Litterfall decomposition of coffee shaded with Tectona grandis or in full sun

ABSTRACT Litterfall is an important source of soil nutrients, but its decomposition can be affected by the crop system used. The objective of this study was to evaluate litterfall decomposition and macronutrient stocks in coffee crop systems in shaded (SHCS) environments and those in full sun (FSCS). The experiment was conducted on a rural property in Cacoal, state of Rondônia, Brazil, in a 2 × 6 factorial scheme with two crop systems (SHCS and FSCS), and six litterfall decomposition evaluation times (0, 30, 60, 180, 300, and 360 days after the litterfall was returned to the soil (DAL)), with seven replicates. The constant of decomposition (k), half-life time (t1/2) at 360 DAL, and phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), and nitrogen (N) concentrations of the remaining litterfall were determined at each evaluation time. The litterfall in the SHCS had a greater weight loss and constant of decomposition and a lower half-life time at the last evaluation, and the weight loss increased as a function of decomposition time. The litterfall stocks of macronutrients N, P, K, Ca, and Mg showed a linear decrease throughout the decomposition time, and increases in sulfur stock were found at the last evaluation.

Improving Walking Economy With an Ankle Exoskeleton Prior to Human-in-the-Loop Optimization

Lower limb robotic exoskeletons have shown the capability to enhance human locomotion for healthy individuals or to assist motion rehabilitation and daily activities for patients. Recent advances in human-in-the-loop optimization that allowed for assistance customization have demonstrated great potential for performance improvement of exoskeletons. In the optimization process, subjects need to experience multiple types of assistance patterns, thus, leading to a long evaluation time. Besides, some patterns may be uncomfortable for the wearers, thereby resulting in unpleasant optimization experiences and inaccurate outcomes. In this study, we investigated the effectiveness of a series of ankle exoskeleton assistance patterns on improving walking economy prior to optimization. We conducted experiments to systematically evaluate the wearers' biomechanical and physiological responses to different assistance patterns on a lightweight cable-driven ankle exoskeleton during walking. We designed nine patterns in the optimization parameters range which varied peak torque magnitude and peak torque timing independently. Results showed that metabolic cost of walking was reduced by 17.1 ± 7.6% under one assistance pattern. Meanwhile, soleus (SOL) muscle activity was reduced by 40.9 ± 19.8% with that pattern. Exoskeleton assistance changed maximum ankle dorsiflexion and plantarflexion angle and reduced biological ankle moment. Assistance pattern with 48% peak torque timing and 0.75 N·m·kg−1 peak torque magnitude was effective in improving walking economy and can be selected as an initial pattern in the optimization procedure. Our results provided a preliminary understanding of how humans respond to different assistances and can be used to guide the initial assistance pattern selection in the optimization.

Pre-Operative, Prophylactic Use of Oral Zinc Tablet in the Management of Post-Operative Sore Throat

Background: Postoperative sore throat (POST) is considered to be quite common complaint. Its symptoms tend to improve with time, but use of oral zinc lozenge has been shown to reduce the incidence and severity of POST. The aim of the present study was to find the efficacy of oral zinc sulfate, given 30 min preoperatively, in reducing POST, primarily caused by endotracheal intubation, till 24 hours after surgery. Method: A prospective, randomized, double-blinded, placebo-control-trial study was conducted on 80 patients, further divided into two groups of 40 patients each, between the age group of 18-60 years, of either gender, in Super Specialty Hospital, GMC Jammu, over a period of 6-months. The two groups received either dispersible zinc tablet or a placebo. The severity of POST was graded on a 4-point scale ranging from 0 to 3 and evaluation was repeated at 30 min, 2, 4, and 24 hour, postoperatively. Results: The difference in severity of sore throat was found to be statistically significant at all evaluation time intervals, except at 24 hours, which was quite lower in Zinc group. The overall incidence of POST in Zinc group was 26.3%, which was significantly higher at 50% in placebo group. Conclusion: A dose 40 mg zinc dispersible tablet, equivalent to 40 mg elemental zinc, given 30 min before surgery, effectively reduced the incidence and severity of POST. Keywords: Post Operative Sore Throat, Oral Zinc Lozenge, Endotracheal Intubation.

