniques and scratch-based end-to-end training.To improve the diagnostic accuracy of the breast CAD system, Dhungel et al  introducIn another study, Dhungel et al  proposedZhu et al  proposedIn another study, Wang et al  presenteRiddli et al  used traSingh et al  presenteSome researchers used image features for lesion detection and classification. One such study by Agarwal and Carson  predicteGao et al  presenteHagos et al  presenteLater, Tuwen et al  proposedJung et al  proposedShen et al  presenteWe summarize the lesion detection and classification methods in details in DL algorithms have shown significant improvements in breast cancer detection and classification problem over the past decade. The deep contextual and texture features allow the classifiers to discriminate between normal and abnormal lesions with varying shapes, size, and orientations. This not only improved the diagnostic capabilities of CAD system but also provided robust solutions for clinical practices.Levy and Jain  demonstrRecently, Samala et al  presenteJadoon et al  presenteIn a similar study, Huynh et al  also useDomingues and Cardoso  used an Wu et al  presenteSarah et al  addresseLately, Wang et al  presenteIn another study, Shams et al  proposedGastitouni et al  presenteDhungel et al  introducGenerally, DL methods have significantly improved the performance of breast cancer detection, classification, and segmentation. We summarize these methods in details in From On the other hand, many attempts have been made to reduce human intervention and produce fully automatic CAD system, which is a very challenging task. In fact, all methods in literature require annotated images (ground truth) to validate their findings during the training and testing stages. Thus, acquisition of labeled mammograms with image-level and pixel-level annotations is one of the obstacles in designing robust DL methods. The main issue is not only the availability of data but also annotations by expert radiologist, which is time consuming, subjective, and expensive.It is noted from the literature that the automated DL method requires extensive experimentation, computational power, and preprocessing of data, which make it inefficient to be used in real time. Moreover, finding the optimal parameters in DL networks is also one of the major challenges in building a CAD system for clinical use. However, this issue can be resolved if sufficient training is provided to clinicians, and CAD systems are made more user friendly. It is also noted that the semisupervised approaches have shown good performance on the public and private datasets for breast cancer diagnosis.From the analysis of methods mentioned in Developing DL models that can learn from limited data is still an open research area not only in breast cancer diagnosis but also for other medical image analysis applications. Moreover, developing data augmentation techniques that can create morphological variations in augmented samples, while also preserving the lesion characteristic, are needed. One of the solutions to address these problems is to explore the capabilities of GANs as successfully demonstrated in studies by Singh et al  and Wu eApart from the development of automatic DL techniques, there are other associated challenges to the medical imaging research community. First, it is very challenging to secure funding for construction of a medical dataset. Also, finding an expert for annotation and the cost of annotation itself is very high. Second, privacy and copyright issues make the medical image difficult to share compared with natural images datasets. Finally, because of the complex anatomy of human organs, a variety of dataset is required using different imaging modalities. Despite these challenges, there has been a significant increase in the number of public datasets. Organizing a grand challenge is one of the good practices devised to share and enrich the datasets. The participants are provided with a certain number of tasks on a particular dataset, and the technique with best results is announced as a winner. Moreover, different research centers join hands in research collaborations as well as common data sharing platforms.From the aforementioned discussions, we can see that both supervised and unsupervised DL methods are used by the image analysis community, but the majority of the work uses the semi supervised approach. The presented literature aims to help in building a CAD system that is robust and computationally efficient to assist the clinicians in the diagnosis of breast cancer at early stages. As DL requires a sufficient amount of annotated data for training, most of the researchers use a combination of public and private data followed by data augmentation techniques to overcome the data scarcity issue. These approaches have provided a feasible solution to the problem of scarcity of data and overfitting.


This work presents a solution to localize Unmanned Autonomous Vehicles with respect to pipes and other cylindrical elements found in inspection and maintenance tasks both in industrial and civilian infrastructures. The proposed system exploits the different features of vision and laser based sensors, combining them to obtain accurate positioning of the robot with respect to the cylindrical structures. A probabilistic (RANSAC-based) procedure is used to segment possible cylinders found in the laser scans, and this is used as a seed to accurately determine the robot position through a computer vision system. The priors obtained from the laser scan registration help to solve the problem of determining the apparent contour of the cylinders. In turn this apparent contour is used in a degenerate quadratic conic estimation, enabling to visually estimate the pose of the cylinder. For a long time now robots and automata can be found in industrial and civilian operations as part of complex systems presenting some degree of automation. The introduction of these technologies has been driven by several factors, mainly the increased efficiency derived from the automation, but also the better and safer conditions for human employees. In many areas, the introduction of these technologies has been delayed because there is still need of human hard to reproduce capabilities. One of these capabilities, characteristic to human beings, is the generality and adaptability of human response, which makes them especially suited for supervisory and monitoring tasks.Monitoring and maintenance tasks rely heavily in availability of the information, which can be obtained from remote/installation sensors, but many times require actual physical inspection of some elements. This is especially true for industry, were it would be impossible to sensorize all the elements/points which must be inspected or monitored at some time as part of the maintenance operations. An example of such elements would be pipes and canalizations, especially in heavy industries, where kilometers of pipes have to be periodically inspected.These pipes and tubes are common structures not only in industry but also in urban environments, and can be frequently found in hard to reach areas. This poses a problem for the mentioned monitoring and maintenance operations, as those operations are commonly performed by human personnel or ground-based unmanned vehicles (UGVs) with great efficiency, become expensive and exceptionally risky in inaccessible areas. In such scenarios, operations with humans in high and/or hard to reach areas generally imply shutting off ordinary operation, building temporary scaffolds, and following complex safety protocols and procedures to minimize risks. In these situations, any opportunity to reduce the participation or risks taken by human personnel can have a great impact, both economically and in safety terms.In this context, Unmanned Aerial Vehicle (UAV)-based solutions have started to appear. While UAV drones have been present for a long time, developments in microelectromechanical systems (MEMS) and battery technologies have produced an explosive growth of the field. This has made them cheaper and easier to deploy, with a big research and development community supporting them, especially for the massively popular rotary-wing multicopters. This kind of UAV has already a strong presence in the audiovisual production and the surveying industry, and is gaining a foothold in other industries.On the other side, the problem of locating pipes from robotic platforms is not new. There are many works that try to locate defects in pipes from the inside ,2 which The objective of this work is to develop a multimodal solution to detect and localize pipes from an UAV in order to enable inspection and monitoring operation in industrial environments inaccessible to humans. An initial attempt to produce independent solutions with monocular cameras and Light Detection and Ranging (LiDAR) sensors, and evaluate which provided a better solution led to the development of several of the methods described. Unlike ground robots, one of the most challenging aspects of robotics in the context of UAV is the limited payload. The equipment deployable on board is strictly limited by weight, its own and that of the batteries required to power the device. This fact is translated into very limited computational power deployable on board, even introducing additional single board computers (SBCs). The chances of delegating computational efforts to other systems are also constrained by the range, bandwidth and latency of wireless communications; so the general assumption is to deploy anything needed at real-time performance on-board.This affects the architecture of the robotic UAV, not only in hardware terms, but also from a high level architecture point of view. Thus, the common approach of deploying a single computing unit in the form of the Flight Management Unit (FMU) is ignored in favour of deploying an additional SBC. This additional computing unit will be responsible of all the hardware and processes not needed in the low-level control loops to guarantee UAV stability and safety. The FMU will receive data from those sensors, which require low computational power to process it  and control the low-level operation of the UAV. This way, the heaviest computational task, such as image processing, localization in maps, video streaming and communications, etc. are delegated to the SBC.Under this architecture, the FMU is still responsible of the odometry estimation, so the research and experiments have to account for the error characterization in these