The year 2021 of the International Journal of Innovation – IJI

The International Journal of Innovation: IJI has been making some changes in recent years and, therefore, increasingly improving its content. These are transformations that would not be possible without the incessant collaboration of the entire IJI team. Ther are always tireless and impeccable. It would not be possible without all the articles submitted by the authors who believe and choose the IJI for their publications. It would not be possible either, without the reviewers who contribute with a lot of dedication, spending their time and knowledge on improving the articles to publish the IJI. We are immensely grateful to everyone! Special thanks to UNINOVE, who have always supported and continue to support IJI.At the end of 2021, we would like to present some data and information that enable readers, authors, and reviewers to follow the evolution of the IJI. We remain dedicated to raising the bar for the IJI. IJI is in the main databases: Dialnet, EBSCO, ERIHPLUS, Latindex, ProQuest, Redalyc, Redib, Spell, Web of Science and Zeitschriften Datenbank. It is classified in stratum B1 of the new QUALIS/CAPES proposal. It is published every four months (January-April; May-August; September-December) and accepts submissions in Portuguese, English, and Spanish, and published in English only.The International Journal of Innovation: IJI's mission is to serve as a vehicle for the periodic publication of scientific and technological works in innovation with a focus on emerging markets, which study individuals, organizations, ecosystems, and policies. The journal's topics of interest focusing on emerging markets are Innovative Entrepreneurship, Innovation and Learning, Innovation and Sustainability, Internationalization of Innovation, Innovation Systems, Emerging Themes of Innovation, and Digital Transformation. As we have already mentioned in another editorial comment, the IJI accepts scientific articles, technology articles, perspectives, and reviews, in addition to the editorial comment.We now present some data from IJI submissions for this year, 2021 (Table 1). Table 1Submissions to the IJIWorks submitted87Rejections65%Acceptance35% Eighty-seven works were submitted to the IJI, of which 65% were rejected and 35% were approved. With these numbers, we observe that rejection is still high. The rejection happens; most of the time, in the desk-reject the editors perform that. The reasons are: a) the submitted works are not following the scope of the IJI; b) the submitted works do not meet the minimum requirements for a publication and are not considered to proceed to a peer review and blind.When articles move to the stage of blind and peer review, the number of rejections gets smaller. Generally, works are rejected at this stage when the authors do not comply with the evaluators' recommendations. Our intention is, with the help of reviewers and authors, to increasingly improve the quality of submitted articles and increase the number of publications in the IJI.Submissions are from different countries. In this year of 2021, we had the following scenario (Table 2). Table 2Submissions to the IJI - by countryCountrySubmissionsBrazil59Argentina1Colombia1Cuba1Algeria2Indonesia2India3Jordan1Sri Lanka1Mexico1Malasia1Nigeria3Portugal2Saudi Arabia3Tunisia1Turkey1Ukraine1Vietnam2South Africa1TOTAL87 The most significant works submitted are from Brazilian authors, but with a balanced distribution among other countries. We are happy that the IJI is considered a possibility for authors from different countries on our continent and other continents. We want to increase the number of foreign submissions and their publications. We believe that this action is important for the academic community due to the diversity of contexts.The IJI counts on the valuable availability of the evaluators, who we know have to dedicate their precious time to help us improve our work. Our evaluation time is still high, up to 98 days. Some reasons impact this deadline: a) the reviewers are very demanding with researchers with little experience, causing back and forth in the assessments until they reach the expected quality for the article; b) holidays in the middle of the year and at the end of the year delay the evaluations a little; c) evaluators also divide their time with evaluations of academic events throughout the year. Due to these reasons, the publication of the works is also compromised. We took up to 122 days to publish the articles.Another interesting piece of information that we would like to share with our readers is the number of registered users of IJI. IJI has 1270 registered users, including authors, reviewers, and readers. In the last year of 2021, there were 534 new users. It makes us very happy, as the number of users has almost doubled. This action makes us hope to have more works submitted and, consequently, more quality works published.We aim to improve and make the IJI a journal with more impact in the academic and practical environment. With the help of everyone involved, we will improve the level of evaluations and significantly improve the published works. We want to bring relevant content that contributes to science in Brazil and other countries around the world.We thank you all for your help and support in this growing IJI journey!