measurements.Detection and positioning of pipes using LiDAR or similar range finder sensors is essentially a problem of shape detection in point clouds. There are many approaches to this problem, but they are generally based on five wide categories: edge based, region based, attribute based, graph based or model based methods. Each category shares a wide set of features, according to its procedures and strategies.Edge based methods try to find the edges of a region of similar points, generally through identification of those points presenting a rapid divergence of the metric with respect to the neighbors. Some methods are based in gradient techniques , while oMethods based on attributes, like , work asGraph-based methods read the whole point-cloud as a graph, with the simplest case matching each point to a node. They can produce very good results, as they can benefit from many techniques commonly applied to graph-based problems, like Markov Random Fields , k-neareModel based approaches are mostly based on the Random Sample Consensus (RANSAC), technique . The pro® is trademark of Velodyne LiDAR, Inc., San José, CA, USA) presents 16 scan lines distributed between +15° to −15° in azimuth, with 360° coverage each , each one with its own normal vector ni per Equations (1) and (2), and using geometrical properties of the dot and cross products, seen in Equations (3) through (6):sc and tc:pc and the director vector vc, for the searched SHCC, per Equations (9) and (10). Notice that, for both tc and sc, when the value of g is below an arbitrarily small n1 and n2 are almost parallel, so an alternative way to obtain tc and sg is applied:Thus, given two sample points vc by themselves, unless they are close to parallel. Once the full parametrization of the SHCC model is achieved, a validation test, using the radius priors, rejects incorrect and/or spurious candidate models, per Expression (11):This method exploits the fact that the normal vectors generally contain enough information to describe the orientation of L. This fact produces either false positives if the seed range for cr was set with wide margins; or failing to find a cylinder C with parameters fitting the SHCC model. In order to avoid this situation, an architecture to produce denser point clouds was developed, exploiting the assumption that odometry measurements of the movements or the multicopter would be available, so an approximation to This formulation was used as part of the implemented RANSAC approach. While synthetic experimentation probed satisfactory, proof of concept tests showed that the application of this RANSAC procedure was vulnerable to the unequal distribution of samples along the different dimensions of the sensor frame The procedure  starts wIn a first step, a geometrical based pass filter removes those points lying on regions, which can be predicted to appear in the cloud, but are known not to contain the target pipe. This includes shadows produced by the body of the UAV itself, and regions of the space determined as not relevant according to the pose or facing of the UAV, like the floor. This relies on previous knowledge and measurement from other sensors to determine the pose and facing of the UAV.In a second step, a voxelization filter is applied, reducing the size of the cloud. The voxel size can be adjusted considering that the nominal ranging accuracy of the LRF sensor used is known, and can be considered accurate, as seen in . Note thNote that this procedure is entirely reliant in the accuracy of the transformation ICP-like  procedurThe final step is a simple removal of statistical outliers based on the neighborhood of the data points. As the objective is detection and segmentation of the surfaces of an object, relevant points will be rarely pruned, as they are not isolated.Once the cloud has been filtered, the RANSAC procedure determines the model of a homogeneous circular cylinder described by an axis (a line with a support Euclidean point and direction vector) and a radius, by fitting the parametric model based on the neighbor surface normal of the data points.Optitrack® arena, T in an image, per Equation (12):Thus, in order to determine the Plückerian coordinates ; the focal length of the optics in terms of pixel size, for the horizontal and vertical axes, αx and αy; and the skew sThe camera intrinsic parameters are used to compose the intrinsic calibration matrix With this data, a degenerate conic equation relating the parametrization of the apparent contour of the cylinder with the calibration matrix for the given image can be obtained through Equation (16):C, in the formre, as the last column of the left singular vectors matrix UThe conic can be solved through singular value decomposition  of matriwe, a vector describing the direction between the camera optical center and the nearest point of the axis, ye, using the second column of the singular vector matrix, scaled using the major singular values to compute σ, (Equation (19))In order to estimate ye is found, a scaling term wn is found, computed according to the radius of the SHCC, cr, according to Equation (21). Solution for the second vector of the Plückerian coordinates, we is then achieved per Equation (22). Notice that as we is orthonormal to both the director vector of the axis of the SHCC, re, and the and director vector from C to the axis through the shortest path, ye, it is obtained through a normalized cross product scaled with wn to force the scale of crOnce This solution was implemented into a ROS node to visually determine the pose of the pipe, as the closed-form solution described meant that the procedure could achieve real-time performance, because