Selection of Diploid and Tetraploid Banana Hybrids Resistant to Pseudocercospora fijiensis

Black Sigatoka, a disease caused by the fungus Pseudocercospora fijiensis, can lead to the complete loss of banana and plantain production in the absence of chemical control. The development of resistant cultivars is the focus of many banana breeding programs and is an alternative to the use of fungicides. In order to define a refined method of selection in genetic breeding programs, this study evaluated 23 improved diploids, seven tetraploids, and two commercial cultivars in the presence of P. fijiensis. Four selection criteria were considered: means of the disease severity index (ID) and area under the disease progress curve (AUDPC) estimated over the total period of the experiment, only in summer, only in winter, and the emission and harvesting of bunches. The selection of genotypes was more effective in the winter, and the evaluation of four leaves no. 3 emitted after six months of growth was efficient at differentiating the resistant and susceptible genotypes. For the improved diploids and tetraploid hybrids, DI varied from 0.0 to 48.8 and from 15.1 to 63.5, respectively, and the AACPD for the improved hybrids and tetraploid hybrids varied from 0.0 to 2439.5 and 1000.2 to 3717.7, respectively. The tetraploid hybrid of the Prata-type CNPMF0906 and the commercial cultivar, which is a hybrid of the BRS Princesa Silk type, showed quantitative resistance and can be used by banana producers. Results suggest that the guidelines adopted for the selection of genotypes resistant to black Sigatoka may include methodologies that reduce the evaluation time. In addition, new sources of resistance to the disease and the influence of its genetic inheritance in future crosses were found.

A Financial Social Work Certificate Program for Community and Family Practitioners

A Financial Social Work Certificate program launched, responding to a need for greater financial knowledge and skill development among social workers. Community leaders who were alumni of a social work school partnered with educators and researchers to plan and organize an intensive 7-day training over the course of 7 months, through the School’s Office of Continuing Professional Education. Content focused on understanding personal finance, learning about interventions to address clients’ financial needs, exploring critical and emerging macro issues, and focusing on how these dovetail with clinical practice. Preliminary results on financial social work self-efficacy indicate greater self-efficacy over three evaluation time points. Next steps will include a more robust evaluation and fostering a network of engaged graduates.

Constructing software countermeasures against instruction manipulation attacks: an approach based on vulnerability evaluation using fault simulator

Fault injection attacks (FIA), which cause information leakage by injecting intentional faults into the data or operations of devices, are one of the most powerful methods compromising the security of confidential data stored on these devices. Previous studies related to FIA report that attackers can skip instructions running on many devices through many means of fault injection. Most existing anti-FIA countermeasures on software are designed to secure against instruction skip (IS). On the other hand, recent studies report that attackers can use laser fault injection to manipulate instructions running on devices as they want. Although the previous studies have shown that instruction manipulation (IM) could attack the existing countermeasures against IS, no effective countermeasures against IM have been proposed. This paper is the first work tackling this problem, aiming to construct software-based countermeasures against IM faults. Evaluating program vulnerabilities to IM faults is required to consider countermeasures against IM faults. We propose three IM simulation environments for that aim and compare them to reveal their performance difference. GDB (GNU debugger)-based simulator that we newly propose in this paper outperforms the QEMU-based simulator that we presented in AICCSA:1–8, 2020 in advance, in terms of evaluation time at most $$\times$$ × 400 faster. Evaluating a target program using the proposed IM simulators reveals that the IM faults leading to attack successes are classified into four classes. We propose secure coding techniques as countermeasures against IMs of each four classes and show the effectiveness of the countermeasures using the IM simulators.

Developing the Capability to Scale and Reuse Learned Knowledge and Functionality in Learning Classifier Systems

<p>Learning is an important activity through which humanity has incrementally improved accomplishing tasks by adapting knowledge and methods based on the related feedback. Although learning is natural to humans, it is much more difficult to achieve in the technological world as tasks are often learned in isolation. Software is capable of learning novel techniques and algorithms in order to solve these basic, individual problems, however transferring said knowledge to other problems in the same or related domains presents challenges. Solutions often cannot be enumerated to discover the best one as many problems of interest can be intractable in terms of the resources needed to successfully complete them. However, many such problems contain key building blocks of knowledge that can be leveraged to achieve a suitable solution. These building blocks encapsulate important structural regularities of the problem. A technique that can learn these regularities without enumeration,may produce general solutions that apply to similar problems of any length. This implies reusing learned information. In order to reuse learned blocks of knowledge, it is important that a program be scalable and flexible. This requires a program capable of taking knowledge from a previous task and applying it to a more complex problem or a problem with a similar pattern. This is anticipated to enable the program to complete the new task in a practical amount of time and with reasonable amounts of resources. In machine learning, the degree of human intervention in solving problems is often important in many tasks. It is generally necessary for a human to provide input to direct and improve learning. In the field of Developmental Learning there is the idea known as the Threshold Concept (TC). A TC is transformative information which advocates learning. TCs are important because without them, the learner cannot progress. In addition, TCs need to be learned in a particular order, much like a curriculum, thus providing the student with viable progress towards learning more difficult ideas at a faster pace than otherwise. Therefore, human input to a learning algorithm can be to partition a problem into constituent subproblems. This is a principal concept of Layered Learning (LL),where a sequence of sub-problems are learned. The sub-problems are self-contained stages which have been separated by a human. This technique is necessary for tasks in which learning a direct mapping from inputs to outputs is intractable given existing learning algorithms. One of the first artificial learning systems developed is Learning Classifier Systems (LCSs). Past work has extended LCSs to provide more expressivity by using richer representations. One such representation is tree-based and is common to the Genetic Programming (GP) technique. GP is part of the Evolutionary Computation (EC) paradigm and produces solutions represented by trees. The tree nodes can contain functions, and leaf nodes problem features, giving GP a rich representation. A more recent technique is Code Fragments (CFs). CFs are GP-like sub-trees with an initial maximum height of two. Initially, CFs contained hard-coded functions at the root nodes and problem features or previously learned CFs at the leaf nodes of the sub-trees. CFs provided improved expressivity and scalability over the original ternary alphabet used by LCSs. Additionally, CF-based systems have successfully learned previously intractable problems, e.g. 135-bit multiplexer. Although CFs have provided increased scalability, they suffer from a structural weakness. As the problem scales, the chains of CFs grow to intractable lengths. This means that at some point the LCS will stop learning. In addition, CFs were originally meant to scale to more complex problems in the same domain. However, it is advantageous to compile cross-domain solutions, as the regularities of a problem might be from different domains to that expressed by the data. The proposed thesis is that a CF-based LCS can scale to complex problems by reusing learned solutions of problems as functions at the inner nodes of CFs together with compaction and Layered Learning. The overall goal is divided into the following three sub-goals: reuse learned functionality from smaller problems in the root nodes of CF sub-trees, identify a compaction technique that facilitates reduced solution size for improved evaluation time of CFs and develop a layered learning methodology for a CF system, which will be demonstrated by learning a general solution to an intractable problem, i.e. n-bit Multiplexer. In this novel work, Code Fragments are extended to include learned functionality at the root nodes of the sub-trees in a technique known as XCSCF². A new compaction technique is designed, which produces an equivalent set of ternary rules from CF rules. This technique is known as XCSCF3. The work culminates with a new technique XCSCF*, which combines Layered Learning, Code Fragments and Transfer Learning (TL) of knowledge and functionality to produce scalable and general solutions, i.e. to the n-bit multiplexer problem. The novel ideas are tested with the multiplexer and hidden multiplexer problems. These problems are chosen because they are difficult due to epistasis, sparsity and non-linearity. Therefore they provide ample opportunity for testing the new contributions. The thesis work has shown that CFs can be used in various ways to increase scalability and to discover solutions to complex problems. Specifically the following three contributions were produced: learned functionality was captured in LCS populations from smaller problems and was reused in the root nodes of CF sub-trees. An online compaction technique that facilitates reduced evaluation time of CFs was designed. A layered learning method to train a CF system in a manner leading to a general solution was developed. This was demonstrated through learning a solution to a previously intractable problem, i.e. the n-bit Multiplexer. The thesis concludes with suggestions for future work aimed at providing better scalability when using compaction techniques.</p>