only a singular value decomposition operation was required to solve the optimization part of the method. Tests with synthetic datasets for apparent contours showed results consistent with those described in the original work. Indoor experiments were also successful, producing average relative error below 3.5% for depth estimation. Still, when the camera optical axis and the pipe axis become close to parallel, which constitutes a degenerate configuration, the method becomes inconsistent.optimal detector [minl.Filter segment lines shorter than Group segments by general orientation, i.e., most likely to be horizontal or vertical.Pair segments by angle and length similarity.Select candidates from those pairs where no other edge is found between them.Candidate validation step using priors.Several approaches were developed in order to extract the apparent contour of a pipe. A simplistic solution based in the Hough transform  was initdetector ). A set A priori knowledge was used during the final validation step to choose the apparent contour candidate to use in the method described earlier to recover the pose. This knowledge was introduced as geometric/model restrictions , or through a human machine interface (HMI). Notice that using HMI knowledge to obtain priors required using accurate odometry to transform the prior knowledge to the relevant coordinate frame of the camera. To add consistency to the method, once an apparent contour has been found and validated, a visual servoing tracking method  searchesThis implementation, including pose recovery, produced robust results in indoor environments in terms of detection, but presented poor performance around 8.64 Hz, while still being affected by multiple challenging issues in terms of computer vision see . A small battery of outdoor tests further revealed some critical weaknesses. Firstly, the global binarization process was not able to properly detect edges under natural uncontrolled lighting, especially when multiple/ambient light produce diffused shadows; the implicit assumption of presenting features similar to a bimodal image taken in the indoor case to use Otsu’s thresholding was not useful in an uncontrolled environment. Additionally, the indoor structured environments presented more easily identified contours, usually presenting stronger edges with approximately known size and structure; thus able to be detected and identified with our assumed model. Finally, in the outdoor operation, the frame to frame contour tracking was unable to track the contour consistently, requiring to reintroduce prior knowledge in the case of the HMI.A modified approach substituted the global binarization with two different local adaptive binarization approaches , but theEarlier sections have discussed work developed with each of the available sensors in order to solve the problem of detection and pose recovery of a pipe with known radius. Of the studied approaches, using LiDAR and vision respectively, each one presented its own weaknesses and strengths. Our study showed that each of the approaches was stronger at one of the steps and noticeable weaker at the other task: the LiDAR registration procedure achieved great robustness at the detection and segmentation task, while the vision based pose recovery presented great accuracy at higher rate, but with very weak detection results. These results led to the development of a combined approach to exploit the best features provided by each sensing technology.The integrated method solves the problem in two different steps, working at different speeds with different sensors. Firstly, a RANSAC-based segmentation step, as described earlier, uses the point cloud data provided by the VLP 16 LiDAR to fit the SHCC model into the environment surrounding the UAV. This process works at an average 4.3 Hz, with an accuracy presenting dependencies w.r.t. the material and texture of the pipe to be detected, and especially to the relative position between the pipe axis and the sensors, as will be discussed in third row of the architecture diagram, which details the visual pipe segmentation and pose recovery. Notice that although some measure of scene registration is still performed, the visual pipeline has been modified to use the data from the LiDAR detected pipe as a prior, so the processes and architecture described in The proposed approach has been validated with experimental data. Each of the different techniques and architectures was tested using the relevant sensors and ground truths. The experiments were performed over sequences captured see  through ® is a trademark of DJI Corp., Shenzhen, China). A second hardware setup was developed in order to test and validate the different techniques developed without having to perform real flights, a standalone rigid frame was built to deploy the sensors, and operate them manually in indoor environments . This meant that although there is no actual difference between X and Y axes in terms of LiDAR sensing capability, as during the capture the camera was pointed towards the pipe, the Y axis of the LiDAR became the depth from the sensor to the pipe, while the X axis mapped the pan or side-scrolling movements. Thus, during the results discussion, those discussions referred to the Y axis of the LiDAR are actually related to the depth between pipe and sensor.In order to evaluate the LiDAR segmentation robustness and accuracy several indoor tests were performed locating a vertical 0.5 m diameter pipe; using an accurate ground truth produced with an Optitrack® system . Several set of sequences, performing multiples passes of similar trajectories were captured and used to test the