Developing the Capability to Scale and Reuse Learned Knowledge and Functionality in Learning Classifier Systems

<p>Learning is an important activity through which humanity has incrementally improved accomplishing tasks by adapting knowledge and methods based on the related feedback. Although learning is natural to humans, it is much more difficult to achieve in the technological world as tasks are often learned in isolation. Software is capable of learning novel techniques and algorithms in order to solve these basic, individual problems, however transferring said knowledge to other problems in the same or related domains presents challenges. Solutions often cannot be enumerated to discover the best one as many problems of interest can be intractable in terms of the resources needed to successfully complete them. However, many such problems contain key building blocks of knowledge that can be leveraged to achieve a suitable solution. These building blocks encapsulate important structural regularities of the problem. A technique that can learn these regularities without enumeration,may produce general solutions that apply to similar problems of any length. This implies reusing learned information. In order to reuse learned blocks of knowledge, it is important that a program be scalable and flexible. This requires a program capable of taking knowledge from a previous task and applying it to a more complex problem or a problem with a similar pattern. This is anticipated to enable the program to complete the new task in a practical amount of time and with reasonable amounts of resources. In machine learning, the degree of human intervention in solving problems is often important in many tasks. It is generally necessary for a human to provide input to direct and improve learning. In the field of Developmental Learning there is the idea known as the Threshold Concept (TC). A TC is transformative information which advocates learning. TCs are important because without them, the learner cannot progress. In addition, TCs need to be learned in a particular order, much like a curriculum, thus providing the student with viable progress towards learning more difficult ideas at a faster pace than otherwise. Therefore, human input to a learning algorithm can be to partition a problem into constituent subproblems. This is a principal concept of Layered Learning (LL),where a sequence of sub-problems are learned. The sub-problems are self-contained stages which have been separated by a human. This technique is necessary for tasks in which learning a direct mapping from inputs to outputs is intractable given existing learning algorithms. One of the first artificial learning systems developed is Learning Classifier Systems (LCSs). Past work has extended LCSs to provide more expressivity by using richer representations. One such representation is tree-based and is common to the Genetic Programming (GP) technique. GP is part of the Evolutionary Computation (EC) paradigm and produces solutions represented by trees. The tree nodes can contain functions, and leaf nodes problem features, giving GP a rich representation. A more recent technique is Code Fragments (CFs). CFs are GP-like sub-trees with an initial maximum height of two. Initially, CFs contained hard-coded functions at the root nodes and problem features or previously learned CFs at the leaf nodes of the sub-trees. CFs provided improved expressivity and scalability over the original ternary alphabet used by LCSs. Additionally, CF-based systems have successfully learned previously intractable problems, e.g. 135-bit multiplexer. Although CFs have provided increased scalability, they suffer from a structural weakness. As the problem scales, the chains of CFs grow to intractable lengths. This means that at some point the LCS will stop learning. In addition, CFs were originally meant to scale to more complex problems in the same domain. However, it is advantageous to compile cross-domain solutions, as the regularities of a problem might be from different domains to that expressed by the data. The proposed thesis is that a CF-based LCS can scale to complex problems by reusing learned solutions of problems as functions at the inner nodes of CFs together with compaction and Layered Learning. The overall goal is divided into the following three sub-goals: reuse learned functionality from smaller problems in the root nodes of CF sub-trees, identify a compaction technique that facilitates reduced solution size for improved evaluation time of CFs and develop a layered learning methodology for a CF system, which will be demonstrated by learning a general solution to an intractable problem, i.e. n-bit Multiplexer. In this novel work, Code Fragments are extended to include learned functionality at the root nodes of the sub-trees in a technique known as XCSCF². A new compaction technique is designed, which produces an equivalent set of ternary rules from CF rules. This technique is known as XCSCF3. The work culminates with a new technique XCSCF*, which combines Layered Learning, Code Fragments and Transfer Learning (TL) of knowledge and functionality to produce scalable and general solutions, i.e. to the n-bit multiplexer problem. The novel ideas are tested with the multiplexer and hidden multiplexer problems. These problems are chosen because they are difficult due to epistasis, sparsity and non-linearity. Therefore they provide ample opportunity for testing the new contributions. The thesis work has shown that CFs can be used in various ways to increase scalability and to discover solutions to complex problems. Specifically the following three contributions were produced: learned functionality was captured in LCS populations from smaller problems and was reused in the root nodes of CF sub-trees. An online compaction technique that facilitates reduced evaluation time of CFs was designed. A layered learning method to train a CF system in a manner leading to a general solution was developed. This was demonstrated through learning a solution to a previously intractable problem, i.e. the n-bit Multiplexer. The thesis concludes with suggestions for future work aimed at providing better scalability when using compaction techniques.</p>