algorithms. The first validation step was finding if the lightweight architecture without scan joining could achieve the same robustness, and how much better performance could be achieved. It was determined that the false positive rate was almost negligible for both see , but at The impact of the distance and orientation between the pipe and the sensor was studied using the ground truth from the motion capture system. It is noticeable how in all the degrees of freedom the error is well bounded, and when studying the 2.10 m point, as the most sampled distance, the error tends to follow a normal-like distribution , and qm is the same segment parametrized through values experimentally measured with the motion capture system. The vision based approach was tested with the same indoor sequences, and some other outdoor sequences, which lacked ground truth or LiDAR information. The accuracy of the results obtained, in terms of pose recovery, was worse than those reported by , with anIn a, b and c in The purely edge attribute-based methods  with respect to a robotic multicopter UAV has been developed. Initial studies tried to determine which of the available sensor devices, namely, monocular vision cameras or LiDAR, could provide a better solution to the detection and positioning challenges. These tests showed that none of the single-sensor solutions developed could provide an all-encompassing satisfactory solution. The LiDAR detection and positioning solutions were implemented based in RANSAC approaches, with two different developed architectures: one based in single LiDAR scan processing, and another one base on joining multiple LiDAR scans. The single scan architecture proved to be functionally as accurate as the approach with multiple scan joining, but presented a fivefold increase in performance measured as rate. This approach achieved very robust detection, with negligible false positives, but at a slow rate with average accuracy.The visual pipelines developed were based in the pose recovery described in . This reThus, the integrated solution proposed uses the LiDAR to detect robustly the presence of the pipe and to produce an approximate estimation of its position, which in turn is projected into the image to use it as a seed to improve visual detection of the pipe. Once the pipe has been detected in the image, the apparent contour is extracted and used to recover the pose of an SHCC, considered the geometrical model of the pipe.All the proposed methods have been tested with experimental data acquired in a motion capture testbed, which provided the ground truth for a rigid frame deploying the sensors used, in a configuration analogous to the one that could be found in and UAV. Additional vision−only sequences, captured with an actual multicopter, were used to test the vision−based approaches as the differences between indoor and outdoor environments greatly influence their performance.


Innovative and effective curricula for medical students and physicians are needed to increase knowledge and confidence for instructing patients on lifestyle management of diseases. We developed an active collaborative session that integrates evidence‐based medicine (EBM), clinical decision-making, nutrition, exercise, and personalized patient care for the instruction of lifestyle management of obesity in the preclinical medical curriculum.Before the session, learners critically appraised an EBM article . In class, there was an EBM discussion assessed and facilitated by multiple-choice questions, followed by a collaborative activity where learners solved a clinical scenario of a patient who wants to use a commercial weight-loss program. Each small group was assigned to a different program but given the same clinical scenario. The objectives of the session were to identify and interpret EBM/non‐EBM resources in order to describe the components, advantages, and disadvantages of the weight-loss programs, make a personalized clinical recommendation, and present it to the class.Generating debate and fostering engagement, the session was perceived as a positive learning experience by the learners. By accomplishing the learning objectives, the participants became well versed in various weight-loss programs.Our results suggest that learners developed interpretation and knowledge integration skills, which may increase their comfort in discussing the lifestyle management of obesity and other diseases. This activity is designed to be implemented at other institutions seeking to integrate active collaborative learning of nutrition, exercise, and clinical decision-making during preclinical and clinical medical education and clinical practice. Importantly, in obese individuals, weight loss improves the control of comorbidities associated with obesity, including type 2 diabetes, hypertension, and hyperlipidemia. Most adult Americans recognize the need to lose weight; however, many physicians are uncomfortable prescribing lifestyle management of obesity.5 This has led to a suboptimal treatment of the obesity epidemic and associated diseases.The need for innovative and effective practical lifestyle management curricula for medical students has been recognized as essential and timely by leading governmental and academic organizations, including the National Institute of Diabetes and Digestive and Kidney Disease, the National Academy of Science, Harvard Medical School, and the Lifestyle Medicine Think Tank.3 The United States Medical Licensing Examination (USMLE) Step 1 exam assesses for nutrition knowledge.6 Several institutions have developed recommendations for medical school nutrition curricula,7 surveys to evaluate