Wa/Ga-Subjects in Japanese and Subdivisions of Tense

<p>WA/GA-SUBJECTS IN JAPANESE AND SUBDIVISIONS OF TENSE Shizuka Torii This thesis takes a semantically based tense/aspect approach to the long-standing problem of wa- and ga-markings of 'subjects' in Japanese. It argues for a correlation between wa/ga-markings of 'subjects' and tense/aspect interpretations of clauses, as illustrated in (1) below, to shed light on a new dimension of the problem. (1) a. John-waki-ta. John come-Past 'John came.' b. John-ga ki-ta. John come-Past 'John has just come/arrived.' <'hot news' perfect> The two types of tense/aspect interpretations correlated with wa- and ga-marked subjects are pinned down in terms of (i) two types of 'evaluation time', which are distinguished as 'original' and 'new' (Enc 1987), (ii) two types of R[eference time] (Reichenbach 1947); one that coincides with S[peech time] but not with E[vent time] (R = S/ inequation E), and the other that coincides with E but not with S (R = E/ inequation S), and (iii) two types of 'viewpoint aspect' (Smith 1991); one that presents 'part' of a situation manifested at a precise temporal point (View part) and the other that presents 'all' of a situation without decomposing it (View all). In order to provide syntactic mechanisms to account for the correlation between wa/ga-markings of 'subjects' and the two distinct types of tense/aspect interpretations, I propose two subdivisions of Tense in line with Chomsky's (1995: 240) suggestion that Tense might have "further subdivisions and implications about event structure and perhaps other properties". I assume that the two subdivisions of Tense are functional categories making up an articulated tense structure (above VP) and contain distinct semantic features responsible for the distinct tense/aspect interpretations correlated with wa- and ga-markings of subjects in Japanese. Being tense categories, they both have T[ense]-features and D[eterminer]-features to be checked by predicates and subject DPs respectively. Due to the distinct semantic content of the two syntactic categories, depending on which T- and D-features predicates and subjects check, we get two distinct types of tense/aspect interpretations of predicates and two distinct types of subjects (which are morphologically distinguished by wa- and ga-markings in Japanese). In this analysis, the T- and D-features of a tense category ensure that a subject and a predicate are necessarily of the same semantic type. The tense system I propose to account for the wa/ga-phenomena unifies tense and aspect to the extent that the wa/ga-phenomena relate to the interpretation of both tense and aspect. A notable consequence of my analysis is that the syntax and semantics of stage- and individual-level predicates (cf. Carlson 1977, Kratzer 1989 and Diesing 1992) fall under the syntax and semantics of tense. The analysis also exhibits some interesting parallelisms to Davis' (1998), in which person features of subjects are related to a temporal structure. In addition the proposed two subject positions within the articulated tense structure are demonstrated to be tenable across languages. Furthermore I show that the reanalysis is extendable to subordinate clause case markings and interpretations, with special attention to factors such as factivity and the distinctions among propositions, states of affairs, and situation-types.</p>