patients' nutrition status,8 online self-learning modules,9 lectures, and team-based learning activities for nutrition education.14 However, to our knowledge, none of these resources have managed to integrate evidence-based medicine (EBM), clinical decision-making, nutrition, exercise, and personalized patient care in a collaborative active learning session. We developed a session for instruction in the lifestyle management of obesity utilizing all of the aforementioned techniques/competencies that permitted collaborative and peer education and allowed instruction on multiple topics in a relatively short amount of time in the curriculum. At the end of the session, learners should be able to develop a personalized recommendation for a patient who wants to use a commercial weight-loss program for the management of obesity. This will move the learners higher on Miller's pyramid of clinical competence,15 up to the Shows How level. The overall goal of this activity is for the learners to develop searching, interpretation, and knowledge integration skills that may increase their confidence in making recommendations for the lifestyle management of obesity and other diseases. We recommend its use in the education of preclinical and clinical medical students, medical residents, and fellows, as well as physicians who have a basic knowledge of the lifestyle management of obesity and the effect of weight loss on associated diseases. A similar approach could be used in teaching lifestyle management of other chronic diseases.There is a need to teach nutrition to medical students in an innovative way that promotes critical thinking in order to maximize retention and stimulate interest.This session was developed after a similar clinical scenario that a medical student encountered in the clinical setting. The student felt that the current class curriculum was largely theoretical and insufficient to make a practical personalized lifestyle recommendation. Several other students at our institution requested more practical nutrition teaching, which supported the need for the development of a practical session. Magdalena Pasarica, a medical educator with expertise in lifestyle management of obesity (family medicine physician with a PhD in nutrition), developed this session in collaboration with David M. Harris, Judith Simms-Cendan, and A. Laurel Gorman, experts in medical education, active learning, and EBM.This session was mandatory for University of Central Florida College of Medicine second-year medical students as part of their preclinical training and incorporated collaborative learning of several skills .The prerequisites, presession task, and active session tasks with their respective time allotments are briefly presented in a flowchart  and are detailed in the text that follows.Prior to the session, learners received a 1-hour lecture on the lifestyle management of obesity with the following objectives: to define obesity, its prevalence, and related comorbidities and to discuss obesity management guidelines. Students had some background knowledge of EBM, useful online search methods, biostatistics, and clinical study design as part of the EBM longitudinal curriculum.16 and completing an individual critical appraisal using an EBM interpretation guide  to answer five EBM questions  designedIn part 2 of the active session, students worked in groups to solve a clinical scenario using the EBM article and other EBM/non‐EBM articles as needed see . Teams wThe entire activity was mandatory but was formatively assessed; it did not count towards students' grades in the endocrine module. Students were encouraged by the instructor to prepare well in order to derive the greatest benefit from the session.Our session was moderated by an instructor with expertise in nutrition, clinical experience in treating obese patients, and extensive knowledge of the EBM article. The session was coordinated by two staff members  in the endocrine module and as part of the nutrition longitudinal curriculum.During part 1 of the active session, students answered five EBM questions , students will report all the resources that they used in addition to the board presentation, and at least 5 minutes will be spent before the session discussing the importance of this delivery method. This can be achieved by mentioning the long-term goal of this activity, which is for students to develop searching, interpretation, and knowledge integration skills that will be useful in clinical practice when making recommendations for the lifestyle management of obesity and other chronic diseases; the fact that nutrition questions will be on their USMLE Step 1 exam; and the importance of active versus passive learning.This session had several challenges. It required 2‐3 hours of curriculum time, which may be perceived as excessive if the significance of the session is not adequately described to module/course directors. Learners who are used to lectures only as an educational modality may perceive this activity as cumbersome, which may be overcome by explaining the benefits of active learning and why the pedagogy was chosen. To make it more engaging, a short video could be filmed to bring the material to life. A limitation of this session is that the survey deployed evaluated the overall instructor's performance in the endocrine module, which may have limited the response rate. For programs that would like to get more representative data on student perception, a survey specifically dedicated to this session should be designed.A. Student Presentation.pptxB. Facilitators Guide.docxAll appendices are peer reviewed as integral parts of the Original Publication.